High spatial resolution mapping of folds and fractures using Unmanned Aerial Vehicle (UAV) photogrammetry

NASA Astrophysics Data System (ADS)

Cruden, A. R.; Vollgger, S.

2016-12-01

The emerging capability of UAV photogrammetry combines a simple and cost-effective method to acquire digital aerial images with advanced computer vision algorithms that compute spatial datasets from a sequence of overlapping digital photographs from various viewpoints. Depending on flight altitude and camera setup, sub-centimeter spatial resolution orthophotographs and textured dense point clouds can be achieved. Orientation data can be collected for detailed structural analysis by digitally mapping such high-resolution spatial datasets in a fraction of time and with higher fidelity compared to traditional mapping techniques. Here we describe a photogrammetric workflow applied to a structural study of folds and fractures within alternating layers of sandstone and mudstone at a coastal outcrop in SE Australia. We surveyed this location using a downward looking digital camera mounted on commercially available multi-rotor UAV that autonomously followed waypoints at a set altitude and speed to ensure sufficient image overlap, minimum motion blur and an appropriate resolution. The use of surveyed ground control points allowed us to produce a geo-referenced 3D point cloud and an orthophotograph from hundreds of digital images at a spatial resolution < 10 mm per pixel, and cm-scale location accuracy. Orientation data of brittle and ductile structures were semi-automatically extracted from these high-resolution datasets using open-source software. This resulted in an extensive and statistically relevant orientation dataset that was used to 1) interpret the progressive development of folds and faults in the region, and 2) to generate a 3D structural model that underlines the complex internal structure of the outcrop and quantifies spatial variations in fold geometries. Overall, our work highlights how UAV photogrammetry can contribute to new insights in structural analysis.

FlyAR: augmented reality supported micro aerial vehicle navigation.

PubMed

Zollmann, Stefanie; Hoppe, Christof; Langlotz, Tobias; Reitmayr, Gerhard

2014-04-01

Micro aerial vehicles equipped with high-resolution cameras can be used to create aerial reconstructions of an area of interest. In that context automatic flight path planning and autonomous flying is often applied but so far cannot fully replace the human in the loop, supervising the flight on-site to assure that there are no collisions with obstacles. Unfortunately, this workflow yields several issues, such as the need to mentally transfer the aerial vehicle’s position between 2D map positions and the physical environment, and the complicated depth perception of objects flying in the distance. Augmented Reality can address these issues by bringing the flight planning process on-site and visualizing the spatial relationship between the planned or current positions of the vehicle and the physical environment. In this paper, we present Augmented Reality supported navigation and flight planning of micro aerial vehicles by augmenting the user’s view with relevant information for flight planning and live feedback for flight supervision. Furthermore, we introduce additional depth hints supporting the user in understanding the spatial relationship of virtual waypoints in the physical world and investigate the effect of these visualization techniques on the spatial understanding.

Tracking stormwater discharge plumes and water quality of the Tijuana River with multispectral aerial imagery

NASA Astrophysics Data System (ADS)

Svejkovsky, Jan; Nezlin, Nikolay P.; Mustain, Neomi M.; Kum, Jamie B.

2010-04-01

Spatial-temporal characteristics and environmental factors regulating the behavior of stormwater runoff from the Tijuana River in southern California were analyzed utilizing very high resolution aerial imagery, and time-coincident environmental and bacterial sampling data. Thirty nine multispectral aerial images with 2.1-m spatial resolution were collected after major rainstorms during 2003-2008. Utilizing differences in color reflectance characteristics, the ocean surface was classified into non-plume waters and three components of the runoff plume reflecting differences in age and suspended sediment concentrations. Tijuana River discharge rate was the primary factor regulating the size of the freshest plume component and its shorelong extensions to the north and south. Wave direction was found to affect the shorelong distribution of the shoreline-connected fresh plume components much more strongly than wind direction. Wave-driven sediment resuspension also significantly contributed to the size of the oldest plume component. Surf zone bacterial samples collected near the time of each image acquisition were used to evaluate the contamination characteristics of each plume component. The bacterial contamination of the freshest plume waters was very high (100% of surf zone samples exceeded California standards), but the oldest plume areas were heterogeneous, including both polluted and clean waters. The aerial imagery archive allowed study of river runoff characteristics on a plume component level, not previously done with coarser satellite images. Our findings suggest that high resolution imaging can quickly identify the spatial extents of the most polluted runoff but cannot be relied upon to always identify the entire polluted area. Our results also indicate that wave-driven transport is important in distributing the most contaminated plume areas along the shoreline.

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States

PubMed Central

Swetnam, Tyson L.; Gillan, Jeffrey K.; Sankey, Temuulen T.; McClaran, Mitchel P.; Nichols, Mary H.; Heilman, Philip; McVay, Jason

2018-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft. PMID:29379511

Considerations for Achieving Cross-Platform Point Cloud Data Fusion across Different Dryland Ecosystem Structural States.

PubMed

Swetnam, Tyson L; Gillan, Jeffrey K; Sankey, Temuulen T; McClaran, Mitchel P; Nichols, Mary H; Heilman, Philip; McVay, Jason

2017-01-01

Remotely sensing recent growth, herbivory, or disturbance of herbaceous and woody vegetation in dryland ecosystems requires high spatial resolution and multi-temporal depth. Three dimensional (3D) remote sensing technologies like lidar, and techniques like structure from motion (SfM) photogrammetry, each have strengths and weaknesses at detecting vegetation volume and extent, given the instrument's ground sample distance and ease of acquisition. Yet, a combination of platforms and techniques might provide solutions that overcome the weakness of a single platform. To explore the potential for combining platforms, we compared detection bias amongst two 3D remote sensing techniques (lidar and SfM) using three different platforms [ground-based, small unmanned aerial systems (sUAS), and manned aircraft]. We found aerial lidar to be more accurate for characterizing the bare earth (ground) in dense herbaceous vegetation than either terrestrial lidar or aerial SfM photogrammetry. Conversely, the manned aerial lidar did not detect grass and fine woody vegetation while the terrestrial lidar and high resolution near-distance (ground and sUAS) SfM photogrammetry detected these and were accurate. UAS SfM photogrammetry at lower spatial resolution under-estimated maximum heights in grass and shrubs. UAS and handheld SfM photogrammetry in near-distance high resolution collections had similar accuracy to terrestrial lidar for vegetation, but difficulty at measuring bare earth elevation beneath dense herbaceous cover. Combining point cloud data and derivatives (i.e., meshes and rasters) from two or more platforms allowed for more accurate measurement of herbaceous and woody vegetation (height and canopy cover) than any single technique alone. Availability and costs of manned aircraft lidar collection preclude high frequency repeatability but this is less limiting for terrestrial lidar, sUAS and handheld SfM. The post-processing of SfM photogrammetry data became the limiting factor at larger spatial scale and temporal repetition. Despite the utility of sUAS and handheld SfM for monitoring vegetation phenology and structure, their spatial extents are small relative to manned aircraft.

Application of Unmanned Aerial Systems in Spatial Downscaling of Landsat VIR imageries of Agricultural Fields

NASA Astrophysics Data System (ADS)

Torres, A.; Hassan Esfahani, L.; Ebtehaj, A.; McKee, M.

2016-12-01

While coarse space-time resolution of satellite observations in visible to near infrared (VIR) is a serious limiting factor for applications in precision agriculture, high resolution remotes sensing observation by the Unmanned Aerial Systems (UAS) systems are also site-specific and still practically restrictive for widespread applications in precision agriculture. We present a modern spatial downscaling approach that relies on new sparse approximation techniques. The downscaling approach learns from a large set of coincident low- and high-resolution satellite and UAS observations to effectively downscale the satellite imageries in VIR bands. We focus on field experiments using the AggieAirTM platform and Landsat 7 ETM+ and Landsat 8 OLI observations obtained in an intensive field campaign in 2013 over an agriculture field in Scipio, Utah. The results show that the downscaling methods can effectively increase the resolution of Landsat VIR imageries by the order of 2 to 4 from 30 m to 15 and 7.5 m, respectively. Specifically, on average, the downscaling method reduces the root mean squared errors up to 26%, considering bias corrected AggieAir imageries as the reference.

Application of airborne thermal imagery to surveys of Pacific walrus

USGS Publications Warehouse

Burn, D.M.; Webber, M.A.; Udevitz, M.S.

2006-01-01

We conducted tests of airborne thermal imagery of Pacific walrus to determine if this technology can be used to detect walrus groups on sea ice and estimate the number of walruses present in each group. In April 2002 we collected thermal imagery of 37 walrus groups in the Bering Sea at spatial resolutions ranging from 1-4 m. We also collected high-resolution digital aerial photographs of the same groups. Walruses were considerably warmer than the background environment of ice, snow, and seawater and were easily detected in thermal imagery. We found a significant linear relation between walrus group size and the amount of heat measured by the thermal sensor at all 4 spatial resolutions tested. This relation can be used in a double-sampling framework to estimate total walrus numbers from a thermal survey of a sample of units within an area and photographs from a subsample of the thermally detected groups. Previous methods used in visual aerial surveys of Pacific walrus have sampled only a small percentage of available habitat, resulting in population estimates with low precision. Results of this study indicate that an aerial survey using a thermal sensor can cover as much as 4 times the area per hour of flight time with greater reliability than visual observation.

Suitability of low cost commercial off-the-shelf aerial platforms and consumer grade digital cameras for small format aerial photography

NASA Astrophysics Data System (ADS)

Turley, Anthony Allen

Many research projects require the use of aerial images. Wetlands evaluation, crop monitoring, wildfire management, environmental change detection, and forest inventory are but a few of the applications of aerial imagery. Low altitude Small Format Aerial Photography (SFAP) is a bridge between satellite and man-carrying aircraft image acquisition and ground-based photography. The author's project evaluates digital images acquired using low cost commercial digital cameras and standard model airplanes to determine their suitability for remote sensing applications. Images from two different sites were obtained. Several photo missions were flown over each site, acquiring images in the visible and near infrared electromagnetic bands. Images were sorted and analyzed to select those with the least distortion, and blended together with Microsoft Image Composite Editor. By selecting images taken within minutes apart, radiometric qualities of the images were virtually identical, yielding no blend lines in the composites. A commercial image stitching program, Autopano Pro, was purchased during the later stages of this study. Autopano Pro was often able to mosaic photos that the free Image Composite Editor was unable to combine. Using telemetry data from an onboard data logger, images were evaluated to calculate scale and spatial resolution. ERDAS ER Mapper and ESRI ArcGIS were used to rectify composite images. Despite the limitations inherent in consumer grade equipment, images of high spatial resolution were obtained. Mosaics of as many as 38 images were created, and the author was able to record detailed aerial images of forest and wetland areas where foot travel was impractical or impossible.

Spatially explicit rangeland erosion monitoring using high-resolution digital aerial imagery

USGS Publications Warehouse

Gillan, Jeffrey K.; Karl, Jason W.; Barger, Nichole N.; Elaksher, Ahmed; Duniway, Michael C.

2016-01-01

Nearly all of the ecosystem services supported by rangelands, including production of livestock forage, carbon sequestration, and provisioning of clean water, are negatively impacted by soil erosion. Accordingly, monitoring the severity, spatial extent, and rate of soil erosion is essential for long-term sustainable management. Traditional field-based methods of monitoring erosion (sediment traps, erosion pins, and bridges) can be labor intensive and therefore are generally limited in spatial intensity and/or extent. There is a growing effort to monitor natural resources at broad scales, which is driving the need for new soil erosion monitoring tools. One remote-sensing technique that can be used to monitor soil movement is a time series of digital elevation models (DEMs) created using aerial photogrammetry methods. By geographically coregistering the DEMs and subtracting one surface from the other, an estimate of soil elevation change can be created. Such analysis enables spatially explicit quantification and visualization of net soil movement including erosion, deposition, and redistribution. We constructed DEMs (12-cm ground sampling distance) on the basis of aerial photography immediately before and 1 year after a vegetation removal treatment on a 31-ha Piñon-Juniper woodland in southeastern Utah to evaluate the use of aerial photography in detecting soil surface change. On average, we were able to detect surface elevation change of ± 8−9cm and greater, which was sufficient for the large amount of soil movement exhibited on the study area. Detecting more subtle soil erosion could be achieved using the same technique with higher-resolution imagery from lower-flying aircraft such as unmanned aerial vehicles. DEM differencing and process-focused field methods provided complementary information and a more complete assessment of soil loss and movement than any single technique alone. Photogrammetric DEM differencing could be used as a technique to quantitatively monitor surface change over time relative to management activities.

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping.

PubMed

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-08-12

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights.

Spatial Quality Evaluation of Resampled Unmanned Aerial Vehicle-Imagery for Weed Mapping

PubMed Central

Borra-Serrano, Irene; Peña, José Manuel; Torres-Sánchez, Jorge; Mesas-Carrascosa, Francisco Javier; López-Granados, Francisca

2015-01-01

Unmanned aerial vehicles (UAVs) combined with different spectral range sensors are an emerging technology for providing early weed maps for optimizing herbicide applications. Considering that weeds, at very early phenological stages, are similar spectrally and in appearance, three major components are relevant: spatial resolution, type of sensor and classification algorithm. Resampling is a technique to create a new version of an image with a different width and/or height in pixels, and it has been used in satellite imagery with different spatial and temporal resolutions. In this paper, the efficiency of resampled-images (RS-images) created from real UAV-images (UAV-images; the UAVs were equipped with two types of sensors, i.e., visible and visible plus near-infrared spectra) captured at different altitudes is examined to test the quality of the RS-image output. The performance of the object-based-image-analysis (OBIA) implemented for the early weed mapping using different weed thresholds was also evaluated. Our results showed that resampling accurately extracted the spectral values from high spatial resolution UAV-images at an altitude of 30 m and the RS-image data at altitudes of 60 and 100 m, was able to provide accurate weed cover and herbicide application maps compared with UAV-images from real flights. PMID:26274960

High Resolution Stratigraphic Mapping in Complex Terrain: A Comparison of Traditional Remote Sensing Techniques with Unmanned Aerial Vehicle - Structure from Motion Photogrammetry

NASA Astrophysics Data System (ADS)

Nesbit, P. R.; Hugenholtz, C.; Durkin, P.; Hubbard, S. M.; Kucharczyk, M.; Barchyn, T.

2016-12-01

Remote sensing and digital mapping have started to revolutionize geologic mapping in recent years as a result of their realized potential to provide high resolution 3D models of outcrops to assist with interpretation, visualization, and obtaining accurate measurements of inaccessible areas. However, in stratigraphic mapping applications in complex terrain, it is difficult to acquire information with sufficient detail at a wide spatial coverage with conventional techniques. We demonstrate the potential of a UAV and Structure from Motion (SfM) photogrammetric approach for improving 3D stratigraphic mapping applications within a complex badland topography. Our case study is performed in Dinosaur Provincial Park (Alberta, Canada), mapping late Cretaceous fluvial meander belt deposits of the Dinosaur Park formation amidst a succession of steeply sloping hills and abundant drainages - creating a challenge for stratigraphic mapping. The UAV-SfM dataset (2 cm spatial resolution) is compared directly with a combined satellite and aerial LiDAR dataset (30 cm spatial resolution) to reveal advantages and limitations of each dataset before presenting a unique workflow that utilizes the dense point cloud from the UAV-SfM dataset for analysis. The UAV-SfM dense point cloud minimizes distortion, preserves 3D structure, and records an RGB attribute - adding potential value in future studies. The proposed UAV-SfM workflow allows for high spatial resolution remote sensing of stratigraphy in complex topographic environments. This extended capability can add value to field observations and has the potential to be integrated with subsurface petroleum models.

Rapid orthophoto development system.

DOT National Transportation Integrated Search

2013-06-01

The DMC system procured in the project represented state-of-the-art, large-format digital aerial camera systems at the start of : project. DMC is based on the frame camera model, and to achieve large ground coverage with high spatial resolution, the ...

Object-Based Arctic Sea Ice Feature Extraction through High Spatial Resolution Aerial photos

NASA Astrophysics Data System (ADS)

Miao, X.; Xie, H.

2015-12-01

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice features, such as melt ponds, submerged ice, water, ice/snow, and pressure ridges, is time-consuming and labor-intensive. An object-based classification algorithm is developed to automatically extract sea ice features efficiently from aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the MIZ near the Alaska coast. The algorithm includes four steps: (1) the image segmentation groups the neighboring pixels into objects based on the similarity of spectral and textural information; (2) the random forest classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice), shadow, and ice/snow; (3) the polygon neighbor analysis separates melt ponds and submerged ice based on spatial relationship; and (4) pressure ridge features are extracted from shadow based on local illumination geometry. The producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection, and shadow shows a user's accuracy of 88.9% and producer's accuracies of 91.4%. Finally, pond density, pond fraction, ice floes, mean ice concentration, average ridge height, ridge profile, and ridge frequency are extracted from batch processing of aerial photos, and their uncertainties are estimated.

Bag of Lines (BoL) for Improved Aerial Scene Representation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sridharan, Harini; Cheriyadat, Anil M.

2014-09-22

Feature representation is a key step in automated visual content interpretation. In this letter, we present a robust feature representation technique, referred to as bag of lines (BoL), for high-resolution aerial scenes. The proposed technique involves extracting and compactly representing low-level line primitives from the scene. The compact scene representation is generated by counting the different types of lines representing various linear structures in the scene. Through extensive experiments, we show that the proposed scene representation is invariant to scale changes and scene conditions and can discriminate urban scene categories accurately. We compare the BoL representation with the popular scalemore » invariant feature transform (SIFT) and Gabor wavelets for their classification and clustering performance on an aerial scene database consisting of images acquired by sensors with different spatial resolutions. The proposed BoL representation outperforms the SIFT- and Gabor-based representations.« less

A Review of Land-Cover Mapping Activities in Coastal Alabama and Mississippi

USGS Publications Warehouse

Smith, Kathryn E.L.; Nayegandhi, Amar; Brock, John C.

2010-01-01

INTRODUCTION Land-use and land-cover (LULC) data provide important information for environmental management. Data pertaining to land-cover and land-management activities are a common requirement for spatial analyses, such as watershed modeling, climate change, and hazard assessment. In coastal areas, land development, storms, and shoreline modification amplify the need for frequent and detailed land-cover datasets. The northern Gulf of Mexico coastal area is no exception. The impact of severe storms, increases in urban area, dramatic changes in land cover, and loss of coastal-wetland habitat all indicate a vital need for reliable and comparable land-cover data. Four main attributes define a land-cover dataset: the date/time of data collection, the spatial resolution, the type of classification, and the source data. The source data are the foundation dataset used to generate LULC classification and are typically remotely sensed data, such as aerial photography or satellite imagery. These source data have a large influence on the final LULC data product, so much so that one can classify LULC datasets into two general groups: LULC data derived from aerial photography and LULC data derived from satellite imagery. The final LULC data can be converted from one format to another (for instance, vector LULC data can be converted into raster data for analysis purposes, and vice versa), but each subsequent dataset maintains the imprint of the source medium within its spatial accuracy and data features. The source data will also influence the spatial and temporal resolution, as well as the type of classification. The intended application of the LULC data typically defines the type of source data and methodology, with satellite imagery being selected for large landscapes (state-wide, national data products) and repeatability (environmental monitoring and change analysis). The coarse spatial scale and lack of refined land-use categories are typical drawbacks to satellite-based land-use classifications. Aerial photography is typically selected for smaller landscapes (watershed-basin scale), for greater definition of the land-use categories, and for increased spatial resolution. Disadvantages of using photography include time-consuming digitization, high costs for imagery collection, and lack of seasonal data. Recently, the availability of high-resolution satellite imagery has generated a new category of LULC data product. These new datasets have similar strengths to the aerial-photo-based LULC in that they possess the potential for refined definition of land-use categories and increased spatial resolution but also have the benefit of satellite-based classifications, such as repeatability for change analysis. LULC classification based on high-resolution satellite imagery is still in the early stages of development but merits greater attention because environmental-monitoring and landscape-modeling programs rely heavily on LULC data. This publication summarizes land-use and land-cover mapping activities for Alabama and Mississippi coastal areas within the U.S. Geological Survey (USGS) Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility Project boundaries. Existing LULC datasets will be described, as well as imagery data sources and ancillary data that may provide ground-truth or satellite training data for a forthcoming land-cover classification. Finally, potential areas for a high-resolution land-cover classification in the Alabama-Mississippi region will be identified.

Use of aerial photographs for assessment of soil organic carbon and delineation of agricultural management zones.

NASA Astrophysics Data System (ADS)

Bartholomeus, H.; Kooistra, L.

2012-04-01

For quantitative estimation of soil properties by means of remote sensing, often hyperspectral data are used. But these data are scarce and expensive, which prohibits wider implementation of the developed techniques in agricultural management. For precision agriculture, observations at a high spatial resolution are required. Colour aerial photographs at this scale are widely available, and can be acquired at no of very low costs. Therefore, we investigated whether publically available aerial photographs can be used to a) automatically delineate management zones and b) estimate levels of organic carbon spatially. We selected three study areas within the Netherlands that cover a large variance in soil type (peat, sand, and clay). For the fields of interest, RGB aerial photographs with a spatial resolution of 50 cm were extracted from a publically available data provider. Further pre-processing exists of geo-referencing only. Since the images originate from different sources and are potentially acquired under unknown illumination conditions, the exact radiometric properties of the data are unknown. Therefore, we used spectral indices to emphasize the differences in reflectance and normalize for differences in radiometry. To delineate management zones we used image segmentation techniques, using the derived indices as input. Comparison with management zone maps as used by the farmers shows that there is good correspondence. Regression analysis between a number of soil properties and the derived indices shows that organic carbon is the major explanatory variable for differences in index values within the fields. However, relations do not hold for large regions, indicating that local models will have to be used, which is a problem that is also still relevant for hyperspectral remote sensing data. With this research, we show that low-cost aerial photographs can be a valuable tool for quantitative analysis of organic carbon and automatic delineation of management zones. Since a lot of data are publically available this offers great possibilities for implementing remote sensing techniques in agricultural management.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

Unmanned aerial survey of fallen trees in a deciduous broadleaved forest in eastern Japan.

PubMed

Inoue, Tomoharu; Nagai, Shin; Yamashita, Satoshi; Fadaei, Hadi; Ishii, Reiichiro; Okabe, Kimiko; Taki, Hisatomo; Honda, Yoshiaki; Kajiwara, Koji; Suzuki, Rikie

2014-01-01

Since fallen trees are a key factor in biodiversity and biogeochemical cycling, information about their spatial distribution is of use in determining species distribution and nutrient and carbon cycling in forest ecosystems. Ground-based surveys are both time consuming and labour intensive. Remote-sensing technology can reduce these costs. Here, we used high-spatial-resolution aerial photographs (0.5-1.0 cm per pixel) taken from an unmanned aerial vehicle (UAV) to survey fallen trees in a deciduous broadleaved forest in eastern Japan. In nine sub-plots we found a total of 44 fallen trees by ground survey. From the aerial photographs, we identified 80% to 90% of fallen trees that were >30 cm in diameter or >10 m in length, but missed many that were narrower or shorter. This failure may be due to the similarity of fallen trees to trunks and branches of standing trees or masking by standing trees. Views of the same point from different angles may improve the detection rate because they would provide more opportunity to detect fallen trees hidden by standing trees. Our results suggest that UAV surveys will make it possible to monitor the spatial and temporal variations in forest structure and function at lower cost.

Unmanned Aerial Survey of Fallen Trees in a Deciduous Broadleaved Forest in Eastern Japan

PubMed Central

Inoue, Tomoharu; Nagai, Shin; Yamashita, Satoshi; Fadaei, Hadi; Ishii, Reiichiro; Okabe, Kimiko; Taki, Hisatomo; Honda, Yoshiaki; Kajiwara, Koji; Suzuki, Rikie

2014-01-01

Since fallen trees are a key factor in biodiversity and biogeochemical cycling, information about their spatial distribution is of use in determining species distribution and nutrient and carbon cycling in forest ecosystems. Ground-based surveys are both time consuming and labour intensive. Remote-sensing technology can reduce these costs. Here, we used high-spatial-resolution aerial photographs (0.5–1.0 cm per pixel) taken from an unmanned aerial vehicle (UAV) to survey fallen trees in a deciduous broadleaved forest in eastern Japan. In nine sub-plots we found a total of 44 fallen trees by ground survey. From the aerial photographs, we identified 80% to 90% of fallen trees that were >30 cm in diameter or >10 m in length, but missed many that were narrower or shorter. This failure may be due to the similarity of fallen trees to trunks and branches of standing trees or masking by standing trees. Views of the same point from different angles may improve the detection rate because they would provide more opportunity to detect fallen trees hidden by standing trees. Our results suggest that UAV surveys will make it possible to monitor the spatial and temporal variations in forest structure and function at lower cost. PMID:25279817

Learning Scene Categories from High Resolution Satellite Image for Aerial Video Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheriyadat, Anil M

2011-01-01

Automatic scene categorization can benefit various aerial video processing applications. This paper addresses the problem of predicting the scene category from aerial video frames using a prior model learned from satellite imagery. We show that local and global features in the form of line statistics and 2-D power spectrum parameters respectively can characterize the aerial scene well. The line feature statistics and spatial frequency parameters are useful cues to distinguish between different urban scene categories. We learn the scene prediction model from highresolution satellite imagery to test the model on the Columbus Surrogate Unmanned Aerial Vehicle (CSUAV) dataset ollected bymore » high-altitude wide area UAV sensor platform. e compare the proposed features with the popular Scale nvariant Feature Transform (SIFT) features. Our experimental results show that proposed approach outperforms te SIFT model when the training and testing are conducted n disparate data sources.« less

Canopy Density Mapping on Ultracam-D Aerial Imagery in Zagros Woodlands, Iran

NASA Astrophysics Data System (ADS)

Erfanifard, Y.; Khodaee, Z.

2013-09-01

Canopy density maps express different characteristics of forest stands, especially in woodlands. Obtaining such maps by field measurements is so expensive and time-consuming. It seems necessary to find suitable techniques to produce these maps to be used in sustainable management of woodland ecosystems. In this research, a robust procedure was suggested to obtain these maps by very high spatial resolution aerial imagery. It was aimed to produce canopy density maps by UltraCam-D aerial imagery, newly taken in Zagros woodlands by Iran National Geographic Organization (NGO), in this study. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The very high spatial resolution aerial imagery of the plot purchased from NGO, was classified by kNN technique and the tree crowns were extracted precisely. The canopy density was determined in each cell of different meshes with different sizes overlaid on the study area map. The accuracy of the final maps was investigated by the ground truth obtained by complete field measurements. The results showed that the proposed method of obtaining canopy density maps was efficient enough in the study area. The final canopy density map obtained by a mesh with 30 Ar (3000 m2) cell size had 80% overall accuracy and 0.61 KHAT coefficient of agreement which shows a great agreement with the observed samples. This method can also be tested in other case studies to reveal its capability in canopy density map production in woodlands.

IMPROVING THE ACCURACY OF HISTORIC SATELLITE IMAGE CLASSIFICATION BY COMBINING LOW-RESOLUTION MULTISPECTRAL DATA WITH HIGH-RESOLUTION PANCHROMATIC DATA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Getman, Daniel J

2008-01-01

Many attempts to observe changes in terrestrial systems over time would be significantly enhanced if it were possible to improve the accuracy of classifications of low-resolution historic satellite data. In an effort to examine improving the accuracy of historic satellite image classification by combining satellite and air photo data, two experiments were undertaken in which low-resolution multispectral data and high-resolution panchromatic data were combined and then classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel resolution) from Landsat 7. These data were augmented with panchromatic datamore » (15m pixel resolution) from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant improvement in the accuracy of classifications made using the ECHO algorithm. Although the inclusion of aerial photography provided an improvement in accuracy, this improvement was only statistically significant at a 40-60% level. These results suggest that once error levels associated with combining aerial photography and multispectral satellite data are reduced, this approach has the potential to significantly enhance the precision and accuracy of classifications made using historic remotely sensed data, as a way to extend the time range of efforts to track temporal changes in terrestrial systems.« less

Potential and Limitations of Low-Cost Unmanned Aerial Systems for Monitoring Altitudinal Vegetation Phenology in the Tropics

NASA Astrophysics Data System (ADS)

Silva, T. S. F.; Torres, R. S.; Morellato, P.

2017-12-01

Vegetation phenology is a key component of ecosystem function and biogeochemical cycling, and highly susceptible to climatic change. Phenological knowledge in the tropics is limited by lack of monitoring, traditionally done by laborious direct observation. Ground-based digital cameras can automate daily observations, but also offer limited spatial coverage. Imaging by low-cost Unmanned Aerial Systems (UAS) combines the fine resolution of ground-based methods with and unprecedented capability for spatial coverage, but challenges remain in producing color-consistent multitemporal images. We evaluated the applicability of multitemporal UAS imaging to monitor phenology in tropical altitudinal grasslands and forests, answering: 1) Can very-high resolution aerial photography from conventional digital cameras be used to reliably monitor vegetative and reproductive phenology? 2) How is UAS monitoring affected by changes in illumination and by sensor physical limitations? We flew imaging missions monthly from Feb-16 to Feb-17, using a UAS equipped with an RGB Canon SX260 camera. Flights were carried between 10am and 4pm, at 120-150m a.g.l., yielding 5-10cm spatial resolution. To compensate illumination changes caused by time of day, season and cloud cover, calibration was attempted using reference targets and empirical models, as well as color space transformations. For vegetative phenological monitoring, multitemporal response was severely affected by changes in illumination conditions, strongly confounding the phenological signal. These variations could not be adequately corrected through calibration due to sensor limitations. For reproductive phenology, the very-high resolution of the acquired imagery allowed discrimination of individual reproductive structures for some species, and its stark colorimetric differences to vegetative structures allowed detection of the reproductive timing on the HSV color space, despite illumination effects. We conclude that reliable vegetative phenology monitoring may exceed the capabilities of consumer cameras, but reproductive phenology can be successfully monitored for species with conspicuous reproductive structures. Further research is being conducted to improve calibration methods and information extraction through machine learning.

Unmanned aerial systems for forest reclamation monitoring: throwing balloons in the air

NASA Astrophysics Data System (ADS)

Andrade, Rita; Vaz, Eric; Panagopoulos, Thomas; Guerrero, Carlos

2014-05-01

Wildfires are a recurrent phenomenon in Mediterranean landscapes, deteriorating environment and ecosystems, calling out for adequate land management. Monitoring burned areas enhances our abilities to reclaim them. Remote sensing has become an increasingly important tool for environmental assessment and land management. It is fast, non-intrusive, and provides continuous spatial coverage. This paper reviews remote sensing methods, based on space-borne, airborne or ground-based multispectral imagery, for monitoring the biophysical properties of forest areas for site specific management. The usage of satellite imagery for land use management has been frequent in the last decades, it is of great use to determine plants health and crop conditions, allowing a synergy between the complexity of environment, anthropogenic landscapes and multi-temporal understanding of spatial dynamics. Aerial photography increments on spatial resolution, nevertheless it is heavily dependent on airborne availability as well as cost. Both these methods are required for wide areas management and policy planning. Comprising an active and high resolution imagery source, that can be brought at a specific instance, reducing cost while maintaining locational flexibility is of utmost importance for local management. In this sense, unmanned aerial vehicles provide maximum flexibility with image collection, they can incorporate thermal and multispectral sensors, however payload and engine operation time limit flight time. Balloon remote sensing is becoming increasingly sought after for site specific management, catering rapid digital analysis, permitting greater control of the spatial resolution as well as of datasets collection in a given time. Different wavelength sensors may be used to map spectral variations in plant growth, monitor water and nutrient stress, assess yield and plant vitality during different stages of development. Proximity could be an asset when monitoring forest plants vitality. Early predictions of re-vegetation success facilitate precise and timely diagnosis of stress, thus remedial actions can be taken at localized detail.

Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes

USGS Publications Warehouse

Beck, Marcus W.; Vondracek, Bruce C.; Hatch, Lorin K.; Vinje, Jason

2013-01-01

Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on ) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors.

Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

NASA Astrophysics Data System (ADS)

Lu, Bing; He, Yuhong

2017-06-01

Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition, owing to its high spatial resolution. The overall accuracy is around 85% for images acquired at different times. Species composition is spatially attributed by topographical features and soil moisture conditions. Spatio-temporal variation of species composition implies the growing process and succession of different species, which is critical for understanding the evolutionary features of grassland ecosystems. Strengths and challenges of applying UAV-acquired imagery for vegetation studies are summarized at the end.

Applications of low altitude photogrammetry for morphometry, displacements, and landform modeling

NASA Astrophysics Data System (ADS)

Gomez, F. G.; Polun, S. G.; Hickcox, K.; Miles, C.; Delisle, C.; Beem, J. R.

2016-12-01

Low-altitude aerial surveying is emerging as a tool that greatly improves the ease and efficiency of measuring landforms for quantitative geomorphic analyses. High-resolution, close-range photogrammetry produces dense, 3-dimensional point clouds that facilitate the construction of digital surface models, as well as a potential means of classifying ground targets using spatial structure. This study presents results from recent applications of UAS-based photogrammetry, including high resolution surface morphometry of a lava flow, repeat-pass applications to mass movements, and fault scarp degradation modeling. Depending upon the desired photographic resolution and the platform/payload flown, aerial photos are typically acquired at altitudes of 40 - 100 meters above the ground surface. In all cases, high-precision ground control points are key for accurate (and repeatable) orientation - relying on low-precision GPS coordinates (whether on the ground or geotags in the aerial photos) typically results in substantial rotations (tilt) of the reference frame. Using common ground control points between repeat surveys results in matching point clouds with RMS residuals better than 10 cm. In arid regions, the point cloud is used to assess lava flow surface roughness using multi-scale measurements of point cloud dimensionality. For the landslide study, the point cloud provides a basis for assessing possible displacements. In addition, the high resolution orthophotos facilitate mapping of fractures and their growth. For neotectonic applications, we compare fault scarp modeling results from UAV-derived point clouds versus field-based surveys (kinematic GPS and electronic distance measurements). In summary, there is a wide ranging toolbox of low-altitude aerial platforms becoming available for field geoscientists. In many instances, these tools will present convenience and reduced cost compared with the effort and expense to contract acquisitions of aerial imagery.

Hyperparameter Classification of Arctic Sea Ice and Snow Based on Aerial Laser Data, Passive Microwave Data and Field Data

NASA Astrophysics Data System (ADS)

Herzfeld, U. C.; Maslanik, J.; Williams, S.; Sturm, M.; Cavalieri, D.

2006-12-01

In the past year, the Arctic sea-ice cover has been shrinking at an alarming rate. Remote-sensing technologies provide opportunities for observations of the sea ice at unprecedented repetition rates and spatial resolutions. The advance of new observational technologies is not only fascinating, it also brings with it the challenge and necessity to derive adequate new geoinformatical and geomathematical methods as a basis for analysis and geophysical interpretation of new data types. Our research includes validation and analysis of NASA EOS data, development of observational instrumentation and advanced geoinformatics. In this talk we emphasize the close linkage between technological development and geoinformatics along case studies of sea-ice near Point Barrow, Alaska, based on the following data types: AMSR-E and PSR passive microwave data, RADARSAT and ERS SAR data, manually-collected snow-depth data and laser-elevation data from unmanned aerial vehicles. The hyperparameter concept is introduced to facilitate characterization and classification of the same sea-ice properties and spatial structures from these data sets, which differ with respect to spatial resolution, measured parameters and observed geophysical variables. Mathematically, this requires parameter identification in undersampled, oversampled or overprinted situations.

Unmanned Aerial Systems and Spectroscopy for Remote Sensing Applications in Archaeology

NASA Astrophysics Data System (ADS)

Themistocleous, K.; Agapiou, A.; Cuca, B.; Hadjimitsis, D. G.

2015-04-01

Remote sensing has open up new dimensions in archaeological research. Although there has been significant progress in increasing the resolution of space/aerial sensors and image processing, the detection of the crop (and soil marks) formations, which relate to buried archaeological remains, are difficult to detect since these marks may not be visible in the images if observed over different period or at different spatial/spectral resolution. In order to support the improvement of earth observation remote sensing technologies specifically targeting archaeological research, a better understanding of the crop/soil marks formation needs to be studied in detail. In this paper the contribution of both Unmanned Aerial Systems as well ground spectroradiometers is discussed in a variety of examples applied in the eastern Mediterranean region (Cyprus and Greece) as well in Central Europe (Hungary). In- situ spectroradiometric campaigns can be applied for the removal of atmospheric impact to simultaneous satellite overpass images. In addition, as shown in this paper, the systematic collection of ground truth data prior to the satellite/aerial acquisition can be used to detect the optimum temporal and spectral resolution for the detection of stress vegetation related to buried archaeological remains. Moreover, phenological studies of the crops from the area of interest can be simulated to the potential sensors based on their Relative Response Filters and therefore prepare better the satellite-aerial campaigns. Ground data and the use of Unmanned Aerial Systems (UAS) can provide an increased insight for studying the formation of crop and soil marks. New algorithms such as vegetation indices and linear orthogonal equations for the enhancement of crop marks can be developed based on the specific spectral characteristics of the area. As well, UAS can be used for remote sensing applications in order to document, survey and model cultural heritage and archaeological sites.

Feature selection methods for object-based classification of sub-decimeter resolution digital aerial imagery

USDA-ARS?s Scientific Manuscript database

Due to the availability of numerous spectral, spatial, and contextual features, the determination of optimal features and class separabilities can be a time consuming process in object-based image analysis (OBIA). While several feature selection methods have been developed to assist OBIA, a robust c...

Employing unmanned aerial vehicle to monitor the health condition of wind turbines

NASA Astrophysics Data System (ADS)

Huang, Yishuo; Chiang, Chih-Hung; Hsu, Keng-Tsang; Cheng, Chia-Chi

2018-04-01

Unmanned aerial vehicle (UAV) can gather the spatial information of huge structures, such as wind turbines, that can be difficult to obtain with traditional approaches. In this paper, the UAV used in the experiments is equipped with high resolution camera and thermal infrared camera. The high resolution camera can provide a series of images with resolution up to 10 Megapixels. Those images can be used to form the 3D model using the digital photogrammetry technique. By comparing the 3D scenes of the same wind turbine at different times, possible displacement of the supporting tower of the wind turbine, caused by ground movement or foundation deterioration may be determined. The recorded thermal images are analyzed by applying the image segmentation methods to the surface temperature distribution. A series of sub-regions are separated by the differences of the surface temperature. The high-resolution optical image and the segmented thermal image are fused such that the surface anomalies are more easily identified for wind turbines.

Using High Spatial Resolution Satellite Imagery to Map Forest Burn Severity Across Spatial Scales in a Pine Barrens Ecosystem

NASA Technical Reports Server (NTRS)

Meng, Ran; Wu, Jin; Schwager, Kathy L.; Zhao, Feng; Dennison, Philip E.; Cook, Bruce D.; Brewster, Kristen; Green, Timothy M.; Serbin, Shawn P.

2017-01-01

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (less than or equal to 5 m) from very-high-resolution (VHR) data. We assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severity was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal - pre- and post-fire event - WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). This work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the less than 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Ran; Wu, Jin; Schwager, Kathy L.

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Using high spatial resolution satellite imagery to map forest burn severity across spatial scales in a Pine Barrens ecosystem

DOE PAGES

Meng, Ran; Wu, Jin; Schwager, Kathy L.; ...

2017-01-21

As a primary disturbance agent, fire significantly influences local processes and services of forest ecosystems. Although a variety of remote sensing based approaches have been developed and applied to Landsat mission imagery to infer burn severity at 30 m spatial resolution, forest burn severity have still been seldom assessed at fine spatial scales (≤ 5 m) from very-high-resolution (VHR) data. Here we assessed a 432 ha forest fire that occurred in April 2012 on Long Island, New York, within the Pine Barrens region, a unique but imperiled fire-dependent ecosystem in the northeastern United States. The mapping of forest burn severitymore » was explored here at fine spatial scales, for the first time using remotely sensed spectral indices and a set of Multiple Endmember Spectral Mixture Analysis (MESMA) fraction images from bi-temporal — pre- and post-fire event — WorldView-2 (WV-2) imagery at 2 m spatial resolution. We first evaluated our approach using 1 m by 1 m validation points at the sub-crown scale per severity class (i.e. unburned, low, moderate, and high severity) from the post-fire 0.10 m color aerial ortho-photos; then, we validated the burn severity mapping of geo-referenced dominant tree crowns (crown scale) and 15 m by 15 m fixed-area plots (inter-crown scale) with the post-fire 0.10 m aerial ortho-photos and measured crown information of twenty forest inventory plots. Our approach can accurately assess forest burn severity at the sub-crown (overall accuracy is 84% with a Kappa value of 0.77), crown (overall accuracy is 82% with a Kappa value of 0.76), and inter-crown scales (89% of the variation in estimated burn severity ratings (i.e. Geo-Composite Burn Index (CBI)). Lastly, this work highlights that forest burn severity mapping from VHR data can capture heterogeneous fire patterns at fine spatial scales over the large spatial extents. This is important since most ecological processes associated with fire effects vary at the < 30 m scale and VHR approaches could significantly advance our ability to characterize fire effects on forest ecosystems.« less

Use of Aerial high resolution visible imagery to produce large river bathymetry: a multi temporal and spatial study over the by-passed Upper Rhine

NASA Astrophysics Data System (ADS)

Béal, D.; Piégay, H.; Arnaud, F.; Rollet, A.; Schmitt, L.

2011-12-01

Aerial high resolution visible imagery allows producing large river bathymetry assuming that water depth is related to water colour (Beer-Bouguer-Lambert law). In this paper we aim at monitoring Rhine River geometry changes for a diachronic study as well as sediment transport after an artificial injection (25.000 m3 restoration operation). For that a consequent data base of ground measurements of river depth is used, built on 3 different sources: (i) differential GPS acquisitions, (ii) sounder data and (iii) lateral profiles realized by experts. Water depth is estimated using a multi linear regression over neo channels built on a principal component analysis over red, green and blue bands and previously cited depth data. The study site is a 12 km long reach of the by-passed section of the Rhine River that draws French and German border. This section has been heavily impacted by engineering works during the last two centuries: channelization since 1842 for navigation purposes and the construction of a 45 km long lateral canal and 4 consecutive hydroelectric power plants of since 1932. Several bathymetric models are produced based on 3 different spatial resolutions (6, 13 and 20 cm) and 5 acquisitions (January, March, April, August and October) since 2008. Objectives are to find the optimal spatial resolution and to characterize seasonal effects. Best performances according to the 13 cm resolution show a 18 cm accuracy when suspended matters impacted less water transparency. Discussions are oriented to the monitoring of the artificial reload after 2 flood events during winter 2010-2011. Bathymetric models produced are also useful to build 2D hydraulic model's mesh.

A new framework for UAV-based remote sensing data processing and its application in almond water stress quantification

USDA-ARS?s Scientific Manuscript database

With the rapid development of small imaging sensors and unmanned aerial vehicles (UAVs), remote sensing is undergoing a revolution with greatly increased spatial and temporal resolutions. While more relevant detail becomes available, it is a challenge to analyze the large number of images to extract...

Medium Spatial Resolution Satellite Characterization

NASA Technical Reports Server (NTRS)

Stensaas, Greg

2007-01-01

This project provides characterization and calibration of aerial and satellite systems in support of quality acquisition and understanding of remote sensing data, and verifies and validates the associated data products with respect to ground and and atmospheric truth so that accurate value-added science can be performed. The project also provides assessment of new remote sensing technologies.

Textural analysis of historical aerial photography to characterize woody plant encroachment in South African savanna

Treesearch

Andrew T. Hudak; Carol A. Wessman

1998-01-01

Transitions from grassland to shrubland through woody plant encroachment result in potentially significant shifts in savanna ecosystem function. Given high resolution imagery, a textural index could prove useful for mapping woody plant densities and monitoring woody plant encroachment across savanna landscapes. Spatial heterogeneity introduced through mixtures of...

Helicopter-based Photography for use in SfM over the West Greenland Ablation Zone

NASA Astrophysics Data System (ADS)

Mote, T. L.; Tedesco, M.; Astuti, I.; Cotten, D.; Jordan, T.; Rennermalm, A. K.

2015-12-01

Results of low-elevation high-resolution aerial photography from a helicopter are reported for a supraglacial watershed in West Greenland. Data were collected at the end of July 2015 over a supraglacial watershed terminating in the Kangerlussuaq region of Greenland and following the Utrecht University K-Transect of meteorological stations. The aerial photography reported here were complementary observations used to support hyperspectral measurements of albedo, discussed in the Greenland Ice sheet hydrology session of this AGU Fall meeting. A compact digital camera was installed inside a pod mounted on the side of the helicopter together with gyroscopes and accelerometers that were used to estimate the relative orientation. Continuous video was collected on 19 and 21 July flights, and frames extracted from the videos are used to create a series of aerial photos. Individual geo-located aerial photos were also taken on a 24 July flight. We demonstrate that by maintaining a constant flight elevation and a near constant ground speed, a helicopter with a mounted camera can produce 3-D structure of the ablation zone of the ice sheet at unprecedented spatial resolution of the order of 5 - 10 cm. By setting the intervalometer on the camera to 2 seconds, the images obtained provide sufficient overlap (>60%) for digital image alignment, even at a flight elevation of ~170m. As a result, very accurate point matching between photographs can be achieved and an extremely dense RGB encoded point cloud can be extracted. Overlapping images provide a series of stereopairs that can be used to create point cloud data consisting of 3 position and 3 color variables, X, Y, Z, R, G, and B. This point cloud is then used to create orthophotos or large scale digital elevation models, thus accurately displaying ice structure. The geo-referenced images provide a ground spatial resolution of approximately 6 cm, permitting analysis of detailed features, such as cryoconite holes, evolving small order streams, and cracks from hydrofracturing.

A Phenology-based Method For Identifying the Planting Fraction of Winter Wheat Using Moderate-resolution Satellite Data

NASA Astrophysics Data System (ADS)

Dong, J.; Liu, W.; Han, W.; Lei, T.; Xia, J.; Yuan, W.

2017-12-01

Winter wheat is a staple food crop for most of the world's population, and the area and spatial distribution of winter wheat are key elements in estimating crop production and ensuring food security. However, winter wheat planting areas contain substantial spatial heterogeneity with mixed pixels for coarse- and moderate-resolution satellite data, leading to significant errors in crop acreage estimation. This study has developed a phenology-based approach using moderate-resolution satellite data to estimate sub-pixel planting fractions of winter wheat. Based on unmanned aerial vehicle (UAV) observations, the unique characteristics of winter wheat with high vegetation index values at the heading stage (May) and low values at the harvest stage (June) were investigated. The differences in vegetation index between heading and harvest stages increased with the planting fraction of winter wheat, and therefore the planting fractions were estimated by comparing the NDVI differences of a given pixel with those of predetermined pure winter wheat and non-winter wheat pixels. This approach was evaluated using aerial images and agricultural statistical data in an intensive agricultural region, Shandong Province in North China. The method explained 60% and 85% of the spatial variation in county- and municipal-level statistical data, respectively. More importantly, the predetermined pure winter wheat and non-winter wheat pixels can be automatically identified using MODIS data according to their NDVI differences, which strengthens the potential to use this method at regional and global scales without any field observations as references.

High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision and hobbyist unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Dandois, J. P.; Ellis, E. C.

2013-12-01

High spatial resolution three-dimensional (3D) measurements of vegetation by remote sensing are advancing ecological research and environmental management. However, substantial economic and logistical costs limit this application, especially for observing phenological dynamics in ecosystem structure and spectral traits. Here we demonstrate a new aerial remote sensing system enabling routine and inexpensive aerial 3D measurements of canopy structure and spectral attributes, with properties similar to those of LIDAR, but with RGB (red-green-blue) spectral attributes for each point, enabling high frequency observations within a single growing season. This 'Ecosynth' methodology applies photogrammetric ''Structure from Motion'' computer vision algorithms to large sets of highly overlapping low altitude (< 130 m) aerial photographs acquired using off-the-shelf digital cameras mounted on an inexpensive (< USD$4000), lightweight (< 2 kg), hobbyist-grade unmanned aerial system (UAS). Ecosynth 3D point clouds with densities of 30 - 67 points m-2 were produced using commercial computer vision software from digital photographs acquired repeatedly by UAS over three 6.25 ha (250 m x 250 m) Temperate Deciduous forest sites in Maryland USA. Ecosynth canopy height maps (CHMs) were strong predictors of field-measured tree heights (R2 0.63 to 0.84) and were highly correlated with a LIDAR CHM (R 0.87) acquired 4 days earlier, though Ecosynth-based estimates of aboveground biomass densities included significant errors (31 - 36% of field-based estimates). Repeated scanning of a 0.25 ha forested area at six different times across a 16 month period revealed ecologically significant dynamics in canopy color at different heights and a structural shift upward in canopy density, as demonstrated by changes in vertical height profiles of point density and relative RGB brightness. Changes in canopy relative greenness were highly correlated (R2 = 0.88) with MODIS NDVI time series for the same area and vertical differences in canopy color revealed the early green up of the dominant canopy species, Liriodendron tulipifera, strong evidence that Ecosynth time series measurements capture vegetation structural and spectral dynamics at the spatial scale of individual trees. Observing canopy phenology in 3D at high temporal resolutions represents a breakthrough in forest ecology. Inexpensive user-deployed technologies for multispectral 3D scanning of vegetation at landscape scales (< 1 km2) heralds a new era of participatory remote sensing by field ecologists, community foresters and the interested public.

Quantifying efficacy and limits of unmanned aerial vehicle (UAV) technology for weed seedling detection as affected by sensor resolution.

PubMed

Peña, José M; Torres-Sánchez, Jorge; Serrano-Pérez, Angélica; de Castro, Ana I; López-Granados, Francisca

2015-03-06

In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5-6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations.

Quantifying Efficacy and Limits of Unmanned Aerial Vehicle (UAV) Technology for Weed Seedling Detection as Affected by Sensor Resolution

PubMed Central

Peña, José M.; Torres-Sánchez, Jorge; Serrano-Pérez, Angélica; de Castro, Ana I.; López-Granados, Francisca

2015-01-01

In order to optimize the application of herbicides in weed-crop systems, accurate and timely weed maps of the crop-field are required. In this context, this investigation quantified the efficacy and limitations of remote images collected with an unmanned aerial vehicle (UAV) for early detection of weed seedlings. The ability to discriminate weeds was significantly affected by the imagery spectral (type of camera), spatial (flight altitude) and temporal (the date of the study) resolutions. The colour-infrared images captured at 40 m and 50 days after sowing (date 2), when plants had 5–6 true leaves, had the highest weed detection accuracy (up to 91%). At this flight altitude, the images captured before date 2 had slightly better results than the images captured later. However, this trend changed in the visible-light images captured at 60 m and higher, which had notably better results on date 3 (57 days after sowing) because of the larger size of the weed plants. Our results showed the requirements on spectral and spatial resolutions needed to generate a suitable weed map early in the growing season, as well as the best moment for the UAV image acquisition, with the ultimate objective of applying site-specific weed management operations. PMID:25756867

Mapping Glacier Dynamics and Proglacial Wetlands with a Multispectral UAV at 5000m in the Cordillera Blanca, Peru

NASA Astrophysics Data System (ADS)

Wigmore, O.; Mark, B. G.

2015-12-01

The glaciers of the Cordillera Blanca, Peru are rapidly retreating as a result of rising temperatures, transforming the hydrology and impacting the socio-economic and environmental systems of the Rio Santa basin. Documenting the heterogeneous spatial patterns of these changes to understand processes of water storage and flow is hindered by technologic and logistic challenges. Highly complex topography, cloud cover and coarse spatial resolution limit the application of satellite data while airborne data collection remains costly and potentially dangerous. However, recent developments have made Unmanned Aerial Vehicle (UAV) technology a viable and potentially transformative method for studying glacier dynamics and proglacial hydrology. The extreme altitudes (4000-6700m) of the Cordillera Blanca limit the use of 'off the shelf' UAVs. Therefore we developed a low cost multispectral (visible, near-infrared and thermal infrared) multirotor UAV capable of conducting fully autonomous aerial surveys at elevations over 5000m within the glacial valleys of the Cordillera Blanca. Using this platform we have completed repeat aerial surveys (in 2014 and 2015) of the debris covered Llaca Glacier, generating highly accurate 10-20cm DEM's and 5cm orthomosaics using a structure from motion workflow. Analysis of these data reveals a highly dynamic system with some areas of the glacier losing as much as 16m of vertical elevation, while other areas have gained up to 5m of elevation over one year. The magnitude and direction of these changes appears to be associated with the presence of debris free ice faces and meltwater ponds. Additionally, we have mapped proglacial meadow and wetland systems. Thermal mosaics at 10-20cm resolution are providing novel insights into the hydrologic pathways of glacier meltwater including mapping the distribution of artesian springs that feed these wetland systems. The high spatial resolution of these UAV datasets facilitates a better understanding of the spatial variability in and controls on glacier dynamics and proglacial surface/subsurface hydrology within the Cordillera Blanca. We discuss the technical details of the platform, the challenges of conducting UAV surveys at high elevation and share insights from our findings at Llaca Glacier and within the proglacial wetland systems.

High Resolution UAV-based Passive Microwave L-band Imaging of Soil Moisture

NASA Astrophysics Data System (ADS)

Gasiewski, A. J.; Stachura, M.; Elston, J.; McIntyre, E. M.

2013-12-01

Due to long electrical wavelengths and aperture size limitations the scaling of passive microwave remote sensing of soil moisture from spaceborne low-resolution applications to high resolution applications suitable for precision agriculture requires use of low flying aerial vehicles. This presentation summarizes a project to develop a commercial Unmanned Aerial Vehicle (UAV) hosting a precision microwave radiometer for mapping of soil moisture in high-value shallow root-zone crops. The project is based on the use of the Tempest electric-powered UAV and a compact digital L-band (1400-1427 MHz) passive microwave radiometer developed specifically for extremely small and lightweight aerial platforms or man-portable, tractor, or tower-based applications. Notable in this combination are a highly integrated UAV/radiometer antenna design and use of both the upwelling emitted signal from the surface and downwelling cold space signal for precise calibration using a lobe-correlating radiometer architecture. The system achieves a spatial resolution comparable to the altitude of the UAV above the ground while referencing upwelling measurements to the constant and well-known background temperature of cold space. The radiometer incorporates digital sampling and radio frequency interference mitigation along with infrared, near-infrared, and visible (red) sensors for surface temperature and vegetation biomass correction. This NASA-sponsored project is being developed both for commercial application in cropland water management, L-band satellite validation, and estuarian plume studies.

Earth mapping - aerial or satellite imagery comparative analysis

NASA Astrophysics Data System (ADS)

Fotev, Svetlin; Jordanov, Dimitar; Lukarski, Hristo

Nowadays, solving the tasks for revision of existing map products and creation of new maps requires making a choice of the land cover image source. The issue of the effectiveness and cost of the usage of aerial mapping systems versus the efficiency and cost of very-high resolution satellite imagery is topical [1, 2, 3, 4]. The price of any remotely sensed image depends on the product (panchromatic or multispectral), resolution, processing level, scale, urgency of task and on whether the needed image is available in the archive or has to be requested. The purpose of the present work is: to make a comparative analysis between the two approaches for mapping the Earth having in mind two parameters: quality and cost. To suggest an approach for selection of the map information sources - airplane-based or spacecraft-based imaging systems with very-high spatial resolution. Two cases are considered: area that equals approximately one satellite scene and area that equals approximately the territory of Bulgaria.

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response.

PubMed

Ofli, Ferda; Meier, Patrick; Imran, Muhammad; Castillo, Carlos; Tuia, Devis; Rey, Nicolas; Briant, Julien; Millet, Pauline; Reinhard, Friedrich; Parkan, Matthew; Joost, Stéphane

2016-03-01

Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resolution of aerial imagery is an order of magnitude higher than the imagery produced by the most sophisticated commercial satellites today. Both the United States Federal Emergency Management Agency (FEMA) and the European Commission's Joint Research Center (JRC) have noted that aerial imagery will inevitably present a big data challenge. The purpose of this article is to get ahead of this future challenge by proposing a hybrid crowdsourcing and real-time machine learning solution to rapidly process large volumes of aerial data for disaster response in a time-sensitive manner. Crowdsourcing can be used to annotate features of interest in aerial images (such as damaged shelters and roads blocked by debris). These human-annotated features can then be used to train a supervised machine learning system to learn to recognize such features in new unseen images. In this article, we describe how this hybrid solution for image analysis can be implemented as a module (i.e., Aerial Clicker) to extend an existing platform called Artificial Intelligence for Disaster Response (AIDR), which has already been deployed to classify microblog messages during disasters using its Text Clicker module and in response to Cyclone Pam, a category 5 cyclone that devastated Vanuatu in March 2015. The hybrid solution we present can be applied to both aerial and satellite imagery and has applications beyond disaster response such as wildlife protection, human rights, and archeological exploration. As a proof of concept, we recently piloted this solution using very high-resolution aerial photographs of a wildlife reserve in Namibia to support rangers with their wildlife conservation efforts (SAVMAP project, http://lasig.epfl.ch/savmap ). The results suggest that the platform we have developed to combine crowdsourcing and machine learning to make sense of large volumes of aerial images can be used for disaster response.

Species–specific contributions to moderate resolution vegetation indices derived from sub-decimeter aerial photography – Prospects for phenological monitoring

USDA-ARS?s Scientific Manuscript database

High spatial heterogeneity in ground cover, large amounts of exposed bare soil, and modest cover from shrubs and grasses in arid and semi-arid ecosystems challenge the integration of field observations of phenology and remotely sensed data to monitor changes in land surface phenology. This research ...

Lessons learned in historical mapping of conifer and oak in the North Coast

Treesearch

Melissa V. Eitzel; Maggi Kelly; Lenya N. Quinn-Davidson

2015-01-01

Conifer encroachment into oak woodlands is becoming a pressing concern for oak conservation, particularly in California's north coast. We use Object-Based Image Analysis (OBIA) with historical aerial imagery from 1948 and recent high-spatial-resolution images from 2009 to explore the potential for mapping encroachment using remote sensing. We find that pre-...

Use of multi-temporal UAV-derived imagery for estimating individual tree growth in Pinus pinea stands

Treesearch

Juan Guerra-Hernández; Eduardo González-Ferreiro; Vicente Monleon; Sonia Faias; Margarida Tomé; Ramón Díaz-Varela

2017-01-01

High spatial resolution imagery provided by unmanned aerial vehicles (UAVs) can yield accurate and efficient estimation of tree dimensions and canopy structural variables at the local scale. We flew a low-cost, lightweight UAV over an experimental Pinus pinea L. plantation (290 trees distributed over 16 ha with different fertirrigation treatments)...

The sky is the limit? 20 years of small-format aerial photography taken from UAS for monitoring geomorphological processes

NASA Astrophysics Data System (ADS)

Marzolff, Irene

2014-05-01

One hundred years after the first publication on aerial photography taken from unmanned aerial platforms (Arthur Batut 1890), small-format aerial photography (SFAP) became a distinct niche within remote sensing during the 1990s. Geographers, plant biologists, archaeologists and other researchers with geospatial interests re-discovered the usefulness of unmanned platforms for taking high-resolution, low-altitude photographs that could then be digitized and analysed with geographical information systems, (softcopy) photogrammetry and image processing techniques originally developed for digital satellite imagery. Even before the ubiquity of digital consumer-grade cameras and 3D analysis software accessible to the photogrammetric layperson, do-it-yourself remote sensing using kites, blimps, drones and micro air vehicles literally enabled the questing researcher to get their own pictures of the world. As a flexible, cost-effective method, SFAP offered images with high spatial and temporal resolutions that could be ideally adapted to the scales of landscapes, forms and distribution patterns to be monitored. During the last five years, this development has been significantly accelerated by the rapid technological advancements of GPS navigation, autopiloting and revolutionary softcopy-photogrammetry techniques. State-of-the-art unmanned aerial systems (UAS) now allow automatic flight planning, autopilot-controlled aerial surveys, ground control-free direct georeferencing and DEM plus orthophoto generation with centimeter accuracy, all within the space of one day. The ease of use of current UAS and processing software for the generation of high-resolution topographic datasets and spectacular visualizations is tempting and has spurred the number of publications on these issues - but which advancements in our knowledge and understanding of geomorphological processes have we seen and can we expect in the future? This presentation traces the development of the last two decades by presenting and discussing examples for geomorphological research using UAS, mostly from the field of soil erosion monitoring.

Spatial consistency of chinook salmon redd distribution within and among years in the Cowlitz River, Washington

USGS Publications Warehouse

Klett, Katherine J.C.; Torgersen, Christian E.; Henning, Julie A.; Murray, Christopher J.

2013-01-01

We investigated the spawning patterns of Chinook Salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington, using a unique set of fine- and coarse-scale temporal and spatial data collected during biweekly aerial surveys conducted in 1991–2009 (500 m to 28 km resolution) and 2008–2009 (100–500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held GPS synchronized with in-flight audio recordings. We examined spatial patterns of Chinook Salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook Salmon spawned in the same sections each year with little variation among years. On a coarse scale, 5 years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years. Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations. On a finer temporal scale, we observed that Chinook Salmon spawned in the same sections during the first and last week. Redds were clustered in both 2008 and 2009. Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook Salmon spawning surveys.

Tradeoff between picture element dimensions and noncoherent averaging in side-looking airborne radar

NASA Technical Reports Server (NTRS)

Moore, R. K.

1979-01-01

An experiment was performed in which three synthetic-aperture images and one real-aperture image were successively degraded in spatial resolution, both retaining the same number of independent samples per pixel and using the spatial degradation to allow averaging of different numbers of independent samples within each pixel. The original and degraded images were provided to three interpreters familiar with both aerial photographs and radar images. The interpreters were asked to grade each image in terms of their ability to interpret various specified features on the image. The numerical interpretability grades were then used as a quantitative measure of the utility of the different kinds of image processing and different resolutions. The experiment demonstrated empirically that the interpretability is related exponentially to the SGL volume which is the product of azimuth, range, and gray-level resolution.

Quantifying forest fragmentation using Geographic Information Systems and Forest Inventory and Analysis plot data

Treesearch

Dacia M. Meneguzzo; Mark H. Hansen

2009-01-01

Fragmentation metrics provide a means of quantifying and describing forest fragmentation. The most common method of calculating these metrics is through the use of Geographic Information System software to analyze raster data, such as a satellite or aerial image of the study area; however, the spatial resolution of the imagery has a significant impact on the results....

Coastal areas mapping using UAV photogrammetry

NASA Astrophysics Data System (ADS)

Nikolakopoulos, Konstantinos G.; Kozarski, Dimitrios; Kogkas, Stefanos

2017-10-01

The coastal areas in the Patras Gulf suffer degradation due to the sea action and other natural and human-induced causes. Changes in beaches, ports, and other man made constructions need to be assessed, both after severe events and on a regular basis, to build models that can predict the evolution in the future. Thus, reliable spatial data acquisition is a critical process for the identification of the coastline and the broader coastal zones for geologists and other scientists involved in the study of coastal morphology. High resolution satellite data, airphotos and airborne Lidar provided in the past the necessary data for the coastline monitoring. High-resolution digital surface models (DSMs) and orthophoto maps had become a necessity in order to map with accuracy all the variations in costal environments. Recently, unmanned aerial vehicles (UAV) photogrammetry offers an alternative solution to the acquisition of high accuracy spatial data along the coastline. This paper presents the use of UAV to map the coastline in Rio area Western Greece. Multiple photogrammetric aerial campaigns were performed. A small commercial UAV (DJI Phantom 3 Advance) was used to acquire thousands of images with spatial resolutions better than 5 cm. Different photogrammetric software's were used to orientate the images, extract point clouds, build a digital surface model and produce orthoimage mosaics. In order to achieve the best positional accuracy signalised ground control points were measured with a differential GNSS receiver. The results of this coastal monitoring programme proved that UAVs can replace many of the conventional surveys, with considerable gains in the cost of the data acquisition and without any loss in the accuracy.

Precision measurements from very-large scale aerial digital imagery.

PubMed

Booth, D Terrance; Cox, Samuel E; Berryman, Robert D

2006-01-01

Managers need measurements and resource managers need the length/width of a variety of items including that of animals, logs, streams, plant canopies, man-made objects, riparian habitat, vegetation patches and other things important in resource monitoring and land inspection. These types of measurements can now be easily and accurately obtained from very large scale aerial (VLSA) imagery having spatial resolutions as fine as 1 millimeter per pixel by using the three new software programs described here. VLSA images have small fields of view and are used for intermittent sampling across extensive landscapes. Pixel-coverage among images is influenced by small changes in airplane altitude above ground level (AGL) and orientation relative to the ground, as well as by changes in topography. These factors affect the object-to-camera distance used for image-resolution calculations. 'ImageMeasurement' offers a user-friendly interface for accounting for pixel-coverage variation among images by utilizing a database. 'LaserLOG' records and displays airplane altitude AGL measured from a high frequency laser rangefinder, and displays the vertical velocity. 'Merge' sorts through large amounts of data generated by LaserLOG and matches precise airplane altitudes with camera trigger times for input to the ImageMeasurement database. We discuss application of these tools, including error estimates. We found measurements from aerial images (collection resolution: 5-26 mm/pixel as projected on the ground) using ImageMeasurement, LaserLOG, and Merge, were accurate to centimeters with an error less than 10%. We recommend these software packages as a means for expanding the utility of aerial image data.

Autonomous aerial observations to extend and complement the Earth Observing System: a science-driven systems-oriented approach

NASA Astrophysics Data System (ADS)

Sandford, Stephen P.; Harrison, F. W.; Langford, John; Johnson, James W.; Qualls, Garry; Emmitt, David; Jones, W. Linwood; Shugart, Herman H., Jr.

2004-12-01

The current Earth observing capability depends primarily on spacecraft missions and ground-based networks to provide the critical on-going observations necessary for improved understanding of the Earth system. Aircraft missions play an important role in process studies but are limited to relatively short-duration flights. Suborbital observations have contributed to global environmental knowledge by providing in-depth, high-resolution observations that space-based and in-situ systems are challenged to provide; however, the limitations of aerial platforms - e.g., limited observing envelope, restrictions associated with crew safety and high cost of operations have restricted the suborbital program to a supporting role. For over a decade, it has been recognized that autonomous aerial observations could potentially be important. Advances in several technologies now enable autonomous aerial observation systems (AAOS) that can provide fundamentally new observational capability for Earth science and applications and thus lead scientists and engineers to rethink how suborbital assets can best contribute to Earth system science. Properly developed and integrated, these technologies will enable new Earth science and operational mission scenarios with long term persistence, higher-spatial and higher-temporal resolution at lower cost than space or ground based approaches. This paper presents the results of a science driven, systems oriented study of broad Earth science measurement needs. These needs identify aerial mission scenarios that complement and extend the current Earth Observing System. These aerial missions are analogous to space missions in their complexity and potential for providing significant data sets for Earth scientists. Mission classes are identified and presented based on science driven measurement needs in atmospheric, ocean and land studies. Also presented is a nominal concept of operations for an AAOS: an innovative set of suborbital assets that complements and augments current and planned space-based observing systems.

Very high resolution aerial films

NASA Astrophysics Data System (ADS)

Becker, Rolf

1986-11-01

The use of very high resolution aerial films in aerial photography is evaluated. Commonly used panchromatic, color, and CIR films and their high resolution equivalents are compared. Based on practical experience and systematic investigations, the very high image quality and improved height accuracy that can be achieved using these films are demonstrated. Advantages to be gained from this improvement and operational restrictions encountered when using high resolution film are discussed.

Ortho-Rectification of Narrow Band Multi-Spectral Imagery Assisted by Dslr RGB Imagery Acquired by a Fixed-Wing Uas

NASA Astrophysics Data System (ADS)

Rau, J.-Y.; Jhan, J.-P.; Huang, C.-Y.

2015-08-01

Miniature Multiple Camera Array (MiniMCA-12) is a frame-based multilens/multispectral sensor composed of 12 lenses with narrow band filters. Due to its small size and light weight, it is suitable to mount on an Unmanned Aerial System (UAS) for acquiring high spectral, spatial and temporal resolution imagery used in various remote sensing applications. However, due to its wavelength range is only 10 nm that results in low image resolution and signal-to-noise ratio which are not suitable for image matching and digital surface model (DSM) generation. In the meantime, the spectral correlation among all 12 bands of MiniMCA images are low, it is difficult to perform tie-point matching and aerial triangulation at the same time. In this study, we thus propose the use of a DSLR camera to assist automatic aerial triangulation of MiniMCA-12 imagery and to produce higher spatial resolution DSM for MiniMCA12 ortho-image generation. Depending on the maximum payload weight of the used UAS, these two kinds of sensors could be collected at the same time or individually. In this study, we adopt a fixed-wing UAS to carry a Canon EOS 5D Mark2 DSLR camera and a MiniMCA-12 multi-spectral camera. For the purpose to perform automatic aerial triangulation between a DSLR camera and the MiniMCA-12, we choose one master band from MiniMCA-12 whose spectral range has overlap with the DSLR camera. However, all lenses of MiniMCA-12 have different perspective centers and viewing angles, the original 12 channels have significant band misregistration effect. Thus, the first issue encountered is to reduce the band misregistration effect. Due to all 12 MiniMCA lenses being frame-based, their spatial offsets are smaller than 15 cm and all images are almost 98% overlapped, we thus propose a modified projective transformation (MPT) method together with two systematic error correction procedures to register all 12 bands of imagery on the same image space. It means that those 12 bands of images acquired at the same exposure time will have same interior orientation parameters (IOPs) and exterior orientation parameters (EOPs) after band-to-band registration (BBR). Thus, in the aerial triangulation stage, the master band of MiniMCA-12 was treated as a reference channel to link with DSLR RGB images. It means, all reference images from the master band of MiniMCA-12 and all RGB images were triangulated at the same time with same coordinate system of ground control points (GCP). Due to the spatial resolution of RGB images is higher than the MiniMCA-12, the GCP can be marked on the RGB images only even they cannot be recognized on the MiniMCA images. Furthermore, a one meter gridded digital surface model (DSM) is created by the RGB images and applied to the MiniMCA imagery for ortho-rectification. Quantitative error analyses show that the proposed BBR scheme can achieve 0.33 pixels of average misregistration residuals length and the co-registration errors among 12 MiniMCA ortho-images and between MiniMCA and Canon RGB ortho-images are all less than 0.6 pixels. The experimental results demonstrate that the proposed method is robust, reliable and accurate for future remote sensing applications.

Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

NASA Astrophysics Data System (ADS)

Li, R.; Zhang, T.; Geng, R.; Wang, L.

2018-04-01

In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

Characterization of surface oil thickness distribution patterns observed during the Deepwater Horizon (MC-252) oil spill with aerial and satellite remote sensing.

PubMed

Svejkovsky, Jan; Hess, Mark; Muskat, Judd; Nedwed, Tim J; McCall, Jenifer; Garcia, Oscar

2016-09-15

Knowledge of the spatial distribution of oil thickness patterns within an on-water spill is of obvious importance for immediate spill response activities as well as for subsequent evaluation of the spill impacts. For long-lasting continuous spills like the 2010 3-month Deepwater Horizon (DWH) event in the Gulf of Mexico, it is also important to identify changes in the dominant oil features through time. This study utilized very high resolution (≤5m) aerial and satellite imagery acquired during the DWH spill to evaluate the shape, size and thickness of surface oil features that dominated the DWH slick. Results indicate that outside of the immediate spill source region, oil distributions did not encompass a broad, varied range of thicknesses. Instead, the oil separated into four primary, distinct characterizations: 1) invisible surface films detectable only with Synthetic Aperture Radar imaging because of the decreased surface backscatter, 2) thicker sheen & rainbow areas (<0.005mm), 3) large regional areas of relatively thin, "metallic appearance" films (0.005-0.08mm), and 4) strands of thick, emulsified oil (>1mm) that were consistently hundreds of meters long but most commonly only 10-50m wide. Where present within the slick footprint, each of the three distinct visible oil thickness classes maintained its shape characteristics both spatially (at different distances from the source and in different portions of the slick), and temporally (from mid-May through July 2010). The region over the source site tended to contain a more continuous range of oil thicknesses, however, our results indicate that the continuous injection of subsurface dispersants starting in late May significantly altered (lowered) that range. In addition to characterizing the oil thickness distribution patterns through the timeline of one of the world's largest oil spills, this paper also details the extension of using high resolution aerial imagery to calibrate medium resolution satellite data sources such as USA's Thematic Mapper (30m) to provide larger-scale spatial views of major spills, and discusses implications for utilizing such data for oil spill characterizations and spill response. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use of Unmanned Aerial Vehicles for Improving Farm Scale Agricultural Water Management in Agriculture at a Farm Scale. A case study for field crops in the California's Central Valley

NASA Astrophysics Data System (ADS)

Medellin-Azuara, J.; Morande, J. A.; Jin, Y.; Chen, Y.; Paw U, K. T.; Viers, J. H.

2016-12-01

Traditional methods for estimating consumptive water use as evapotranspiration (ET) for agriculture in areas with water limitations such as California have always been a challenge for farmers, water managers, researchers and government agencies. Direct measurement of evapotranspiration (ET) and crop water stress in agriculture can be a cumbersome and costly task. Furthermore, spatial variability of applied water and irrigation and stress level in crops, due to inherent heterogeneity in soil conditions, topography, management practices, and lack of uniformity in water applications may affect estimates water use efficiency and water balances. This situation difficult long-term management of agroecosystems. This paper presents a case study for various areas in California's Central Valley using Unmanned Aerial Vehicles (UAVs) for a late portion of the 2016 irrigation season These estimates are compared those obtained by direct measurement (from previously deployed stations), and energy balance approaches with remotely sensed data in a selection of field crop parcels. This research improves information on water use and site conditions in agriculture by enhancing remote sensing-based estimations through the use of higher resolution multi-spectral and thermal imagery captured by UAV. We assess whether more frequent information at higher spatial resolution from UAVs can improve estimations of overall ET through energy balance and imagery. Stress levels and ET are characterized spatially to examine irrigation practices and their performance to improve water use in the agroecosystem. Ground based data such as air and crop temperature and stem water potential is collected to validate UAV aerial measurements. Preliminary results show the potential of UAV technology to improve timing, resolution and accuracy in the ET estimation and assessment of crop stress at a farm scales. Side to side comparison with ground level stations employing surface renewal, eddy covariance and energy balance provides a testbed to improve understanding of consumptive use and crop water management in water scarce irrigated agriculture regions. Keywords. California Central Valley, Agricultural Water Use, Remote Sensing, Energy Balance, Evapotranspiration, Water management,

Does the Data Resolution/origin Matter? Satellite, Airborne and Uav Imagery to Tackle Plant Invasions

NASA Astrophysics Data System (ADS)

Müllerová, Jana; Brůna, Josef; Dvořák, Petr; Bartaloš, Tomáš; Vítková, Michaela

2016-06-01

Invasive plant species represent a serious threat to biodiversity and landscape as well as human health and socio-economy. To successfully fight plant invasions, new methods enabling fast and efficient monitoring, such as remote sensing, are needed. In an ongoing project, optical remote sensing (RS) data of different origin (satellite, aerial and UAV), spectral (panchromatic, multispectral and color), spatial (very high to medium) and temporal resolution, and various technical approaches (object-, pixelbased and combined) are tested to choose the best strategies for monitoring of four invasive plant species (giant hogweed, black locust, tree of heaven and exotic knotweeds). In our study, we address trade-offs between spectral, spatial and temporal resolutions required for balance between the precision of detection and economic feasibility. For the best results, it is necessary to choose best combination of spatial and spectral resolution and phenological stage of the plant in focus. For species forming distinct inflorescences such as giant hogweed iterative semi-automated object-oriented approach was successfully applied even for low spectral resolution data (if pixel size was sufficient) whereas for lower spatial resolution satellite imagery or less distinct species with complicated architecture such as knotweed, combination of pixel and object based approaches was used. High accuracies achieved for very high resolution data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparably precise, fast and efficient. This knowledge serves as a basis for prediction, monitoring and prioritization of management targets.

Application possibilities of aerial and terrain data evaluation in particulate pollution effects

NASA Astrophysics Data System (ADS)

Kozma-Bognar, V.; Berke, J.; Martin, G.

2012-04-01

Recently, remote sensing has become a widely used technology in order to acquire information about our environment. Data collected using remote sensing technology indispensible criteria to recognise and monitor environmental problems caused by contamination from various human activities. According to great technological change and development in the previous decade high spectral and geometric resolution sensors are more often used. The higher resolution technology allows getting more accurate and reliable results in the research processes of the environmental pollution impacts. At University of Pannonia, Georgikon Faculty (Hungary) plant-soil-atmosphere system analyses are carried out for detecting the potential harmful effects of heavy metal pollution originated from vehicle industry. Related to this research at the Department of Meteorology and Water Management, black carbon and cadmium pollution effects are being analysed on maize crops. Testing area is situated at Agro-meteorological Research Station in Keszthely, where the first time in 2011 aerial imaging technology was used in parallel with field analyses. The experiment aims to analyses correlation of the field data with aerial data. During aerial photography were taken in different spectral bands (Visible, Near Infrared, Far Infrared). High intensity, spectral and spatial resolution data was an important part of the multitemporal imagine sensing and evaluating technology, therefore original technical solutions were applied. These resolutions served accurate plot-level evaluation. Fractal structure and intensity measurement evaluation methods were applied to examine black carbon and cadmium polluted and control maize canopy after data pre-processing. Research also focused on the examination of potential negative or positive effects of irrigation so that differences between irrigated and non-irrigated maize was investigated. For the period of growing season of 2011 time-series analyses were carried out in various phonological phases of maize. Finally, valued aerial and terrain parameters - including e.g. micro-climatic conditions, relative humidity, albedo, etc. - were compared. This article was made under the project TÁMOP-4.2.1/B-09/1/KONV-2010-0003 and TÁMOP-4.2.2/B-10/1-2010-0025. These projects are supported by the European Union and co-financed by the European Social Fund.

Monitoring tropical debris-covered glacier dynamics from high-resolution unmanned aerial vehicle photogrammetry, Cordillera Blanca, Peru

NASA Astrophysics Data System (ADS)

Wigmore, Oliver; Mark, Bryan

2017-11-01

The glaciers of the Cordillera Blanca, Peru, are rapidly retreating and thinning as a result of climate change, altering the timing, quantity and quality of water available to downstream users. Furthermore, increases in the number and size of proglacial lakes associated with these melting glaciers is increasing potential exposure to glacier lake outburst floods (GLOFs). Understanding how these glaciers are changing and their connection to proglacial lake systems is thus of critical importance. Most satellite data are too coarse for studying small mountain glaciers and are often affected by cloud cover, while traditional airborne photogrammetry and lidar are costly. Recent developments have made unmanned aerial vehicles (UAVs) a viable and potentially transformative method for studying glacier change at high spatial resolution, on demand and at relatively low cost.Using a custom designed hexacopter built for high-altitude (4000-6000 m a. s. l. ) operation, we completed repeat aerial surveys (2014 and 2015) of the debris-covered Llaca Glacier tongue and proglacial lake system. High-resolution orthomosaics (5 cm) and digital elevation models (DEMs) (10 cm) were produced and their accuracy assessed. Analysis of these datasets reveals highly heterogeneous patterns of glacier change. The most rapid areas of ice loss were associated with exposed ice cliffs and meltwater ponds on the glacier surface. Considerable subsidence and low surface velocities were also measured on the sediments within the pro-glacial lake, indicating the presence of extensive regions of buried ice and continued connection to the glacier tongue. Only limited horizontal retreat of the glacier tongue was observed, indicating that measurements of changes in aerial extent alone are inadequate for monitoring changes in glacier ice quantity.

Demand-based urban forest planning using high-resolution remote sensing and AHP

NASA Astrophysics Data System (ADS)

Kolanuvada, Srinivasa Raju; Mariappan, Muneeswaran; Krishnan, Vani

2016-05-01

Urban forest planning is important for providing better urban ecosystem services and conserve the natural carbon sinks inside the urban area. In this study, a demand based urban forest plan was developed for Chennai city by using Analytical Hierarchy Process (AHP) method. Population density, Tree cover, Air quality index and Carbon stocks are the parameters were considered in this study. Tree cover and Above Ground Biomass (AGB) layers were prepared at a resolution of 1m from airborne LiDAR and aerial photos. The ranks and weights are assigned by the spatial priority using AHP. The results show that, the actual status of the urban forest is not adequate to provide ecosystem services on spatial priority. From this perspective, we prepared a demand based plan for improving the urban ecosystem.

Assessing the impacts of canopy openness and flight parameters on detecting a sub-canopy tropical invasive plant using a small unmanned aerial system

NASA Astrophysics Data System (ADS)

Perroy, Ryan L.; Sullivan, Timo; Stephenson, Nathan

2017-03-01

Small unmanned aerial systems (sUAS) have great potential to facilitate the early detection and management of invasive plants. Here we show how very high-resolution optical imagery, collected from small consumer-grade multirotor UAS platform at altitudes of 30-120 m above ground level (agl), can be used to detect individual miconia (Miconia calvescens) plants in a highly invaded tropical rainforest environment on the island of Hawai'i. The central aim of this research was to determine how overstory vegetation cover, imagery resolution, and camera look-angle impact the aerial detection of known individual miconia plants. For our finest resolution imagery (1.37 cm ground sampling distance collected at 30 m agl), we obtained a 100% detection rate for sub-canopy plants with above-crown openness values >40% and a 69% detection rate for those with >20% openness. We were unable to detect any plants with <10% above crown openness. Detection rates progressively declined with coarser spatial resolution imagery, ending in a 0% detection rate for the 120 m agl flights (ground sampling distance of 5.31 cm). The addition of forward-looking oblique imagery improved detection rates for plants below overstory vegetation, though this effect decreased with increasing flight altitude. While dense overstory canopy cover, limited flight times, and visual line of sight regulations present formidable obstacles for detecting miconia and other invasive plant species, we show that sUAS platforms carrying optical sensors can be an effective component of an integrated management plan within challenging subcanopy forest environments.

Mapping snow depth in complex alpine terrain with close range aerial imagery - estimating the spatial uncertainties of repeat autonomous aerial surveys over an active rock glacier

NASA Astrophysics Data System (ADS)

Goetz, Jason; Marcer, Marco; Bodin, Xavier; Brenning, Alexander

2017-04-01

Snow depth mapping in open areas using close range aerial imagery is just one of the many cases where developments in structure-from-motion and multi-view-stereo (SfM-MVS) 3D reconstruction techniques have been applied for geosciences - and with good reason. Our ability to increase the spatial resolution and frequency of observations may allow us to improve our understanding of how snow depth distribution varies through space and time. However, to ensure accurate snow depth observations from close range sensing we must adequately characterize the uncertainty related to our measurement techniques. In this study, we explore the spatial uncertainties of snow elevation models for estimation of snow depth in a complex alpine terrain from close range aerial imagery. We accomplish this by conducting repeat autonomous aerial surveys over a snow-covered active-rock glacier located in the French Alps. The imagery obtained from each flight of an unmanned aerial vehicle (UAV) is used to create an individual digital elevation model (DEM) of the snow surface. As result, we obtain multiple DEMs of the snow surface for the same site. These DEMs are obtained from processing the imagery with the photogrammetry software Agisoft Photoscan. The elevation models are also georeferenced within Photoscan using the geotagged imagery from an onboard GNSS in combination with ground targets placed around the rock glacier, which have been surveyed with highly accurate RTK-GNSS equipment. The random error associated with multi-temporal DEMs of the snow surface is estimated from the repeat aerial survey data. The multiple flights are designed to follow the same flight path and altitude above the ground to simulate the optimal conditions of repeat survey of the site, and thus try to estimate the maximum precision associated with our snow-elevation measurement technique. The bias of the DEMs is assessed with RTK-GNSS survey observations of the snow surface elevation of the area on and surrounding the rock glacier. Additionally, one of the challenges with processing snow cover imagery with SfM-MVS is dealing with the general homogeneity of the surface, which makes is difficult for automated-feature detection algorithms to identify key features for point matching. This challenge depends on the snow cover surface conditions, such as scale, lighting conditions (high vs. low contrast), and availability of snow-free features within a scene, among others. We attempt to explore this aspect by spatial modelling the factors influencing the precision and bias of the DEMs from image, flight, and terrain attributes.

Using aerial photography to estimate wood suitable for charcoal in managed oak forests

NASA Astrophysics Data System (ADS)

Ramírez-Mejía, D.; Gómez-Tagle, A.; Ghilardi, A.

2018-02-01

Mexican oak forests (genus Quercus) are frequently used for traditional charcoal production. Appropriate management programs are needed to ensure their long-term use, while conserving the biodiversity and ecosystem services, and associated benefits. A key variable needed to design these programs is the spatial distribution of standing woody biomass. A state-of-the-art methodology using small format aerial photographs was developed to estimate the total aboveground biomass (AGB) and aboveground woody biomass suitable for charcoal making (WSC) in intensively managed oak forests. We used tree crown area (CAap) measurements from very high-resolution (30 cm) orthorectified small format digital aerial photographs as the predictive variable. The CAap accuracy was validated using field measurements of the crown area (CAf). Allometric relationships between: (a) CAap versus AGB, and (b) CAap versus WSC had a high significance level (R 2 > 0.91, p < 0.0001). This approach shows that it is possible to obtain sound biomass estimates as a function of the crown area derived from digital small format aerial photographs.

Topographic data acquisition in tsunami-prone coastal area using Unmanned Aerial Vehicle (UAV)

NASA Astrophysics Data System (ADS)

Marfai, M. A.; Sunarto; Khakim, N.; Cahyadi, A.; Rosaji, F. S. C.; Fatchurohman, H.; Wibowo, Y. A.

2018-04-01

The southern coastal area of Java Island is one of the nine seismic gaps prone to tsunamis. The entire coastline in one of the regencies, Gunungkidul, is exposed to the subduction zone in the Indian Ocean. Also, the growing tourism industries in the regency increase its vulnerability, which places most of its areas at high risk of tsunamis. The same case applies to Kukup, i.e., one of the most well-known beaches in Gunungkidul. Structurally shaped cliffs that surround it experience intensive wave erosion process, but it has very minimum access for evacuation routes. Since tsunami modeling is a very advanced analysis, it requires an accurate topographic data. Therefore, the research aimed to generate the topographic data of Kukup Beach as the baseline in tsunami risk reduction analysis and disaster management. It used aerial photograph data, which was acquired using Unmanned Aerial Vehicle (UAV). The results showed that the aerial photographs captured by drone had accurate elevation and spatial resolution. Therefore, they are applicable for tsunami modeling and disaster management.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

USGS Publications Warehouse

Husak, G.J.; Marshall, M. T.; Michaelsen, J.; Pedreros, Diego; Funk, Christopher C.; Galu, G.

2008-01-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

NASA Astrophysics Data System (ADS)

Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

2008-07-01

Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

An evaluation of a UAV guidance system with consumer grade GPS receivers

NASA Astrophysics Data System (ADS)

Rosenberg, Abigail Stella

Remote sensing has been demonstrated an important tool in agricultural and natural resource management and research applications, however there are limitations that exist with traditional platforms (i.e., hand held sensors, linear moves, vehicle mounted, airplanes, remotely piloted vehicles (RPVs), unmanned aerial vehicles (UAVs) and satellites). Rapid technological advances in electronics, computers, software applications, and the aerospace industry have dramatically reduced the cost and increased the availability of remote sensing technologies. Remote sensing imagery vary in spectral, spatial, and temporal resolutions and are available from numerous providers. Appendix A presented results of a test project that acquired high-resolution aerial photography with a RPV to map the boundary of a 0.42 km2 fire area. The project mapped the boundaries of the fire area from a mosaic of the aerial images collected and compared this with ground-based measurements. The project achieved a 92.4% correlation between the aerial assessment and the ground truth data. Appendix B used multi-objective analysis to quantitatively assess the tradeoffs between different sensor platform attributes to identify the best overall technology. Experts were surveyed to identify the best overall technology at three different pixel sizes. Appendix C evaluated the positional accuracy of a relatively low cost UAV designed for high resolution remote sensing of small areas in order to determine the positional accuracy of sensor readings. The study evaluated the accuracy and uncertainty of a UAV flight route with respect to the programmed waypoints and of the UAV's GPS position, respectively. In addition, the potential displacement of sensor data was evaluated based on (1) GPS measurements on board the aircraft and (2) the autopilot's circuit board with 3-axis gyros and accelerometers (i.e., roll, pitch, and yaw). The accuracies were estimated based on a 95% confidence interval or similar methods. The accuracy achieved in the second and third manuscripts demonstrates that reasonably priced, high resolution remote sensing via RPVs and UAVs is practical for agriculture and natural resource professionals.

Impact of spatial variation in snow water equivalent and snow ablation on spring snowcover depletion over an alpine ridge

NASA Astrophysics Data System (ADS)

Schirmer, Michael; Harder, Phillip; Pomeroy, John

2016-04-01

The spatial and temporal dynamics of mountain snowmelt are controlled by the spatial distribution of snow accumulation and redistribution and the pattern of melt energy applied to this snowcover. In order to better quantify the spatial variations of accumulation and ablation, Structure-from-Motion techniques were applied to sequential aerial photographs of an alpine ridge in the Canadian Rocky Mountains taken from an Unmanned Aerial Vehicle (UAV). Seven spatial maps of snow depth and changes to depth during late melt (May-July) were generated at very high resolutions covering an area of 800 x 600 m. The accuracy was assessed with over 100 GPS measurements and RMSE were found to be less than 10 cm. Low resolution manual measurements of density permitted calculation of snow water equivalent (SWE) and change in SWE (ablation rate). The results indicate a highly variable initial SWE distribution, which was five times more variable than the spatial variation in ablation rate. Spatial variation in ablation rate was still substantial, with a factor of two difference between north and south aspects and small scale variations due to local dust deposition. However, the impact of spatial variations in ablation rate on the snowcover depletion curve could not be discerned. The reason for this is that only a weak spatial correlation developed between SWE and ablation rate. These findings suggest that despite substantial variations in ablation rate, snowcover depletion curve calculations should emphasize the spatial variation of initial SWE rather than the variation in ablation rate. While there is scientific evidence from other field studies that support this, there are also studies that suggest that spatial variations in ablation rate can influence snowcover depletion curves in complex terrain, particularly in early melt. The development of UAV photogrammetry has provided an opportunity for further detailed measurement of ablation rates, SWE and snowcover depletion over complex terrain and UAV field studies are recommended to clarify the relative importance of SWE and melt variability on snowcover depletion in various environmental conditions.

Kite aerial photography for low-cost, ultra-high spatial resolution multi-spectral mapping of intertidal landscapes.

PubMed

Bryson, Mitch; Johnson-Roberson, Matthew; Murphy, Richard J; Bongiorno, Daniel

2013-01-01

Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales.

Kite Aerial Photography for Low-Cost, Ultra-high Spatial Resolution Multi-Spectral Mapping of Intertidal Landscapes

PubMed Central

Bryson, Mitch; Johnson-Roberson, Matthew; Murphy, Richard J.; Bongiorno, Daniel

2013-01-01

Intertidal ecosystems have primarily been studied using field-based sampling; remote sensing offers the ability to collect data over large areas in a snapshot of time that could complement field-based sampling methods by extrapolating them into the wider spatial and temporal context. Conventional remote sensing tools (such as satellite and aircraft imaging) provide data at limited spatial and temporal resolutions and relatively high costs for small-scale environmental science and ecologically-focussed studies. In this paper, we describe a low-cost, kite-based imaging system and photogrammetric/mapping procedure that was developed for constructing high-resolution, three-dimensional, multi-spectral terrain models of intertidal rocky shores. The processing procedure uses automatic image feature detection and matching, structure-from-motion and photo-textured terrain surface reconstruction algorithms that require minimal human input and only a small number of ground control points and allow the use of cheap, consumer-grade digital cameras. The resulting maps combine imagery at visible and near-infrared wavelengths and topographic information at sub-centimeter resolutions over an intertidal shoreline 200 m long, thus enabling spatial properties of the intertidal environment to be determined across a hierarchy of spatial scales. Results of the system are presented for an intertidal rocky shore at Jervis Bay, New South Wales, Australia. Potential uses of this technique include mapping of plant (micro- and macro-algae) and animal (e.g. gastropods) assemblages at multiple spatial and temporal scales. PMID:24069206

Comparison of Terrestrial Laser Scanning (TLS) and Structure from Motion (SfM) photogrammetry from unmanned aerial systems (UAS) for geomorphic change detection in beach-dune systems.

NASA Astrophysics Data System (ADS)

Heathfield, D.; Walker, I. J.; Grilliot, M. J.

2016-12-01

The recent emergence of terrestrial laser scanning (TLS) and unmanned aerial systems (UAS) as mapping platforms in geomorphology research has allowed for expedited acquisition of high spatial and temporal resolution, three-dimensional topographic datasets. TLS provides dense 3D `point cloud' datasets that require careful acquisition strategies and appreciable post-processing to produce accurate digital elevation models (DEMs). UAS provide overlapping nadir and oblique imagery that can be analysed using Structure from Motion (SfM) photogrammetry software to provide accurate, high-resolution orthophoto mosaics and accurate digital surface models (DSMs). Both methods yield centimeter to decimeter scale accuracy, depending on various hardware and field acquisition considerations (e.g., camera resolution, flight height, on-site GNSS control, etc.). Combined, the UAS-SfM workflow provides a comparable and more affordable solution to the more expensive TLS or aerial LiDAR methods. This paper compares and contrasts SfM and TLS survey methodologies and related workflow costs and benefits as used to quantify and examine seasonal beach-dune erosion and recovery processes at a site (Calvert Island) on British Columbia's central coast in western Canada. Seasonal SfM- and TLS-derived DEMs were used to quantify spatial patterns of surface elevation change, geomorphic responses, and related significant sediment volume changes. Cluster maps of positive (depositional) and negative (erosional) change are analysed to detect and interpret the geomorphic and sediment budget responses following an erosive water level event during winter 2016 season (Oct. 2015 - Apr. 2016). Vantage cameras also provided qualitative data on the frequency and magnitude of environmental drivers (e.g., tide, wave, wind forcing) of erosion and deposition events during the observation period. In addition, we evaluate the costs, time expenditures, and accuracy considerations for both SfM and TLS methodologies.

Configuration and specifications of an Unmanned Aerial Vehicle (UAV) for early site specific weed management.

PubMed

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches).

Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management

PubMed Central

Torres-Sánchez, Jorge; López-Granados, Francisca; De Castro, Ana Isabel; Peña-Barragán, José Manuel

2013-01-01

A new aerial platform has risen recently for image acquisition, the Unmanned Aerial Vehicle (UAV). This article describes the technical specifications and configuration of a UAV used to capture remote images for early season site- specific weed management (ESSWM). Image spatial and spectral properties required for weed seedling discrimination were also evaluated. Two different sensors, a still visible camera and a six-band multispectral camera, and three flight altitudes (30, 60 and 100 m) were tested over a naturally infested sunflower field. The main phases of the UAV workflow were the following: 1) mission planning, 2) UAV flight and image acquisition, and 3) image pre-processing. Three different aspects were needed to plan the route: flight area, camera specifications and UAV tasks. The pre-processing phase included the correct alignment of the six bands of the multispectral imagery and the orthorectification and mosaicking of the individual images captured in each flight. The image pixel size, area covered by each image and flight timing were very sensitive to flight altitude. At a lower altitude, the UAV captured images of finer spatial resolution, although the number of images needed to cover the whole field may be a limiting factor due to the energy required for a greater flight length and computational requirements for the further mosaicking process. Spectral differences between weeds, crop and bare soil were significant in the vegetation indices studied (Excess Green Index, Normalised Green-Red Difference Index and Normalised Difference Vegetation Index), mainly at a 30 m altitude. However, greater spectral separability was obtained between vegetation and bare soil with the index NDVI. These results suggest that an agreement among spectral and spatial resolutions is needed to optimise the flight mission according to every agronomical objective as affected by the size of the smaller object to be discriminated (weed plants or weed patches). PMID:23483997

A Three-Dimensional Target Depth-Resolution Method with a Single-Vector Sensor

PubMed Central

Zhao, Anbang; Bi, Xuejie; Hui, Juan; Zeng, Caigao; Ma, Lin

2018-01-01

This paper mainly studies and verifies the target number category-resolution method in multi-target cases and the target depth-resolution method of aerial targets. Firstly, target depth resolution is performed by using the sign distribution of the reactive component of the vertical complex acoustic intensity; the target category and the number resolution in multi-target cases is realized with a combination of the bearing-time recording information; and the corresponding simulation verification is carried out. The algorithm proposed in this paper can distinguish between the single-target multi-line spectrum case and the multi-target multi-line spectrum case. This paper presents an improved azimuth-estimation method for multi-target cases, which makes the estimation results more accurate. Using the Monte Carlo simulation, the feasibility of the proposed target number and category-resolution algorithm in multi-target cases is verified. In addition, by studying the field characteristics of the aerial and surface targets, the simulation results verify that there is only amplitude difference between the aerial target field and the surface target field under the same environmental parameters, and an aerial target can be treated as a special case of a surface target; the aerial target category resolution can then be realized based on the sign distribution of the reactive component of the vertical acoustic intensity so as to realize three-dimensional target depth resolution. By processing data from a sea experiment, the feasibility of the proposed aerial target three-dimensional depth-resolution algorithm is verified. PMID:29649173

A Three-Dimensional Target Depth-Resolution Method with a Single-Vector Sensor.

PubMed

Zhao, Anbang; Bi, Xuejie; Hui, Juan; Zeng, Caigao; Ma, Lin

2018-04-12

This paper mainly studies and verifies the target number category-resolution method in multi-target cases and the target depth-resolution method of aerial targets. Firstly, target depth resolution is performed by using the sign distribution of the reactive component of the vertical complex acoustic intensity; the target category and the number resolution in multi-target cases is realized with a combination of the bearing-time recording information; and the corresponding simulation verification is carried out. The algorithm proposed in this paper can distinguish between the single-target multi-line spectrum case and the multi-target multi-line spectrum case. This paper presents an improved azimuth-estimation method for multi-target cases, which makes the estimation results more accurate. Using the Monte Carlo simulation, the feasibility of the proposed target number and category-resolution algorithm in multi-target cases is verified. In addition, by studying the field characteristics of the aerial and surface targets, the simulation results verify that there is only amplitude difference between the aerial target field and the surface target field under the same environmental parameters, and an aerial target can be treated as a special case of a surface target; the aerial target category resolution can then be realized based on the sign distribution of the reactive component of the vertical acoustic intensity so as to realize three-dimensional target depth resolution. By processing data from a sea experiment, the feasibility of the proposed aerial target three-dimensional depth-resolution algorithm is verified.

Employing UAVs to Acquire Detailed Vegetation and Bare Ground Data for Assessing Rangeland Health

NASA Astrophysics Data System (ADS)

Rango, A.; Laliberte, A.; Herrick, J. E.; Winters, C.

2007-12-01

Because of its value as a historical record (extending back to the mid 1930s), aerial photography is an important tool used in many rangeland studies. However, these historical photos are not very useful for detailed analysis of rangeland health because of inadequate spatial resolution and scheduling limitations. These issues are now being resolved by using Unmanned Aerial Vehicles (UAVs) over rangeland study areas. Spatial resolution improvements have been rapid in the last 10 years from the QuickBird satellite through improved aerial photography to the new UAV coverage and have utilized improved sensors and the more simplistic approach of low altitude flights. Our rangeland health experiments have shown that the low altitude UAV digital photography is preferred by rangeland scientists because it allows, for the first time, their identification of vegetation and land surface patterns and patches, gap sizes, bare soil percentages, and vegetation type. This hyperspatial imagery (imagery with a resolution finer than the object of interest) is obtained at about 5cm resolution by flying at an altitude of 150m above the surface of the Jornada Experimental Range in southern New Mexico. Additionally, the UAV provides improved temporal flexibility, such as flights immediately following fires, floods, and other catastrophic disturbances, because the flight capability is located near the study area and the vehicles are under the direct control of the users, eliminating the additional steps associated with budgets and contracts. There are significant challenges to improve the data to make them useful for operational agencies, namely, image distortion with inexpensive, consumer grade digital cameras, difficulty in detecting sufficient ground control points in small scenes (152m by 114m), accuracy of exterior UAV information on X,Y, Z, roll, pitch, and heading, the sheer number of images collected, and developing reliable relationships with ground-based data across a broad range of topographies and plant communities. Our efforts are currently focused on developing a complete and efficient workflow for UAV operational missions consisting of flight planning, image acquisition, image rectification and mosaicking, and image classification. The remote sensing capability is being incorporated into existing rangeland health assessment and monitoring protocols.

Scaling forest phenology from trees to the landscape using an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Klosterman, S.; Melaas, E. K.; Martinez, A.; Richardson, A. D.

2013-12-01

Vegetation phenology monitoring has yielded a decades-long archive documenting the impacts of global change on the biosphere. However, the coarse spatial resolution of remote sensing obscures the organismic level processes driving phenology, while point measurements on the ground limit the extent of observation. Unmanned aerial vehicles (UAVs) enable low altitude remote sensing at higher spatial and temporal resolution than available from space borne platforms, and have the potential to elucidate the links between organism scale processes and landscape scale analyses of terrestrial phenology. This project demonstrates the use of a low cost multirotor UAV, equipped with a consumer grade digital camera, for observation of deciduous forest phenology and comparison to ground- and tower-based data as well as remote sensing. The UAV was flown approximately every five days during the spring green-up period in 2013, to obtain aerial photography over an area encompassing a 250m resolution MODIS (Moderate Resolution Imaging Spectroradiometer) pixel at Harvard Forest in central Massachusetts, USA. The imagery was georeferenced and tree crowns were identified using a detailed species map of the study area. Image processing routines were used to extract canopy 'greenness' time series, which were used to calculate phenology transition dates corresponding to early, middle, and late stages of spring green-up for the dominant canopy trees. Aggregated species level phenology estimates from the UAV data, including the mean and variance of phenology transition dates within species in the study area, were compared to model predictions based on visual assessment of a smaller sample size of individual trees, indicating the extent to which limited ground observations represent the larger landscape. At an intermediate scale, the UAV data was compared to data from repeat digital photography, integrating over larger portions of canopy within and near the study area, as a validation step and to see how well tower-based approaches characterize the surrounding landscape. Finally, UAV data was compared to MODIS data to determine how tree crowns within a remote sensing pixel combine to create the aggregate landscape phenology measured by remote sensing, using an area weighted average of the phenology of all dominant crowns.

Configuration and Specifications of AN Unmanned Aerial Vehicle for Precision Agriculture

NASA Astrophysics Data System (ADS)

Erena, M.; Montesinos, S.; Portillo, D.; Alvarez, J.; Marin, C.; Fernandez, L.; Henarejos, J. M.; Ruiz, L. A.

2016-06-01

Unmanned Aerial Vehicles (UAVs) with multispectral sensors are increasingly attractive in geosciences for data capture and map updating at high spatial and temporal resolutions. These autonomously-flying systems can be equipped with different sensors, such as a six-band multispectral camera (Tetracam mini-MCA-6), GPS Ublox M8N, and MEMS gyroscopes, and miniaturized sensor systems for navigation, positioning, and mapping purposes. These systems can be used for data collection in precision viticulture. In this study, the efficiency of a light UAV system for data collection, processing, and map updating in small areas is evaluated, generating correlations between classification maps derived from remote sensing and production maps. Based on the comparison of the indices derived from UAVs incorporating infrared sensors with those obtained by satellites (Sentinel 2A and Landsat 8), UAVs show promise for the characterization of vineyard plots with high spatial variability, despite the low vegetative coverage of these crops. Consequently, a procedure for zoning map production based on UAV/UV images could provide important information for farmers.

Water level observations from Unmanned Aerial Vehicles (UAVs) for improving probabilistic estimations of interaction between rivers and groundwater

NASA Astrophysics Data System (ADS)

Bandini, Filippo; Butts, Michael; Vammen Jacobsen, Torsten; Bauer-Gottwein, Peter

2016-04-01

Integrated hydrological models are generally calibrated against observations of river discharge and piezometric head in groundwater aquifers. Integrated hydrological models are rarely calibrated against spatially distributed water level observations measured by either in-situ stations or spaceborne platforms. Indeed in-situ observations derived from ground-based stations are generally spaced too far apart to capture spatial patterns in the water surface. On the other hand spaceborne observations have limited spatial resolution. Additionally satellite observations have a temporal resolution which is not ideal for observing the temporal patterns of the hydrological variables during extreme events. UAVs (Unmanned Aerial Vehicles) offer several advantages: i) high spatial resolution; ii) tracking of the water body better than any satellite technology; iii) timing of the sampling merely depending on the operators. In this case study the Mølleåen river (Denmark) and its catchment have been simulated through an integrated hydrological model (MIKE 11-MIKE SHE). This model was initially calibrated against observations of river discharge retrieved by in-situ stations and against piezometric head of the aquifers. Subsequently the hydrological model has been calibrated against dense spatially distributed water level observations, which could potentially be retrieved by UAVs. Error characteristics of synthetic UAV water level observations were taken from a recent proof-of-concept study. Since the technology for ranging water level is under development, UAV synthetic water level observations were extracted from another model of the river with higher spatial resolution (cross sections located every 10 m). This model with high resolution is assumed to be absolute truth for the purpose of this work. The river model with the coarser resolution has been calibrated against the synthetic water level observations through Differential Evolution Adaptive Metropolis (DREAM) algorithm, an efficient global Markov Chain Monte Carlo (MCMC) in high-dimensional spaces. Calibration against water level has demonstrated a significant improvement of the estimation of the exchange flow between groundwater and river branch. Groundwater flux and direction are now better simulated. Reliability and sharpness of the probabilistic forecasts are assessed with the sharpness, the interval skill score (ISS) of the 95{%} confidence interval, and with the root mean square error (RMSE) of the maximum a posteriori probability (MAP). The binary outcome (either gaining or loosing stream) of the flow direction is assessed with Brier score (BS). After water level calibration the sharpness of the estimations is approximately doubled with respect to the model calibrated only against discharge, ISS has improved from 2.4-7to 7.8-8 m^3/s\\cdot m, RMSE from 9.2-8 to 2.4-8 m^3/s\\cdot m^and BS is halved from 0.58 to 0.25.

An Assessment of Stream Confluence Flow Dynamics using Large Scale Particle Image Velocimetry Captured from Unmanned Aerial Systems

NASA Astrophysics Data System (ADS)

Lewis, Q. W.; Rhoads, B. L.

2017-12-01

The merging of rivers at confluences results in complex three-dimensional flow patterns that influence sediment transport, bed morphology, downstream mixing, and physical habitat conditions. The capacity to characterize comprehensively flow at confluences using traditional sensors, such as acoustic Doppler velocimeters and profiles, is limited by the restricted spatial resolution of these sensors and difficulties in measuring velocities simultaneously at many locations within a confluence. This study assesses two-dimensional surficial patterns of flow structure at a small stream confluence in Illinois, USA, using large scale particle image velocimetry (LSPIV) derived from videos captured by unmanned aerial systems (UAS). The method captures surface velocity patterns at high spatial and temporal resolution over multiple scales, ranging from the entire confluence to details of flow within the confluence mixing interface. Flow patterns at high momentum ratio are compared to flow patterns when the two incoming flows have nearly equal momentum flux. Mean surface flow patterns during the two types of events provide details on mean patterns of surface flow in different hydrodynamic regions of the confluence and on changes in these patterns with changing momentum flux ratio. LSPIV data derived from the highest resolution imagery also reveal general characteristics of large-scale vortices that form along the shear layer between the flows during the high-momentum ratio event. The results indicate that the use of LSPIV and UAS is well-suited for capturing in detail mean surface patterns of flow at small confluences, but that characterization of evolving turbulent structures is limited by scale considerations related to structure size, image resolution, and camera instability. Complementary methods, including camera platforms mounted at fixed positions close to the water surface, provide opportunities to accurately characterize evolving turbulent flow structures in confluences.

Unmanned aerial monitoring of fluvial changes in the vicinity of selected gauges of the Local System for Flood Monitoring in Klodzko County, SW Poland

NASA Astrophysics Data System (ADS)

Jeziorska, Justyna; Witek, Matylda; Niedzielski, Tomasz

2013-04-01

Only high resolution spatial data enable precise measurements of various morphometric characteristics of river channels and ensure meaningful effects of research into fluvial changes. Using ground-based measurement tools is time-consuming and expensive. Traditional photogrammetry often does not reach a desired resolution, and the technology is cost effective only for the large-area coverage. The present research introduces potentials of UAV (Unmanned Aerial Vehicle) for monitoring fluvial changes. Observations were carried out with the ultralight UAV swinglet CAM produced by senseFly. This lightweight (0,5 kg), small (wingspan: 80 cm) aircraft allowed frequent (with approximately monthly sampling resolution) and low-cost missions. Three hydrologic gauges, the surroundings of which were the target of series of photos taken by camera placed in airplane frame, belong to the Local System for Flood Monitoring in Kłodzko County (SW Poland). The only way of obtaining reliable results is an appropriate image rectification, in order to measure morphometric characteristics of terrain, free of geometrical deformations induced by the topographical relief, the tilt of the camera axis and the distortion of the optics. Commercially available software for the production of digital orthophotos and digital surface models (DSMs) from a range of uncalibrated oblique and vertical aerial images was successfully used to achieve this aim. As a result of completing the above procedure 9 orthophotos were generated (one for each of 3 study areas during 3 missions). For extraction of terrain parameters, a DSM was produced as a result of bundle block adjustment. Both products reached ultra-high resolution of 4cm/px. Various fluvial forms were classified and recognized, and a few time series of maps from each study area were compared in order to detect potential changes within the fluvial system. We inferred on the origins of the short-term responses of fluvial systems, and such an inference was feasible due to the analysis of metrological and hydrological data recorded by the Local System for Flood Monitoring in Kłodzko County. Orthophotos and DSMs, generated from imagery obtained by UAV, show high accuracy of results and are suitable for measuring fluvial changes. This approach moves beyond current restrictions of traditional data collecting, due to its unprecedented spatial and temporal resolution and low cost of application.

Spatial consistency of Chinook salmon redd distribution within and among years in the Cowlitz River, Washington

DOE Office of Scientific and Technical Information (OSTI.GOV)

Klett, Katherine J.; Torgersen, Christian; Henning, Julie

2013-04-28

We investigated the spawning patterns of Chinook salmon Oncorhynchus tshawytscha on the lower Cowlitz River, Washington (USA) using a unique set of fine- and coarse-scale 35 temporal and spatial data collected during bi-weekly aerial surveys conducted in 1991-2009 (500 m to 28 km resolution) and 2008-2009 (100-500 m resolution). Redd locations were mapped from a helicopter during 2008 and 2009 with a hand-held global positioning system (GPS) synchronized with in-flight audio recordings. We examined spatial patterns of Chinook salmon redd reoccupation among and within years in relation to segment-scale geomorphic features. Chinook salmon spawned in the same sections each yearmore » with little variation among years. On a coarse scale, five years (1993, 1998, 2000, 2002, and 2009) were compared for reoccupation. Redd locations were highly correlated among years resulting in a minimum correlation coefficient of 0.90 (adjusted P = 0.002). Comparisons on a fine scale (500 m) between 2008 and 2009 also revealed a high degree of consistency among redd locations (P < 0.001). On a finer temporal scale, we observed that salmon spawned in the same sections during the first and last week (2008: P < 0.02; and 2009: P < 0.001). Redds were clustered in both 2008 and 2009 (P < 0.001). Regression analysis with a generalized linear model at the 500-m scale indicated that river kilometer and channel bifurcation were positively associated with redd density, whereas sinuosity was negatively associated with redd density. Collecting data on specific redd locations with a GPS during aerial surveys was logistically feasible and cost effective and greatly enhanced the spatial precision of Chinook salmon spawning surveys.« less

Satellite image analysis for surveillance, vegetation and climate change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cai, D Michael

2011-01-18

Recently, many studies have provided abundant evidence to show the trend of tree mortality is increasing in many regions, and the cause of tree mortality is associated with drought, insect outbreak, or fire. Unfortunately, there is no current capability available to monitor vegetation changes, and correlate and predict tree mortality with CO{sub 2} change, and climate change on the global scale. Different survey platforms (methods) have been used for forest management. Typical ground-based forest surveys measure tree stem diameter, species, and alive or dead. The measurements are low-tech and time consuming, but the sample sizes are large, running into millionsmore » of trees, covering large areas, and spanning many years. These field surveys provide powerful ground validation for other survey methods such as photo survey, helicopter GPS survey, and aerial overview survey. The satellite imagery has much larger coverage. It is easier to tile the different images together, and more important, the spatial resolution has been improved such that close to or even higher than aerial survey platforms. Today, the remote sensing satellite data have reached sub-meter spatial resolution for panchromatic channels (IKONOS 2: 1 m; Quickbird-2: 0.61 m; Worldview-2: 0.5 m) and meter spatial resolution for multi-spectral channels (IKONOS 2: 4 meter; Quickbird-2: 2.44 m; Worldview-2: 2 m). Therefore, high resolution satellite imagery can allow foresters to discern individual trees. This vital information should allow us to quantify physiological states of trees, e.g. healthy or dead, shape and size of tree crowns, as well as species and functional compositions of trees. This is a powerful data resource, however, due to the vast amount of the data collected daily, it is impossible for human analysts to review the imagery in detail to identify the vital biodiversity information. Thus, in this talk, we will discuss the opportunities and challenges to use high resolution satellite imagery and machine learning theory to monitor tree mortality at the level of individual trees.« less

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona.

PubMed

Marsac, Kara E; Burnley, Pamela C; Adcock, Christopher T; Haber, Daniel A; Malchow, Russell L; Hausrath, Elisabeth M

2016-12-01

This study compares high resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (National Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.

Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (Nationalmore » Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.« less

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

DOE PAGES

Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.; ...

2016-09-16

Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (Nationalmore » Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.« less

A Compact L-band Radiometer for High Resolution sUAS-based Imaging of Soil Moisture and Surface Salinity Variations

NASA Astrophysics Data System (ADS)

Gasiewski, A. J.; Stachura, M.; Dai, E.; Elston, J.; McIntyre, E.; Leuski, V.

2014-12-01

Due to the long electrical wavelengths required along with practical aperture size limitations the scaling of passive microwave remote sensing of soil moisture and salinity from spaceborne low-resolution (~10-100 km) applications to high resolution (~10-1000 m) applications requires use of low flying aerial vehicles. This presentation summarizes the status of a project to develop a commercial small Unmanned Aerial System (sUAS) hosting a microwave radiometer for mapping of soil moisture in precision agriculture and sea surface salinity studies. The project is based on the Tempest electric-powered UAS and a compact L-band (1400-1427 MHz) radiometer developed specifically for extremely small and lightweight aerial platforms or man-portable, tractor, or tower-based applications. Notable in this combination are a highly integrated sUAS/radiometer antenna design and use of both the upwelling emitted signal from the surface and downwelling cold space signal for precise calibration using a unique lobe-differencing correlating radiometer architecture. The system achieves a spatial resolution comparable to the altitude of the UAS above the surface while referencing upwelling measurements to the constant and well-known background temperature of cold space. The radiometer has been tested using analog correlation detection, although future builds will include infrared, near-infrared, and visible (red) sensors for surface temperature and vegetation biomass correction and digital sampling for radio frequency interference mitigation. This NASA-sponsored project is being developed for commercial application in cropland water management (for example, high-value shallow root-zone crops), landslide risk assessment, NASA SMAP satellite validation, and NASA Aquarius salinity stratification studies. The system will ultimately be capable of observing salinity events caused by coastal glacier and estuary fresh water outflow plumes and open ocean rainfall events.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos.

PubMed

Aghamohammadi, Amirhossein; Ang, Mei Choo; A Sundararajan, Elankovan; Weng, Ng Kok; Mogharrebi, Marzieh; Banihashem, Seyed Yashar

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods.

A parallel spatiotemporal saliency and discriminative online learning method for visual target tracking in aerial videos

PubMed Central

2018-01-01

Visual tracking in aerial videos is a challenging task in computer vision and remote sensing technologies due to appearance variation difficulties. Appearance variations are caused by camera and target motion, low resolution noisy images, scale changes, and pose variations. Various approaches have been proposed to deal with appearance variation difficulties in aerial videos, and amongst these methods, the spatiotemporal saliency detection approach reported promising results in the context of moving target detection. However, it is not accurate for moving target detection when visual tracking is performed under appearance variations. In this study, a visual tracking method is proposed based on spatiotemporal saliency and discriminative online learning methods to deal with appearance variations difficulties. Temporal saliency is used to represent moving target regions, and it was extracted based on the frame difference with Sauvola local adaptive thresholding algorithms. The spatial saliency is used to represent the target appearance details in candidate moving regions. SLIC superpixel segmentation, color, and moment features can be used to compute feature uniqueness and spatial compactness of saliency measurements to detect spatial saliency. It is a time consuming process, which prompted the development of a parallel algorithm to optimize and distribute the saliency detection processes that are loaded into the multi-processors. Spatiotemporal saliency is then obtained by combining the temporal and spatial saliencies to represent moving targets. Finally, a discriminative online learning algorithm was applied to generate a sample model based on spatiotemporal saliency. This sample model is then incrementally updated to detect the target in appearance variation conditions. Experiments conducted on the VIVID dataset demonstrated that the proposed visual tracking method is effective and is computationally efficient compared to state-of-the-art methods. PMID:29438421

The evaluation of unmanned aerial system-based photogrammetry and terrestrial laser scanning to generate DEMs of agricultural watersheds

NASA Astrophysics Data System (ADS)

Ouédraogo, Mohamar Moussa; Degré, Aurore; Debouche, Charles; Lisein, Jonathan

2014-06-01

Agricultural watersheds tend to be places of intensive farming activities that permanently modify their microtopography. The surface characteristics of the soil vary depending on the crops that are cultivated in these areas. Agricultural soil microtopography plays an important role in the quantification of runoff and sediment transport because the presence of crops, crop residues, furrows and ridges may impact the direction of water flow. To better assess such phenomena, 3-D reconstructions of high-resolution agricultural watershed topography are essential. Fine-resolution topographic data collection technologies can be used to discern highly detailed elevation variability in these areas. Knowledge of the strengths and weaknesses of existing technologies used for data collection on agricultural watersheds may be helpful in choosing an appropriate technology. This study assesses the suitability of terrestrial laser scanning (TLS) and unmanned aerial system (UAS) photogrammetry for collecting the fine-resolution topographic data required to generate accurate, high-resolution digital elevation models (DEMs) in a small watershed area (12 ha). Because of farming activity, 14 TLS scans (≈ 25 points m- 2) were collected without using high-definition surveying (HDS) targets, which are generally used to mesh adjacent scans. To evaluate the accuracy of the DEMs created from the TLS scan data, 1098 ground control points (GCPs) were surveyed using a real time kinematic global positioning system (RTK-GPS). Linear regressions were then applied to each DEM to remove vertical errors from the TLS point elevations, errors caused by the non-perpendicularity of the scanner's vertical axis to the local horizontal plane, and errors correlated with the distance to the scanner's position. The scans were then meshed to generate a DEMTLS with a 1 × 1 m spatial resolution. The Agisoft PhotoScan and MicMac software packages were used to process the aerial photographs and generate a DEMPSC (Agisoft PhotoScan) and DEMMCM (MicMac), respectively, with spatial resolutions of 1 × 1 m. Comparing the DEMs with the 1098 GCPs showed that the DEMTLS was the most accurate data product, with a root mean square error (RMSE) of 4.5 cm, followed by the DEMMCM and the DEMPSC, which had RMSE values of 9.0 and 13.9 cm, respectively. The DEMPSC had absolute errors along the border of the study area that ranged from 15.0 to 52.0 cm, indicating the presence of systematic errors. Although the derived DEMMCM was accurate, an error analysis along a transect showed that the errors in the DEMMCM data tended to increase in areas of lower elevation. Compared with TLS, UAS is a promising tool for data collection because of its flexibility and low operational cost. However, improvements are needed in the photogrammetric processing of the aerial photographs to remove non-linear distortions.

Implications of sensor design for coral reef detection: Upscaling ground hyperspectral imagery in spatial and spectral scales

NASA Astrophysics Data System (ADS)

Caras, Tamir; Hedley, John; Karnieli, Arnon

2017-12-01

Remote sensing offers a potential tool for large scale environmental surveying and monitoring. However, remote observations of coral reefs are difficult especially due to the spatial and spectral complexity of the target compared to sensor specifications as well as the environmental implications of the water medium above. The development of sensors is driven by technological advances and the desired products. Currently, spaceborne systems are technologically limited to a choice between high spectral resolution and high spatial resolution, but not both. The current study explores the dilemma of whether future sensor design for marine monitoring should prioritise on improving their spatial or spectral resolution. To address this question, a spatially and spectrally resampled ground-level hyperspectral image was used to test two classification elements: (1) how the tradeoff between spatial and spectral resolutions affects classification; and (2) how a noise reduction by majority filter might improve classification accuracy. The studied reef, in the Gulf of Aqaba (Eilat), Israel, is heterogeneous and complex so the local substrate patches are generally finer than currently available imagery. Therefore, the tested spatial resolution was broadly divided into four scale categories from five millimeters to one meter. Spectral resolution resampling aimed to mimic currently available and forthcoming spaceborne sensors such as (1) Environmental Mapping and Analysis Program (EnMAP) that is characterized by 25 bands of 6.5 nm width; (2) VENμS with 12 narrow bands; and (3) the WorldView series with broadband multispectral resolution. Results suggest that spatial resolution should generally be prioritized for coral reef classification because the finer spatial scale tested (pixel size < 0.1 m) may compensate for some low spectral resolution drawbacks. In this regard, it is shown that the post-classification majority filtering substantially improves the accuracy of all pixel sizes up to the point where the kernel size reaches the average unit size (pixel < 0.25 m). However, careful investigation as to the effect of band distribution and choice could improve the sensor suitability for the marine environment task. This in mind, while the focus in this study was on the technologically limited spaceborne design, aerial sensors may presently provide an opportunity to implement the suggested setup.

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges.

PubMed

Fernández-Guisuraga, José Manuel; Sanz-Ablanedo, Enoc; Suárez-Seoane, Susana; Calvo, Leonor

2018-02-14

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas.

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges

PubMed Central

2018-01-01

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas. PMID:29443914

Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography

NASA Astrophysics Data System (ADS)

Li, Xiaoxiao; Myint, Soe W.; Zhang, Yujia; Galletti, Chritopher; Zhang, Xiaoxiang; Turner, Billie L.

2014-12-01

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.

Derivation of high spatial resolution albedo from UAV digital imagery: application over the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

Ryan, Jonathan C.; Hubbard, Alun; Box, Jason E.; Brough, Stephen; Cameron, Karen; Cook, Joseph M.; Cooper, Matthew; Doyle, Samuel H.; Edwards, Arwyn; Holt, Tom; Irvine-Fynn, Tristram; Jones, Christine; Pitcher, Lincoln H.; Rennermalm, Asa K.; Smith, Laurence C.; Stibal, Marek; Snooke, Neal

2017-05-01

Measurements of albedo are a prerequisite for modelling surface melt across the Earth's cryosphere, yet available satellite products are limited in spatial and/or temporal resolution. Here, we present a practical methodology to obtain centimetre resolution albedo products with accuracies of 5% using consumer-grade digital camera and unmanned aerial vehicle (UAV) technologies. Our method comprises a workflow for processing, correcting and calibrating raw digital images using a white reference target, and upward and downward shortwave radiation measurements from broadband silicon pyranometers. We demonstrate the method with a set of UAV sorties over the western, K-sector of the Greenland Ice Sheet. The resulting albedo product, UAV10A1, covers 280 km2, at a resolution of 20 cm per pixel and has a root-mean-square difference of 3.7% compared to MOD10A1 and 4.9% compared to ground-based broadband pyranometer measurements. By continuously measuring downward solar irradiance, the technique overcomes previous limitations due to variable illumination conditions during and between surveys over glaciated terrain. The current miniaturization of multispectral sensors and incorporation of upward facing radiation sensors on UAV packages means that this technique will likely become increasingly attractive in field studies and used in a wide range of applications for high temporal and spatial resolution surface mapping of debris, dust, cryoconite and bioalbedo and for directly constraining surface energy balance models.

BOREAS Level-0 C-130 Aerial Photography

NASA Technical Reports Server (NTRS)

Newcomer, Jeffrey A.; Dominguez, Roseanne; Hall, Forrest G. (Editor)

2000-01-01

For BOReal Ecosystem-Atmosphere Study (BOREAS), C-130 and other aerial photography was collected to provide finely detailed and spatially extensive documentation of the condition of the primary study sites. The NASA C-130 Earth Resources aircraft can accommodate two mapping cameras during flight, each of which can be fitted with 6- or 12-inch focal-length lenses and black-and-white, natural-color, or color-IR film, depending upon requirements. Both cameras were often in operation simultaneously, although sometimes only the lower resolution camera was deployed. When both cameras were in operation, the higher resolution camera was often used in a more limited fashion. The acquired photography covers the period of April to September 1994. The aerial photography was delivered as rolls of large format (9 x 9 inch) color transparency prints, with imagery from multiple missions (hundreds of prints) often contained within a single roll. A total of 1533 frames were collected from the C-130 platform for BOREAS in 1994. Note that the level-0 C-130 transparencies are not contained on the BOREAS CD-ROM set. An inventory file is supplied on the CD-ROM to inform users of all the data that were collected. Some photographic prints were made from the transparencies. In addition, BORIS staff digitized a subset of the tranparencies and stored the images in JPEG format. The CD-ROM set contains a small subset of the collected aerial photography that were the digitally scanned and stored as JPEG files for most tower and auxiliary sites in the NSA and SSA. See Section 15 for information about how to acquire additional imagery.

Direct Penguin Counting Using Unmanned Aerial Vehicle Image

NASA Astrophysics Data System (ADS)

Hyun, C. U.; Kim, H. C.; Kim, J. H.; Hong, S. G.

2015-12-01

This study presents an application of unmanned aerial vehicle (UAV) images to monitor penguin colony in Baton Peninsula, King George Island, Antarctica. The area around Narębski Point located on the southeast coast of Barton Peninsula was designated as Antarctic Specially Protected Area No. 171 (ASPA 171), and Chinstrap and Gentoo penguins inhabit in this area. The UAV images were acquired in a part of ASPA 171 from four flights in a single day, Jan 18, 2014. About 360 images were mosaicked as an image of about 3 cm spatial resolution and then a subset including representative penguin rookeries was selected. The subset image was segmented based on gradient map of pixel values, and spectral and spatial attributes were assigned to each segment. The object based image analysis (OBIA) was conducted with consideration of spectral attributes including mean and minimum values of each segment and various shape attributes such as area, length, compactness and roundness to detect individual penguin. The segments indicating individual penguin were effectively detected on rookeries with high contrasts in the spectral and shape attributes. The importance of periodic and precise monitoring of penguins has been recognized because variations of their populations reflect environmental changes and disturbance from human activities. Utilization of very high resolution imaging method shown in this study can be applied to other penguin habitats in Antarctica, and the results will be able to support establishing effective environmental management plans.

Semantic Segmentation and Difference Extraction via Time Series Aerial Video Camera and its Application

NASA Astrophysics Data System (ADS)

Amit, S. N. K.; Saito, S.; Sasaki, S.; Kiyoki, Y.; Aoki, Y.

2015-04-01

Google earth with high-resolution imagery basically takes months to process new images before online updates. It is a time consuming and slow process especially for post-disaster application. The objective of this research is to develop a fast and effective method of updating maps by detecting local differences occurred over different time series; where only region with differences will be updated. In our system, aerial images from Massachusetts's road and building open datasets, Saitama district datasets are used as input images. Semantic segmentation is then applied to input images. Semantic segmentation is a pixel-wise classification of images by implementing deep neural network technique. Deep neural network technique is implemented due to being not only efficient in learning highly discriminative image features such as road, buildings etc., but also partially robust to incomplete and poorly registered target maps. Then, aerial images which contain semantic information are stored as database in 5D world map is set as ground truth images. This system is developed to visualise multimedia data in 5 dimensions; 3 dimensions as spatial dimensions, 1 dimension as temporal dimension, and 1 dimension as degenerated dimensions of semantic and colour combination dimension. Next, ground truth images chosen from database in 5D world map and a new aerial image with same spatial information but different time series are compared via difference extraction method. The map will only update where local changes had occurred. Hence, map updating will be cheaper, faster and more effective especially post-disaster application, by leaving unchanged region and only update changed region.

NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands

USGS Publications Warehouse

Nouri, Hamideh; Anderson, Sharolyn; Sutton, Paul; Beecham, Simon; Nagler, Pamela L.; Jarchow, Christopher J.; Roberts, Dar A.

2017-01-01

This research addresses the question as to whether or not the Normalised Difference Vegetation Index (NDVI) is scale invariant (i.e. constant over spatial aggregation) for pure pixels of urban vegetation. It has been long recognized that there are issues related to the modifiable areal unit problem (MAUP) pertaining to indices such as NDVI and images at varying spatial resolutions. These issues are relevant to using NDVI values in spatial analyses. We compare two different methods of calculation of a mean NDVI: 1) using pixel values of NDVI within feature/object boundaries and 2) first calculating the mean red and mean near-infrared across all feature pixels and then calculating NDVI. We explore the nature and magnitude of these differences for images taken from two sensors, a 1.24 m resolution WorldView-3 and a 0.1 m resolution digital aerial image. We apply these methods over an urban park located in the Adelaide Parklands of South Australia. We demonstrate that the MAUP is not an issue for calculation of NDVI within a sensor for pure urban vegetation pixels. This may prove useful for future rule-based monitoring of the ecosystem functioning of green infrastructure.

Characterization and delineation of caribou habitat on Unimak Island using remote sensing techniques

NASA Astrophysics Data System (ADS)

Atkinson, Brain M.

The assessment of herbivore habitat quality is traditionally based on quantifying the forages available to the animal across their home range through ground-based techniques. While these methods are highly accurate, they can be time-consuming and highly expensive, especially for herbivores that occupy vast spatial landscapes. The Unimak Island caribou herd has been decreasing in the last decade at rates that have prompted discussion of management intervention. Frequent inclement weather in this region of Alaska has provided for little opportunity to study the caribou forage habitat on Unimak Island. The overall objectives of this study were two-fold 1) to assess the feasibility of using high-resolution color and near-infrared aerial imagery to map the forage distribution of caribou habitat on Unimak Island and 2) to assess the use of a new high-resolution multispectral satellite imagery platform, RapidEye, and use of the "red-edge" spectral band on vegetation classification accuracy. Maximum likelihood classification algorithms were used to create land cover maps in aerial and satellite imagery. Accuracy assessments and transformed divergence values were produced to assess vegetative spectral information and classification accuracy. By using RapidEye and aerial digital imagery in a hierarchical supervised classification technique, we were able to produce a high resolution land cover map of Unimak Island. We obtained overall accuracy rates of 71.4 percent which are comparable to other land cover maps using RapidEye imagery. The "red-edge" spectral band included in the RapidEye imagery provides additional spectral information that allows for a more accurate overall classification, raising overall accuracy 5.2 percent.

Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery.

PubMed

Martinuzzi, Sebastián; Ramos-González, Olga M; Muñoz-Erickson, Tischa A; Locke, Dexter H; Lugo, Ariel E; Radeloff, Volker C

2018-04-01

Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planning in tropical cities, and emphasizes the need for more studies in tropical cities. © 2017 by the Ecological Society of America.

THE IDEA IS TO USEMODIS IN CONJUNCTION WITH THE CURRENT LIMITED LANDSAT CAPABILITY, COMMERCIAL SATELLITES, ANDUNMANNED AERIAL VEHICLES (UAV), IN A MULTI-STAGE APPROACH TO MEET EPA INFORMATION NEEDS.REMOTE SENSING OVERVIEW: EPA CAPABILITIES, PRIORITY AGENCY APPLICATIONS, SENSOR/AIRCRAFT CAPABILITIES, COST CONSIDERATIONS, SPECTRAL AND SPATIAL RESOLUTIONS, AND TEMPORAL CONSIDERATIONS

EPA Science Inventory

EPA remote sensing capabilities include applied research for priority applications and technology support for operational assistance to clients across the Agency. The idea is to use MODIS in conjunction with the current limited Landsat capability, commercial satellites, and Unma...

UAV-based detection and spatial analyses of periglacial landforms on Demay Point (King George Island, South Shetland Islands, Antarctica)

NASA Astrophysics Data System (ADS)

Dąbski, Maciej; Zmarz, Anna; Pabjanek, Piotr; Korczak-Abshire, Małgorzata; Karsznia, Izabela; Chwedorzewska, Katarzyna J.

2017-08-01

High-resolution aerial images allow detailed analyses of periglacial landforms, which is of particular importance in light of climate change and resulting changes in active layer thickness. The aim of this study is to show possibilities of using UAV-based photography to perform spatial analysis of periglacial landforms on the Demay Point peninsula, King George Island, and hence to supplement previous geomorphological studies of the South Shetland Islands. Photogrammetric flights were performed using a PW-ZOOM fixed-winged unmanned aircraft vehicle. Digital elevation models (DEM) and maps of slope and contour lines were prepared in ESRI ArcGIS 10.3 with the Spatial Analyst extension, and three-dimensional visualizations in ESRI ArcScene 10.3 software. Careful interpretation of orthophoto and DEM, allowed us to vectorize polygons of landforms, such as (i) solifluction landforms (solifluction sheets, tongues, and lobes); (ii) scarps, taluses, and a protalus rampart; (iii) patterned ground (hummocks, sorted circles, stripes, nets and labyrinths, and nonsorted nets and stripes); (iv) coastal landforms (cliffs and beaches); (v) landslides and mud flows; and (vi) stone fields and bedrock outcrops. We conclude that geomorphological studies based on commonly accessible aerial and satellite images can underestimate the spatial extent of periglacial landforms and result in incomplete inventories. The PW-ZOOM UAV is well suited to gather detailed geomorphological data and can be used in spatial analysis of periglacial landforms in the Western Antarctic Peninsula region.

Pict'Earth: A new Method of Virtual Globe Data Acquisition

NASA Astrophysics Data System (ADS)

Johnson, J.; Long, S.; Riallant, D.; Hronusov, V.

2007-12-01

Georeferenced aerial imagery facilitates and enhances Earth science investigations. The realized value of imagery as a tool is measured from the spatial, temporal and radiometric resolution of the imagery. Currently, there is an need for a system which facilitates the rapid acquisition and distribution of high-resolution aerial earth images of localized areas. The Pict'Earth group has developed an apparatus and software algorithms which facilitate such tasks. Hardware includes a small radio-controlled model airplane (RC UAV); Light smartphones with high resolution cameras (Nokia NSeries Devices); and a GPS connected to the smartphone via the bluetooth protocol, or GPS-equipped phone. Software includes python code which controls the functions of the smartphone and GPS to acquire data in-flight; Online Virtual Globe applications including Google Earth, AJAX/Web2.0 technologies and services; APIs and libraries for developers, all of which are based on open XML-based GIS data standards. This new process for acquisition and distribution of high-resolution aerial earth images includes the following stages: Perform Survey over area of interest (AOI) with the RC UAV (Mobile Liveprocessing). In real-time our software collects images from the smartphone camera and positional data (latitude, longitude, altitude and heading) from the GPS. The software then calculates the earth footprint (geoprint) of each image and creates KML files which incorporate the georeferenced images and tracks of UAV. Optionally, it is possible to send the data in- flight via SMS/MMS (text and multimedia messages), or cellular internet networks via FTP. In Post processing the images are filtered, transformed, and assembled into a orthorectified image mosaic. The final mosaic is then cut into tiles and uploaded as a user ready product to web servers in kml format for use in Virtual Globes and other GIS applications. The obtained images and resultant data have high spatial resolution, can be updated in near-real time (high temporal resolution), and provide current radiance values (which is important for seasonal work). The final mosaics can also be assembled into time-lapse sequences and presented temporally. The suggested solution is cost effective when compared to the alternative methods of acquiring similar imagery. The systems are compact, mobile, and do not require a substantial amount of auxiliary equipment. Ongoing development of the software makes it possible to adapt the technology to different platforms, smartphones, sensors, and types of data. The range of application of this technology potentially covers a large part of the spectrum of Earth sciences including the calibration and validation of high-resolution satellite-derived products. These systems are currently being used for monitoring of dynamic land and water surface processes, and can be used for reconnaissance when locating and establishing field measurement sites.

Spatial dynamics of thermokarst and thermo-erosion at lakes and ponds in North Siberia and Northwest Alaska using high-resolution remote sensing

NASA Astrophysics Data System (ADS)

Grosse, G.; Tillapaugh, M.; Romanovsky, V. E.; Walter, K. M.; Plug, L. J.

2008-12-01

Formation, growth, and drainage of thermokarst lakes in ice-rich permafrost deposits are important factors of landscape dynamics in extent Arctic lowlands. Monitoring of spatial and temporal dynamics of such lakes will allow an assessment of permafrost stability and enhance the capabilities for modelling and quantifying biogeochemical processes related to permafrost degradation in a warming Arctic. In this study we use high-resolution remote sensing and GIS to analyze the development of thermokarst lakes and ponds in two study regions in North Siberia and Northwest Alaska. The sites are 1) the Cherskii region in the Kolyma lowland (Siberia) and 2) the Kitluk River area on the northern Seward Peninsula (Alaska). Both regions are characterized by continuous permafrost, a highly dissected and dynamic thermokarst landscape, uplands of Late Pleistocene permafrost deposits with high excess ice contents, and a large total volume of permafrost-stored carbon. These ice-rich Yedoma or Yedoma-like deposits are highly vulnerable to permafrost degradation forced by climate warming or other surface disturbance. Time series of high- resolution imagery (aerial, Corona, Ikonos, Alos Prism) covering more than 50 years of lake dynamics allow detailed assessments of processes and spatial patterns of thermokarst lake expansion and drainage in continuous permafrost. Time series of high-resolution imagery (aerial, Corona, Ikonos, Alos Prism) covering more than 50 years of lake dynamics allow detailed assessments of processes and spatial patterns of thermokarst lake expansion and drainage in continuous permafrost. Processes identified include thaw slumping, wave undercutting of frozen sediments or peat blocks and subsequent mass wasting, thaw collapse of near-shore zones, sinkhole formation and ice-wedge tunnelling, and gully formation by thermo-erosion. We use GIS-based tools to relate the remote sensing results to field data (ground ice content, topography, lithology, and relative age of landscape units). Results exhibit a very dynamic lake environment at both sites strongly related to landscape history and past cryolithological development. Lake shore erosion rates reach values of more than 1 m per year over the 50 year observation period at some sites. Permafrost degradation processes are identified as a key driver of both lake expansion and drainage.

Characterizing the Diurnal Cycle of Land Surface Temperature and Evapotranspiration at High Spatial Resolution Using Thermal Observations from sUAS.

NASA Astrophysics Data System (ADS)

Dutta, D.; Drewry, D.; Johnson, W. R.

2017-12-01

The surface temperature of plant canopies is an important indicator of the stomatal regulation of plant water use and the associated water flux from plants to atmosphere (evapotranspiration (ET)). Remotely sensed thermal observations using compact, low-cost, lightweight sensors from small unmanned aerial systems (sUAS) have the potential to provide surface temperature (ST) and ET estimates at unprecedented spatial and temporal resolutions, allowing us to characterize the intra-field diurnal variations in canopy ST and ET for a variety of vegetation systems. However, major challenges exist for obtaining accurate surface temperature estimates from low-cost uncooled microbolometer-type sensors. Here we describe the development of calibration methods using thermal chamber experiments, taking into account the ambient optics and sensor temperatures, and applying simple models of spatial non-uniformity correction to the sensor focal-plane-array. We present a framework that can be used to derive accurate surface temperatures using radiometric observations from low-cost sensors, and demonstrate this framework using a sUAS-mounted sensor across a diverse set of calibration and vegetation targets. Further, we demonstrate the use of the Surface Temperature Initiated Closure (STIC) model for computing spatially explicit, high spatial resolution ET estimates across several well-monitored agricultural systems, as driven by sUAS acquired surface temperatures. STIC provides a physically-based surface energy balance framework for the simultaneous retrieval of the surface and atmospheric vapor conductances and surface energy fluxes, by physically integrating radiometric surface temperature information into the Penman-Monteith equation. Results of our analysis over agricultural systems in Ames, IA and Davis, CA demonstrate the power of this approach for quantifying the intra-field spatial variability in the diurnal cycle of plant water use at sub-meter resolutions.

Semantic Building FAÇADE Segmentation from Airborne Oblique Images

NASA Astrophysics Data System (ADS)

Lin, Y.; Nex, F.; Yang, M. Y.

2018-05-01

With the introduction of airborne oblique camera systems and the improvement of photogrammetric techniques, high-resolution 2D and 3D data can be acquired in urban areas. This high-resolution data allows us to perform detailed investigations on building roofs and façades which can contribute to LoD3 city modeling. Normally, façade segmentation is achieved from terrestrial views. In this paper, we address the problem from aerial views by using high resolution oblique aerial images as the data source in urban areas. In addition to traditional image features, such as RGB and SIFT, normal vector and planarity are also extracted from dense matching point clouds. Then, these 3D geometrical features are projected back to 2D space to assist façade interpretation. Random forest is trained and applied to label façade pixels. Fully connected conditional random field (CRF), capturing long-range spatial interactions, is used as a post-processing to refine our classification results. Its pairwise potential is defined by a linear combination of Gaussian kernels and the CRF model is efficiently solved by mean field approximation. Experiments show that 3D features can significantly improve classification results. Also, fully connected CRF performs well in correcting noisy pixels.

Measuring and Monitoring Long Term Disaster Recovery Using Remote Sensing: A Case Study of Post Katrina New Orleans

NASA Astrophysics Data System (ADS)

Archer, Reginald S.

This research focuses on measuring and monitoring long term recovery progress from the impacts of Hurricane Katrina on New Orleans, LA. Remote sensing has frequently been used for emergency response and damage assessment after natural disasters. However, techniques for analysis of long term disaster recovery using remote sensing have not been widely explored. With increased availability and lower costs, remote sensing offers an objective perspective, systematic and repeatable analysis, and provides a substitute to multiple site visits. In addition, remote sensing allows access to large geographical areas and areas where ground access may be disrupted, restricted or denied. This dissertation addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators. Maximum likelihood classification and post-classification change detection were applied to multi-temporal high resolution aerial images to quantitatively measure the progress of recovery. Images were classified to automatically identify disaster recovery indicators and exploit the indicators that are visible within each image. The spectral analysis demonstrated that employing maximum likelihood classification to high resolution true color aerial images performed adequately and provided a good indication of spectral pattern recognition, despite the limited spectral information. Applying the change detection to the classified images was effective for determining the temporal trajectory of indicators categorized as blue tarps, FEMA trailers, houses, vegetation, bare earth and pavement. The results of the post classification change detection revealed a dominant change trajectory from bluetarp to house, as damaged houses became permanently repaired. Specifically, the level of activity of blue tarps, housing, vegetation, FEMA trailers (temporary housing) pavement and bare earth were derived from aerial image processing to measure and monitor the progress of recovery. Trajectories of recovery for each individual indicator were examined to provide a better understanding of activity during reconstruction. A collection of spatial metrics was explored in order to identify spatial patterns and characterize classes in terms of patches of pixels. One of the key findings of the spatial analysis is that patch shapes were more complex in the presence of debris and damaged or destroyed buildings. The combination of spectral, temporal, and spatial analysis provided a satisfactory, though limited, solution to the question of whether remote sensing alone, can be used to quantitatively assess and monitor the progress of long term recovery following a major disaster. The research described in this dissertation provided a detailed illustration of the level of activity experienced by different recovery indicators during the long term recovery process. It also addressed the primary difficulties involved in the development of change detection methods capable of detecting changes experienced by disaster recovery indicators identified from classified high resolution true color aerial imagery. The results produced in this research demonstrate that the observed trajectories for actual indicators of recovery indicate different levels of recovery activity even within the same community. The level of activity of the long term reconstruction phase observed in the Kates model is not consistent with the level of activity of key recovery indicators in the Lower 9th Ward during the same period. Used in the proper context, these methods and results provide decision making information for determining resources. KEYWORDS: Change detection, classification, Katrina, New Orleans, remote sensing, disaster recovery, spatial metrics

Retrospective farm scale spatial analysis of viticultural terroir fertility using a 70 y-aerial photograph time series, soil survey and very high resolution Pléiades and EM38 data

NASA Astrophysics Data System (ADS)

Vaudour, Emmanuelle; Leclercq, Léa; Gilliot, Jean-Marc; Chaignon, Benoît

2016-04-01

In order to elaborate adequate and sustainable practices while better controlling harvest composition at farm scale, the detailed spatial assessment of terroir units is needed. Although such assessment is made in the present time, it reflects vine behaviour and soil quality according to cumulated past choices in vineyard management. in addition to demarcate homogeneous within-vineyard zones, there is a need, in cases where the winegrower starts up its activities, to retrace the behaviour of these zones in the past, so as to consolidate the diagnosis of vine fertility, and determine further adoption of new soil and vineyard management practices that are likely to favour a long-term preservation of quality production together with soil ecosystem functions. In this study we aimed at performing such historical and spatial tracing using a long term time-series of aerial survey images, in combination with a set of very high resolution data: resistivity EM38 measurements and very high resolution Pléiades satellite images. This study was conducted over a 6 ha-farm mainly planted with rainfed black Grenache and Syrah varieties in the Southern Rhone Valley. In a previous study carried out at regional scale, soil landscape and potential terroir units had been characterized. A new field survey carried out in January 2015 considered a total of 98 topsoil sampling sites in addition to 14 soil pits, the horizons of which were described and sampled. Physico-chemical analyses were made for all soil samples, and for those horizons having the highest root development, additional analytical parameters such as copper, active lime and mineral nutrients contents were determined. Along with soil parameters, soil surface condition, vine biological parameters including vigour, presence of diseases, stock-unearthing were collected. A total of 25 aerial photographs in digitized format from the French National Institute of Geographic and Forest Information (IGN) were examined over the 1947-2010 period, of which 7 were retained for further rectification and processing (having either panchromatic, color and infrared emulsions). This dataset was used to retrace the landuse and planting history for each field. In order to assess actual vine vigour, NDVI was calculated from two atmospherically corrected Pléiades images (2.8 m-colour) acquired on 18 May 2014 and 28 July 2015 and clipped over the farm area. Moreover, within-field zones containing missing vines were isolated parameterizing the ENVI Feature Extraction® module for both the 0.7 m panchromatic band of the Pléiades image of July and 6 aerial photographs from 1972 to 2010. This enabled to demarcate those zones according to their frequency of missing and/or low vigour vines. Actual soil organic carbon and copper contents and stock unearthing intensity appeared to be significantly correlated with past landuse, particularly forest or orchard dating back the 1940s. The temporal dataset exhibited a repeated spatial pattern of missing vines, despite several plantings and uprootings, in adequation with actual soil nutrient properties.

Geohazard reconnaissance mapping for potential rock boulder fall using low altitude UAV photogrammetry

NASA Astrophysics Data System (ADS)

Sharan Kumar, N.; Ashraf Mohamad Ismail, Mohd; Sukor, Nur Sabahiah Abdul; Cheang, William

2018-05-01

This paper discusses potential applications of unmanned aerial vehicles (UAVs) for evaluation of risk immediately with photos and 3-dimensional digital element. Aerial photography using UAV ready to give a powerful technique for potential rock boulder fall recognition. High-resolution outputs from this method give the chance to evaluate the site for potential rock boulder falls spatially. The utilization of UAV to capture the aerial photos is a quick, reliable, and cost-effective technique contrasted with terrestrial laser scanning method. Reconnaissance of potential rock boulder susceptible to fall is very crucial during the geotechnical investigation. This process is essential in the view of the rock fall hazards nearby site before the beginning of any preliminary work. Photogrammetric applications have empowered the automated way to deal with identification of rock boulder susceptible to fall by recognizing the location, size, and position. A developing examination of the utilization of digital photogrammetry gives numerous many benefits for civil engineering application. These advancements have made important contributions to our capabilities to create the geohazard map on potential rock boulder fall.

Evaluation and development of unmanned aircraft (UAV) for UDOT needs.

DOT National Transportation Integrated Search

2012-05-01

This research involved the use of high-resolution aerial photography obtained from Unmanned Aerial Vehicles (UAV) to aid UDOT in monitoring and documenting State Roadway structures and associated issues. Using geo-referenced UAV high resolution aeria...

Measuring water level in rivers and lakes from lightweight Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Bandini, Filippo; Jakobsen, Jakob; Olesen, Daniel; Reyna-Gutierrez, Jose Antonio; Bauer-Gottwein, Peter

2017-05-01

The assessment of hydrologic dynamics in rivers, lakes, reservoirs and wetlands requires measurements of water level, its temporal and spatial derivatives, and the extent and dynamics of open water surfaces. Motivated by the declining number of ground-based measurement stations, research efforts have been devoted to the retrieval of these hydraulic properties from spaceborne platforms in the past few decades. However, due to coarse spatial and temporal resolutions, spaceborne missions have several limitations when assessing the water level of terrestrial surface water bodies and determining complex water dynamics. Unmanned Aerial Vehicles (UAVs) can fill the gap between spaceborne and ground-based observations, and provide high spatial resolution and dense temporal coverage data, in quick turn-around time, using flexible payload design. This study focused on categorizing and testing sensors, which comply with the weight constraint of small UAVs (around 1.5 kg), capable of measuring the range to water surface. Subtracting the measured range from the vertical position retrieved by the onboard Global Navigation Satellite System (GNSS) receiver, we can determine the water level (orthometric height). Three different ranging payloads, which consisted of a radar, a sonar and an in-house developed camera-based laser distance sensor (CLDS), have been evaluated in terms of accuracy, precision, maximum ranging distance and beam divergence. After numerous flights, the relative accuracy of the overall system was estimated. A ranging accuracy better than 0.5% of the range and a maximum ranging distance of 60 m were achieved with the radar. The CLDS showed the lowest beam divergence, which is required to avoid contamination of the signal from interfering surroundings for narrow fields of view. With the GNSS system delivering a relative vertical accuracy better than 3-5 cm, water level can be retrieved with an overall accuracy better than 5-7 cm.

Improving Understanding of Spatial Heterogeneity in Mountain Ecohydrology with Multispectral Unmanned Aerial Systems (UAS).

NASA Astrophysics Data System (ADS)

Wigmore, O.; Molotch, N. P.

2017-12-01

Mountain regions are a critical component of the hydrologic system. These regions are extremely heterogeneous, with dramatic topographic, climatic, ecologic and hydrologic variations occurring over very short distances. This heterogeneity makes understanding changes in these environments difficult. Commonly used satellite data are often too coarse to resolve processes at appropriate scales and point measurements are typically unrepresentative of the wider region. The rapid rise of Unmanned Aerial Systems (UAS) offers a potential solution to the scale-related inadequacies of satellite and ground-based observing systems. Using UAS, spatially distributed datasets can be collected at high resolution (i.e. cm), on demand, and can therefore facilitate improved understanding of mountain ecohydrology. We deployed a custom built multispectral - visible (RGB), near infrared (NIR) and thermal infrared (TIR) - UAS at a weekly interval over the Niwot Ridge Long Term Ecological Research (NWT LTER) saddle catchment at 3500masl in the Colorado Rockies. This system was used to map surface water pathways, land cover and topography, and quantify ecohydrologic variables including, snow depth, vegetation productivity and surface soil moisture at 5-50cm resolution across an 80ha study area. This presentation will discuss the techniques, methods and merits of using UAS derived multispectral data for ecohydrologic research in mountain regions. We will also present preliminary findings from our survey time series at NWT LTER and a discussion of the potential insights that these datasets can provide. Key questions to be addressed are: 1) how does spatial variability in snow depth impact soil moisture and vegetation productivity, 2) how can UAS help us to identify ecohydrologic `hotspots' and `hot moments' across heterogeneous landscapes.

A Methodological Intercomparison of Topographic and Aerial Photographic Habitat Survey Techniques

NASA Astrophysics Data System (ADS)

Bangen, S. G.; Wheaton, J. M.; Bouwes, N.

2011-12-01

A severe decline in Columbia River salmonid populations and subsequent Federal listing of subpopulations has mandated both the monitoring of populations and evaluation of the status of available habitat. Numerous field and analytical methods exist to assist in the quantification of the abundance and quality of in-stream habitat for salmonids. These methods range from field 'stick and tape' surveys to spatially explicit topographic and aerial photographic surveys from a mix of ground-based and remotely sensed airborne platforms. Although several previous studies have assessed the quality of specific individual survey methods, the intercomparison of competing techniques across a diverse range of habitat conditions (wadeable headwater channels to non-wadeable mainstem channels) has not yet been elucidated. In this study, we seek to enumerate relative quality (i.e. accuracy, precision, extent) of habitat metrics and inventories derived from an array of ground-based and remotely sensed surveys of varying degrees of sophistication, as well as quantify the effort and cost in conducting the surveys. Over the summer of 2010, seven sample reaches of varying habitat complexity were surveyed in the Lemhi River Basin, Idaho, USA. Complete topographic surveys were attempted at each site using rtkGPS, total station, ground-based LiDaR and traditional airborne LiDaR. Separate high spatial resolution aerial imagery surveys were acquired using a tethered blimp, a drone UAV, and a traditional fixed-wing aircraft. Here we also developed a relatively simplistic methodology for deriving bathymetry from aerial imagery that could be readily employed by instream habitat monitoring programs. The quality of bathymetric maps derived from aerial imagery was compared with rtkGPS topographic data. The results are helpful for understanding the strengths and weaknesses of different approaches in specific conditions, and how a hybrid of data acquisition methods can be used to build a more complete quantification of salmonid habitat conditions in streams.

L-band Soil Moisture Mapping using Small UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.

2015-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 promises to provide global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of soil moisture on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform) .Compared with various other proposed methods of validation based on either situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed (~km scale) coverage at very high spatial resolution (~15 m) suitable for scaling scale studies, and at comparatively low operator cost. The LDCR on Tempest unit can supply the soil moisture mapping with different resolution which is of order the Tempest altitude.

The Impact of Conflicting Spatial Representations in Airborne Unmanned Aerial System Sensor Control

DTIC Science & Technology

2016-02-01

Spatial Discordance 1 Running head: SPATIAL DISCORDANCE IN AIRBORNE UAS OPERATIONS The impact of conflicting spatial...representations in airborne unmanned aerial system sensor control Joseph W Geeseman, James E Patrey, Caroline Davy, Katherine Peditto, & Christine Zernickow...system (UAS) simulation while riding in the fuselage of an airborne Lockheed P-3 Orion. The P-3 flew a flight profile of intermittent ascending

D Object Classification Based on Thermal and Visible Imagery in Urban Area

NASA Astrophysics Data System (ADS)

Hasani, H.; Samadzadegan, F.

2015-12-01

The spatial distribution of land cover in the urban area especially 3D objects (buildings and trees) is a fundamental dataset for urban planning, ecological research, disaster management, etc. According to recent advances in sensor technologies, several types of remotely sensed data are available from the same area. Data fusion has been widely investigated for integrating different source of data in classification of urban area. Thermal infrared imagery (TIR) contains information on emitted radiation and has unique radiometric properties. However, due to coarse spatial resolution of thermal data, its application has been restricted in urban areas. On the other hand, visible image (VIS) has high spatial resolution and information in visible spectrum. Consequently, there is a complementary relation between thermal and visible imagery in classification of urban area. This paper evaluates the potential of aerial thermal hyperspectral and visible imagery fusion in classification of urban area. In the pre-processing step, thermal imagery is resampled to the spatial resolution of visible image. Then feature level fusion is applied to construct hybrid feature space include visible bands, thermal hyperspectral bands, spatial and texture features and moreover Principle Component Analysis (PCA) transformation is applied to extract PCs. Due to high dimensionality of feature space, dimension reduction method is performed. Finally, Support Vector Machines (SVMs) classify the reduced hybrid feature space. The obtained results show using thermal imagery along with visible imagery, improved the classification accuracy up to 8% respect to visible image classification.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture.

PubMed

Yamamoto, Kyosuke; Togami, Takashi; Yamaguchi, Norio

2017-11-06

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture-in cooperation with image processing technologies-for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis.

Super-Resolution of Plant Disease Images for the Acceleration of Image-based Phenotyping and Vigor Diagnosis in Agriculture

PubMed Central

Togami, Takashi; Yamaguchi, Norio

2017-01-01

Unmanned aerial vehicles (UAVs or drones) are a very promising branch of technology, and they have been utilized in agriculture—in cooperation with image processing technologies—for phenotyping and vigor diagnosis. One of the problems in the utilization of UAVs for agricultural purposes is the limitation in flight time. It is necessary to fly at a high altitude to capture the maximum number of plants in the limited time available, but this reduces the spatial resolution of the captured images. In this study, we applied a super-resolution method to the low-resolution images of tomato diseases to recover detailed appearances, such as lesions on plant organs. We also conducted disease classification using high-resolution, low-resolution, and super-resolution images to evaluate the effectiveness of super-resolution methods in disease classification. Our results indicated that the super-resolution method outperformed conventional image scaling methods in spatial resolution enhancement of tomato disease images. The results of disease classification showed that the accuracy attained was also better by a large margin with super-resolution images than with low-resolution images. These results indicated that our approach not only recovered the information lost in low-resolution images, but also exerted a beneficial influence on further image analysis. The proposed approach will accelerate image-based phenotyping and vigor diagnosis in the field, because it not only saves time to capture images of a crop in a cultivation field but also secures the accuracy of these images for further analysis. PMID:29113104

The use of unmanned aerial vehicle imagery in intertidal monitoring

NASA Astrophysics Data System (ADS)

Konar, Brenda; Iken, Katrin

2018-01-01

Intertidal monitoring projects are often limited in their practicality because traditional methods such as visual surveys or removal of biota are often limited in the spatial extent for which data can be collected. Here, we used imagery from a small unmanned aerial vehicle (sUAV) to test their potential use in rocky intertidal and intertidal seagrass surveys in the northern Gulf of Alaska. Images captured by the sUAV in the high, mid and low intertidal strata on a rocky beach and within a seagrass bed were compared to data derived concurrently from observer visual surveys and to images taken by observers on the ground. Observer visual data always resulted in the highest taxon richness, but when observer data were aggregated to the lower taxonomic resolution obtained by the sUAV images, overall community composition was mostly similar between the two methods. Ground camera images and sUAV images yielded mostly comparable community composition despite the typically higher taxonomic resolution obtained by the ground camera. We conclude that monitoring goals or research questions that can be answered on a relatively coarse taxonomic level can benefit from an sUAV-based approach because it allows much larger spatial coverage within the time constraints of a low tide interval than is possible by observers on the ground. We demonstrated this large-scale applicability by using sUAV images to develop maps that show the distribution patterns and patchiness of seagrass.

Measuring slip in paleoearthquakes using high-resolution aerial lidar data: Combined analysis of the Wairau, Awatere, Clarence, and Hope faults, South Island, New Zealand

NASA Astrophysics Data System (ADS)

Zinke, R. W.; Dolan, J. F.; Hatem, A. E.; Van Dissen, R. J.; Langridge, R.; Grenader, J.; McGuire, C. P.; Rhodes, E. J.; Nicol, A., , Prof

2016-12-01

Analysis of a large new high-resolution aerial lidar microtopographic data set provides > 500 measured fault offsets from sections of the four primary right-lateral strike-slip faults of the Marlborough Fault System (MFS), in northern South Island, New Zealand. With a shot density of >12 shots/m2 (and locally up to 18 shots/m2) these high-quality data allow us to resolve topographically defined geomorphic offsets with decimeter precision along 250 km of combined fault length. The measured offsets range in size from 2 m to > 100 m, and allow us to constrain displacements in the past one to several surface ruptures along stretches of the Wairau, Awatere, Clarence, and Hope faults. Our results reveal a number of important details of the rupture history of these faults, including: (1) the amount of slip and spatial variability (along and across strike) of strain released in the most recent event along sections of each of the four faults; (2) the consistency of slip throughout the past several ruptures on specific faults; and (3) suggestions of potential linkages and segment boundaries along each fault. The lidar data also facilitate precise measurements of larger offsets that, when combined with age data collected as part of our broader collaborative analyses of incremental fault slip rates and paleoearthquake ages, help to constrain the broader spatial and temporal patterns of strain release across the MFS during Holocene and latest Pleistocene time.

Island building in the South China Sea: detection of turbidity plumes and artificial islands using Landsat and MODIS data

PubMed Central

Barnes, Brian B.; Hu, Chuanmin

2016-01-01

The South China Sea is currently in a state of intense geopolitical conflict, with six countries claiming sovereignty over some or all of the area. Recently, several countries have carried out island building projects in the Spratly Islands, converting portions of coral reefs into artificial islands. Aerial photography and high resolution satellites can capture snapshots of this construction, but such data are lacking in temporal resolution and spatial scope. In contrast, lower resolution satellite sensors with regular repeat sampling allow for more rigorous assessment and monitoring of changes to the reefs and surrounding areas. Using Landsat-8 data at ≥15-m resolution, we estimated that over 15 km2 of submerged coral reef area was converted to artificial islands between June 2013 and December 2015, mostly by China. MODIS data at ≥250-m resolution were used to locate previously underreported island building activities, as well as to assess resulting in-water turbidity plumes. The combined spatial extent of observed turbidity plumes for island building activities at Mischief, Subi, and Fiery Cross Reefs was over 4,300 km2, although nearly 40% of this area was only affected once. Together, these activities represent widespread damage to coral ecosystems through physical burial as well as indirect turbidity effects. PMID:27628096

Island building in the South China Sea: detection of turbidity plumes and artificial islands using Landsat and MODIS data

NASA Astrophysics Data System (ADS)

Barnes, Brian B.; Hu, Chuanmin

2016-09-01

The South China Sea is currently in a state of intense geopolitical conflict, with six countries claiming sovereignty over some or all of the area. Recently, several countries have carried out island building projects in the Spratly Islands, converting portions of coral reefs into artificial islands. Aerial photography and high resolution satellites can capture snapshots of this construction, but such data are lacking in temporal resolution and spatial scope. In contrast, lower resolution satellite sensors with regular repeat sampling allow for more rigorous assessment and monitoring of changes to the reefs and surrounding areas. Using Landsat-8 data at ≥15-m resolution, we estimated that over 15 km2 of submerged coral reef area was converted to artificial islands between June 2013 and December 2015, mostly by China. MODIS data at ≥250-m resolution were used to locate previously underreported island building activities, as well as to assess resulting in-water turbidity plumes. The combined spatial extent of observed turbidity plumes for island building activities at Mischief, Subi, and Fiery Cross Reefs was over 4,300 km2, although nearly 40% of this area was only affected once. Together, these activities represent widespread damage to coral ecosystems through physical burial as well as indirect turbidity effects.

Island building in the South China Sea: detection of turbidity plumes and artificial islands using Landsat and MODIS data.

PubMed

Barnes, Brian B; Hu, Chuanmin

2016-09-15

The South China Sea is currently in a state of intense geopolitical conflict, with six countries claiming sovereignty over some or all of the area. Recently, several countries have carried out island building projects in the Spratly Islands, converting portions of coral reefs into artificial islands. Aerial photography and high resolution satellites can capture snapshots of this construction, but such data are lacking in temporal resolution and spatial scope. In contrast, lower resolution satellite sensors with regular repeat sampling allow for more rigorous assessment and monitoring of changes to the reefs and surrounding areas. Using Landsat-8 data at ≥15-m resolution, we estimated that over 15 km(2) of submerged coral reef area was converted to artificial islands between June 2013 and December 2015, mostly by China. MODIS data at ≥250-m resolution were used to locate previously underreported island building activities, as well as to assess resulting in-water turbidity plumes. The combined spatial extent of observed turbidity plumes for island building activities at Mischief, Subi, and Fiery Cross Reefs was over 4,300 km(2), although nearly 40% of this area was only affected once. Together, these activities represent widespread damage to coral ecosystems through physical burial as well as indirect turbidity effects.

Proceedings of the 2004 High Spatial Resolution Commercial Imagery Workshop

NASA Technical Reports Server (NTRS)

2006-01-01

Topics covered include: NASA Applied Sciences Program; USGS Land Remote Sensing: Overview; QuickBird System Status and Product Overview; ORBIMAGE Overview; IKONOS 2004 Calibration and Validation Status; OrbView-3 Spatial Characterization; On-Orbit Modulation Transfer Function (MTF) Measurement of QuickBird; Spatial Resolution Characterization for QuickBird Image Products 2003-2004 Season; Image Quality Evaluation of QuickBird Super Resolution and Revisit of IKONOS: Civil and Commercial Application Project (CCAP); On-Orbit System MTF Measurement; QuickBird Post Launch Geopositional Characterization Update; OrbView-3 Geometric Calibration and Geopositional Accuracy; Geopositional Statistical Methods; QuickBird and OrbView-3 Geopositional Accuracy Assessment; Initial On-Orbit Spatial Resolution Characterization of OrbView-3 Panchromatic Images; Laboratory Measurement of Bidirectional Reflectance of Radiometric Tarps; Stennis Space Center Verification and Validation Capabilities; Joint Agency Commercial Imagery Evaluation (JACIE) Team; Adjacency Effects in High Resolution Imagery; Effect of Pulse Width vs. GSD on MTF Estimation; Camera and Sensor Calibration at the USGS; QuickBird Geometric Verification; Comparison of MODTRAN to Heritage-based Results in Vicarious Calibration at University of Arizona; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Estimating Sub-Pixel Proportions of Sagebrush with a Regression Tree; How Do YOU Use the National Land Cover Dataset?; The National Map Hazards Data Distribution System; Recording a Troubled World; What Does This-Have to Do with This?; When Can a Picture Save a Thousand Homes?; InSAR Studies of Alaska Volcanoes; Earth Observing-1 (EO-1) Data Products; Improving Access to the USGS Aerial Film Collections: High Resolution Scanners; Improving Access to the USGS Aerial Film Collections: Phoenix Digitizing System Product Distribution; System and Product Characterization: Issues Approach; Innovative Approaches to Analysis of Lidar Data for the National Map; Changes in Imperviousness near Military Installations; Geopositional Accuracy Evaluations of QuickBird and OrbView-3: Civil and Commercial Applications Project (CCAP); Geometric Accuracy Assessment: OrbView ORTHO Products; QuickBird Radiometric Calibration Update; OrbView-3 Radiometric Calibration; QuickBird Radiometric Characterization; NASA Radiometric Characterization; Establishing and Verifying the Traceability of Remote-Sensing Measurements to International Standards; QuickBird Applications; Airport Mapping and Perpetual Monitoring Using IKONOS; OrbView-3 Relative Accuracy Results and Impacts on Exploitation and Accuracy Improvement; Using Remotely Sensed Imagery to Determine Impervious Surface in Sioux Falls, South Dakota; Applying High-Resolution Satellite Imagery and Remotely Sensed Data to Local Government Applications: Sioux Falls, South Dakota; Automatic Co-Registration of QuickBird Data for Change Detection Applications; Developing Coastal Surface Roughness Maps Using ASTER and QuickBird Data Sources; Automated, Near-Real Time Cloud and Cloud Shadow Detection in High Resolution VNIR Imagery; Science Applications of High Resolution Imagery at the USGS EROS Data Center; Draft Plan for Characterizing Commercial Data Products in Support of Earth Science Research; Atmospheric Correction Prototype Algorithm for High Spatial Resolution Multispectral Earth Observing Imaging Systems; Determining Regional Arctic Tundra Carbon Exchange: A Bottom-Up Approach; Using IKONOS Imagery to Assess Impervious Surface Area, Riparian Buffers and Stream Health in the Mid-Atlantic Region; Commercial Remote Sensing Space Policy Civil Implementation Update; USGS Commercial Remote Sensing Data Contracts (CRSDC); and Commercial Remote Sensing Space Policy (CRSSP): Civil Near-Term Requirements Collection Update.

NDVI, scale invariance and the modifiable areal unit problem: An assessment of vegetation in the Adelaide Parklands.

PubMed

Nouri, Hamideh; Anderson, Sharolyn; Sutton, Paul; Beecham, Simon; Nagler, Pamela; Jarchow, Christopher J; Roberts, Dar A

2017-04-15

This research addresses the question as to whether or not the Normalised Difference Vegetation Index (NDVI) is scale invariant (i.e. constant over spatial aggregation) for pure pixels of urban vegetation. It has been long recognized that there are issues related to the modifiable areal unit problem (MAUP) pertaining to indices such as NDVI and images at varying spatial resolutions. These issues are relevant to using NDVI values in spatial analyses. We compare two different methods of calculation of a mean NDVI: 1) using pixel values of NDVI within feature/object boundaries and 2) first calculating the mean red and mean near-infrared across all feature pixels and then calculating NDVI. We explore the nature and magnitude of these differences for images taken from two sensors, a 1.24m resolution WorldView-3 and a 0.1m resolution digital aerial image. We apply these methods over an urban park located in the Adelaide Parklands of South Australia. We demonstrate that the MAUP is not an issue for calculation of NDVI within a sensor for pure urban vegetation pixels. This may prove useful for future rule-based monitoring of the ecosystem functioning of green infrastructure. Copyright © 2017 Elsevier B.V. All rights reserved.

Collection and Analysis of Crowd Data with Aerial, Rooftop, and Ground Views

DTIC Science & Technology

2014-11-10

collected these datasets using different aircrafts. Erista 8 HL OctaCopter is a heavy-lift aerial platform capable of using high-resolution cinema ...is another high-resolution camera that is cinema grade and high quality, with the capability of capturing videos with 4K resolution at 30 frames per...292.58 Imaging Systems and Accessories Blackmagic Production Camera 4 Crowd Counting using 4K Cameras High resolution cinema grade digital video

EnviroAtlas -- Fresno, California -- One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The Fresno, CA EnviroAtlas One-Meter-scale Urban Land Cover Data were generated via supervised classification of combined aerial photography and LiDAR data. The air photos were United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1-m spatial resolution. Aerial photography ('imagery') was collected on multiple dates in summer 2010. Seven land cover classes were mapped: Water, impervious surfaces (Impervious), soil and barren (Soil), trees and forest (Tree), and grass and herbaceous non-woody vegetation (Grass), agriculture (Ag), and Orchards. An accuracy assessment of 500 completely random and 103 stratified random points yielded an overall User's fuzzy accuracy of 81.1 percent (see below). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Fresno, CA plus a 1-km buffer. Where imagery was available, additional areas outside the 1-km boundary were also mapped but not included in the accuracy assessment. We expect the accuracy of the areas outside of the 1-km boundary to be consistent with those within. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The da

EnviroAtlas -- Fresno, California -- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The Fresno, CA EnviroAtlas One-Meter-scale Urban Land Cover Data were generated via supervised classification of combined aerial photography and LiDAR data. The air photos were United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1-m spatial resolution. Aerial photography ('imagery') was collected on multiple dates in summer 2010. Seven land cover classes were mapped: Water, impervious surfaces (Impervious), soil and barren (Soil), trees and forest (Tree), and grass and herbaceous non-woody vegetation (Grass), agriculture (Ag), and Orchards. An accuracy assessment of 500 completely random and 103 stratified random points yielded an overall User's fuzzy accuracy of 81.1 percent (see below). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Fresno, CA plus a 1-km buffer. Where imagery was available, additional areas outside the 1-km boundary were also mapped but not included in the accuracy assessment. We expect the accuracy of the areas outside of the 1-km boundary to be consistent with those within. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with

Vehicle detection from very-high-resolution (VHR) aerial imagery using attribute belief propagation (ABP)

NASA Astrophysics Data System (ADS)

Wang, Yanli; Li, Ying; Zhang, Li; Huang, Yuchun

2016-10-01

With the popularity of very-high-resolution (VHR) aerial imagery, the shape, color, and context attribute of vehicles are better characterized. Due to the various road surroundings and imaging conditions, vehicle attributes could be adversely affected so that vehicle is mistakenly detected or missed. This paper is motivated to robustly extract the rich attribute feature for detecting the vehicles of VHR imagery under different scenarios. Based on the hierarchical component tree of vehicle context, attribute belief propagation (ABP) is proposed to detect salient vehicles from the statistical perspective. With the Max-tree data structure, the multi-level component tree around the road network is efficiently created. The spatial relationship between vehicle and its belonging context is established with the belief definition of vehicle attribute. To effectively correct single-level belief error, the inter-level belief linkages enforce consistency of belief assignment between corresponding components at different levels. ABP starts from an initial set of vehicle belief calculated by vehicle attribute, and then iterates through each component by applying inter-level belief passing until convergence. The optimal value of vehicle belief of each component is obtained via minimizing its belief function iteratively. The proposed algorithm is tested on a diverse set of VHR imagery acquired in the city and inter-city areas of the West and South China. Experimental results show that the proposed algorithm can detect vehicle efficiently and suppress the erroneous effectively. The proposed ABP framework is promising to robustly classify the vehicles from VHR Aerial imagery.

Image degradation in aerial imagery duplicates. [photographic processing of photographic film and reproduction (copying)

NASA Technical Reports Server (NTRS)

Lockwood, H. E.

1975-01-01

A series of Earth Resources Aircraft Program data flights were made over an aerial test range in Arizona for the evaluation of large cameras. Specifically, both medium altitude and high altitude flights were made to test and evaluate a series of color as well as black-and-white films. Image degradation, inherent in duplication processing, was studied. Resolution losses resulting from resolution characteristics of the film types are given. Color duplicates, in general, are shown to be degraded more than black-and-white films because of the limitations imposed by available aerial color duplicating stock. Results indicate that a greater resolution loss may be expected when the original has higher resolution. Photographs of the duplications are shown.

Landsat multispectral sharpening using a sensor system model and panchromatic image

USGS Publications Warehouse

Lemeshewsky, G.P.; ,

2003-01-01

The thematic mapper (TM) sensor aboard Landsats 4, 5 and enhanced TM plus (ETM+) on Landsat 7 collect imagery at 30-m sample distance in six spectral bands. New with ETM+ is a 15-m panchromatic (P) band. With image sharpening techniques, this higher resolution P data, or as an alternative, the 10-m (or 5-m) P data of the SPOT satellite, can increase the spatial resolution of the multispectral (MS) data. Sharpening requires that the lower resolution MS image be coregistered and resampled to the P data before high spatial frequency information is transferred to the MS data. For visual interpretation and machine classification tasks, it is important that the sharpened data preserve the spectral characteristics of the original low resolution data. A technique was developed for sharpening (in this case, 3:1 spatial resolution enhancement) visible spectral band data, based on a model of the sensor system point spread function (PSF) in order to maintain spectral fidelity. It combines high-pass (HP) filter sharpening methods with iterative image restoration to reduce degradations caused by sensor-system-induced blurring and resembling. Also there is a spectral fidelity requirement: sharpened MS when filtered by the modeled degradations should reproduce the low resolution source MS. Quantitative evaluation of sharpening performance was made by using simulated low resolution data generated from digital color-IR aerial photography. In comparison to the HP-filter-based sharpening method, results for the technique in this paper with simulated data show improved spectral fidelity. Preliminary results with TM 30-m visible band data sharpened with simulated 10-m panchromatic data are promising but require further study.

The use of unmanned aerial systems for the mapping of legacy uranium mines.

PubMed

Martin, P G; Payton, O D; Fardoulis, J S; Richards, D A; Scott, T B

2015-05-01

Historical mining of uranium mineral veins within Cornwall, England, has resulted in a significant amount of legacy radiological contamination spread across numerous long disused mining sites. Factors including the poorly documented and aged condition of these sites as well as the highly localised nature of radioactivity limit the success of traditional survey methods. A newly developed terrain-independent unmanned aerial system [UAS] carrying an integrated gamma radiation mapping unit was used for the radiological characterisation of a single legacy mining site. Using this instrument to produce high-spatial-resolution maps, it was possible to determine the radiologically contaminated land areas and to rapidly identify and quantify the degree of contamination and its isotopic nature. The instrument was demonstrated to be a viable tool for the characterisation of similar sites worldwide. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Ratio maps of iron ore deposits Atlantic City district, Wyoming

NASA Technical Reports Server (NTRS)

Vincent, R. K.

1973-01-01

Preliminary results of a spectral rationing technique are shown for a region at the southern end of the Wind River Range, Wyoming. Digital ratio graymaps and analog ratio images have been produced for the test site, but ground truth is not yet available for thorough interpretation of these products. ERTS analog ratio images were found generally better than either ERTS single-channel images or high altitude aerial photos for the discrimination of vegetation from non-vegetation in the test site region. Some linear geological features smaller than the ERTS spatial resolution are seen as well in ERTS ratio and single-channel images as in high altitude aerial photography. Geochemical information appears to be extractable from ERTS data. Good preliminary quantitative agreement between ERTS-derived ratios and laboratory-derived reflectance ratios of rocks and minerals encourage plans to use lab data as training sets for a simple ratio gating logic approach to automatic recognition maps.

Low altitude unmanned aerial vehicle for characterising remediation effectiveness following the FDNPP accident.

PubMed

Martin, P G; Payton, O D; Fardoulis, J S; Richards, D A; Yamashiki, Y; Scott, T B

2016-01-01

On the 12th of March 2011, The Great Tōhoku Earthquake occurred 70 km off the eastern coast of Japan, generating a large 14 m high tsunami. The ensuing catalogue of events over the succeeding 12 d resulted in the release of considerable quantities of radioactive material into the environment. Important to the large-scale remediation of the affected areas is the accurate and high spatial resolution characterisation of contamination, including the verification of decontaminated areas. To enable this, a low altitude unmanned aerial vehicle equipped with a lightweight gamma-spectrometer and height normalisation system was used to produce sub-meter resolution maps of contamination. This system provided a valuable method to examine both contaminated and remediated areas rapidly, whilst greatly reducing the dose received by the operator, typically in localities formerly inaccessible to ground-based survey methods. The characterisation of three sites within Fukushima Prefecture is presented; one remediated (and a site of much previous attention), one un-remediated and a third having been subjected to an alternative method to reduce emitted radiation dose. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Melon yield prediction using small unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Zhao, Tiebiao; Wang, Zhongdao; Yang, Qi; Chen, YangQuan

2017-05-01

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.

Mapping Urban Tree Canopy Coverage and Structure using Data Fusion of High Resolution Satellite Imagery and Aerial Lidar

NASA Astrophysics Data System (ADS)

Elmes, A.; Rogan, J.; Williams, C. A.; Martin, D. G.; Ratick, S.; Nowak, D.

2015-12-01

Urban tree canopy (UTC) coverage is a critical component of sustainable urban areas. Trees provide a number of important ecosystem services, including air pollution mitigation, water runoff control, and aesthetic and cultural values. Critically, urban trees also act to mitigate the urban heat island (UHI) effect by shading impervious surfaces and via evaporative cooling. The cooling effect of urban trees can be seen locally, with individual trees reducing home HVAC costs, and at a citywide scale, reducing the extent and magnitude of an urban areas UHI. In order to accurately model the ecosystem services of a given urban forest, it is essential to map in detail the condition and composition of these trees at a fine scale, capturing individual tree crowns and their vertical structure. This paper presents methods for delineating UTC and measuring canopy structure at fine spatial resolution (<1m). These metrics are essential for modeling the HVAC benefits from UTC for individual homes, and for assessing the ecosystem services for entire urban areas. Such maps have previously been made using a variety of methods, typically relying on high resolution aerial or satellite imagery. This paper seeks to contribute to this growing body of methods, relying on a data fusion method to combine the information contained in high resolution WorldView-3 satellite imagery and aerial lidar data using an object-based image classification approach. The study area, Worcester, MA, has recently undergone a large-scale tree removal and reforestation program, following a pest eradication effort. Therefore, the urban canopy in this location provides a wide mix of tree age class and functional type, ideal for illustrating the effectiveness of the proposed methods. Early results show that the object-based classifier is indeed capable of identifying individual tree crowns, while continued research will focus on extracting crown structural characteristics using lidar-derived metrics. Ultimately, the resulting fine resolution UTC map will be compared with previously created UTC maps of the same area but for earlier dates, producing a canopy change map corresponding to the Worcester area tree removal and replanting effort.

Monitoring Cloud-prone Complex Landscapes At Multiple Spatial Scales Using Medium And High Resolution Optical Data: A Case Study In Central Africa

NASA Astrophysics Data System (ADS)

Basnet, Bikash

Tracking land surface dynamics over cloud-prone areas with complex mountainous terrain and a landscape that is heterogeneous at a scale of approximately 10 m, is an important challenge in the remote sensing of tropical regions in developing nations, due to the small plot sizes. Persistent monitoring of natural resources in these regions at multiple spatial scales requires development of tools to identify emerging land cover transformation due to anthropogenic causes, such as agricultural expansion and climate change. Along with the cloud cover and obstructions by topographic distortions due to steep terrain, there are limitations to the accuracy of monitoring change using available historical satellite imagery, largely due to sparse data access and the lack of high quality ground truth for classifier training. One such complex region is the Lake Kivu region in Central Africa. This work addressed these problems to create an effective process for monitoring the Lake Kivu region located in Central Africa. The Lake Kivu region is a biodiversity hotspot with a complex and heterogeneous landscape and intensive agricultural development, where individual plot sizes are often at the scale of 10m. Procedures were developed that use optical data from satellite and aerial observations at multiple scales to tackle the monitoring challenges. First, a novel processing chain was developed to systematically monitor the spatio-temporal land cover dynamics of this region over the years 1988, 2001, and 2011 using Landsat data, complemented by ancillary data. Topographic compensation was performed on Landsat reflectances to avoid the strong illumination angle impacts and image compositing was used to compensate for frequent cloud cover and thus incomplete annual data availability in the archive. A systematic supervised classification, using the state-of-the-art machine learning classifier Random Forest, was applied to the composite Landsat imagery to obtain land cover thematic maps with overall accuracies of 90% and higher. Subsequent change analysis between these years found extensive conversions of the natural environment as a result of human related activities. The gross forest cover loss for 1988--2001 and 2001--2011 periods was 216.4 and 130.5 thousand hectares, respectively, signifying significant deforestation in the period of civil war and a relatively stable and lower deforestation rate later, possibly due to conservation and reforestation efforts in the region. The other dominant land cover changes in the region were aggressive subsistence farming and urban expansion displacing natural vegetation and arable lands. Despite limited data availability, this study fills the gap of much needed detailed and updated land cover change information for this biologically important region of Central Africa. While useful on a regional scale, Landsat data can be inadequate for more detailed studies of land cover change. Based on an increasing availability of high resolution imagery and light detection and ranging (LiDAR) data from manned and unmanned aerial platforms (<1m resolution), a study was performed leading to a novel generic framework for land cover monitoring at fine spatial scales. The approach fuses high spatial resolution aerial imagery and LiDAR data to produce land cover maps with high spatial detail using object-based image analysis techniques. The classification framework was tested for a scene with both natural and cultural features and was found to be more than 90 percent accurate, sufficient for detailed land cover change studies.

A New Perspective: Assessing the Spatial Distribution of Coral Bleaching with Unmanned Low Altitude Remote Sensing Systems

NASA Astrophysics Data System (ADS)

Levy, J.; Franklin, E. C.; Hunter, C. L.

2016-12-01

Coral reefs are biodiversity hotspots that are vital to the function of global economic and biological processes. Coral bleaching is a significant contributor to the global decline of reefs and can impact an expansive reef area over short timescales. In order to understand the dynamics of coral bleaching and how these stress events impact reef ecosystems, it is important to conduct rapid bleaching surveys at functionally important spatial scales. Due to the inherent heterogeneity, size, and in some cases, remoteness of coral reefs, it is difficult to routinely monitor coral bleaching dynamics before, during, and after bleaching. Additionally, current in situ survey methods only collect snippets of discrete reef data over small reef areas, which are unable to accurately represent the reef as a whole. We present a new technique using small unmanned aerial systems (sUAS) as cost effective, efficient monitoring tools that target small to intermediate-scale reef dynamics to understand the spatial distribution of bleached coral colonies during the 2015 bleaching event on patch reefs in Kaneohe Bay, Oahu. Overlapping low altitude aerial images were collected at four reefs during the bleaching period and processed using Structure-from-Motion techniques to produce georeferenced and spatially accurate orthomosaics of complete reef areas. Mosaics were analyzed using manual and heuristic neural network classification schemes to identify comprehensive populations of bleached and live coral on each patch reef. We found that bleached colonies had random and clumped distributions on patch reefs in Kaneohe Bay depending on local environmental conditions. Our work demonstrates that sUAS provide a low cost, efficient platform that can rapidly and repeatedly collect high-resolution imagery (1 cm/pixel) and map large areas of shallow reef ecosystems (5 hectares). This study proves the feasibility of utilizing sUAS as a tool to collect spatially rich reef data that will provide reef scientists a new perspective on meso-scale coral reef dynamics. We envision that similar low altitude aerial surveys will be incorporated as a standard component of shallow-water reef studies, especially on reefs too dangerous or remote for in situ surveys.

Remote Sensing, GIS, and Vector-Borne Disease

NASA Technical Reports Server (NTRS)

Beck, Louisa R.

2001-01-01

The concept of global climate change encompasses more than merely an alteration in temperature; it also includes spatial and temporal covariations in precipitation and humidity, and more frequent occurrence of extreme weather events. The impact of these variations, which can occur at a variety of temporal and spatial scales, could have a direct impact on disease transmission through their environmental consequences for pathogen, vector, and host survival, as well as indirectly through human demographic and behavioral responses. New and future sensor systems will allow scientists to investigate the relationships between climate change and environmental risk factors at multiple spatial, temporal and spectral scales. Higher spatial resolution will provide better opportunities for mapping urban features previously only possible with high resolution aerial photography. These opportunities include housing quality (e.g., Chagas'disease, leishmaniasis) and urban mosquito habitats (e.g., dengue fever, filariasis, LaCrosse encephalitis). There are or will be many new sensors that have higher spectral resolution, enabling scientists to acquire more information about parameters such as soil moisture, soil type, better vegetation discrimination, and ocean color, to name a few. Although soil moisture content is now detectable using Landsat, the new thermal, shortwave infrared, and radar sensors will be able to provide this information at a variety of scales not achievable using Landsat. Soil moisture could become a key component in transmission risk models for Lyme disease (tick survival), helminthiases (worm habitat), malaria (vector-breeding habitat), and schistosomiasis (snail habitat).

Integrated remote sensing and visualization (IRSV) system for transportation infrastructure operations and management, phase two, volume 5 : aerial bridge deck imaging data collection and software revision.

DOT National Transportation Integrated Search

2012-02-01

For rapid deployment of bridge scan missions, sub-inch aerial imaging using small format aerial photography : is suggested. Under-belly photography is used to generate high resolution aerial images that can be geo-referenced and : used for quantifyin...

Quadcopter applications for wildlife monitoring

NASA Astrophysics Data System (ADS)

Radiansyah, S.; Kusrini, M. D.; Prasetyo, L. B.

2017-01-01

Recently, Unmanned Aerial Vehicle (UAV) had been use as an instrument for wildlife research. Most of that, using an airplane type which need space for runaway. Copter is UAV type that can fly at canopy space and do not need runaway. The research aims are to examine quadcopter application for wildlife monitoring, measure the accuracy of data generated and determine effective, efficient and appropriate technical recommendation in accordance with the ethics of wildlife photography. Flight trials with a camera 12 - 24 MP at altitude ranges from 50-200 m above ground level (agl), producing aerial photographs with spatial resolution of 0.85 - 4.79 cm/pixel. Aerial photos quality depends on the type and setting of camera, vibration damper system, flight altitude and punctuality of the shooting. For wildlife monitoring the copter is recommended to take off at least 300 m from the target, and flies at 50 - 100 m agl with flight speed of 5 - 7 m/sec on fine weather. Quadcopter presence with a distance more than 30 m from White-bellied Sea Eagles (Haliaeetus leucogaster) nest and Proboscis Monkey (Nasalis larvatus) did not cause negative response. Quadcopter application should pay attention to the behaviour and characteristic of wildlife.

Gyrocopter-Based Remote Sensing Platform

NASA Astrophysics Data System (ADS)

Weber, I.; Jenal, A.; Kneer, C.; Bongartz, J.

2015-04-01

In this paper the development of a lightweight and highly modularized airborne sensor platform for remote sensing applications utilizing a gyrocopter as a carrier platform is described. The current sensor configuration consists of a high resolution DSLR camera for VIS-RGB recordings. As a second sensor modality, a snapshot hyperspectral camera was integrated in the aircraft. Moreover a custom-developed thermal imaging system composed of a VIS-PAN camera and a LWIR-camera is used for aerial recordings in the thermal infrared range. Furthermore another custom-developed highly flexible imaging system for high resolution multispectral image acquisition with up to six spectral bands in the VIS-NIR range is presented. The performance of the overall system was tested during several flights with all sensor modalities and the precalculated demands with respect to spatial resolution and reliability were validated. The collected data sets were georeferenced, georectified, orthorectified and then stitched to mosaics.

High-Resolution Digital Terrain Models of the Sacramento/San Joaquin Delta Region, California

USGS Publications Warehouse

Coons, Tom; Soulard, Christopher E.; Knowles, Noah

2008-01-01

The U.S. Geological Survey (USGS) Western Region Geographic Science Center, in conjunction with the USGS Water Resources Western Branch of Regional Research, has developed a high-resolution elevation dataset covering the Sacramento/San Joaquin Delta region of California. The elevation data were compiled photogrammically from aerial photography (May 2002) with a scale of 1:15,000. The resulting dataset has a 10-meter horizontal resolution grid of elevation values. The vertical accuracy was determined to be 1 meter. Two versions of the elevation data are available: the first dataset has all water coded as zero, whereas the second dataset has bathymetry data merged with the elevation data. The projection of both datasets is set to UTM Zone 10, NAD 1983. The elevation data are clipped into files that spatially approximate 7.5-minute USGS quadrangles, with about 100 meters of overlap to facilitate combining the files into larger regions without data gaps. The files are named after the 7.5-minute USGS quadrangles that cover the same general spatial extent. File names that include a suffix (_b) indicate that the bathymetry data are included (for example, sac_east versus sac_east_b). These files are provided in ESRI Grid format.

Brillouin Optical Correlation Domain Analysis in Composite Material Beams

PubMed Central

Stern, Yonatan; London, Yosef; Preter, Eyal; Antman, Yair; Diamandi, Hilel Hagai; Silbiger, Maayan; Adler, Gadi; Shalev, Doron; Zadok, Avi

2017-01-01

Structural health monitoring is a critical requirement in many composites. Numerous monitoring strategies rely on measurements of temperature or strain (or both), however these are often restricted to point-sensing or to the coverage of small areas. Spatially-continuous data can be obtained with optical fiber sensors. In this work, we report high-resolution distributed Brillouin sensing over standard fibers that are embedded in composite structures. A phase-coded, Brillouin optical correlation domain analysis (B-OCDA) protocol was employed, with spatial resolution of 2 cm and sensitivity of 1 °K or 20 micro-strain. A portable measurement setup was designed and assembled on the premises of a composite structures manufacturer. The setup was successfully utilized in several structural health monitoring scenarios: (a) monitoring the production and curing of a composite beam over 60 h; (b) estimating the stiffness and Young’s modulus of a composite beam; and (c) distributed strain measurements across the surfaces of a model wing of an unmanned aerial vehicle. The measurements are supported by the predictions of structural analysis calculations. The results illustrate the potential added values of high-resolution, distributed Brillouin sensing in the structural health monitoring of composites. PMID:28974041

Brillouin Optical Correlation Domain Analysis in Composite Material Beams.

PubMed

Stern, Yonatan; London, Yosef; Preter, Eyal; Antman, Yair; Diamandi, Hilel Hagai; Silbiger, Maayan; Adler, Gadi; Levenberg, Eyal; Shalev, Doron; Zadok, Avi

2017-10-02

Structural health monitoring is a critical requirement in many composites. Numerous monitoring strategies rely on measurements of temperature or strain (or both), however these are often restricted to point-sensing or to the coverage of small areas. Spatially-continuous data can be obtained with optical fiber sensors. In this work, we report high-resolution distributed Brillouin sensing over standard fibers that are embedded in composite structures. A phase-coded, Brillouin optical correlation domain analysis (B-OCDA) protocol was employed, with spatial resolution of 2 cm and sensitivity of 1 °K or 20 micro-strain. A portable measurement setup was designed and assembled on the premises of a composite structures manufacturer. The setup was successfully utilized in several structural health monitoring scenarios: (a) monitoring the production and curing of a composite beam over 60 h; (b) estimating the stiffness and Young's modulus of a composite beam; and (c) distributed strain measurements across the surfaces of a model wing of an unmanned aerial vehicle. The measurements are supported by the predictions of structural analysis calculations. The results illustrate the potential added values of high-resolution, distributed Brillouin sensing in the structural health monitoring of composites.

UAS applications in high alpine, snow-covered terrain

NASA Astrophysics Data System (ADS)

Bühler, Y.; Stoffel, A.; Ginzler, C.

2017-12-01

Access to snow-covered, alpine terrain is often difficult and dangerous. Hence parameters such as snow depth or snow avalanche release and deposition zones are hard to map in situ with adequate spatial and temporal resolution and with spatial continuous coverage. These parameters are currently operationally measured at automated weather stations and by observer networks. However such isolated point measurements are not able to capture the information spatial continuous and to describe the high spatial variability present in complex mountain topography. Unmanned Aerial Systems (UAS) have the potential to fill this gap by frequently covering selected high alpine areas with high spatial resolution down to ground resolutions of even few millimeters. At the WSL Institute for Snow and Avalanche Research SLF we test different photogrammetric UAS with visual and near infrared bands. During the last three years we were able to gather experience in more than 100 flight missions in extreme terrain. By processing the imagery applying state-of-the-art structure from motion (SfM) software, we were able to accurately document several avalanche events and to photogrammetrically map snow depth with accuracies from 1 to 20 cm (dependent on the flight height above ground) compare to manual snow probe measurements. This was even possible on homogenous snow surfaces with very little texture. A key issue in alpine terrain is flight planning. We need to cover regions at high elevations with large altitude differences (up to 1 km) with high wind speeds (up to 20 m/s) and cold temperatures (down to - 25°C). Only a few UAS are able to cope with these environmental conditions. We will give an overview on our applications of UAS in high alpine terrain that demonstrate the big potential of such systems to acquire frequent, accurate and high spatial resolution geodata in high alpine, snow covered terrain that could be essential to answer longstanding questions in avalanche and snow hydrology research.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

Accuracy Assessment of Coastal Topography Derived from Uav Images

NASA Astrophysics Data System (ADS)

Long, N.; Millescamps, B.; Pouget, F.; Dumon, A.; Lachaussée, N.; Bertin, X.

2016-06-01

To monitor coastal environments, Unmanned Aerial Vehicle (UAV) is a low-cost and easy to use solution to enable data acquisition with high temporal frequency and spatial resolution. Compared to Light Detection And Ranging (LiDAR) or Terrestrial Laser Scanning (TLS), this solution produces Digital Surface Model (DSM) with a similar accuracy. To evaluate the DSM accuracy on a coastal environment, a campaign was carried out with a flying wing (eBee) combined with a digital camera. Using the Photoscan software and the photogrammetry process (Structure From Motion algorithm), a DSM and an orthomosaic were produced. Compared to GNSS surveys, the DSM accuracy is estimated. Two parameters are tested: the influence of the methodology (number and distribution of Ground Control Points, GCPs) and the influence of spatial image resolution (4.6 cm vs 2 cm). The results show that this solution is able to reproduce the topography of a coastal area with a high vertical accuracy (< 10 cm). The georeferencing of the DSM require a homogeneous distribution and a large number of GCPs. The accuracy is correlated with the number of GCPs (use 19 GCPs instead of 10 allows to reduce the difference of 4 cm); the required accuracy should be dependant of the research problematic. Last, in this particular environment, the presence of very small water surfaces on the sand bank does not allow to improve the accuracy when the spatial resolution of images is decreased.

An algorithm for approximate rectification of digital aerial images

USDA-ARS?s Scientific Manuscript database

High-resolution aerial photography is one of the most valuable tools available for managing extensive landscapes. With recent advances in digital camera technology, computer hardware, and software, aerial photography is easier to collect, store, and transfer than ever before. Images can be automa...

Monitoring the changing position of coastlines using aerial and satellite image data: an example from the eastern coast of Trabzon, Turkey.

PubMed

Sesli, Faik Ahmet; Karsli, Fevzi; Colkesen, Ismail; Akyol, Nihat

2009-06-01

Coastline mapping and coastline change detection are critical issues for safe navigation, coastal resource management, coastal environmental protection, and sustainable coastal development and planning. Changes in the shape of coastline may fundamentally affect the environment of the coastal zone. This may be caused by natural processes and/or human activities. Over the past 30 years, the coastal sites in Turkey have been under an intensive restraint associated with a population press due to the internal and external touristic demand. In addition, urbanization on the filled up areas, settlements, and the highways constructed to overcome the traffic problems and the other applications in the coastal region clearly confirm an intensive restraint. Aerial photos with medium spatial resolution and high resolution satellite imagery are ideal data sources for mapping coastal land use and monitoring their changes for a large area. This study introduces an efficient method to monitor coastline and coastal land use changes using time series aerial photos (1973 and 2002) and satellite imagery (2005) covering the same geographical area. Results show the effectiveness of the use of digital photogrammetry and remote sensing data on monitoring large area of coastal land use status. This study also showed that over 161 ha areas were filled up in the research area and along the coastal land 12.2 ha of coastal erosion is determined for the period of 1973 to 2005. Consequently, monitoring of coastal land use is thus necessary for coastal area planning in order to protecting the coastal areas from climate changes and other coastal processes.

Linking high-resolution geomorphic mapping, sediment sources, and channel types in a formerly glaciated basin of northeastern Alto-Adige/Sudtirol, Italy

NASA Astrophysics Data System (ADS)

Brardinoni, F.; Perina, E.; Bonfanti, G.; Falsitta, G.; Agliardi, F.

2012-04-01

To characterize channel-network morphodynamics and response potential in the Gadria-Strimm basin (14.8 km^2) we conduct a concerted effort entailing: (i) high-resolution mapping of landforms, channel reaches, and sediment sources; and (ii) historical evolution of colluvial channel disturbance through sequential aerial photosets (1945-59-69-82-90-00-06-11). The mapping was carried out via stereographic inspection of aerial photographs, examination of 2.5-m gridded DTM and DSM, and extensive field work. The study area is a formerly glaciated basin characterized by peculiar landform assemblages imposed by a combination of tectonic and glacial first-order structures. The most striking feature in Strimm Creek is a structurally-controlled valley step separating an upper hanging valley, dominated by periglacial and fluvial processes, and a V-notched lower part in which lateral colluvial channels are directly connected to Strimm's main stem. In Gadria Creek, massive kame terraces located in proximity of the headwaters provide virtually unlimited sediment supply to frequent debris-flow activity, making this sub-catchment an ideal site for monitoring, hence studying the mechanics of these processes. Preliminary results point to a high spatial variability of the colluvial channel network, in which sub-sectors have remained consistently active during the study period while others have become progressively dormant with notable forest re-growth. In an attempt to link sediment flux to topography and substrate type, future work will involve photogrammetric analysis across the sequential aerial photosets as well as a morphometric/geomechanical characterization of the surficial materials.

Looking for Off-Fault Deformation and Measuring Strain Accumulation During the Past 70 years on a Portion of the Locked San Andreas Fault

NASA Astrophysics Data System (ADS)

Vadman, M.; Bemis, S. P.

2017-12-01

Even at high tectonic rates, detection of possible off-fault plastic/aseismic deformation and variability in far-field strain accumulation requires high spatial resolution data and likely decades of measurements. Due to the influence that variability in interseismic deformation could have on the timing, size, and location of future earthquakes and the calculation of modern geodetic estimates of strain, we attempt to use historical aerial photographs to constrain deformation through time across a locked fault. Modern photo-based 3D reconstruction techniques facilitate the creation of dense point clouds from historical aerial photograph collections. We use these tools to generate a time series of high-resolution point clouds that span 10-20 km across the Carrizo Plain segment of the San Andreas fault. We chose this location due to the high tectonic rates along the San Andreas fault and lack of vegetation, which may obscure tectonic signals. We use ground control points collected with differential GPS to establish scale and georeference the aerial photograph-derived point clouds. With a locked fault assumption, point clouds can be co-registered (to one another and/or the 1.7 km wide B4 airborne lidar dataset) along the fault trace to calculate relative displacements away from the fault. We use CloudCompare to compute 3D surface displacements, which reflect the interseismic strain accumulation that occurred in the time interval between photo collections. As expected, we do not observe clear surface displacements along the primary fault trace in our comparisons of the B4 lidar data against the aerial photograph-derived point clouds. However, there may be small scale variations within the lidar swath area that represent near-fault plastic deformation. With large-scale historical photographs available for the Carrizo Plain extending back to at least the 1940s, we can potentially sample nearly half the interseismic period since the last major earthquake on this portion of this fault (1857). Where sufficient aerial photograph coverage is available, this approach has the potential to illuminate complex fault zone processes for this and other major strike-slip faults.

The Generic Resolution Advisor and Conflict Evaluator (GRACE) for Unmanned Aircraft Detect-And-Avoid Systems

NASA Technical Reports Server (NTRS)

Abramson, Michael; Refai, Mohamad; Santiago, Confesor

2017-01-01

The paper describes the Generic Resolution Advisor and Conflict Evaluator (GRACE), a novel alerting and guidance algorithm that combines flexibility, robustness, and computational efficiency. GRACE is generic since it was designed without any assumptions regarding temporal or spatial scales, aircraft performance, or its sensor and communication systems. Therefore, GRACE was adopted as a core component of the Java Architecture for Detect-And-Avoid (DAA) Extensibility and Modeling, developed by NASA as a research and modeling tool for Unmanned Aerial Systems Integration in the National Airspace System (NAS). GRACE has been used in a number of real-time and fast-time experiments supporting evolving requirements of DAA research, including parametric studies, NAS-wide simulations, human-in-the-loop experiments, and live flight tests.

Low-altitude aerial color digital photographic survey of the San Andreas Fault

USGS Publications Warehouse

Lynch, David K.; Hudnut, Kenneth W.; Dearborn, David S.P.

2010-01-01

Ever since 1858, when Gaspard-Félix Tournachon (pen name Félix Nadar) took the first aerial photograph (Professional Aerial Photographers Association 2009), the scientific value and popular appeal of such pictures have been widely recognized. Indeed, Nadar patented the idea of using aerial photographs in mapmaking and surveying. Since then, aerial imagery has flourished, eventually making the leap to space and to wavelengths outside the visible range. Yet until recently, the availability of such surveys has been limited to technical organizations with significant resources. Geolocation required extensive time and equipment, and distribution was costly and slow. While these situations still plague older surveys, modern digital photography and lidar systems acquire well-calibrated and easily shared imagery, although expensive, platform-specific software is sometimes still needed to manage and analyze the data. With current consumer-level electronics (cameras and computers) and broadband internet access, acquisition and distribution of large imaging data sets are now possible for virtually anyone. In this paper we demonstrate a simple, low-cost means of obtaining useful aerial imagery by reporting two new, high-resolution, low-cost, color digital photographic surveys of selected portions of the San Andreas fault in California. All pictures are in standard jpeg format. The first set of imagery covers a 92-km-long section of the fault in Kern and San Luis Obispo counties and includes the entire Carrizo Plain. The second covers the region from Lake of the Woods to Cajon Pass in Kern, Los Angeles, and San Bernardino counties (151 km) and includes Lone Pine Canyon soon after the ground was largely denuded by the Sheep Fire of October 2009. The first survey produced a total of 1,454 oblique digital photographs (4,288 x 2,848 pixels, average 6 Mb each) and the second produced 3,762 nadir images from an elevation of approximately 150 m above ground level (AGL) on the southeast leg and 300 m AGL on the northwest leg. Spatial resolution (pixel size or ground sample distance) is a few centimeters. Time and geographic coordinates of the aircraft were automatically written into the exchangeable image file format (EXIF) data within each jpeg photograph. A few hours after acquisition and validation, the photographs were uploaded to a publically accessible Web page. The goal was to obtain quick-turnaround, low-cost, high-resolution, overlapping, and contiguous imagery for use in planning field operations, and to provide imagery for a wide variety of land use and educational studies. This work was carried out in support of ongoing geological research on the San Andreas fault, but the technique is widely applicable beyond geology.

Examining fire-induced forest changes using novel remote sensing technique: a case study in a mixed pine-oak forest

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Cook, B.; Hanavan, R. P.; Serbin, S.

2017-12-01

Fire-induced forest changes has long been a central focus for forest ecology and global carbon cycling studies, and is becoming a pressing issue for global change biologists particularly with the projected increases in the frequency and intensity of fire with a warmer and drier climate. Compared with time-consuming and labor intensive field-based approaches, remote sensing offers a promising way to efficiently assess fire effects and monitor post-fire forest responses across a range of spatial and temporal scales. However, traditional remote sensing studies relying on simple optical spectral indices or coarse resolution imagery still face a number of technical challenges, including confusion or contamination of the signal by understory dynamics and mixed pixels with moderate to coarse resolution data (>= 30 m). As such, traditional remote sensing may not meet the increasing demand for more ecologically-meaningful monitoring and quantitation of fire-induced forest changes. Here we examined the use of novel remote sensing technique (i.e. airborne imaging spectroscopy and LiDAR measurement, very high spatial resolution (VHR) space-borne multi-spectral measurement, and high temporal-spatial resolution UAS-based (Unmanned Aerial System) imagery), in combination with field and phenocam measurements to map forest burn severity across spatial scales, quantify crown-scale post-fire forest recovery rate, and track fire-induced phenology changes in the burned areas. We focused on a mixed pine-oak forest undergoing multiple fire disturbances for the past several years in Long Island, NY as a case study. We demonstrate that (1) forest burn severity mapping from VHR remote sensing measurement can capture crown-scale heterogeneous fire patterns over large-scale; (2) the combination of VHR optical and structural measurements provides an efficient means to remotely sense species-level post-fire forest responses; (3) the UAS-based remote sensing enables monitoring of fire-induced forest phenology changes at unprecedented temporal and spatial resolutions. This work provides the methodological approach monitor fire-induced forest changes in a spatially explicit manner across scales, with important implications for fire-related forest management and for constraining/benchmarking process models.

Research applications of night-time aerial photography, from local to global scales

NASA Astrophysics Data System (ADS)

Hale, J.; Sadler, J.

2012-12-01

Artificial lighting of the earth's surface is changing at a global scale, with numerous social, economic and environmental implications. In many regions, the extent, brightness and spectral range of lighting is increasing, reflecting economic and technological development, population growth and urbanization. Its benefits include improving the perception of neighbourhood safety and increasing people's options for when activities can take place. Impacts range from the disruption of sleep patterns by a single street lamp to obscured views of the night sky for tens of kilometers surrounding an urban area. There is therefore a need to secure baseline maps of artificial lighting, and to detect changes in their extent and quality over time. Considerable success has been achieved in generating global lighting datasets from Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) data, which have been used to support a broad range of research and policy applications. However, their coarse spatial and spectral resolution and difficulties in radiance calibration have been recognised as barriers to some potential applications. We present the first multi-spectral radiance calibrated lighting data for cities at a fine spatial resolution (10cm). We then illustrate how these data can be effective for exploring relationships between lighting and urban form, and that they can support the radiance calibration of lighting datasets with much greater spatial extents. Color night photography was collected for two major English cities - Birmingham and London. Ground photometry and radiometry surveys were undertaken, permitting the reclassification of the images to represent incident lux and the identification and classification of individual lamps. Total illuminated area and lamp density both correlated positively with percentage built surface cover, although the strength of these relationships differed between cities. This suggests that artificial lighting may not be useful as a consistent predictor of built density, but that some form of collinearity should still be expected in studies that employ built density gradients. The collection of digital color night photography from the International Space Station (ISS) presents an opportunity to rapidly map artificial lighting at a medium resolution and large extent, but radiance calibrated data do not yet exist. We therefore used our ground surveys and aerial night photographs of London to reclassify pixels within an ISS image of SE England to represent upward radiant flux. In addition, we were able to explore whether the estimated radiance values for each pixel resulted from a few bright light sources or multiple dim lamps, raising the possibility of improved estimates of lighting character based on prior probability models. Given the global step-change underway in artificial lighting and the high demand for data on urban systems, our results suggest that a suite of complimentary lighting measurement techniques that includes night-time aerial photography would be beneficial.

Long-term monitoring of a large landslide by using an Unmanned Aerial Vehicle (UAV)

NASA Astrophysics Data System (ADS)

Lindner, Gerald; Schraml, Klaus; Mansberger, Reinfried; Hübl, Johannes

2015-04-01

Currently UAVs become more and more important in various scientific areas, including forestry, precision farming, archaeology and hydrology. Using these drones in natural hazards research enables a completely new level of data acquisition being flexible of site, invariant in time, cost-efficient and enabling arbitrary spatial resolution. In this study, a rotary-wing Mini-UAV carrying a DSLR camera was used to acquire time series of overlapping aerial images. These photographs were taken as input to extract Digital Surface Models (DSM) as well as orthophotos in the area of interest. The "Pechgraben" area in Upper Austria has a catchment area of approximately 2 km². Geology is mainly dominated by limestone and sandstone. Caused by heavy rainfalls in the late spring of 2013, an area of about 70 ha began to move towards the village in the valley. In addition to the urgent measures, the slow-moving landslide was monitored approximately every month over a time period of more than 18 months. A detailed documentation of the change process was the result. Moving velocities and height differences were quantified and validated using a dense network of Ground Control Points (GCP). For further analysis, 14 image flights with a total amount of 10.000 photographs were performed to create multi-temporal geodata in in sub-decimeter-resolution for two depicted areas of the landslide. Using a UAV for this application proved to be an excellent choice, as it allows short repetition times, low flying heights and high spatial resolution. Furthermore, the UAV acts almost weather independently as well as highly autonomously. High-quality results can be expected within a few hours after the photo flight. The UAV system performs very well in an alpine environment. Time series of the assessed geodata detect changes in topography and provide a long-term documentation of the measures taken in order to stop the landslide and to prevent infrastructure from damage.

Quantifying Subsurface Water and Heat Distribution and its Linkage with Landscape Properties in Terrestrial Environment using Hydro-Thermal-Geophysical Monitoring and Coupled Inverse Modeling

NASA Astrophysics Data System (ADS)

Dafflon, B.; Tran, A. P.; Wainwright, H. M.; Hubbard, S. S.; Peterson, J.; Ulrich, C.; Williams, K. H.

2015-12-01

Quantifying water and heat fluxes in the subsurface is crucial for managing water resources and for understanding the terrestrial ecosystem where hydrological properties drive a variety of biogeochemical processes across a large range of spatial and temporal scales. Here, we present the development of an advanced monitoring strategy where hydro-thermal-geophysical datasets are continuously acquired and further involved in a novel inverse modeling framework to estimate the hydraulic and thermal parameter that control heat and water dynamics in the subsurface and further influence surface processes such as evapotranspiration and vegetation growth. The measured and estimated soil properties are also used to investigate co-interaction between subsurface and surface dynamics by using above-ground aerial imaging. The value of this approach is demonstrated at two different sites, one in the polygonal shaped Arctic tundra where water and heat dynamics have a strong impact on freeze-thaw processes, vegetation and biogeochemical processes, and one in a floodplain along the Colorado River where hydrological fluxes between compartments of the system (surface, vadose zone and groundwater) drive biogeochemical transformations. Results show that the developed strategy using geophysical, point-scale and aerial measurements is successful to delineate the spatial distribution of hydrostratigraphic units having distinct physicochemical properties, to monitor and quantify in high resolution water and heat distribution and its linkage with vegetation, geomorphology and weather conditions, and to estimate hydraulic and thermal parameters for enhanced predictions of water and heat fluxes as well as evapotranspiration. Further, in the Colorado floodplain, results document the potential presence of only periodic infiltration pulses as a key hot moment controlling soil hydro and biogeochemical functioning. In the arctic, results show the strong linkage between soil water content, thermal parameters, thaw layer thickness and vegetation distribution. Overall, results of these efforts demonstrate the value of coupling various datasets at high spatial and temporal resolution to improve predictive understanding of subsurface and surface dynamics.

Uncertainties in mapping forest carbon in urban ecosystems.

PubMed

Chen, Gang; Ozelkan, Emre; Singh, Kunwar K; Zhou, Jun; Brown, Marilyn R; Meentemeyer, Ross K

2017-02-01

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m 2 , aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape. Copyright © 2016 Elsevier Ltd. All rights reserved.

Delineating wetland catchments and modeling hydrologic connectivity using lidar data and aerial imagery

NASA Astrophysics Data System (ADS)

Wu, Qiusheng; Lane, Charles R.

2017-07-01

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling-spilling-merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that our proposed framework is promising for improving overland flow simulation and hydrologic connectivity analysis.

Building Change Detection from Harvey using Unmanned Aerial System (UAS)

NASA Astrophysics Data System (ADS)

Chang, A.; Yeom, J.; Jung, J.; Choi, I.

2017-12-01

Unmanned Aerial System (UAS) is getting to be the most important technique in recent days since the fine spatial and high temporal resolution data previously unobtainable from traditional remote sensing platforms. Advanced UAS data can provide a great opportunity for disaster monitoring. Especially, building change detection is the one of the most important topics for damage assessment and recovery from disasters. This study is proposing a method to monitor building change with UAS data for Holiday Beach in Texas, where was directly hit by Harvey on 25 August 2017. This study adopted 3D change detection to monitor building damage and recovery levels with building height as well as natural color information. We used a rotorcraft UAS to collect RGB data twice on 9 September and 18 October 2017 after the hurricane. The UAS data was processed using Agisoft Photoscan Pro Software to generate super high resolution dataset including orthomosaic, DSM (Digital Surface Model), and 3D point cloud. We compared the processed dataset with an airborne image considerable as before-hurricane data, which was acquired on January 2016. Building damage and recovery levels were determined by height and color change. The result will show that UAS data is useful to assess building damage and recovery for affected area by the natural disaster such as Harvey.

Using Small Unmanned Aerial Systems to Advance Hydrological Models in Coastal Watersheds

NASA Astrophysics Data System (ADS)

Moorhead, R.; Hathcock, L.; Coffey, J. J.; Hood, R. E.; van Cooten, S.; Choate, K.; Rawson, H.; Kosturock, A.

2014-12-01

Small unmanned aerial systems (sUASs) have the potential to provide highly useful information for models of earth systems that vary over time intervals of days and for which sub-meter resolution is crucial. In particular, the state of coastal watershed plains are highly dependent on vegetation type and cover, soil type, weather, river flooding, and coastal inundation. The vegetation type and cover affect the drying potential, as well as the watershed's resistance to flood water movement. The soil type, soil moisture, and pond depths affect the ability of the watershed to absorb river flood waters and inundation from the sea. In this presentation we will describe a data collection campaign and model modification effort for hydrological models in a coastal watershed. The data collection campaign is obtaining data bimonthly using multiple UASs to capture the state of the watershed quicker. In particular, the vegetation cover and the extent of the water surface expression are captured at approximately a 1 inch spatial resolution over a few days with sUASs that can image 1-2 square miles per hour. The vegetation data provides a time-varying input to improve the estimation of the roughness coefficient and the dry potential from the traditionally static datasets. By correlating the high spatio-temporal resolution surface water expression with data from approximately ten river gauges, models can be improved and validated under more conditions. The presentation will also discuss the requisite sUAS capabilities and our experience in using them.

Multi-Criteria GIS Analyses with the Use of Uavs for the Needs of Spatial Planning

NASA Astrophysics Data System (ADS)

Zawieska, D.; Markiewicz, J.; Turek, A.; Bakuła, K.; Kowalczyk, M.; Kurczyński, Z.; Ostrowski, W.; Podlasiak, P.

2016-06-01

Utilization of Unmanned Aerial Systems (UAVs) in agriculture, forestry, or other environmental contexts has recently become common. However, in the case of spatial planning, the role of UAVs still seems to be underestimated. At present, sections of municipal development use UAVs mainly for promotional purposes (films, folders, brochures, etc.). The use of UAVs for spatial management provides results, first of all, in the form of savings in human resources and time; however, more frequently, it is also connected with financial savings (given the decreasing cost of UAVs and photogrammetric software). The performed research presented here relates to the possibilities of using UAVs to update planning documents, and, in particular, to update the study of conditions and directions of spatial management and preparation of local plans for physical management. Based on acquired photographs with a resolution of 3 cm, a cloud of points is generated, as well as 3D models and the true orthophotomap. These data allow multi-criteria spatial analyses. Additionally, directions of development and changes in physical management are analysed for the given area.

Unmanned Aerial Vehicle (UAV) Data Acquisition for Archaeological Site Identification and Mapping

NASA Astrophysics Data System (ADS)

Handayani, W.; Ayuningtyas, E. A.; Candra R, F. S.; Arif S, B.; Argadyanto, B.

2017-12-01

Archaeological sites as part of human history and located around community are important to be preserved for connecting historical information from generation to generation. Mapping of archaeological sites can be done as one of preservation efforts. Yogyakarta has several archaeological sites such as Pleret Palace, the former royal palace of Mataram Islam in the 16th Century. Data limitations and the difficulty of reconstructing the site sketches into a map become obstacles in archaeological sites mapping. Unmanned Aerial Vehicle (UAV) can be an alternative of high-resolution spatial data acquisition for detail mapping, including archaeological sites mapping. This study aims to see how far the UAV acquisition results can be used for Archaeological Site mapping in Pleret Palace. Data acquisition using UAV generated to mosaic orthophoto, Digital Surface Model (DSM), and Digital Terrain Model (DTM). Archaeological sites identified using DTM and matched with site sketch made by Cultural Agency. From these data, it can be recognized some relics form, such as palace fortress, moats and canals, and also dikes of Segarayasa. This research is expected to be a reference in archaeological site mapping using detailed spatial data, especially UAV. Furthermore, it can be obtained archaeological site map close to real condition; as well as archaeological sites preservation in Indonesia.

BOREAS Level-0 ER-2 Aerial Photography

NASA Technical Reports Server (NTRS)

Newcomer, Jeffrey A.; Dominquez, Roseanne; Hall, Forrest G. (Editor)

2000-01-01

For BOReal Ecosystem-Atmosphere Study (BOREAS), the ER-2 and other aerial photography was collected to provide finely detailed and spatially extensive documentation of the condition of the primary study sites. The ER-2 aerial photography consists of color-IR transparencies collected during flights in 1994 and 1996 over the study areas.

Integration of In Situ Radon Modeling with High Resolution Aerial Remote Sensing for Mapping and Quantifying Local to Regional Flow and Transport of Submarine Groundwater Discharge from Coastal Aquifers

NASA Astrophysics Data System (ADS)

Glenn, C. R.; Kennedy, J. J.; Dulaiova, H.; Kelly, J. L.; Lucey, P. G.; Lee, E.; Fackrell, J.

2015-12-01

Submarine groundwater discharge (SGD) is a principal conduit for huge volumes of fresh groundwater loss and is a key transport mechanism for nutrient and contaminant pollution to coastal zones worldwide. However, the volumes and spatially and temporally variable nature of SGD is poorly known and requires rapid and high-resolution data acquisition at the scales in which it is commonly observed. Airborne thermal infrared (TIR) remote sensing, using high-altitude manned aircraft and low-altitude remote-controlled unmanned aerial vehicles (UAVs or "Drones") are uniquely qualified for this task, and applicable wherever 0.1°C temperature contrasts exist between discharging and receiving waters. We report on the use of these technologies in combination with in situ radon model studies of SGD volume and nutrient flux from three of the largest Hawaiian Islands. High altitude manned aircraft results produce regional (~300m wide x 100s km coastline) 0.5 to 3.2 m-resolution sea-surface temperature maps accurate to 0.7°C that show point-source and diffuse flow in exquisite detail. Using UAVs offers cost-effective advantages of higher spatial and temporal resolution and instantaneous deployments that can be coordinated simultaneously with any ground-based effort. We demonstrate how TIR-mapped groundwater discharge plume areas may be linearly and highly correlated to in situ groundwater fluxes. We also illustrate how in situ nutrient data may be incorporated into infrared imagery to produce nutrient distribution maps of regional worth. These results illustrate the potential for volumetric quantification and up-scaling of small- to regional-scale SGD. These methodologies provide a tremendous advantage for identifying and differentiating spring-fed, point-sourced, and/or diffuse groundwater discharge into oceans, estuaries, and streams. The integrative techniques are also important precursors for developing best-use and cost-effective strategies for otherwise time-consuming in situ studies, and represent a substantial new asset for land use and coastal zone research and management.

Characterization of Shrubland-Atmosphere Interactions through Use of the Eddy Covariance Method, Distributed Footprint Sampling, and Imagery from Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Anderson, C.; Vivoni, E. R.; Pierini, N.; Robles-Morua, A.; Rango, A.; Laliberte, A.; Saripalli, S.

2012-12-01

Ecohydrological dynamics can be evaluated from field observations of land-atmosphere states and fluxes, including water, carbon, and energy exchanges measured through the eddy covariance method. In heterogeneous landscapes, the representativeness of these measurements is not well understood due to the variable nature of the sampling footprint and the mixture of underlying herbaceous, shrub, and soil patches. In this study, we integrate new field techniques to understand how ecosystem surface states are related to turbulent fluxes in two different semiarid shrubland settings in the Jornada (New Mexico) and Santa Rita (Arizona) Experimental Ranges. The two sites are characteristic of Chihuahuan (NM) and Sonoran (AZ) Desert mixed-shrub communities resulting from woody plant encroachment into grassland areas. In each study site, we deployed continuous soil moisture and soil temperature profile observations at twenty sites around an eddy covariance tower after local footprint estimation revealed the optimal sensor network design. We then characterized the tower footprint through terrain and vegetation analyses derived at high resolution (<1 m) from imagery obtained from a fixed-wing and rotary-wing Unmanned Aerial Vehicles (UAV). Our analysis focuses on the summertime land-atmosphere states and fluxes during which each ecosystem responded differentially to the North American monsoon. We found that vegetation heterogeneity induces spatial differences in soil moisture and temperature that are important to capture when relating these states to the eddy covariance flux measurements. Spatial distributions of surface states at different depths reveal intricate patterns linked to vegetation cover that vary between the two sites. Furthermore, single site measurements at the tower are insufficient to capture the footprint conditions and their influence on turbulent fluxes. We also discuss techniques for aggregating the surface states based upon the vegetation and soil classifications obtained from the high-resolution aerial imagery. Overall, the integration of the different techniques yielded new insight into the spatiotemporal variation of land surface states and their relation to sensible and latent heat fluxes in two shrubland sites, with the potential application in other ecosystems worldwide.

Unmanned Aerial System Aids Dry-season Stream Temperature Sensing

NASA Astrophysics Data System (ADS)

Chung, M.; Detweiler, C.; Higgins, J.; Ore, J. P.; Dralle, D.; Thompson, S. E.

2016-12-01

In freshwater ecosystems, temperature affects biogeochemistry and ecology, and is thus a primary physical determinant of habitat quality. Measuring temperatures in spatially heterogeneous water bodies poses a serious challenge to researchers due to constraints associated with currently available methods: in situ loggers record temporally continuous temperature measurements but are limited to discrete spatial locations, while distributed temperature and remote sensing provide fine-resolution spatial measurements that are restricted to only two-dimensions (i.e. streambed and surface, respectively). Using a commercially available quadcopter equipped with a 6m cable and temperature-pressure sensor system, we measured stream temperatures at two confluences at the South Fork Eel River, where cold water inputs from the tributary to the mainstem create thermal refugia for juvenile salmonids during the dry season. As a mobile sensing platform, unmanned aerial systems (UAS) can facilitate quick and repeated sampling with minimal disturbance to the ecosystem, and their datasets can be interpolated to create a three-dimensional thermal map of a water body. The UAS-derived data was compared to data from in situ data loggers to evaluate whether the UAS is better able to capture fine-scale temperature dynamics at each confluence. The UAS has inherent limitations defined by battery life and flight times, as well as operational constraints related to maneuverability under wind and streamflow conditions. However, the platform is able to serve as an additional field tool for researchers to capture complex thermal structures in water bodies.

Mapping coastal marine debris using aerial imagery and spatial analysis.

PubMed

Moy, Kirsten; Neilson, Brian; Chung, Anne; Meadows, Amber; Castrence, Miguel; Ambagis, Stephen; Davidson, Kristine

2017-12-19

This study is the first to systematically quantify, categorize, and map marine macro-debris across the main Hawaiian Islands (MHI), including remote areas (e.g., Niihau, Kahoolawe, and northern Molokai). Aerial surveys were conducted over each island to collect high resolution photos, which were processed into orthorectified imagery and visually analyzed in GIS. The technique provided precise measurements of the quantity, location, type, and size of macro-debris (>0.05m 2 ), identifying 20,658 total debris items. Northeastern (windward) shorelines had the highest density of debris. Plastics, including nets, lines, buoys, floats, and foam, comprised 83% of the total count. In addition, the study located six vessels from the 2011 Tōhoku tsunami. These results created a baseline of the location, distribution, and composition of marine macro-debris across the MHI. Resource managers and communities may target high priority areas, particularly along remote coastlines where macro-debris counts were largely undocumented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multiple Point Statistics algorithm based on direct sampling and multi-resolution images

NASA Astrophysics Data System (ADS)

Julien, S.; Renard, P.; Chugunova, T.

2017-12-01

Multiple Point Statistics (MPS) has become popular for more than one decade in Earth Sciences, because these methods allow to generate random fields reproducing highly complex spatial features given in a conceptual model, the training image, while classical geostatistics techniques based on bi-point statistics (covariance or variogram) fail to generate realistic models. Among MPS methods, the direct sampling consists in borrowing patterns from the training image to populate a simulation grid. This latter is sequentially filled by visiting each of these nodes in a random order, and then the patterns, whose the number of nodes is fixed, become narrower during the simulation process, as the simulation grid is more densely informed. Hence, large scale structures are caught in the beginning of the simulation and small scale ones in the end. However, MPS may mix spatial characteristics distinguishable at different scales in the training image, and then loose the spatial arrangement of different structures. To overcome this limitation, we propose to perform MPS simulation using a decomposition of the training image in a set of images at multiple resolutions. Applying a Gaussian kernel onto the training image (convolution) results in a lower resolution image, and iterating this process, a pyramid of images depicting fewer details at each level is built, as it can be done in image processing for example to lighten the space storage of a photography. The direct sampling is then employed to simulate the lowest resolution level, and then to simulate each level, up to the finest resolution, conditioned to the level one rank coarser. This scheme helps reproduce the spatial structures at any scale of the training image and then generate more realistic models. We illustrate the method with aerial photographies (satellite images) and natural textures. Indeed, these kinds of images often display typical structures at different scales and are well-suited for MPS simulation techniques.

USGS Releases New Digital Aerial Products

USGS Publications Warehouse

,

2005-01-01

The U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) has initiated distribution of digital aerial photographic products produced by scanning or digitizing film from its historical aerial photography film archive. This archive, located in Sioux Falls, South Dakota, contains thousands of rolls of film that contain more than 8 million frames of historic aerial photographs. The largest portion of this archive consists of original film acquired by Federal agencies from the 1930s through the 1970s to produce 1:24,000-scale USGS topographic quadrangle maps. Most of this photography is reasonably large scale (USGS photography ranges from 1:8,000 to 1:80,000) to support the production of the maps. Two digital products are currently available for ordering: high-resolution scanned products and medium-resolution digitized products.

From high spatial resolution imagery to spatial indicators : Application for hydromorphy follow-up on Bourgneuf wetland

NASA Astrophysics Data System (ADS)

Bailly, J. S.; Puech, C.; Lukac, F.; Massé, J.

2003-04-01

On Atlantic coastal wetlands, the understanding of hydrological processes may refer to hydraulic surface structures characterization as small ditches or channels networks, permanent and temporary water bodies. Moreover to improve the understanding, this characerization should be realized regarding different seasons and different spatial scales: elementary parcel, managment unit and whole wetland scales. In complement to usual observations on a few local ground points, high spatial resolution remote sensing may be a good information support for extraction and characterization on elementary objects, especially water bodies, permanents or temporary ones and ditches. To carry out a floow-up on wetlands, a seasonal image acquisition rate, reachable from most of satelite systems, is in that case informative for hydrological needs. In this work, georeferencing methods on openfield wetlands have been handled with care in order to use diachronic images or combined geographical data; lack of relief, short vegetation and well structured landscape make this preprocess easier in comparison to other landscape situations. In this presentation we focus on spatial hydromorphy parameters constructed from images with specific processes. Especially, hydromorphy indicators for parcels or managment units have been developped using an IRC winter-spring-summer metric resolution set of images: these descriptors are based on water areas evolution or hydrophyl vegetations presence traducing hydrodynamic submersion behaviour in temporary water bodies. An other example presents a surface water network circulation indicator elaborated on IRC aerial photography combined with vectorized geographic database. This indicator is based on ditches width and vegetation presence : a specific process uses vectorized geo data set to define transects across ditches on which classified image analysis is carried out (supervised classification). These first results proposing hydromorphy descriptors from very high resolution don't give complete indicators for follow-up and monitoring of coastal wetlands, but their combinaison, aggregation should present good technical bases to carry it out with success.

Evaluating RGB photogrammetry and multi-temporal digital surface models for detecting soil erosion

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Seeger, Manuel

2013-04-01

Photogrammetry is a widely used tool for generating high-resolution digital surface models. Unmanned Aerial Vehicles (UAVs), equipped with a Red Green Blue (RGB) camera, have great potential in quickly acquiring multi-temporal high-resolution orthophotos and surface models. Such datasets would ease the monitoring of geomorphological processes, such as local soil erosion and rill formation after heavy rainfall events. In this study we test a photogrammetric setup to determine data requirements for soil erosion studies with UAVs. We used a rainfall simulator (5 m2) and above a rig with attached a Panasonic GX1 16 megapixel digital camera and 20mm lens. The soil material in the simulator consisted of loamy sand at an angle of 5 degrees. Stereo pair images were taken before and after rainfall simulation with 75-85% overlap. Acquired images were automatically mosaicked to create high-resolution orthorectified images and digital surface models (DSM). We resampled the DSM to different spatial resolutions to analyze the effect of cell size to the accuracy of measured rill depth and soil loss estimations, and determined an optimal cell size (thus flight altitude). Furthermore, the high spatial accuracy of the acquired surface models allows further analysis of rill formation and channel initiation related to e.g. surface roughness. We suggest implementing near-infrared and temperature sensors to combine soil moisture and soil physical properties with surface morphology for future investigations.

Analysis of Biophysical Mechanisms of Gilgai Microrelief Formation in Dryland Swelling Soils Using Ultra-High Resolution Aerial Imagery

NASA Astrophysics Data System (ADS)

Krell, N.; DeCarlo, K. F.; Caylor, K. K.

2015-12-01

Microrelief formations ("gilgai"), which form due to successive wetting-drying cycles typical of swelling soils, provide ecological hotspots for local fauna and flora, including higher and more robust vegetative growth. The distribution of these gilgai suggests a remarkable degree of regularity. However, it is unclear to what extent the mechanisms that drive gilgai formation are physical, such as desiccation-induced fracturing, or biological in nature, namely antecedent vegetative clustering. We investigated gilgai genesis and pattern formation in a 100 x 100 meter study area with swelling soils in a semiarid grassland at the Mpala Research Center in central Kenya. Our ongoing experiment is composed of three 9m2 treatments: we removed gilgai and limited vegetative growth by herbicide application in one plot, allowed for unrestricted seed dispersal in another, and left gilgai unobstructed in a control plot. To estimate the spatial frequencies of the repeating patterns of gilgai, we obtained ultra-high resolution (0.01-0.03m/pixel) images with an unmanned aerial vehicle (UAV) from which digital elevation models were also generated. Geostatistical analyses using wavelet and fourier methods in 1- and 2-dimensions were employed to characterize gilgai size and distribution. Preliminary results support regular spatial patterning across the gilgaied landscape and heterogeneities may be related to local soil properties and biophysical influences. Local data on gilgai and fracture characteristics suggest that gilgai form at characteristic heights and spacing based on fracture morphology: deep, wide cracks result in large, highly vegetated mounds whereas shallow cracks, induced by animal trails, are less correlated with gilgai size and shape. Our experiments will help elucidate the links between shrink-swell processes and gilgai-vegetation patterning in high activity clay soils and advance our understanding of the mechanisms of gilgai formation in drylands.

Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches.

PubMed

Meneguzzo, Dacia M; Liknes, Greg C; Nelson, Mark D

2013-08-01

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis-[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

PubMed Central

Shafian, Sanaz; Schnell, Ronnie; Bagavathiannan, Muthukumar; Valasek, John; Shi, Yeyin; Olsenholler, Jeff

2018-01-01

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April–October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for within-season data collection of agricultural crops such as sorghum. PMID:29715311

Study on analysis from sources of error for Airborne LIDAR

NASA Astrophysics Data System (ADS)

Ren, H. C.; Yan, Q.; Liu, Z. J.; Zuo, Z. Q.; Xu, Q. Q.; Li, F. F.; Song, C.

2016-11-01

With the advancement of Aerial Photogrammetry, it appears that to obtain geo-spatial information of high spatial and temporal resolution provides a new technical means for Airborne LIDAR measurement techniques, with unique advantages and broad application prospects. Airborne LIDAR is increasingly becoming a new kind of space for earth observation technology, which is mounted by launching platform for aviation, accepting laser pulses to get high-precision, high-density three-dimensional coordinate point cloud data and intensity information. In this paper, we briefly demonstrates Airborne laser radar systems, and that some errors about Airborne LIDAR data sources are analyzed in detail, so the corresponding methods is put forwarded to avoid or eliminate it. Taking into account the practical application of engineering, some recommendations were developed for these designs, which has crucial theoretical and practical significance in Airborne LIDAR data processing fields.

Assessing Hurricane Katrina Damage to the Mississippi Gulf Coast Using IKONOS Imagery

NASA Technical Reports Server (NTRS)

Spruce, Joseph; McKellip, Rodney

2006-01-01

Hurricane Katrina hit southeastern Louisiana and the Mississippi Gulf Coast as a Category 3 hurricane with storm surges as high as 9 m. Katrina devastated several coastal towns by destroying or severely damaging hundreds of homes. Several Federal agencies are assessing storm impacts and assisting recovery using high-spatial-resolution remotely sensed data from satellite and airborne platforms. High-quality IKONOS satellite imagery was collected on September 2, 2005, over southwestern Mississippi. Pan-sharpened IKONOS multispectral data and ERDAS IMAGINE software were used to classify post-storm land cover for coastal Hancock and Harrison Counties. This classification included a storm debris category of interest to FEMA for disaster mitigation. The classification resulted from combining traditional unsupervised and supervised classification techniques. Higher spatial resolution aerial and handheld photography were used as reference data. Results suggest that traditional classification techniques and IKONOS data can map wood-dominated storm debris in open areas if relevant training areas are used to develop the unsupervised classification signatures. IKONOS data also enabled other hurricane damage assessment, such as flood-deposited mud on lawns and vegetation foliage loss from the storm. IKONOS data has also aided regional Katrina vegetation damage surveys from multidate Land Remote Sensing Satellite and Moderate Resolution Imaging Spectroradiometer data.

Applying aerial digital photography as a spectral remote sensing technique for macrophytic cover assessment in small rural streams

NASA Astrophysics Data System (ADS)

Anker, Y.; Hershkovitz, Y.; Gasith, A.; Ben-Dor, E.

2011-12-01

Although remote sensing of fluvial ecosystems is well developed, the tradeoff between spectral and spatial resolutions prevents its application in small streams (<3m width). In the current study, a remote sensing approach for monitoring and research of small ecosystem was developed. The method is based on differentiation between two indicative vegetation species out of the ecosystem flora. Since when studied, the channel was covered mostly by a filamentous green alga (Cladophora glomerata) and watercress (Nasturtium officinale), these species were chosen as indicative; nonetheless, common reed (Phragmites australis) was also classified in order to exclude it from the stream ROI. The procedure included: A. For both section and habitat scales classifications, acquisition of aerial digital RGB datasets. B. For section scale classification, hyperspectral (HSR) dataset acquisition. C. For calibration, HSR reflectance measurements of specific ground targets, in close proximity to each dataset acquisition swath. D. For habitat scale classification, manual, in-stream flora grid transects classification. The digital RGB datasets were converted to reflectance units by spectral calibration against colored reference plates. These red, green, blue, white, and black EVA foam reference plates were measured by an ASD field spectrometer and each was given a spectral value. Each spectral value was later applied to the spectral calibration and radiometric correction of spectral RGB (SRGB) cube. Spectral calibration of the HSR dataset was done using the empirical line method, based on reference values of progressive grey scale targets. Differentiation between the vegetation species was done by supervised classification both for the HSR and for the SRGB datasets. This procedure was done using the Spectral Angle Mapper function with the spectral pattern of each vegetation species as a spectral end member. Comparison between the two remote sensing techniques and between the SRGB classification and the in-situ transects indicates that: A. Stream vegetation classification resolution is about 4 cm by the SRGB method compared to about 1 m by HSR. Moreover, this resolution is also higher than of the manual grid transect classification. B. The SRGB method is by far the most cost-efficient. The combination of spectral information (rather than the cognitive color) and high spatial resolution of aerial photography provides noise filtration and better sub-water detection capabilities than the HSR technique. C. Only the SRGB method applies for habitat and section scales; hence, its application together with in-situ grid transects for validation, may be optimal for use in similar scenarios.
The HSR dataset was first degraded to 17 bands with the same spectral range as the RGB dataset and also to a dataset with 3 equivalent bands

Assessing the consistency of UAV-derived point clouds and images acquired at different altitudes

NASA Astrophysics Data System (ADS)

Ozcan, O.

2016-12-01

Unmanned Aerial Vehicles (UAVs) offer several advantages in terms of cost and image resolution compared to terrestrial photogrammetry and satellite remote sensing system. Nowadays, UAVs that bridge the gap between the satellite scale and field scale applications were initiated to be used in various application areas to acquire hyperspatial and high temporal resolution imageries due to working capacity and acquiring in a short span of time with regard to conventional photogrammetry methods. UAVs have been used for various fields such as for the creation of 3-D earth models, production of high resolution orthophotos, network planning, field monitoring and agricultural lands as well. Thus, geometric accuracy of orthophotos and volumetric accuracy of point clouds are of capital importance for land surveying applications. Correspondingly, Structure from Motion (SfM) photogrammetry, which is frequently used in conjunction with UAV, recently appeared in environmental sciences as an impressive tool allowing for the creation of 3-D models from unstructured imagery. In this study, it was aimed to reveal the spatial accuracy of the images acquired from integrated digital camera and the volumetric accuracy of Digital Surface Models (DSMs) which were derived from UAV flight plans at different altitudes using SfM methodology. Low-altitude multispectral overlapping aerial photography was collected at the altitudes of 30 to 100 meters and georeferenced with RTK-GPS ground control points. These altitudes allow hyperspatial imagery with the resolutions of 1-5 cm depending upon the sensor being used. Preliminary results revealed that the vertical comparison of UAV-derived point clouds with respect to GPS measurements pointed out an average distance at cm-level. Larger values are found in areas where instantaneous changes in surface are present.

Re-examining data-intensive surface water models with high-resolution topography derived from unmanned aerial system photogrammetry

NASA Astrophysics Data System (ADS)

Pai, H.; Tyler, S.

2017-12-01

Small, unmanned aerial systems (sUAS) are quickly becoming a cost-effective and easily deployable tool for high spatial resolution environmental sensing. Land surface studies from sUAS imagery have largely focused on accurate topographic mapping, quantifying geomorphologic changes, and classification/identification of vegetation, sediment, and water quality tracers. In this work, we explore a further application of sUAS-derived topographic mapping to a two-dimensional (2-d), depth-averaged river hydraulic model (Flow and Sediment Transport with Morphological Evolution of Channels, FaSTMECH) along a short, meandering reach of East River, Colorado. On August 8, 2016, we flew a sUAS as part of the Center for Transformative Environmental Monitoring Programs with a consumer-grade visible camera and created a digital elevation map ( 1.5 cm resolution; 5 cm accuracy; 500 m long river corridor) with Agisoft Photoscan software. With the elevation map, we created a longitudinal water surface elevation (WSE) profile by manually delineating the bank-water interface and river bathymetry by applying refraction corrections for more accurate water depth estimates, an area of ongoing research for shallow and clear river systems. We tested both uncorrected and refraction-corrected bathymetries with the steady-state, 2-d model, applying sensitivities for dissipation parameters (bed roughness and eddy characteristics). Model performance was judged from the WSE data and measured stream velocities. While the models converged, performance and insights from model output could be improved with better bed roughness characterization and additional water depth cross-validation for refraction corrections. Overall, this work shows the applicability of sUAS-derived products to a multidimensional river model, where bathymetric data of high resolution and accuracy are key model input requirements.

Assessing Long-Term Seagrass Changes by Integrating a High-Spatial Resolution Image, Historical Aerial Photography and Field Data

NASA Astrophysics Data System (ADS)

Leon-Perez, M.; Hernandez, W. J.; Armstrong, R.

2016-02-01

Reported cases of seagrass loss have increased over the last 40 years, increasing the awareness of the need for assessing seagrass health. In situ monitoring has been the main method to assess spatial and temporal changes in seagrass ecosystem. Although remote sensing techniques with multispectral imagery have been recently used for these purposes, long-term analysis is limited to the sensor's mission life. The objective of this project is to determine long-term changes in seagrass habitat cover at Caja de Muertos Island Nature Reserve, by combining in situ data with a satellite image and historical aerial photography. A current satellite imagery of the WorldView-2 sensor was used to generate a 2014 benthic habitat map for the study area. The multispectral image was pre-processed using: conversion of digital numbers to radiance, and atmospheric and water column corrections. Object-based image analysis was used to segment the image into polygons representing different benthic habitats and to classify those habitats according to the classification scheme developed for this project. The scheme include the following benthic habitat categories: seagrass (sparse, dense and very dense), colonized hard bottom (sparse, dense and very dense), sand and mix algae on unconsolidated sediments. Field work was used to calibrate the satellite-derived benthic maps and to asses accuracy of the final products. In addition, a time series of satellite imagery and historic aerial photography from 1950 to 2014 provided data to assess long-term changes in seagrass habitat cover within the Reserve. Preliminary results show an increase in seagrass habitat cover, contrasting with the worldwide declining trend. The results of this study will provide valuable information for the conservation and management of seagrass habitat in the Caja de Muertos Island Nature Reserve.

Retrospective 70 y-spatial analysis of repeated vine mortality patterns using ancient aerial time series, Pléiades images and multi-source spatial and field data

NASA Astrophysics Data System (ADS)

Vaudour, E.; Leclercq, L.; Gilliot, J. M.; Chaignon, B.

2017-06-01

For any wine estate, there is a need to demarcate homogeneous within-vineyard zones ('terroirs') so as to manage grape production, which depends on vine biological condition. Until now, the studies performing digital zoning of terroirs have relied on recent spatial data and scant attention has been paid to ancient geoinformation likely to retrace past biological condition of vines and especially occurrence of vine mortality. Is vine mortality characterized by recurrent and specific patterns and if so, are these patterns related to terroir units and/or past landuse? This study aimed at performing a historical and spatial tracing of vine mortality patterns using a long time-series of aerial survey images (1947-2010), in combination with recent data: soil apparent electrical conductivity EM38 measurements, very high resolution Pléiades satellite images, and a detailed field survey. Within a 6 ha-estate in the Southern Rhone Valley, landuse and planting history were retraced and the map of missing vines frequency was constructed from the whole time series including a 2015-Pléiades panchromatic band. Within-field terroir units were obtained from a support vector machine classifier computed on the spectral bands and NDVI of Pléiades images, EM38 data and morphometric data. Repeated spatial patterns of missing vines were highlighted throughout several plantings, uprootings, and vine replacements, and appeared to match some within-field terroir units, being explained by their specific soil characteristics, vine/soil management choices and the past landuse of the 1940s. Missing vines frequency was spatially correlated with topsoil CaCO3 content, and negatively correlated with topsoil iron, clay, total N, organic C contents and NDVI. A retrospective spatio-temporal assessment of terroir therefore brings a renewed focus on some key parameters for maintaining a sustainable grape production.

Spatial and temporal variation in distribution of mangroves in Moreton Bay, subtropical Australia: a comparison of pattern metrics and change detection analyses based on aerial photographs

NASA Astrophysics Data System (ADS)

Manson, F. J.; Loneragan, N. R.; Phinn, S. R.

2003-07-01

An assessment of the changes in the distribution and extent of mangroves within Moreton Bay, southeast Queensland, Australia, was carried out. Two assessment methods were evaluated: spatial and temporal pattern metrics analysis, and change detection analysis. Currently, about 15,000 ha of mangroves are present in Moreton Bay. These mangroves are important ecosystems, but are subject to disturbance from a number of sources. Over the past 25 years, there has been a loss of more than 3800 ha, as a result of natural losses and mangrove clearing (e.g. for urban and industrial development, agriculture and aquaculture). However, areas of new mangroves have become established over the same time period, offsetting these losses to create a net loss of about 200 ha. These new mangroves have mainly appeared in the southern bay region and the bay islands, particularly on the landward edge of existing mangroves. In addition, spatial patterns and species composition of mangrove patches have changed. The pattern metrics analysis provided an overview of mangrove distribution and change in the form of single metric values, while the change detection analysis gave a more detailed and spatially explicit description of change. An analysis of the effects of spatial scales on the pattern metrics indicated that they were relatively insensitive to scale at spatial resolutions less than 50 m, but that most metrics became sensitive at coarser resolutions, a finding which has implications for mapping of mangroves based on remotely sensed data.

Uav Photogrammetry for Mapping and Monitoring of Northern Permafrost Landscapes

NASA Astrophysics Data System (ADS)

Fraser, R. H.; Olthof, I.; Maloley, M.; Fernandes, R.; Prevost, C.; van der Sluijs, J.

2015-08-01

Northern environments are changing in response to recent climate warming, resource development, and natural disturbances. The Arctic climate has warmed by 2-3°C since the 1950's, causing a range of cryospheric changes including declines in sea ice extent, snow cover duration, and glacier mass, and warming permafrost. The terrestrial Arctic has also undergone significant temperature-driven changes in the form of increased thermokarst, larger tundra fires, and enhanced shrub growth. Monitoring these changes to inform land managers and decision makers is challenging due to the vast spatial extents involved and difficult access. Environmental monitoring in Canada's North is often based on local-scale measurements derived from aerial reconnaissance and photography, and ecological, hydrologic, and geologic sampling and surveying. Satellite remote sensing can provide a complementary tool for more spatially comprehensive monitoring but at coarser spatial resolutions. Satellite remote sensing has been used to map Arctic landscape changes related to vegetation productivity, lake expansion and drainage, glacier retreat, thermokarst, and wildfire activity. However, a current limitation with existing satellite-based techniques is the measurement gap between field measurements and high resolution satellite imagery. Bridging this gap is important for scaling up field measurements to landscape levels, and validating and calibrating satellite-based analyses. This gap can be filled to a certain extent using helicopter or fixed-wing aerial surveys, but at a cost that is often prohibitive. Unmanned aerial vehicle (UAV) technology has only recently progressed to the point where it can provide an inexpensive and efficient means of capturing imagery at this middle scale of measurement with detail that is adequate to interpret Arctic vegetation (i.e. 1-5 cm) and coverage that can be directly related to satellite imagery (1-10 km2). Unlike satellite measurements, UAVs permit frequent surveys (e.g. for monitoring vegetation phenology, fires, and hydrology), are not constrained by repeat cycle or cloud cover, can be rapidly deployed following a significant event, and are better suited than manned aircraft for mapping small areas. UAVs are becoming more common for agriculture, law enforcement, and marketing, but their use in the Arctic is still rare and represents untapped technology for northern mapping, monitoring, and environmental research. We are conducting surveys over a range of sensitive or changing northern landscapes using a variety of UAV multicopter platforms and small sensors. Survey targets include retrogressive thaw slumps, tundra shrub vegetation, recently burned vegetation, road infrastructure, and snow. Working with scientific partners involved in northern monitoring programs (NWT CIMP, CHARS, NASA ABOVE, NRCan-GSC) we are investigating the advantages, challenges, and best practices for acquiring high resolution imagery from multicopters to create detailed orthomosaics and co-registered 3D terrain models. Colour and multispectral orthomosaics are being integrated with field measurements and satellite imagery to conduct spatial scaling of environmental parameters. Highly detailed digital terrain models derived using structure from motion (SfM) photogrammetry are being applied to measure thaw slump morphology and change, snow depth, tundra vegetation structure, and surface condition of road infrastructure. These surveys and monitoring applications demonstrate that UAV-based photogrammetry is poised to make a rapid contribution to a wide range of northern monitoring and research applications.

Analytical 3D views and virtual globes — scientific results in a familiar spatial context

NASA Astrophysics Data System (ADS)

Tiede, Dirk; Lang, Stefan

In this paper we introduce analytical three-dimensional (3D) views as a means for effective and comprehensible information delivery, using virtual globes and the third dimension as an additional information carrier. Four case studies are presented, in which information extraction results from very high spatial resolution (VHSR) satellite images were conditioned and aggregated or disaggregated to regular spatial units. The case studies were embedded in the context of: (1) urban life quality assessment (Salzburg/Austria); (2) post-disaster assessment (Harare/Zimbabwe); (3) emergency response (Lukole/Tanzania); and (4) contingency planning (faked crisis scenario/Germany). The results are made available in different virtual globe environments, using the implemented contextual data (such as satellite imagery, aerial photographs, and auxiliary geodata) as valuable additional context information. Both day-to-day users and high-level decision makers are addressees of this tailored information product. The degree of abstraction required for understanding a complex analytical content is balanced with the ease and appeal by which the context is conveyed.

Remote sensing of environmental impact of land use activities

NASA Technical Reports Server (NTRS)

Paul, C. K.

1977-01-01

The capability to monitor land cover, associated in the past with aerial film cameras and radar systems, was discussed in regard to aircraft and spacecraft multispectral scanning sensors. A proposed thematic mapper with greater spectral and spatial resolutions for the fourth LANDSAT is expected to usher in new environmental monitoring capability. In addition, continuing improvements in image classification by supervised and unsupervised computer techniques are being operationally verified for discriminating environmental impacts of human activities on the land. The benefits of employing remote sensing for this discrimination was shown to far outweigh the incremental costs of converting to an aircraft-satellite multistage system.

Predictive modeling of terrestrial radiation exposure from geologic materials

NASA Astrophysics Data System (ADS)

Haber, Daniel A.

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials in an area by creating a model using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low spatial resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas, referred to as background radiation units, homogenous in terms of K, U, and Th are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by our partner National Security Technologies, LLC (NSTec), allowing for the refinement of the technique. High resolution radiation exposure rate models have been developed for two study areas in Southern Nevada that include the alluvium on the western shore of Lake Mohave, and Government Wash north of Lake Mead; both of these areas are arid with little soil moisture and vegetation. We determined that by using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide radiation background units of alluvium, regions of homogeneous geochemistry can be defined allowing for the exposure rate to be predicted. Soil and rock samples have been collected at Government Wash and Lake Mohave as well as a third site near Cameron, Arizona. K, U, and Th concentrations of these samples have been determined using inductively coupled mass spectrometry (ICP-MS) and laboratory counting using radiation detection equipment. In addition, many sample locations also have concentrations determined via in situ radiation measurements with high purity germanium detectors (HPGe) and aerial survey measurements. These various measurement techniques have been compared and found to produce consistent results. Finally, modeling using Monte Carlo N-Particle Transport Code (MCNP), a particle physics modeling code, has allowed us to derive concentration to exposure rate coefficients. These simulations also have shown that differences in major element chemistry have little impact on the gamma ray emissions of geologic materials.

Super-resolution reconstruction of hyperspectral images.

PubMed

Akgun, Toygar; Altunbasak, Yucel; Mersereau, Russell M

2005-11-01

Hyperspectral images are used for aerial and space imagery applications, including target detection, tracking, agricultural, and natural resource exploration. Unfortunately, atmospheric scattering, secondary illumination, changing viewing angles, and sensor noise degrade the quality of these images. Improving their resolution has a high payoff, but applying super-resolution techniques separately to every spectral band is problematic for two main reasons. First, the number of spectral bands can be in the hundreds, which increases the computational load excessively. Second, considering the bands separately does not make use of the information that is present across them. Furthermore, separate band super-resolution does not make use of the inherent low dimensionality of the spectral data, which can effectively be used to improve the robustness against noise. In this paper, we introduce a novel super-resolution method for hyperspectral images. An integral part of our work is to model the hyperspectral image acquisition process. We propose a model that enables us to represent the hyperspectral observations from different wavelengths as weighted linear combinations of a small number of basis image planes. Then, a method for applying super resolution to hyperspectral images using this model is presented. The method fuses information from multiple observations and spectral bands to improve spatial resolution and reconstruct the spectrum of the observed scene as a combination of a small number of spectral basis functions.

High-resolution mapping based on an Unmanned Aerial Vehicle (UAV) to capture paleoseismic offsets along the Altyn-Tagh fault, China.

PubMed

Gao, Mingxing; Xu, Xiwei; Klinger, Yann; van der Woerd, Jerome; Tapponnier, Paul

2017-08-15

The recent dramatic increase in millimeter- to centimeter- resolution topographic datasets obtained via multi-view photogrammetry raises the possibility of mapping detailed offset geomorphology and constraining the spatial characteristics of active faults. Here, for the first time, we applied this new method to acquire high-resolution imagery and generate topographic data along the Altyn Tagh fault, which is located in a remote high elevation area and shows preserved ancient earthquake surface ruptures. A digital elevation model (DEM) with a resolution of 0.065 m and an orthophoto with a resolution of 0.016 m were generated from these images. We identified piercing markers and reconstructed offsets based on both the orthoimage and the topography. The high-resolution UAV data were used to accurately measure the recent seismic offset. We obtained the recent offset of 7 ± 1 m. Combined with the high resolution satellite image, we measured cumulative offsets of 15 ± 2 m, 20 ± 2 m, 30 ± 2 m, which may be due to multiple paleo-earthquakes. Therefore, UAV mapping can provide fine-scale data for the assessment of the seismic hazards.

Mapping and monitoring Louisiana's mangroves in the aftermath of the 2010 Gulf of Mexico Oil spill

USGS Publications Warehouse

Giri, C.; Long, J.; Tieszen, L.

2011-01-01

Information regarding the present condition, historical status, and dynamics of mangrove forests is needed to study the impacts of the Gulf of Mexico oil spill and other stressors affecting mangrove ecosystems. Such information is unavailable for Louisiana at sufficient spatial and thematic detail. We prepared mangrove forest distribution maps of Louisiana (prior to the oil spill) at 1 m and 30 m spatial resolution using aerial photographs and Landsat satellite data, respectively. Image classification was performed using a decision-tree classification approach. We also prepared land-cover change pairs for 1983, 1984, and every 2 y from 1984 to 2010 depicting “ecosystem shifts” (e.g., expansion, retraction, and disappearance). This new spatiotemporal information could be used to assess short-term and long-term impacts of the oil spill on mangroves. Finally, we propose an operational methodology based on remote sensing (Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer [ASTER], hyperspectral, light detection and ranging [LIDAR], aerial photographs, and field inventory data) to monitor the existing and emerging mangrove areas and their disturbance and regrowth patterns. Several parameters such as spatial distribution, ecosystem shifts, species composition, and tree height/biomass could be measured to assess the impact of the oil spill and mangrove recovery and restoration. Future research priorities will be to quantify the impacts and recovery of mangroves considering multiple stressors and perturbations, including oil spill, winter freeze, sea-level rise, land subsidence, and land-use/land-cover change for the entire Gulf Coast.

Mapping snow depth distribution in forested terrain using unmanned aerial vehicles and structure-from-motion

NASA Astrophysics Data System (ADS)

Webster, C.; Bühler, Y.; Schirmer, M.; Stoffel, A.; Giulia, M.; Jonas, T.

2017-12-01

Snow depth distribution in forests exhibits strong spatial heterogeneity compared to adjacent open sites. Measurement of snow depths in forests is currently limited to a) manual point measurements, which are sparse and time-intensive, b) ground-penetrating radar surveys, which have limited spatial coverage, or c) airborne LiDAR acquisition, which are expensive and may deteriorate in denser forests. We present the application of unmanned aerial vehicles in combination with structure-from-motion (SfM) methods to photogrammetrically map snow depth distribution in forested terrain. Two separate flights were carried out 10 days apart across a heterogeneous forested area of 900 x 500 m. Corresponding snow depth maps were derived using both, LiDAR-based and SfM-based DTM data, obtained during snow-off conditions. Manual measurements collected following each flight were used to validate the snow depth maps. Snow depths were resolved at 5cm resolution and forest snow depth distribution structures such as tree wells and other areas of preferential melt were represented well. Differential snow depth maps showed maximum ablation in the exposed south sides of trees and smaller differences in the centre of gaps and on the north side of trees. This new application of SfM to map snow depth distribution in forests demonstrates a straightforward method for obtaining information that was previously only available through manual spatially limited ground-based measurements. These methods could therefore be extended to more frequent observation of snow depths in forests as well as estimating snow accumulation and depletion rates.

Semi-automatic mapping of linear-trending bedforms using 'Self-Organizing Maps' algorithm

NASA Astrophysics Data System (ADS)

Foroutan, M.; Zimbelman, J. R.

2017-09-01

Increased application of high resolution spatial data such as high resolution satellite or Unmanned Aerial Vehicle (UAV) images from Earth, as well as High Resolution Imaging Science Experiment (HiRISE) images from Mars, makes it necessary to increase automation techniques capable of extracting detailed geomorphologic elements from such large data sets. Model validation by repeated images in environmental management studies such as climate-related changes as well as increasing access to high-resolution satellite images underline the demand for detailed automatic image-processing techniques in remote sensing. This study presents a methodology based on an unsupervised Artificial Neural Network (ANN) algorithm, known as Self Organizing Maps (SOM), to achieve the semi-automatic extraction of linear features with small footprints on satellite images. SOM is based on competitive learning and is efficient for handling huge data sets. We applied the SOM algorithm to high resolution satellite images of Earth and Mars (Quickbird, Worldview and HiRISE) in order to facilitate and speed up image analysis along with the improvement of the accuracy of results. About 98% overall accuracy and 0.001 quantization error in the recognition of small linear-trending bedforms demonstrate a promising framework.

Moving object detection in top-view aerial videos improved by image stacking

NASA Astrophysics Data System (ADS)

Teutsch, Michael; Krüger, Wolfgang; Beyerer, Jürgen

2017-08-01

Image stacking is a well-known method that is used to improve the quality of images in video data. A set of consecutive images is aligned by applying image registration and warping. In the resulting image stack, each pixel has redundant information about its intensity value. This redundant information can be used to suppress image noise, resharpen blurry images, or even enhance the spatial image resolution as done in super-resolution. Small moving objects in the videos usually get blurred or distorted by image stacking and thus need to be handled explicitly. We use image stacking in an innovative way: image registration is applied to small moving objects only, and image warping blurs the stationary background that surrounds the moving objects. Our video data are coming from a small fixed-wing unmanned aerial vehicle (UAV) that acquires top-view gray-value images of urban scenes. Moving objects are mainly cars but also other vehicles such as motorcycles. The resulting images, after applying our proposed image stacking approach, are used to improve baseline algorithms for vehicle detection and segmentation. We improve precision and recall by up to 0.011, which corresponds to a reduction of the number of false positive and false negative detections by more than 3 per second. Furthermore, we show how our proposed image stacking approach can be implemented efficiently.

Unmanned Aerial Vehicle (UAV) data analysis for fertilization dose assessment

NASA Astrophysics Data System (ADS)

Kavvadias, Antonis; Psomiadis, Emmanouil; Chanioti, Maroulio; Tsitouras, Alexandros; Toulios, Leonidas; Dercas, Nicholas

2017-10-01

The growth rate monitoring of crops throughout their biological cycle is very important as it contributes to the achievement of a uniformly optimum production, a proper harvest planning, and reliable yield estimation. Fertilizer application often dramatically increases crop yields, but it is necessary to find out which is the ideal amount that has to be applied in the field. Remote sensing collects spatially dense information that may contribute to, or provide feedback about, fertilization management decisions. There is a potential goal to accurately predict the amount of fertilizer needed so as to attain an ideal crop yield without excessive use of fertilizers cause financial loss and negative environmental impacts. The comparison of the reflectance values at different wavelengths, utilizing suitable vegetation indices, is commonly used to determine plant vigor and growth. Unmanned Aerial Vehicles (UAVs) have several advantages; because they can be deployed quickly and repeatedly, they are flexible regarding flying height and timing of missions, and they can obtain very high-resolution imagery. In an experimental crop field in Eleftherio Larissa, Greece, different dose of pre-plant and in-season fertilization was applied in 27 plots. A total of 102 aerial photos in two flights were taken using an Unmanned Aerial Vehicle based on the scheduled fertilization. Α correlation of experimental fertilization with the change of vegetation indices values and with the increase of the vegetation cover rate during those days was made. The results of the analysis provide useful information regarding the vigor and crop growth rate performance of various doses of fertilization.

Image Registration of High-Resolution Uav Data: the New Hypare Algorithm

NASA Astrophysics Data System (ADS)

Bahr, T.; Jin, X.; Lasica, R.; Giessel, D.

2013-08-01

Unmanned aerial vehicles play an important role in the present-day civilian and military intelligence. Equipped with a variety of sensors, such as SAR imaging modes, E/O- and IR sensor technology, they are due to their agility suitable for many applications. Hence, the necessity arises to use fusion technologies and to develop them continuously. Here an exact image-to-image registration is essential. It serves as the basis for important image processing operations such as georeferencing, change detection, and data fusion. Therefore we developed the Hybrid Powered Auto-Registration Engine (HyPARE). HyPARE combines all available spatial reference information with a number of image registration approaches to improve the accuracy, performance, and automation of tie point generation and image registration. We demonstrate this approach by the registration of 39 still images from a high-resolution image stream, acquired with a Aeryon Photo3S™ camera on an Aeryon Scout micro-UAV™.

Thresholds of Detection and Identification of Halite Nodule Habitats in the Atacama Desert Using Remote Imaging

NASA Technical Reports Server (NTRS)

Phillips, M. S.; Moersch, J. E.; Cabrol, N. A.; Davila, A. F.

2018-01-01

The guiding theme of Mars exploration is shifting from global and regional habitability assessment to biosignature detection. To locate features likely to contain biosignatures, it is useful to focus on the reliable identification of specific habitats with high biosignature preservation potential. Proposed chloride deposits on Mars may represent evaporitic environments conducive to the preservation of biosignatures. Analogous chloride- bearing, salt-encrusted playas (salars) are a habitat for life in the driest parts of the Atacama Desert, and are also environments with a taphonomic window. The specific geologic features that harbor and preserve microorganisms in Atacama salars are sub- meter to meter scale salt protuberances, or halite nodules. This study focuses on the ability to recognize and map halite nodules using images acquired from an unmanned aerial vehicle (UAV) at spatial resolutions ranging from mm/pixel to that of the highest resolution orbital images available for Mars.

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification

PubMed Central

Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references. PMID:29581722

A Two-Stream Deep Fusion Framework for High-Resolution Aerial Scene Classification.

PubMed

Yu, Yunlong; Liu, Fuxian

2018-01-01

One of the challenging problems in understanding high-resolution remote sensing images is aerial scene classification. A well-designed feature representation method and classifier can improve classification accuracy. In this paper, we construct a new two-stream deep architecture for aerial scene classification. First, we use two pretrained convolutional neural networks (CNNs) as feature extractor to learn deep features from the original aerial image and the processed aerial image through saliency detection, respectively. Second, two feature fusion strategies are adopted to fuse the two different types of deep convolutional features extracted by the original RGB stream and the saliency stream. Finally, we use the extreme learning machine (ELM) classifier for final classification with the fused features. The effectiveness of the proposed architecture is tested on four challenging datasets: UC-Merced dataset with 21 scene categories, WHU-RS dataset with 19 scene categories, AID dataset with 30 scene categories, and NWPU-RESISC45 dataset with 45 challenging scene categories. The experimental results demonstrate that our architecture gets a significant classification accuracy improvement over all state-of-the-art references.

Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery.

PubMed

Casado, Monica Rivas; Gonzalez, Rocio Ballesteros; Kriechbaumer, Thomas; Veal, Amanda

2015-11-04

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management.

Model-Based Building Detection from Low-Cost Optical Sensors Onboard Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Karantzalos, K.; Koutsourakis, P.; Kalisperakis, I.; Grammatikopoulos, L.

2015-08-01

The automated and cost-effective building detection in ultra high spatial resolution is of major importance for various engineering and smart city applications. To this end, in this paper, a model-based building detection technique has been developed able to extract and reconstruct buildings from UAV aerial imagery and low-cost imaging sensors. In particular, the developed approach through advanced structure from motion, bundle adjustment and dense image matching computes a DSM and a true orthomosaic from the numerous GoPro images which are characterised by important geometric distortions and fish-eye effect. An unsupervised multi-region, graphcut segmentation and a rule-based classification is responsible for delivering the initial multi-class classification map. The DTM is then calculated based on inpaininting and mathematical morphology process. A data fusion process between the detected building from the DSM/DTM and the classification map feeds a grammar-based building reconstruction and scene building are extracted and reconstructed. Preliminary experimental results appear quite promising with the quantitative evaluation indicating detection rates at object level of 88% regarding the correctness and above 75% regarding the detection completeness.

Hybrid Automatic Building Interpretation System

NASA Astrophysics Data System (ADS)

Pakzad, K.; Klink, A.; Müterthies, A.; Gröger, G.; Stroh, V.; Plümer, L.

2011-09-01

HABIS (Hybrid Automatic Building Interpretation System) is a system for an automatic reconstruction of building roofs used in virtual 3D building models. Unlike most of the commercially available systems, HABIS is able to work to a high degree automatically. The hybrid method uses different sources intending to exploit the advantages of the particular sources. 3D point clouds usually provide good height and surface data, whereas spatial high resolution aerial images provide important information for edges and detail information for roof objects like dormers or chimneys. The cadastral data provide important basis information about the building ground plans. The approach used in HABIS works with a multi-stage-process, which starts with a coarse roof classification based on 3D point clouds. After that it continues with an image based verification of these predicted roofs. In a further step a final classification and adjustment of the roofs is done. In addition some roof objects like dormers and chimneys are also extracted based on aerial images and added to the models. In this paper the used methods are described and some results are presented.

Observations of Near-Surface Relative Humidity in a Wind Turbine Array Boundary Layer Using an Instrumented Unmanned Aerial System

NASA Astrophysics Data System (ADS)

Adkins, K. A.; Sescu, A.

2016-12-01

Simulation and modeling have shown that wind farms have an impact on the near-surface atmospheric boundary layer (ABL) as turbulent wakes generated by the turbines enhance vertical mixing. These changes alter downstream atmospheric properties. With a large portion of wind farms hosted within an agricultural context, changes to the environment can potentially have secondary impacts such as to the productivity of crops. With the exception of a few observational data sets that focus on the impact to near-surface temperature, little to no observational evidence exists. These few studies also lack high spatial resolution due to their use of a limited number of meteorological towers or remote sensing techniques. This study utilizes an instrumented small unmanned aerial system (sUAS) to gather in-situ field measurements from two Midwest wind farms, focusing on the impact that large utility-scale wind turbines have on relative humidity. Wind turbines are found to differentially alter the relative humidity in the downstream, spanwise and vertical directions under a variety of atmospheric stability conditions.

Key parameters design of an aerial target detection system on a space-based platform

NASA Astrophysics Data System (ADS)

Zhu, Hanlu; Li, Yejin; Hu, Tingliang; Rao, Peng

2018-02-01

To ensure flight safety of an aerial aircraft and avoid recurrence of aircraft collisions, a method of multi-information fusion is proposed to design the key parameter to realize aircraft target detection on a space-based platform. The key parameters of a detection wave band and spatial resolution using the target-background absolute contrast, target-background relative contrast, and signal-to-clutter ratio were determined. This study also presented the signal-to-interference ratio for analyzing system performance. Key parameters are obtained through the simulation of a specific aircraft. And the simulation results show that the boundary ground sampling distance is 30 and 35 m in the mid- wavelength infrared (MWIR) and long-wavelength infrared (LWIR) bands for most aircraft detection, and the most reasonable detection wavebands is 3.4 to 4.2 μm and 4.35 to 4.5 μm in the MWIR bands, and 9.2 to 9.8 μm in the LWIR bands. We also found that the direction of detection has a great impact on the detection efficiency, especially in MWIR bands.

[Retrieval of crown closure of moso bamboo forest using unmanned aerial vehicle (UAV) remotely sensed imagery based on geometric-optical model].

PubMed

Wang, Cong; Du, Hua-qiang; Zhou, Guo-mo; Xu, Xiao-jun; Sun, Shao-bo; Gao, Guo-long

2015-05-01

This research focused on the application of remotely sensed imagery from unmanned aerial vehicle (UAV) with high spatial resolution for the estimation of crown closure of moso bamboo forest based on the geometric-optical model, and analyzed the influence of unconstrained and fully constrained linear spectral mixture analysis (SMA) on the accuracy of the estimated results. The results demonstrated that the combination of UAV remotely sensed imagery and geometric-optical model could, to some degrees, achieve the estimation of crown closure. However, the different SMA methods led to significant differentiation in the estimation accuracy. Compared with unconstrained SMA, the fully constrained linear SMA method resulted in higher accuracy of the estimated values, with the coefficient of determination (R2) of 0.63 at 0.01 level, against the measured values acquired during the field survey. Root mean square error (RMSE) of approximate 0.04 was low, indicating that the usage of fully constrained linear SMA could bring about better results in crown closure estimation, which was closer to the actual condition in moso bamboo forest.

Diurnal Cycles of High Resolution Land Surface Temperatures (LSTs) Determined from UAV Platforms Across a Range of Surface Types

NASA Astrophysics Data System (ADS)

McCabe, M.; Rosas Aguilar, J.; Parkes, S. D.; Aragon, B.

2017-12-01

Observation of land surface temperature (LST) has many practical uses, from studying boundary layer dynamics and land-atmosphere coupling, to investigating surface properties such as soil moisture status, heat stress and surface heat fluxes. Typically, LST is observed via satellite based sensors such as LandSat or via point measurements using IR radiometers. These measurements provide either good spatial coverage and resolution or good temporal coverage. However, neither are able to provide the needed spatial and temporal resolution for many of the research applications described above. Technological developments in the use of Unmanned Aerial Vehicles (UAVs), together with small thermal frame cameras, has enabled a capacity to overcome this spatiotemporal constraint. Utilising UAV platforms to collect LST measurements across diurnal cycles provides an opportunity to study how meteorological and surface properties vary in both space and time. Here we describe the collection of LST data from a multi-rotor UAV across a study domain that is observed multiple times throughout the day. Flights over crops of Rhodes grass and alfalfa, along with a bare desert surface, were repeated with between 8 and 11 surveys covering the period from early morning to sunset. Analysis of the collected thermal imagery shows that the constructed LST maps illustrate a strong diurnal cycle consistent with expected trends, but with considerable spatial and temporal variability observed within and between the different domains. These results offer new insights into the dynamics of land surface behavior in both dry and wet soil conditions and at spatiotemporal scales that are unable to be replicated using traditional satellite platforms.

In with the new, out with the old? Auto-extraction for remote sensing archaeology

NASA Astrophysics Data System (ADS)

Cowley, David C.

2012-09-01

This paper explores aspects of the inter-relationships between traditional archaeological interpretation of remote sensed data (principally visual examination of aerial photographs/satellite) and those drawing on automated feature extraction and processing. Established approaches to archaeological interpretation of aerial photographs are heavily reliant on individual observation (eye/brain) in an experience and knowledge-based process. Increasingly, however, much more complex and extensive datasets are becoming available to archaeology and these require critical reflection on analytical and interpretative processes. Archaeological applications of Airborne Laser Scanning (ALS) are becoming increasingly routine, and as the spatial resolution of hyper-spectral data improves, its potentially massive implications for archaeological site detection may prove to be a sea-change. These complex datasets demand new approaches, as traditional methods based on direct observation by an archaeological interpreter will never do more than scratch the surface, and will fail to fully extend the boundaries of knowledge. Inevitably, changing analytical and interpretative processes can create tensions, especially, as has been the case in archaeology, when the innovations in data and analysis come from outside the discipline. These tensions often centre on the character of the information produced, and a lack of clarity on the place of archaeological interpretation in the workflow. This is especially true for ALS data and autoextraction techniques, and carries implications for all forms of remote sensed archaeological datasets, including hyperspectral data and aerial photographs.

Timescale bias in measuring river migration rate

NASA Astrophysics Data System (ADS)

Donovan, M.; Belmont, P.; Notebaert, B.

2016-12-01

River channel migration plays an important role in sediment routing, water quality, riverine ecology, and infrastructure risk assessment. Migration rates may change in time and space due to systematic changes in hydrology, sediment supply, vegetation, and/or human land and water management actions. The ability to make detailed measurements of lateral migration over a wide range of temporal and spatial scales has been enhanced from increased availability of historical landscape-scale aerial photography and high-resolution topography (HRT). Despite a surge in the use of historical and contemporary aerial photograph sequences in conjunction with evolving methods to analyze such data for channel change, we found no research considering the biases that may be introduced as a function of the temporal scales of measurement. Unsteady processes (e.g.; sedimentation, channel migration, width changes) exhibit extreme discontinuities over time and space, resulting in distortion when measurements are averaged over longer temporal scales, referred to as `Sadler effects' (Sadler, 1981; Gardner et al., 1987). Using 12 sets of aerial photographs for the Root River (Minnesota), we measure lateral migration over space (110 km) and time (1937-2013) assess whether bias arises from different measurement scales and whether rates shift systematically with increased discharge over time. Results indicate that measurement-scale biases indeed arise from the time elapsed between measurements. We parsed the study reach into three distinct reaches and examine if/how recent increases in river discharge translate into changes in migration rate.

Geomorphology and vegetation mapping the ice-free terrains of the Western Antarctic Peninsula region using very high resolution imagery from an UAV

NASA Astrophysics Data System (ADS)

Vieira, G.; Mora, C.; Pina, P.; Bandeira, L.; Hong, S. G.

2014-12-01

The West Antarctic Peninsula (WAP) is one of the Earth's regions with a fastest warming signal since the 1950's with an increase of over +2.5 ºC in MAAT. Significant changes have been reported for glaciers, ice-shelves, sea-ice and also for the permafrost environment. Mapping and monitoring the ice-free areas of the WAP has been until recently limited by the available aerial photo surveys, but also by the scarce high resolution satellite imagery (e.g. QuickBird, WorldView, etc.) that are seriously constrained by the high cloudiness of the region. Recent developments in Unmanned Aerial Vehicles (UAV's), which have seen significant technological advances and price reduction in the last few years, allow for its systematical use for mapping and monitoring in remote environments. In the framework of projects PERMANTAR-3 (PTDC/AAG-GLO/3908/2012 - FCT) and 3DAntártida (Ciência Viva), we complement traditional terrain surveying and mapping, satellite remote sensing (SAR and optical) and D-GPS deformation monitoring, with the application of an UAV. In this communication, we present the results from the application of a Sensefly ebee UAV in mapping the vegetation and geomorphological processes (e.g. sorted circles), as well as for digital elevation model generation in a test site in Barton Pen., King George Isl.. The UAV is a lightweight (ci. 700g) aircraft, with a 96 cm wingspan, which is portable and easy to transport. It allows for up to 40 min flight time, with application of RGB or NIR cameras. We have tested the ebee successfully with winds up to 10 m/s and obtained aerial photos with a ground resolution of 4 cm/pixel. The digital orthophotomaps, high resolution DEM's together with field observations have allowed for deriving geomorphological maps with unprecedented detail and accuracy, providing new insight into the controls on the spatial distribution of geomorphological processes. The talk will focus on the first results from the field surveys of February and March 2014 and will also include some test data from the mountains of serra da Estrela, Portugal. New surveying is planned for the season of 2014-15 with mapping to be conducted in Livingston and King George Islands.

EnviroAtlas - New York, NY - One Meter Resolution Urban Land Cover Data (2008) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas ). The New York, NY EnviroAtlas Meter-scale Urban Land Cover (MULC) Data were generated by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O'Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. Seven classes were mapped using LiDAR and high resolution orthophotography: Tree Canopy, Grass/Shrub, Bare Soil, Water, Buildings, Roads/Railroads, and Other Paved Surfaces. These data were subsequently merged to fit with the EPA classification. The SAL project covered the five boroughs within the NYC city limits. However the EPA study area encompassed that area plus a 1 kilometer buffer. Additional land cover for the buffer area was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from July, 2011 and LiDAR from 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for New Yor

UAS close range remote sensing for mapping coastal environments

NASA Astrophysics Data System (ADS)

Papakonstantinou, Apostolos; Topouzelis, Kostantinos; Doukari, Michaela

2017-09-01

Coastline change and marine litter concentration in shoreline zones are two different emerging problems indicating the vulnerability as well as the quality of a coastal environment. Both problems present spatiotemporal changes due to weather and anthropogenic factors. Traditionally spatiotemporal changes in coastal environments are monitored using high-resolution satellite images and manned surveys. The last years, Unmanned Aerial Systems (UAS) are used as additional tool for monitoring environmental phenomena in sensitive coastal areas. In this study, two different case studies for mapping emerging coastal phenomena i.e. coastline changes and marine litter in Lesvos island, are presented. Both phenomena have increasing interest among scientists monitoring sensitive coastal areas. This paper outlines the integration of UAS for data acquisition and Structure from Motion (SfM) pipeline for the visualization of selected coastal areas in the Aegean Sea. The followed UAS-SfM methodology produces very detailed orthophoto maps. This high resolution spatial information is used for mapping and detecting primarily, marine litter on coastal and underwater zones and secondly, coastline changes and coastal erosion. More specific the produced orthophoto maps analyzed through GIS and with the use of the appropriate cartographic techniques the objective environmental parameters were mapped. Results showed that UAS-SfM pipeline produces geoinformation with high accuracy and spatial resolution that helps scientists to map with confidence environmental changes that take place in shoreline zones.

Satellite and aerial data as a tool for digs localisation and their verification using geophysical methods

NASA Astrophysics Data System (ADS)

Pavelka, Karel; Faltynova, Martina; Bila, Zdenka

2013-04-01

The Middle Europe such as next world cultural centres are inhabited by humans tens of thousands years. In the last ten years, new methods are implemented in archaeology. It means new sensitive geophysical methods, very high resolution remote sensing and Airborne Laser Scanning (ALS). This contribution will refer about new technological possibilities for archaeology in the Czech Republic to two project examples. VHR satellite data or aerial image data can be used for searching of potential archaeological sites. In some cases, orthophoto mosaic is very useful; nowadays, different aerial orthophotomosaic layers are available in the Czech Republic (2002-3, 2006 and 2009) with pixel resolution 25cm. The archaeological findings are best visible in the Czech Republic by their vegetation indices. For this reason, the best time for data acquiring is mid of spring, in rapid vegetation process. Another option is the soil indices - the best time is early spring or autumn, after crop. A new progressive method is ALS, which can be used for spatial indices. Since autumn 2009 the entire area of the Czech Republic is mapped by technology of ALS. The aim of mapping is to get authentic and detailed digital terrain model (DTM) of the Czech Republic. About 80% (autumn 2012) of the Czech territory is currently covered by the DTM based on ALS. The standard deviation of model points in altitude is better than 20cm. The DTM displayed in appropriate form (as shaded surface) can be used as a data source for searching and description of archaeological sites - mainly in forested areas. By using of above mentioned methods a lot of interesting historical sites were discovered. The logical next step is a verification of these findings by using terrestrial methods - in this case by using of geophysical instruments. At the CTU Prague, the walking gradiometer GSM-19 and georadar SIR-3000 are at disposal. In first example the former fortification from Prussia - Austrian was localized on orthophoto mosaic and QuickBird satellite data. Normally is not visible from the surface. Secondary was fortification localized on shaded relief by spatial indices. Last verification has been made by walking magnetometer. Second example is joining of both magnetometers and GPR data. These technology and 3D modelling was used for localisation and verification of unknown tomb in the neighbourhood of church ruins in Panensky Tynec.

A classification-based assessment of the optimal spatial and spectral resolution of coastal wetland imagery

NASA Astrophysics Data System (ADS)

Becker, Brian L.

Great Lakes wetlands are increasingly being recognized as vital ecosystem components that provide valuable functions such as sediment retention, wildlife habitat, and nutrient removal. Aerial photography has traditionally provided a cost effective means to inventory and monitor coastal wetlands, but is limited by its broad spectral sensitivity and non-digital format. Airborne sensor advancements have now made the acquisition of digital imagery with high spatial and spectral resolution a reality. In this investigation, we selected two Lake Huron coastal wetlands, each from a distinct eco-region, over which, digital, airborne imagery (AISA or CASI-II) was acquired. The 1-meter images contain approximately twenty, 10-nanometer-wide spectral bands strategically located throughout the visible and near-infrared. The 4-meter hyperspectral imagery contains 48 contiguous bands across the visible and short-wavelength near-infrared. Extensive, in-situ, reflectance spectra (SE-590) and sub-meter GPS locations were acquired for the dominant botanical and substrate classes field-delineated at each location. Normalized in-situ spectral signatures were subjected to Principal Components and 2nd Derivative analyses in order to identify the most botanically explanative image bands. Three image-based investigations were implemented in order to evaluate the ability of three classification algorithms (ISODATA, Spectral Angle Mapper and Maximum-Likelihood) to differentiate botanical regions-of-interest. Two additional investigations were completed in order to assess classification changes associated with the independent manipulation of both spatial and spectral resolution. Of the three algorithms tested, the Maximum-Likelihood classifier best differentiated (89%) the regions-of-interest in both study sites. Covariance-based PCA rotation consistently enhanced the performance of the Maximum-Likelihood classifier. Seven non-overlapping bands (425.4, 514.9, 560.1, 685.5, 731.5, 812.3 and 916.7 nanometers) were identified that represented the best performing bands with respect to classification performance. A spatial resolution of 2 meters or less was determined to be the as being most appropriate in Great Lakes coastal wetland environments. This research represents the first step in evaluating the effectiveness of applying high-resolution, narrow-band imagery to the detailed mapping of coastal wetlands in the Great Lakes region.

Enabling high-quality observations of surface imperviousness for water runoff modelling from unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Tokarczyk, Piotr; Leitao, Joao Paulo; Rieckermann, Jörg; Schindler, Konrad; Blumensaat, Frank

2015-04-01

Modelling rainfall-runoff in urban areas is increasingly applied to support flood risk assessment particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the area. While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increase as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data is unavailable. Modern unmanned air vehicles (UAVs) allow acquiring high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements, and obtaining a degree of detail tailored for the purpose of the study. In this study, we investigate for the first time the possibility to derive high-resolution imperviousness maps for urban areas from UAV imagery and to use this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is tested and applied in a state-of-the-art urban drainage modelling exercise. In a real-life case study in the area of Lucerne, Switzerland, we compare imperviousness maps generated from a consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their correctness, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyze the surface runoff of the 307 individual sub-catchments regarding relevant attributes, such as peak runoff and volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet. We show that imperviousness maps generated using UAV imagery processed with modern classification methods achieve accuracy comparable with standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on modelled surface runoff and pipe flows. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility to flexibly acquire up-to-date aerial images at a superior quality and a competitive price. Our analyses furthermore suggest that spatially more detailed urban drainage models can even better benefit from the full detail of UAV imagery.

Evaluation of remote sensing aerial systems in existing transportation practices, phase II.

DOT National Transportation Integrated Search

2011-06-01

A low-cost aerial platform represents a flexible tool for acquiring high-resolution images for ground areas of interest. The geo-referencing of objects within these images could benefit civil engineers in a variety of research areas including, but no...

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data

PubMed Central

Huang, Huabing; Gong, Peng; Cheng, Xiao; Clinton, Nick; Li, Zengyuan

2009-01-01

Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data. PMID:22573971

All Source Solution Decision Support Products Created for Stennis Space Center in Response to Hurricane Katrina

NASA Technical Reports Server (NTRS)

Ross, Kenton W.; Graham, William D.

2007-01-01

In the aftermath of Hurricane Katrina and in response to the needs of SSC (Stennis Space Center), NASA required the generation of decision support products with a broad range of geospatial inputs. Applying a systems engineering approach, the NASA ARTPO (Applied Research and Technology Project Office) at SSC evaluated the Center's requirements and source data quality. ARTPO identified data and information products that had the potential to meet decision-making requirements; included were remotely sensed data ranging from high-spatial-resolution aerial images through high-temporal-resolution MODIS (Moderate Resolution Imaging Spectroradiometer) products. Geospatial products, such as FEMA's (Federal Emergency Management Agency's) Advisory Base Flood Elevations, were also relevant. Where possible, ARTPO applied SSC calibration/validation expertise to both clarify the quality of various data source options and to validate that the inputs that were finally chosen met SSC requirements. ARTPO integrated various information sources into multiple decision support products, including two maps: Hurricane Katrina Inundation Effects at Stennis Space Center (highlighting surge risk posture) and Vegetation Change In and Around Stennis Space Center: Katrina and Beyond (highlighting fire risk posture).

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors

PubMed Central

Zheng, Guang; Moskal, L. Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels. PMID:22574042

Retrieving Leaf Area Index (LAI) Using Remote Sensing: Theories, Methods and Sensors.

PubMed

Zheng, Guang; Moskal, L Monika

2009-01-01

The ability to accurately and rapidly acquire leaf area index (LAI) is an indispensable component of process-based ecological research facilitating the understanding of gas-vegetation exchange phenomenon at an array of spatial scales from the leaf to the landscape. However, LAI is difficult to directly acquire for large spatial extents due to its time consuming and work intensive nature. Such efforts have been significantly improved by the emergence of optical and active remote sensing techniques. This paper reviews the definitions and theories of LAI measurement with respect to direct and indirect methods. Then, the methodologies for LAI retrieval with regard to the characteristics of a range of remotely sensed datasets are discussed. Remote sensing indirect methods are subdivided into two categories of passive and active remote sensing, which are further categorized as terrestrial, aerial and satellite-born platforms. Due to a wide variety in spatial resolution of remotely sensed data and the requirements of ecological modeling, the scaling issue of LAI is discussed and special consideration is given to extrapolation of measurement to landscape and regional levels.

Combining structure-from-motion derived point clouds from satellites and unmanned aircraft systems images with ground-truth data to create high-resolution digital elevation models

NASA Astrophysics Data System (ADS)

Palaseanu, M.; Thatcher, C.; Danielson, J.; Gesch, D. B.; Poppenga, S.; Kottermair, M.; Jalandoni, A.; Carlson, E.

2016-12-01

Coastal topographic and bathymetric (topobathymetric) data with high spatial resolution (1-meter or better) and high vertical accuracy are needed to assess the vulnerability of Pacific Islands to climate change impacts, including sea level rise. According to the Intergovernmental Panel on Climate Change reports, low-lying atolls in the Pacific Ocean are extremely vulnerable to king tide events, storm surge, tsunamis, and sea-level rise. The lack of coastal topobathymetric data has been identified as a critical data gap for climate vulnerability and adaptation efforts in the Republic of the Marshall Islands (RMI). For Majuro Atoll, home to the largest city of RMI, the only elevation dataset currently available is the Shuttle Radar Topography Mission data which has a 30-meter spatial resolution and 16-meter vertical accuracy (expressed as linear error at 90%). To generate high-resolution digital elevation models (DEMs) in the RMI, elevation information and photographic imagery have been collected from field surveys using GNSS/total station and unmanned aerial vehicles for Structure-from-Motion (SfM) point cloud generation. Digital Globe WorldView II imagery was processed to create SfM point clouds to fill in gaps in the point cloud derived from the higher resolution UAS photos. The combined point cloud data is filtered and classified to bare-earth and georeferenced using the GNSS data acquired on roads and along survey transects perpendicular to the coast. A total station was used to collect elevation data under tree canopies where heavy vegetation cover blocked the view of GNSS satellites. A subset of the GPS / total station data was set aside for error assessment of the resulting DEM.

Issues of Authenticity of Spatial Data.

ERIC Educational Resources Information Center

McGlamery, Patrick

This paper discusses the authenticity of digital spatial data. The first section describes three formats for digital spatial data: vector, raster, and thematic. The second section addresses the integrity of spatial data, including six possible formats for the same information: (1) aerial photographic prints, time stamped, primary, remotely sensed…

Construction of a small and lightweight hyperspectral imaging system

NASA Astrophysics Data System (ADS)

Vogel, Britta; Hünniger, Dirk; Bastian, Georg

2014-05-01

The analysis of the reflected sunlight offers great opportunity to gain information about the environment, including vegetation and soil. In the case of plants the wavelength ratio of the reflected light usually undergoes a change if the state of growth or state of health changes. So the measurement of the reflected light allows drawing conclusions about the state of, amongst others, vegetation. Using a hyperspectral imaging system for data acquisition leads to a large dataset, which can be evaluated with respect to several different questions to obtain various information by one measurement. Based on commercially available plain optical components we developed a small and lightweight hyperspectral imaging system within the INTERREG IV A-Project SMART INSPECTORS. The project SMART INSPECTORS [Smart Aerial Test Rigs with Infrared Spectrometers and Radar] deals with the fusion of airborne visible and infrared imaging remote sensing instruments and wireless sensor networks for precision agriculture and environmental research. A high performance camera was required in terms of good signal, good wavelength resolution and good spatial resolution, while severe constraints of size, proportions and mass had to be met due to the intended use on small unmanned aerial vehicles. The detector was chosen to operate without additional cooling. The refractive and focusing optical components were identified by supporting works with an optical raytracing software and a self-developed program. We present details of design and construction of our camera system, test results to confirm the optical simulation predictions as well as our first measurements.

The use of LiDAR-derived high-resolution DSM and intensity data to support modelling of urban flooding

NASA Astrophysics Data System (ADS)

Aktaruzzaman, Md.; Schmitt, Theo G.

2011-11-01

This paper addresses the issue of a detailed representation of an urban catchment in terms of hydraulic and hydrologic attributes. Modelling of urban flooding requires a detailed knowledge of urban surface characteristics. The advancement in spatial data acquisition technology such as airborne LiDAR (Light Detection and Ranging) has greatly facilitated the collection of high-resolution topographic information. While the use of the LiDAR-derived Digital Surface Model (DSM) has gained popularity over the last few years as input data for a flood simulation model, the use of LiDAR intensity data has remained largely unexplored in this regard. LiDAR intensity data are acquired along with elevation data during the data collection mission by an aircraft. The practice of using of just aerial images with RGB (Red, Green and Blue) wavebands is often incapable of identifying types of surface under the shadow. On the other hand, LiDAR intensity data can provide surface information independent of sunlight conditions. The focus of this study is the use of intensity data in combination with aerial images to accurately map pervious and impervious urban areas. This study presents an Object-Based Image Analysis (OBIA) framework for detecting urban land cover types, mainly pervious and impervious surfaces in order to improve the rainfall-runoff modelling. Finally, this study shows the application of highresolution DSM and land cover maps to flood simulation software in order to visualize the depth and extent of urban flooding phenomena.

Reproducibility of UAV-based earth surface topography based on structure-from-motion algorithms.

NASA Astrophysics Data System (ADS)

Clapuyt, François; Vanacker, Veerle; Van Oost, Kristof

2014-05-01

A representation of the earth surface at very high spatial resolution is crucial to accurately map small geomorphic landforms with high precision. Very high resolution digital surface models (DSM) can then be used to quantify changes in earth surface topography over time, based on differencing of DSMs taken at various moments in time. However, it is compulsory to have both high accuracy for each topographic representation and consistency between measurements over time, as DSM differencing automatically leads to error propagation. This study investigates the reproducibility of reconstructions of earth surface topography based on structure-from-motion (SFM) algorithms. To this end, we equipped an eight-propeller drone with a standard reflex camera. This equipment can easily be deployed in the field, as it is a lightweight, low-cost system in comparison with classic aerial photo surveys and terrestrial or airborne LiDAR scanning. Four sets of aerial photographs were created for one test field. The sets of airphotos differ in focal length, and viewing angles, i.e. nadir view and ground-level view. In addition, the importance of the accuracy of ground control points for the construction of a georeferenced point cloud was assessed using two different GPS devices with horizontal accuracy at resp. the sub-meter and sub-decimeter level. Airphoto datasets were processed with SFM algorithm and the resulting point clouds were georeferenced. Then, the surface representations were compared with each other to assess the reproducibility of the earth surface topography. Finally, consistency between independent datasets is discussed.

Multi-temporal analysis of aerial images for the investigation of spatial-temporal dynamics of shallow erosion - a case study from the Tyrolean Alps

NASA Astrophysics Data System (ADS)

Wiegand, C.; Geitner, C.; Heinrich, K.; Rutzinger, M.

2012-04-01

Small and shallow eroded areas characterize the landscape of many pastures and meadows in the Alps. The extent of such erosion phenomena varies between 2 m2 and 200 m2. These patches tend to be only a few decimetres thick, with a maximum depth of 2 m. The processes involved are shallow landslides, superficial erosion by snow and livestock trampling. Key parameters that influence the emergence of shallow erosion are the geological, topographical and climatic circumstances in an area as well as its soils, vegetation and land use. The negative impact of this phenomenon includes not only the loss of soil but also the reduced attractiveness of the landscape, especially in tourist regions. One approach identifying and mapping geomorphological elements is remote sensing. The analysis of aerial images is a suitable method for identifying the multi-temporal dynamics of shallow eroded areas because of the good spatial and temporal resolution. For this purpose, we used a pixel-based approach to detect these areas semi-automatically in an orthophoto. In a first step, each aerial image was classified using dynamic thresholds derived from the histogram of the orthophoto. In a second step, the identified areas of erosion were filtered and visually in-terpreted. Based on this procedure, eroded areas with a minimum size of 5 m2 were detected in a test site located in the Inner Schmirn Valley (Tyrol, Austria). The altitude of the test site ranges between 1,980 m and 2,370 m, with a mean inclination of 36°, facing E to SE. Geologically, the slope is part of the "Hohe Tauern Window", characterized by "Bündner schists" deficient in lime and regolith. Until the 1960s, the slope was used as a hay meadow. Orthophotos from 2000, 2003, 2007 and 2010 were used for this investigation. Older aerial images were not suitable because of their lower resolution and poor ortho-rectification. However, they are useful for relating the results of the ten-year time-span to a larger temporal context. No significant increase of erosion could be observed for the investigated ten-year period. The majority of the eroded areas show no distinct trend but rather an irregular pattern of increase and decrease. The results fit well in a larger temporal context: in aerial images of the 1950s, the slope already shows several eroded patches, which did not change until the year 2000. The owners also confirm that erosion was even a problem before abandonment. In this case, the inclination of the terrain seems to exceed the influence of land-use activities. With the semi-automated detection of such eroded areas, a more objective and time-saving method was found. The results contribute to an improved understanding of the process and can initiate a long-term observation. In subsequent studies we will apply the approach to further test sites and adapt it for the detection of smaller eroded areas.

Characterization of Vegetation using the UC Davis Remote Sensing Testbed

NASA Astrophysics Data System (ADS)

Falk, M.; Hart, Q. J.; Bowen, K. S.; Ustin, S. L.

2006-12-01

Remote sensing provides information about the dynamics of the terrestrial biosphere with continuous spatial and temporal coverage on many different scales. We present the design and construction of a suite of instrument modules and network infrastructure with size, weight and power constraints suitable for small scale vehicles, anticipating vigorous growth in unmanned aerial vehicles (UAV) and other mobile platforms. Our approach provides the rapid deployment and low cost acquisition of high aerial imagery for applications requiring high spatial resolution and revisits. The testbed supports a wide range of applications, encourages remote sensing solutions in new disciplines and demonstrates the complete range of engineering knowledge required for the successful deployment of remote sensing instruments. The initial testbed is deployed on a Sig Kadet Senior remote controlled plane. It includes an onboard computer with wireless radio, GPS, inertia measurement unit, 3-axis electronic compass and digital cameras. The onboard camera is either a RGB digital camera or a modified digital camera with red and NIR channels. Cameras were calibrated using selective light sources, an integrating spheres and a spectrometer, allowing for the computation of vegetation indices such as the NDVI. Field tests to date have investigated technical challenges in wireless communication bandwidth limits, automated image geolocation, and user interfaces; as well as image applications such as environmental landscape mapping focusing on Sudden Oak Death and invasive species detection, studies on the impact of bird colonies on tree canopies, and precision agriculture.

Geodetic mass balance measurements on debris and clean-ice tropical glaciers in Ecuador

NASA Astrophysics Data System (ADS)

La Frenierre, J.; Decker, C. R.; Jordan, E.; Wigmore, O.; Hodge, B. E.; Niederriter, C.; Michels, A.

2017-12-01

Glaciers are recognized as highly sensitive indicators of climate change in high altitude, low latitude environments. In the tropical Andes, various analyses of glacier surface area change have helped illuminate the manifestation of climate change in this region, however, information about actual glacier mass balance behavior is much more limited given the relatively small glaciers, difficult access, poor weather, and/or limited local resources common here. Several new technologies, including aerial and terrestrial LIDAR and structure-from-motion photogrammetry using small unmanned aerial vehicles (UAVs), make mass balance measurements using geodetic approaches increasingly feasible in remote mountain locations, which can both further our understanding of changing climatic conditions, and improve our ability to evaluate the downstream hydrologic impacts of ice loss. At Volcán Chimborazo, Ecuador, these new technologies, combined with a unique, 5-meter resolution digital elevation model derived from 1997 aerial imagery, make possible an analysis of the magnitude and spatial patterns of mass balance behavior over the past two decades. Here, we evaluate ice loss between 1997 and 2017 at the tongues of two adjacent glaciers, one debris-covered and detached from its accumulation area (Reschreiter Glacier), and one debris-free and intact (Hans Meyer Glacier). Additionally, we incorporate data from 2012 and 2013 terrestrial LIDAR surveys to evaluate the behavior of the Reschreiter at a finer temporal resolution. We find that on the Hans Meyer, the mean surface deflation rate since 1997 at the present-day tongue has been nearly 3 m yr-1, while on the lower-elevation Reschreiter, the mean deflation rate has been approximately 1 m yr-1. However, the processes by which debris-covered ice becomes exposed results in highly heterogeneous patterns of ice loss, with some areas experiencing surface deflation rates approaching 15 m yr-1 when energy absorption is unimpeded.

Estimation of canopy attributes in beech forests using true colour digital images from a small fixed-wing UAV

NASA Astrophysics Data System (ADS)

Chianucci, Francesco; Disperati, Leonardo; Guzzi, Donatella; Bianchini, Daniele; Nardino, Vanni; Lastri, Cinzia; Rindinella, Andrea; Corona, Piermaria

2016-05-01

Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but their application is limited by the spectral and temporal resolution available from these systems, which is often not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remote sensing platforms has recently gained increasing attention, but their applications in forestry are still at an experimental stage. In this study we described a methodology to obtain rapid and reliable estimates of forest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digital numbers were converted to the green leaf algorithm (GLA) and to the CIE L*a*b* colour space to obtain estimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributes were compared with in situ estimates obtained from two digital canopy photographic techniques (cover and fisheye photography). The method was tested in beech forests. UAV images accurately quantified canopy cover even in very dense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images, leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photography. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, but the accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution. We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and meaningful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage of high resolution imagery with quick turnaround series, being therefore suitable for routine forest stand monitoring and real-time applications.

Monitoring channel head erosion processes in response to an artificially induced abrupt base level change using time-lapse photography

NASA Astrophysics Data System (ADS)

Nichols, M. H.; Nearing, M.; Hernandez, M.; Polyakov, V. O.

2016-07-01

Gullies that terminate at a vertical-wall are ubiquitous throughout arid and semiarid regions. Multi-year assessments of gully evolution and headcut advance are typically accomplished using traditional ground surveys and aerial photographs, with much recent research focused on integrating data collected at very high spatial resolutions using new techniques such as aerial surveys with blimps or kites and ground surveys with LiDar scanners. However, knowledge of specific processes that drive headcut advance is limited due to inadequate observation and documentation of flash floods and subsequent erosion that can occur at temporal resolutions not captured through repeat surveys. This paper presents a method for using very-high temporal resolution ground-based time-lapse photography to capture short-duration flash floods and gully head evolution in response. In 2004, a base level controlling concrete weir was removed from the outlet of a 1.29 ha semiarid headwater drainage on the Walnut Gulch Experimental Watershed in southeastern Arizona, USA. During the ten year period from 2004 to 2014 the headcut migrated upchannel a total of 14.5 m reducing the contributing area at the headwall by 9.5%. Beginning in July 2012, time-lapse photography was employed to observe event scale channel evolution dynamics. The most frequent erosion processes observed during three seasons of time-lapse photography were plunge pool erosion and mass wasting through sidewall or channel headwall slumping that occurred during summer months. Geomorphic change during the ten year period was dominated by a single piping event in August 2014 that advanced the channel head 7.4 m (51% of the overall advance) and removed 11.3 m3 of sediment. High temporal resolution time-lapse photography was critical for identifying subsurface erosion processes, in the absence of time-lapse images piping would not have been identified as an erosion mechanism responsible for advancing the gully headwall at this site.

Integration of Palmer Drought Severity Index and remote sensing data to simulate wetland water surface from 1910 to 2009 in Cottonwood Lake area, North Dakota

USGS Publications Warehouse

Huang, Shengli; Dahal, Devendra; Young, Claudia; Chander, Gyanesh; Liu, Shuguang

2011-01-01

Spatiotemporal variations of wetland water in the Prairie Pothole Region are controlled by many factors; two of them are temperature and precipitation that form the basis of the Palmer Drought Severity Index (PDSI). Taking the 196 km2 Cottonwood Lake area in North Dakota as our pilot study site, we integrated PDSI, Landsat images, and aerial photography records to simulate monthly water surface. First, we developed a new Wetland Water Area Index (WWAI) from PDSI to predict water surface area. Second, we developed a water allocation model to simulate the spatial distribution of water bodies at a resolution of 30 m. Third, we used an additional procedure to model the small wetlands (less than 0.8 ha) that could not be detected by Landsat. Our results showed that i) WWAI was highly correlated with water area with an R2 of 0.90, resulting in a simple regression prediction of monthly water area to capture the intra- and inter-annual water change from 1910 to 2009; ii) the spatial distribution of water bodies modeled from our approach agreed well with the water locations visually identified from the aerial photography records; and iii) the R2 between our modeled water bodies (including both large and small wetlands) and those from aerial photography records could be up to 0.83 with a mean average error of 0.64 km2 within the study area where the modeled wetland water areas ranged from about 2 to 14 km2. These results indicate that our approach holds great potential to simulate major changes in wetland water surface for ecosystem service; however, our products could capture neither the short-term water change caused by intensive rainstorm events nor the wetland change caused by human activities.

ARISE (Antarctic Remote Ice Sensing Experiment) in the East 2003: Validation of Satellite-derived Sea-ice Data Product

NASA Technical Reports Server (NTRS)

Massom, Robert A.; Worby, Anthony; Lytle, Victoria; Markus, Thorsten; Allison, Ian; Scambos, Theodore; Enomoto, Hiroyuki; Tateyama, Kazutaka; Haran, Terence; Comiso, Josefino C.;

Unravelling earth flow dynamics with 3-D time series derived from UAV-SfM models

NASA Astrophysics Data System (ADS)

Clapuyt, François; Vanacker, Veerle; Schlunegger, Fritz; Van Oost, Kristof

2017-12-01

Accurately assessing geo-hazards and quantifying landslide risks in mountainous environments are gaining importance in the context of the ongoing global warming. For an in-depth understanding of slope failure mechanisms, accurate monitoring of the mass movement topography at high spatial and temporal resolutions remains essential. The choice of the acquisition framework for high-resolution topographic reconstructions will mainly result from the trade-off between the spatial resolution needed and the extent of the study area. Recent advances in the development of unmanned aerial vehicle (UAV)-based image acquisition combined with the structure-from-motion (SfM) algorithm for three-dimensional (3-D) reconstruction make the UAV-SfM framework a competitive alternative to other high-resolution topographic techniques. In this study, we aim at gaining in-depth knowledge of the Schimbrig earthflow located in the foothills of the Central Swiss Alps by monitoring ground surface displacements at very high spatial and temporal resolution using the efficiency of the UAV-SfM framework. We produced distinct topographic datasets for three acquisition dates between 2013 and 2015 in order to conduct a comprehensive 3-D analysis of the landslide. Therefore, we computed (1) the sediment budget of the hillslope, and (2) the horizontal and (3) the three-dimensional surface displacements. The multitemporal UAV-SfM based topographic reconstructions allowed us to quantify rates of sediment redistribution and surface movements. Our data show that the Schimbrig earthflow is very active, with mean annual horizontal displacement ranging between 6 and 9 m. Combination and careful interpretation of high-resolution topographic analyses reveal the internal mechanisms of the earthflow and its complex rotational structure. In addition to variation in horizontal surface movements through time, we interestingly showed that the configuration of nested rotational units changes through time. Although there are major changes in the internal structure of the earthflow in the 2013-2015 period, the sediment budget of the drainage basin is nearly in equilibrium. As a consequence, our data show that the time lag between sediment mobilization by landslides and enhanced sediment fluxes in the river network can be considerable.

Aerial projection of three-dimensional motion pictures by electro-holography and parabolic mirrors.

PubMed

Kakue, Takashi; Nishitsuji, Takashi; Kawashima, Tetsuya; Suzuki, Keisuke; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2015-07-08

We demonstrate an aerial projection system for reconstructing 3D motion pictures based on holography. The system consists of an optical source, a spatial light modulator corresponding to a display and two parabolic mirrors. The spatial light modulator displays holograms calculated by computer and can reconstruct holographic motion pictures near the surface of the modulator. The two parabolic mirrors can project floating 3D images of the motion pictures formed by the spatial light modulator without mechanical scanning or rotating. In this demonstration, we used a phase-modulation-type spatial light modulator. The number of pixels and the pixel pitch of the modulator were 1,080 × 1,920 and 8.0 μm × 8.0 μm, respectively. The diameter, the height and the focal length of each parabolic mirror were 288 mm, 55 mm and 100 mm, respectively. We succeeded in aerially projecting 3D motion pictures of size ~2.5 mm(3) by this system constructed by the modulator and mirrors. In addition, by applying a fast computational algorithm for holograms, we achieved hologram calculations at ~12 ms per hologram with 4 CPU cores.

Aerial projection of three-dimensional motion pictures by electro-holography and parabolic mirrors

PubMed Central

Kakue, Takashi; Nishitsuji, Takashi; Kawashima, Tetsuya; Suzuki, Keisuke; Shimobaba, Tomoyoshi; Ito, Tomoyoshi

2015-01-01

We demonstrate an aerial projection system for reconstructing 3D motion pictures based on holography. The system consists of an optical source, a spatial light modulator corresponding to a display and two parabolic mirrors. The spatial light modulator displays holograms calculated by computer and can reconstruct holographic motion pictures near the surface of the modulator. The two parabolic mirrors can project floating 3D images of the motion pictures formed by the spatial light modulator without mechanical scanning or rotating. In this demonstration, we used a phase-modulation-type spatial light modulator. The number of pixels and the pixel pitch of the modulator were 1,080 × 1,920 and 8.0 μm × 8.0 μm, respectively. The diameter, the height and the focal length of each parabolic mirror were 288 mm, 55 mm and 100 mm, respectively. We succeeded in aerially projecting 3D motion pictures of size ~2.5 mm3 by this system constructed by the modulator and mirrors. In addition, by applying a fast computational algorithm for holograms, we achieved hologram calculations at ~12 ms per hologram with 4 CPU cores. PMID:26152453

Using UAV photogrammetry to study topographic change: application to Saskatchewan Glacier, Alberta, Canada

NASA Astrophysics Data System (ADS)

Meunier Cardinal, G.; Demuth, M. N.; Kinnard, C.

2016-12-01

Glaciers are an important source of fresh water in the headwaters of the Canadian Rocky Mountains, and ongoing climate warming could reduce their future hydrological contribution. Unmanned Aerial Vehicles UAVs) are an emergent technology that allow studying glacial processes with an unprecedented level of detail, but their usefulness for deriving accurate topographic data on glaciers has not yet been fully assessed. In this perspective we tested the use of a UAV platform to acquire images at a very high spatial resolution (<10cm) in order to estimate topographical and dynamic changes over a one year period on the ablation zone of Saskatchewan glacier, the main outlet of the Columbia Icefield in Alberta, Canada (52°06N, 117°15W). Two data acquisition campaigns were carried out, in August 2014 and 2015. Orthomosaics and digital elevation models (DEMs) with a high spatial resolution (<10cm) were produced for each year, using the Structure from Motion (SfM) algorithm. A detailed assessment of DEM errors was performed by cross-validation of an network of ground control points (GCPs) deployed on the glacier surface. The influence of checkpoint position in the network, border effects, number of photos calibrated and GPS accuracy were examined. Topographical changes were measured from the DEM difference and surface displacements estimated by applying feature tracking techniques to the orthomosaics. Further, the dominant scales of topographic spatial variability were examined using a semivariogram analysis of the DEMs. Results show that UAV-based photogrammetry is promising to further our understanding of high-resolution glacier surface processes and to perform repeat, on-demand monitoring of glacier changes, but their application on remote glaciers remains challenging.

Shoreline Erosion and Slope Failure Detection over Southwest Lakeshore Michigan using Temporal Radar and Digital Elevation Model

NASA Astrophysics Data System (ADS)

Sataer, G.; Sultan, M.; Yellich, J. A.; Becker, R.; Emil, M. K.; Palaseanu, M.

2017-12-01

Throughout the 20th century and into the 21st century, significant losses of residential, commercial and governmental property were reported along the shores of the Great Lakes region due to one or more of the following factors: high lake levels, wave actions, groundwater discharge. A collaborative effort (Western Michigan University, University of Toledo, Michigan Geological Survey [MGS], United States Geological Survey [USGS], National Oceanographic and Atmospheric Administration [NOAA]) is underway to examine the temporal topographic variations along the shoreline and the adjacent bluff extending from the City of South Haven in the south to the City of Saugatuck in the north within the Allegan County. Our objectives include two main tasks: (1) identification of the timing of, and the areas, witnessing slope failure and shoreline erosion, and (2) investigating the factors causing the observed failures and erosion. This is being accomplished over the study area by: (1) detecting and measuring slope subsidence rates (velocities along line of site) and failures using radar interferometric persistent scatter (PS) techniques applied to ESA's European Remote Sensing (ERS) satellites, ERS-1 and -2 (spatial resolution: 25 m) that were acquired in 1995 to 2007, (2) extracting temporal high resolution (20 cm) digital elevation models (DEM) for the study area from temporal imagery acquired by Unmanned Aerial Vehicles (UAVs), and applying change detection techniques to the extracted DEMs, (3) detecting change in elevation and slope profiles extracted from two LIDAR Coastal National Elevation Database (CoNED) DEMs (spatial resolution: 0.5m), acquired on 2008 and 2012, and (4) spatial and temporal correlation of the detected changes in elevation with relevant data sets (e.g., lake levels, precipitation, groundwater levels) in search of causal effects.

The Spatial Resolution in the Computer Modelling of Atmospheric Flow over a Double-Hill Forested Region

NASA Astrophysics Data System (ADS)

Palma, J. L.; Rodrigues, C. V.; Lopes, A. S.; Carneiro, A. M. C.; Coelho, R. P. C.; Gomes, V. C.

2017-12-01

With the ever increasing accuracy required from numerical weather forecasts, there is pressure to increase the resolution and fidelity employed in computational micro-scale flow models. However, numerical studies of complex terrain flows are fundamentally bound by the digital representation of the terrain and land cover. This work assess the impact of the surface description on micro-scale simulation results at a highly complex site in Perdigão, Portugal, characterized by a twin parallel ridge topography, densely forested areas and an operating wind turbine. Although Coriolis and stratification effects cannot be ignored, the study is done under neutrally stratified atmosphere and static inflow conditions. The understanding gained here will later carry over to WRF-coupled simulations, where those conditions do not apply and the flow physics is more accurately modelled. With access to very fine digital mappings (<1m horizontal resolution) of both topography and land cover (roughness and canopy cover, both obtained through aerial LIDAR scanning of the surface) the impact of each element of the surface description on simulation results can be individualized, in order to estimate the resolution required to satisfactorily resolve them. Starting from the bare topographic description, in its coursest form, these include: a) the surface roughness mapping, b) the operating wind turbine, c) the canopy cover, as either body forces or added surface roughness (akin to meso-scale modelling), d) high resolution topography and surface cover mapping. Each of these individually will have an impact near the surface, including the rotor swept area of modern wind turbines. Combined they will considerably change flow up to boundary layer heights. Sensitivity to these elements cannot be generalized and should be assessed case-by-case. This type of in-depth study, unfeasible using WRF-coupled simulations, should provide considerable insight when spatially allocating mesh resolution for accurate resolution of complex flows.

Using high-resolution digital aerial imagery to map land cover

USGS Publications Warehouse

Dieck, J.J.; Robinson, Larry

2014-01-01

The Upper Midwest Environmental Sciences Center (UMESC) has used aerial photography to map land cover/land use on federally owned and managed lands for over 20 years. Until recently, that process used 23- by 23-centimeter (9- by 9-inch) analog aerial photos to classify vegetation along the Upper Mississippi River System, on National Wildlife Refuges, and in National Parks. With digital aerial cameras becoming more common and offering distinct advantages over analog film, UMESC transitioned to an entirely digital mapping process in 2009. Though not without challenges, this method has proven to be much more accurate and efficient when compared to the analog process.

Unmanned aerial vehicle: A unique platform for low-altitude remote sensing for crop management

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAV) provide a unique platform for remote sensing to monitor crop fields that complements remote sensing from satellite, aircraft and ground-based platforms. The UAV-based remote sensing is versatile at ultra-low altitude to be able to provide an ultra-high-resolution imag...

Map Scale, Proportion, and Google[TM] Earth

ERIC Educational Resources Information Center

Roberge, Martin C.; Cooper, Linda L.

2010-01-01

Aerial imagery has a great capacity to engage and maintain student interest while providing a contextual setting to strengthen their ability to reason proportionally. Free, on-demand, high-resolution, large-scale aerial photography provides both a bird's eye view of the world and a new perspective on one's own community. This article presents an…

Evaluation of eelgrass beds mapping using a high-resolution airborne multispectral scanner

USGS Publications Warehouse

Su, H.; Karna, D.; Fraim, E.; Fitzgerald, M.; Dominguez, R.; Myers, J.S.; Coffland, B.; Handley, L.R.; Mace, T.

2006-01-01

Eelgrass (Zostera marina) can provide vital ecological functions in stabilizing sediments, influencing current dynamics, and contributing significant amounts of biomass to numerous food webs in coastal ecosystems. Mapping eelgrass beds is important for coastal water and nearshore estuarine monitoring, management, and planning. This study demonstrated the possible use of high spatial (approximately 5 m) and temporal (maximum low tide) resolution airborne multispectral scanner on mapping eelgrass beds in Northern Puget Sound, Washington. A combination of supervised and unsupervised classification approaches were performed on the multispectral scanner imagery. A normalized difference vegetation index (NDVI) derived from the red and near-infrared bands and ancillary spatial information, were used to extract and mask eelgrass beds and other submerged aquatic vegetation (SAV) in the study area. We evaluated the resulting thematic map (geocoded, classified image) against a conventional aerial photograph interpretation using 260 point locations randomly stratified over five defined classes from the thematic map. We achieved an overall accuracy of 92 percent with 0.92 Kappa Coefficient in the study area. This study demonstrates that the airborne multispectral scanner can be useful for mapping eelgrass beds in a local or regional scale, especially in regions for which optical remote sensing from space is constrained by climatic and tidal conditions. ?? 2006 American Society for Photogrammetry and Remote Sensing.

EnviroAtlas - New York, NY - One Meter Resolution Urban Land Cover Data (2008)

EPA Pesticide Factsheets

The New York, NY EnviroAtlas Meter-scale Urban Land Cover (MULC) Data were generated by the University of Vermont Spatial Analysis Laboratory (SAL) under the direction of Jarlath O'Neil-Dunne as part of the United States Forest Service Urban Tree Canopy (UTC) assessment program. Seven classes were mapped using LiDAR and high resolution orthophotography: Tree Canopy, Grass/Shrub, Bare Soil, Water, Buildings, Roads/Railroads, and Other Paved Surfaces. These data were subsequently merged to fit with the EPA classification. The SAL project covered the five boroughs within the NYC city limits. However the EPA study area encompassed that area plus a 1 kilometer buffer. Additional land cover for the buffer area was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from July, 2011 and LiDAR from 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for New York City plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAt

River morphodynamics from space: the Landsat frontier

NASA Astrophysics Data System (ADS)

Schwenk, Jon; Khandelwal, Ankush; Fratkin, Mulu; Kumar, Vipin; Foufoula-Georgiou, Efi

2017-04-01

NASA's Landsat family of satellites have been observing the entire globe since 1984, providing over 30 years of snapshots with an 18 day frequency and 30 meter resolution. These publicly-available Landsat data are particularly exciting to researchers interested in river morphodynamics, who are often limited to use of historical maps, aerial photography, and field surveys with poor and irregular time resolutions and limited spatial extents. Landsat archives show potential for overcoming these limitations, but techniques and tools for accurately and efficiently mining the vault of scenes must first be developed. In this PICO presentation, we detail the problems we encountered while mapping and quantifying planform dynamics of over 1,300 km of the actively-migrating, meandering Ucayali River in Peru from Landsat imagery. We also present methods to overcome these obstacles and introduce the Matlab-based RivMAP (River Morphodynamics from Analysis of Planforms) toolbox that we developed to extract banklines and centerlines, compute widths, curvatures, and angles, identify cutoffs, and quantify planform changes via centerline migration and erosion/accretion over large spatial domains with high temporal resolution. Measurement uncertainties were estimated by analyzing immobile, abandoned oxbow lakes. Our results identify hotspots of planform changes, and combined with limited precipitation, stage, and topography data, we parse three simultaneous controls on river migration: climate, sediment, and meander cutoff. Overall, this study demonstrates the vast potential locked within Landsat archives to identify multi-scale controls on river migration, observe the co-evolution of width, curvature, discharge, and migration, and discover and develop new geomorphic insights.

Cubesats and drones: bridging the spatio-temporal divide for enhanced earth observation

NASA Astrophysics Data System (ADS)

McCabe, M. F.; Aragon, B.; Parkes, S. D.; Mascaro, J.; Houborg, R.

2017-12-01

In just the last few years, a range of advances in remote sensing technologies have enabled an unprecedented opportunity in earth observation. Parallel developments in cubesats and unmanned aerial vehicles (UAVs) have overcome one of the outstanding challenges in observing the land surface: the provision of timely retrievals at a spatial resolution that is sufficiently detailed to make field-level decisions. Planet cubesats have revolutionized observing capacity through their objective of near daily global retrieval. These nano-satellite systems provide high resolution (approx. 3 m) retrievals in red-green-blue and near-infrared wavelengths, offering capacity to develop vegetation metrics for both hydrological and precision agricultural applications. Apart from satellite based advances, nearer to earth technology is being exploited for a range of observation needs. UAVs provide an adaptable platform from which a variety of sensing systems can be deployed. Combinations of optical, thermal, multi- and hyper-spectral systems allow for the estimation of a range of land surface variables, including vegetation structure, vegetation health, land surface temperature and evaporation. Here we explore some of these exciting developments in the context of agricultural hydrology, providing examples of cubesat and UAV imagery that has been used to inform upon crop health and water use. An investigation of the spatial and temporal advantage of these complementary systems is undertaken, with examples of multi-day high-resolution vegetation dynamics from cubesats presented alongside diurnal-cycle responses derived from multiple within-day UAV flights.

Measuring atmospheric aerosols of organic origin on multirotor Unmanned Aerial Vehicles (UAVs).

NASA Astrophysics Data System (ADS)

Crazzolara, Claudio; Platis, Andreas; Bange, Jens

2017-04-01

In-situ measurements of the spatial distribution and transportation of atmospheric organic particles such as pollen and spores are of great interdisciplinary interest such as: - In agriculture to investigate the spread of transgenetic material, - In paleoclimatology to improve the accuracy of paleoclimate models derived from pollen grains retrieved from sediments, and - In meteorology/climate research to determine the role of spores and pollen acting as nuclei in cloud formation processes. The few known state of the art in-situ measurement systems are using passive sampling units carried by fixed wing UAVs, thus providing only limited spatial resolution of aerosol concentration. Also the passively sampled air volume is determined with low accuracy as it is only calculated by the length of the flight path. We will present a new approach, which is based on the use of a multirotor UAV providing a versatile platform. On this UAV an optical particle counter in addition to a particle collecting unit, e.g. a conventional filter element and/or a inertial mass separator were installed. Both sampling units were driven by a mass flow controlled blower. This allows not only an accurate determination of the number and size concentration, but also an exact classification of the type of collected aerosol particles as well as an accurate determination of the sampled air volume. In addition, due to the application of a multirotor UAV with its automated position stabilisation system, the aerosol concentration can be measured with a very high spatial resolution of less than 1 m in all three dimensions. The combination of comprehensive determination of number, type and classification of aerosol particles in combination with the very high spatial resolution provides not only valuable progress in agriculture, paleoclimatology and meteorology, but also opens up the application of multirotor UAVs in new fields, for example for precise determination of the mechanisms of generation and distribution of fine particulate matter as the result of road traffic.

Application of UAV-SfM photogrammetry and aerial lidar to a disastrous flood: repeated topographic measurement of a newly formed crevasse splay of the Kinu River, central Japan

NASA Astrophysics Data System (ADS)

Izumida, Atsuto; Uchiyama, Shoichiro; Sugai, Toshihiko

2017-09-01

Geomorphic impacts of a disastrous crevasse splay that formed in September 2015 and its post-formation modifications were quantitatively documented by using repeated, high-definition digital surface models (DSMs) of an inhabited and cultivated floodplain of the Kinu River, central Japan. The DSMs were based on pre-flood (resolution: 2 m) and post-flood (resolution: 1 m) aerial light detection and ranging (lidar) data from January 2007 and September 2015, respectively, and on structure-from-motion (SfM) photogrammetry data (resolution: 3.84 cm) derived from aerial photos taken by an unmanned aerial vehicle (UAV) in December 2015. After elimination of systematic errors among the DSMs and down-sampling of the SfM-derived DSM, elevation changes on the order of 10-1 m - including not only topography but also growth of vegetation, vanishing of flood waters, and restoration and repair works - were detected. Comparison of the DSMs showed that the volume eroded by the flood was more than twice the deposited volume in the area within 300-500 m of the breached artificial levee, where the topography was significantly affected. The results suggest that DSMs based on a combination of UAV-SfM and lidar data can be used to quantify, rapidly and in rich detail, topographic changes on floodplains caused by floods.

Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Yan; Hill, Michael J.; Zhang, Xiaoyang

tThe Mediterranean-type oak/grass savanna of California is composed of widely spaced oak trees withunderstory grasses. These savanna regions are interspersed with large areas of more open grasslands.The ability of remotely sensed data (with various spatial resolutions) to monitor the phenology in thesewater-limited oak/grass savannas and open grasslands is explored over the 2012–2015 timeframe usingdata from Landsat (30 m), the MODerate resolution Imaging Spectroradiometer (MODIS – gridded 500 m),and the Visible Infrared Imaging Radiometer Suite (VIIRS – gridded 500 m) data. Vegetation phenologydetected from near-ground level, webcam based PhenoCam imagery from two sites in the Ameriflux Net-work (long-term flux measurement networkmore » of the Americas) (Tonzi Ranch and Vaira Ranch) is upscaled,using a National Agriculture Imagery Program (NAIP) aerial image (1 m), to evaluate the detection ofvegetation phenology of these savannas and grasslands with the satellite data. Results show that the Nor-malized Difference Vegetation Index (NDVI) time series observed from the satellite sensors are all stronglycorrelated with the PhenoCam NDVI values from Tonzi Ranch (R2> 0.67) and Vaira Ranch (R2> 0.81). How-ever, the different viewing geometries and spatial coverage of the PhenoCams and the various satellitesensors may cause differences in the absolute phenological transition dates. Analysis of frequency his-tograms of phenological dates illustrate that the phenological dates in the relatively homogeneous opengrasslands are consistent across the different spatial resolutions, in contrast, the relatively heterogeneousoak/grass savannas display has somewhat later greenup, maturity, and dormancy dates at 30 m resolu-tion than at 500 m scale due to the different phenological cycles exhibited by the overstory trees and theunderstory grasses. In addition, phenologies derived from the MODIS view angle corrected reflectance(Nadir BRDF-Adjusted Reflectance – NBAR) and the newly developed VIIRS NBAR are shown to providecomparable phenological dates (majority absolute bias ≤2 days) in this area.« less

Using data from Landsat, MODIS, VIIRS and PhenoCams to monitor the phenology of California oak/grass savanna and open grassland across spatial scales

DOE PAGES

Liu, Yan; Hill, Michael J.; Zhang, Xiaoyang; ...

2017-03-03

tThe Mediterranean-type oak/grass savanna of California is composed of widely spaced oak trees withunderstory grasses. These savanna regions are interspersed with large areas of more open grasslands.The ability of remotely sensed data (with various spatial resolutions) to monitor the phenology in thesewater-limited oak/grass savannas and open grasslands is explored over the 2012–2015 timeframe usingdata from Landsat (30 m), the MODerate resolution Imaging Spectroradiometer (MODIS – gridded 500 m),and the Visible Infrared Imaging Radiometer Suite (VIIRS – gridded 500 m) data. Vegetation phenologydetected from near-ground level, webcam based PhenoCam imagery from two sites in the Ameriflux Net-work (long-term flux measurement networkmore » of the Americas) (Tonzi Ranch and Vaira Ranch) is upscaled,using a National Agriculture Imagery Program (NAIP) aerial image (1 m), to evaluate the detection ofvegetation phenology of these savannas and grasslands with the satellite data. Results show that the Nor-malized Difference Vegetation Index (NDVI) time series observed from the satellite sensors are all stronglycorrelated with the PhenoCam NDVI values from Tonzi Ranch (R2> 0.67) and Vaira Ranch (R2> 0.81). How-ever, the different viewing geometries and spatial coverage of the PhenoCams and the various satellitesensors may cause differences in the absolute phenological transition dates. Analysis of frequency his-tograms of phenological dates illustrate that the phenological dates in the relatively homogeneous opengrasslands are consistent across the different spatial resolutions, in contrast, the relatively heterogeneousoak/grass savannas display has somewhat later greenup, maturity, and dormancy dates at 30 m resolu-tion than at 500 m scale due to the different phenological cycles exhibited by the overstory trees and theunderstory grasses. In addition, phenologies derived from the MODIS view angle corrected reflectance(Nadir BRDF-Adjusted Reflectance – NBAR) and the newly developed VIIRS NBAR are shown to providecomparable phenological dates (majority absolute bias ≤2 days) in this area.« less

Spectral characteristics and the extent of paleosols of the Palouse formation

NASA Technical Reports Server (NTRS)

Frazier, B. E.; Busacca, Alan; Cheng, Yaan; Wherry, David; Hart, Judy; Gill, Steve

1988-01-01

The objective of this study is to test the hypothesis that TM data is adequate in band selection and width and in spatial resolution to distinguish soil organic matter, iron oxide, and lime-silica contents to map several severity classes of erosion in soils of the Palouse region. The methodology used is as follows: (1) To develop spectral relationships from TM data that define the spatial distribution of soil areas by levels of (1) organic matter in the surface soil, (2) iron oxide and clay in exposed paleosol B horizons, and (3) lime-silica accumulations in exposed paleosol B horizons; (2) To compare areas determined by the method outlined in 1 to patterns interpreted from color aerial photos, and to ground observations on bare-soil fields; and (3) To define, on the basis of results of 1 and 2 to the extent possible, where exposed paleosols exist within fields that are not bare, but have a crop cover, and the distribution of desirable and undesirable soil properties in each field.

Long-Term Monitoring of Desert Land and Natural Resources and Application of Remote Sensing Technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; Rollins, Katherine E.

2016-11-01

Monitoring environmental impacts over large, remote desert regions for long periods of time can be very costly. Remote sensing technologies present a promising monitoring tool because they entail the collection of spatially contiguous data, automated processing, and streamlined data analysis. This report provides a summary of remote sensing products and refinement of remote sensing data interpretation methodologies that were generated as part of the U.S. Department of the Interior Bureau of Land Management Solar Energy Program. In March 2015, a team of researchers from Argonne National Laboratory (Argonne) collected field data of vegetation and surface types from more than 5,000more » survey points within the eastern part of the Riverside East Solar Energy Zone (SEZ). Using the field data, remote sensing products that were generated in 2014 using very high spatial resolution (VHSR; 15 cm) multispectral aerial images were validated in order to evaluate potential refinements to the previous methodologies to improve the information extraction accuracy.« less

A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery.

PubMed

Huang, Huasheng; Deng, Jizhong; Lan, Yubin; Yang, Aqing; Deng, Xiaoling; Zhang, Lei

2018-01-01

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

Passive millimeter-wave imaging polarimeter system

NASA Astrophysics Data System (ADS)

Persons, Christopher M.; Martin, Christopher A.; Jones, Michael W.; Kolinko, Vladimir; Lovberg, John A.

2009-05-01

The Army has identified a need to rapidly identify, map, and classify natural and manmade features to aid situational awareness as well as mission and tactical planning. To address these needs, Digital Fusion and Trex Enterprises have designed a full Stokes, passive MMW imaging polarimeter that is capable of being deployed on an unmanned aerial vehicle. Results of a detailed trade study are presented, where an architecture, waveband and target platform are selected. The selected architecture is a pushbroom phased-array system, which allows the system to collect a wide fieldof- view image with minimal components and weight. W band is chosen as a trade-off between spatial resolution, weather penetration, and component availability. The trade study considers several unmanned aerial system (UAS) platforms that are capable of low-level flight and that can support the MMW antenna. The utility of the passive Stokes imager is demonstrated through W band phenomenology data collections at horizontal and vertical polarization using a variety of natural and manmade materials. The concept design is detailed, along with hardware and procedures for both radiometric and polarimetric calibration. Finally, a scaled version of the concept design is presented, which is being fabricated for an upcoming demonstration on a small, manned aircraft.

Analysis of near-surface relative humidity in a wind turbine array boundary layer using an instrumented unmanned aerial system and large-eddy simulation

NASA Astrophysics Data System (ADS)

Adkins, Kevin; Elfajri, Oumnia; Sescu, Adrian

2016-11-01

Simulation and modeling have shown that wind farms have an impact on the near-surface atmospheric boundary layer (ABL) as turbulent wakes generated by the turbines enhance vertical mixing. These changes alter downstream atmospheric properties. With a large portion of wind farms hosted within an agricultural context, changes to the environment can potentially have secondary impacts such as to the productivity of crops. With the exception of a few observational data sets that focus on the impact to near-surface temperature, little to no observational evidence exists. These few studies also lack high spatial resolution due to their use of a limited number of meteorological towers or remote sensing techniques. This study utilizes an instrumented small unmanned aerial system (sUAS) to gather in-situ field measurements from two Midwest wind farms, focusing on the impact that large utility-scale wind turbines have on relative humidity. Results are also compared to numerical experiments conducted using large eddy simulation (LES). Wind turbines are found to differentially alter the relative humidity in the downstream, spanwise and vertical directions under a variety of atmospheric stability conditions.

Field validation of 1930s aerial photography: What are we missing?

USDA-ARS?s Scientific Manuscript database

Aerial photography from the 1930s serves as the earliest synoptic depiction of vegetation cover. We generated a spatially explicit database of shrub (Prosopis velutina) stand structure within two 1.8 ha field plots established in 1932 to address two questions: (1) What are the detection limits of p...

Comprehensive geo-spatial data creation for Najran region in the KSA

NASA Astrophysics Data System (ADS)

Alrajhi, M.; Hawarey, M.

2009-04-01

The General Directorate for Surveying and Mapping (GDSM) of the Deputy Ministry for Land and Surveying (DMLS) of the Ministry of Municipal and Rural Affairs (MOMRA) in the Kingdom of Saudi Arabia (KSA) has the exclusive mandate to carry out aerial photography and produce large-scale detailed maps for about 220 cities and villages in the KSA. This presentation is about the comprehensive geo-spatial data creation for the Najran region, South KSA, that was founded on country-wide horizontal geodetic ground control using Global Navigation Satellite Systems (GNSS) within the MOMRA's Terrestrial Reference Frame 2000 (MTRF2000) that is tied to International Terrestrial Reference Frame 2000 (ITRF2000) Epoch 2004.0, and vertical geodetic ground control using precise digital leveling in reference to Jeddah 1969 mean sea level, and included aerial photography of area 917 km2 at 1:5,500 scale and 14,304 km2 at 1:45,000 scale, full aerial triangulation, and production of orthophoto maps at scale of 1:10,000 (298 sheets) for 14,304 km2, with aerial photography lasting from May 2006 until July 2006.

Comprehensive geo-spatial data creation for Ar-Riyadh region in the KSA

NASA Astrophysics Data System (ADS)

Alrajhi, M.; Hawarey, M.

2009-04-01

The General Directorate for Surveying and Mapping (GDSM) of the Deputy Ministry for Land and Surveying (DMLS) of the Ministry of Municipal and Rural Affairs (MOMRA) in the Kingdom of Saudi Arabia (KSA) has the exclusive mandate to carry out aerial photography and produce large-scale detailed maps for about 220 cities and villages in the KSA. This presentation is about the comprehensive geo-spatial data creation for the Ar-Riyadh region, Central KSA, that was founded on country-wide horizontal geodetic ground control using Global Navigation Satellite Systems (GNSS) within the MOMRA's Terrestrial Reference Frame 2000 (MTRF2000) that is tied to International Terrestrial Reference Frame 2000 (ITRF2000) Epoch 2004.0, and vertical geodetic ground control using precise digital leveling in reference to Jeddah 1969 mean sea level, and included aerial photography of area 3,000 km2 at 1:5,500 scale and 10,000 km2 at 1:45,000 scale, full aerial triangulation, and production of orthophoto maps at scale of 1:10,000 (480 sheets) for 10,000 km2, with aerial photography lasting from July 2007 thru August 2007.

Comprehensive geo-spatial data creation for Asir region in the KSA

NASA Astrophysics Data System (ADS)

Alrajhi, M.; Hawarey, M.

2009-04-01

The General Directorate for Surveying and Mapping (GDSM) of the Deputy Ministry for Land and Surveying (DMLS) of the Ministry of Municipal and Rural Affairs (MOMRA) in the Kingdom of Saudi Arabia (KSA) has the exclusive mandate to carry out aerial photography and produce large-scale detailed maps for about 220 cities and villages in the KSA. This presentation is about the comprehensive geo-spatial data creation for the Asir region, South West KSA, that was founded on country-wide horizontal geodetic ground control using Global Navigation Satellite Systems (GNSS) within the MOMRA's Terrestrial Reference Frame 2000 (MTRF2000) that is tied to International Terrestrial Reference Frame 2000 (ITRF2000) Epoch 2004.0, and vertical geodetic ground control using precise digital leveling in reference to Jeddah 1969 mean sea level, and included aerial photography of area 2,188 km2 at 1:5,500 scale and 32,640 km2 at 1:45,000 scale, full aerial triangulation, and production of orthophoto maps at scale of 1:10,000 (680 sheets) for 32,640 km2, with aerial photography lasting from July 2007 thru October 2007.

DUV or EUV: that is the question

NASA Astrophysics Data System (ADS)

Williamson, David M.

2000-11-01

Lord Rayleigh's well-known equations for resolution and depth of focus indicate that resolution is better improved by reducing the wavelength of light rather than by increasing the numerical aperture (NA) of the projection optics, particularly when NA is approaching its physical limit of 1.0 in air (or vacuum). Vector aerial image simulations of diffraction-limited Deep Ultraviolet (DUV) and Extreme Ultraviolet (EUV) lithographic systems verify this simple view, even though Rayleigh's constants in Microlithography are not constant because of a variety of image enhancement techniques that attempt to compensate for the shortcomings of the aerial image when it is pushed to the limit. The aerial image is not the whole story, however. The competition between DUV and EUV systems will be decided more by economic and technological factors such as risk, time and cost of development and cost of ownership. These in turn depend on cost, availability and quality of light sources, refracting materials, photoresists and reticles.

Comparing two ground-cover measurement methodologies for semiarid rangelands

USDA-ARS?s Scientific Manuscript database

The limited field-of-view (FOV) associated with single-resolution very-large scale aerial (VLSA) imagery requires users to balance FOV and resolution needs. This balance varies by the specific questions being asked of the data. Here, we tested a FOV-resolution question by comparing ground-cover meas...

Using drone-mounted cameras for on-site body documentation: 3D mapping and active survey.

PubMed

Urbanová, Petra; Jurda, Mikoláš; Vojtíšek, Tomáš; Krajsa, Jan

2017-12-01

Recent advances in unmanned aerial technology have substantially lowered the cost associated with aerial imagery. As a result, forensic practitioners are today presented with easy low-cost access to aerial photographs at remote locations. The present paper aims to explore boundaries in which the low-end drone technology can operate as professional crime scene equipment, and to test the prospects of aerial 3D modeling in the forensic context. The study was based on recent forensic cases of falls from height admitted for postmortem examinations. Three mock outdoor forensic scenes featuring a dummy, skeletal remains and artificial blood were constructed at an abandoned quarry and subsequently documented using a commercial DJI Phantom 2 drone equipped with a GoPro HERO 4 digital camera. In two of the experiments, the purpose was to conduct aerial and ground-view photography and to process the acquired images with a photogrammetry protocol (using Agisoft PhotoScan ® 1.2.6) in order to generate 3D textured models. The third experiment tested the employment of drone-based video recordings in mapping scattered body parts. The results show that drone-based aerial photography is capable of producing high-quality images, which are appropriate for building accurate large-scale 3D models of a forensic scene. If, however, high-resolution top-down three-dimensional scene documentation featuring details on a corpse or other physical evidence is required, we recommend building a multi-resolution model by processing aerial and ground-view imagery separately. The video survey showed that using an overview recording for seeking out scattered body parts was efficient. In contrast, the less easy-to-spot evidence, such as bloodstains, was detected only after having been marked properly with crime scene equipment. Copyright © 2017 Elsevier B.V. All rights reserved.

Multi-scale analysis of a household level agent-based model of landcover change.

PubMed

Evans, Tom P; Kelley, Hugh

2004-08-01

Scale issues have significant implications for the analysis of social and biophysical processes in complex systems. These same scale implications are likewise considerations for the design and application of models of landcover change. Scale issues have wide-ranging effects from the representativeness of data used to validate models to aggregation errors introduced in the model structure. This paper presents an analysis of how scale issues affect an agent-based model (ABM) of landcover change developed for a research area in the Midwest, USA. The research presented here explores how scale factors affect the design and application of agent-based landcover change models. The ABM is composed of a series of heterogeneous agents who make landuse decisions on a portfolio of cells in a raster-based programming environment. The model is calibrated using measures of fit derived from both spatial composition and spatial pattern metrics from multi-temporal landcover data interpreted from historical aerial photography. A model calibration process is used to find a best-fit set of parameter weights assigned to agents' preferences for different landuses (agriculture, pasture, timber production, and non-harvested forest). Previous research using this model has shown how a heterogeneous set of agents with differing preferences for a portfolio of landuses produces the best fit to landcover changes observed in the study area. The scale dependence of the model is explored by varying the resolution of the input data used to calibrate the model (observed landcover), ancillary datasets that affect land suitability (topography), and the resolution of the model landscape on which agents make decisions. To explore the impact of these scale relationships the model is run with input datasets constructed at the following spatial resolutions: 60, 90, 120, 150, 240, 300 and 480 m. The results show that the distribution of landuse-preference weights differs as a function of scale. In addition, with the gradient descent model fitting method used in this analysis the model was not able to converge to an acceptable fit at the 300 and 480 m spatial resolutions. This is a product of the ratio of the input cell resolution to the average parcel size in the landscape. This paper uses these findings to identify scale considerations in the design, development, validation and application of ABMs of landcover change.

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data

NASA Astrophysics Data System (ADS)

Gajda, Agnieszka; Wójtowicz-Nowakowska, Anna

2013-04-01

A comparison of the accuracy of pixel based and object based classifications of integrated optical and LiDAR data Land cover maps are generally produced on the basis of high resolution imagery. Recently, LiDAR (Light Detection and Ranging) data have been brought into use in diverse applications including land cover mapping. In this study we attempted to assess the accuracy of land cover classification using both high resolution aerial imagery and LiDAR data (airborne laser scanning, ALS), testing two classification approaches: a pixel-based classification and object-oriented image analysis (OBIA). The study was conducted on three test areas (3 km2 each) in the administrative area of Kraków, Poland, along the course of the Vistula River. They represent three different dominating land cover types of the Vistula River valley. Test site 1 had a semi-natural vegetation, with riparian forests and shrubs, test site 2 represented a densely built-up area, and test site 3 was an industrial site. Point clouds from ALS and ortophotomaps were both captured in November 2007. Point cloud density was on average 16 pt/m2 and it contained additional information about intensity and encoded RGB values. Ortophotomaps had a spatial resolution of 10 cm. From point clouds two raster maps were generated: intensity (1) and (2) normalised Digital Surface Model (nDSM), both with the spatial resolution of 50 cm. To classify the aerial data, a supervised classification approach was selected. Pixel based classification was carried out in ERDAS Imagine software. Ortophotomaps and intensity and nDSM rasters were used in classification. 15 homogenous training areas representing each cover class were chosen. Classified pixels were clumped to avoid salt and pepper effect. Object oriented image object classification was carried out in eCognition software, which implements both the optical and ALS data. Elevation layers (intensity, firs/last reflection, etc.) were used at segmentation stage due to proper wages usage. Thus a more precise and unambiguous boundaries of segments (objects) were received. As a results of the classification 5 classes of land cover (buildings, water, high and low vegetation and others) were extracted. Both pixel-based image analysis and OBIA were conducted with a minimum mapping unit of 10m2. Results were validated on the basis on manual classification and random points (80 per test area), reference data set was manually interpreted using ortophotomaps and expert knowledge of the test site areas.

High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Leitao, J. P.; Rieckermann, J.; Schindler, K.; Blumensaat, F.

2015-10-01

Modelling rainfall-runoff in urban areas is increasingly applied to support flood risk assessment, particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the catchment area as model input. While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increases as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data are often unavailable. Modern unmanned aerial vehicles (UAVs) allow one to acquire high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements and obtaining a degree of detail tailored for the purpose of the study. In this study, we investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and of using this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is proposed and evaluated in a state-of-the-art urban drainage modelling exercise. In a real-life case study (Lucerne, Switzerland), we compare imperviousness maps generated using a fixed-wing consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their overall accuracy, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyse the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and runoff volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet. We show that imperviousness maps generated from UAV images processed with modern classification methods achieve an accuracy comparable to standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on predicted surface runoff and pipe flows, when traditional workflows are used. We expect that they will have a substantial influence when more detailed modelling approaches are employed to characterize land use and to predict surface runoff. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility of flexibly acquiring up-to-date aerial images at a quality compared with off-the-shelf image products and a competitive price at the same time. We believe that in the future, urban drainage models representing a higher degree of spatial detail will fully benefit from the strengths of UAV imagery.

3D display considerations for rugged airborne environments

NASA Astrophysics Data System (ADS)

Barnidge, Tracy J.; Tchon, Joseph L.

2015-05-01

The KC-46 is the next generation, multi-role, aerial refueling tanker aircraft being developed by Boeing for the United States Air Force. Rockwell Collins has developed the Remote Vision System (RVS) that supports aerial refueling operations under a variety of conditions. The system utilizes large-area, high-resolution 3D displays linked with remote sensors to enhance the operator's visual acuity for precise aerial refueling control. This paper reviews the design considerations, trade-offs, and other factors related to the selection and ruggedization of the 3D display technology for this military application.

Applications of remote sensing to estuarine management

NASA Technical Reports Server (NTRS)

Munday, J. C., Jr.; Gordon, H. H.; Hennigar, H. F.

1977-01-01

Remote sensing was used in the resolution of estuarine problems facing federal and Virginia governmental agencies. A prototype Elizabeth River Surface Circulation Atlas was produced from photogrammetry to aid in oil spill cleanup and source identification. Aerial photo analysis twice led to selection of alternative plans for dredging and spoil disposal which minimized marsh damage. Marsh loss due to a mud wave from a highway dyke was measured on sequential aerial photographs. An historical aerial photographic sequence gave basis to a potential Commonwealth of Virginia legal claim to accreting and migrating coastal islands.

Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring

PubMed Central

Müllerová, Jana; Brůna, Josef; Bartaloš, Tomáš; Dvořák, Petr; Vítková, Michaela; Pyšek, Petr

2017-01-01

The rapid spread of invasive plants makes their management increasingly difficult. Remote sensing offers a means of fast and efficient monitoring, but still the optimal methodologies remain to be defined. The seasonal dynamics and spectral characteristics of the target invasive species are important factors, since, at certain time of the vegetation season (e.g., at flowering or senescing), plants are often more distinct (or more visible beneath the canopy). Our aim was to establish fast, repeatable and a cost-efficient, computer-assisted method applicable over larger areas, to reduce the costs of extensive field campaigns. To achieve this goal, we examined how the timing of monitoring affects the detection of noxious plant invaders in Central Europe, using two model herbaceous species with markedly different phenological, structural, and spectral characteristics. They are giant hogweed (Heracleum mantegazzianum), a species with very distinct flowering phase, and the less distinct knotweeds (Fallopia japonica, F. sachalinensis, and their hybrid F. × bohemica). The variety of data generated, such as imagery from purposely-designed, unmanned aircraft vehicle (UAV), and VHR satellite, and aerial color orthophotos enabled us to assess the effects of spectral, spatial, and temporal resolution (i.e., the target species' phenological state) for successful recognition. The demands for both spatial and spectral resolution depended largely on the target plant species. In the case that a species was sampled at the most distinct phenological phase, high accuracy was achieved even with lower spectral resolution of our low-cost UAV. This demonstrates that proper timing can to some extent compensate for the lower spectral resolution. The results of our study could serve as a basis for identifying priorities for management, targeted at localities with the greatest risk of invasive species' spread and, once eradicated, to monitor over time any return. The best mapping strategy should reflect morphological and structural features of the target plant and choose appropriate spatial, spectral, and temporal resolution. The UAV enables flexible data acquisition for required time periods at low cost and is, therefore, well-suited for targeted monitoring; while satellite imagery provides the best solution for larger areas. Nonetheless, users must be aware of their limits. PMID:28620399

L-band Soil Moisture Mapping using Small UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.; Gasiewski, A. J.; Stachura, M.; Elston, J.; Venkitasubramony, A.

2016-12-01

1. IntroductionSoil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 promises to provide global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of soil moisture on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform). Compared with various other proposed methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling scale studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site on September 8th and 9th, 2015 and Yuma Colorado Irrigation Research Foundation (IRF) site from June to August, 2016. These tests were flown at 25-50 m altitude to obtain differing spatial resolutions. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. 2. References[1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C-Band Microwave Soil Moisture Retrieval During CLASIC 2007," Proc. IGARSS, 2008. [2] Robock, A., S. Steele-Dunne, J. Basara, W. Crow, and M. Moghaddam M, "In Situ Network and Scaling," SMAP Algorithm and Cal/Val Workshop, 2009. [3] Walker, A., "Airborne Microwave Radiometer Measurements During CanEx-SM10," Second SMAP Cal/Val Workshop, 2011.

Stocking rate effects on spatial heterogeneity in vegetation cover in a grazing-resistant grassland

USDA-ARS?s Scientific Manuscript database

Spatial patterns in rangeland vegetation serve as indicators of rangeland condition and are an important component of wildlife habitat. We illustrate the use of very-large-scale aerial photography (VLSA) to quantify spatial patterns in bare soil of the northeastern Colorado shortgrass steppe. Using ...

Grazing intensity and spatial heterogeneity in bare soil in a grazing-resistant grassland

USDA-ARS?s Scientific Manuscript database

Spatial patterns in rangeland vegetation serve as indicators of rangeland condition and are an important component of wildlife habitat. We illustrate the use of very-large-scale aerial photography (VLSA) to quantify spatial patterns in bare soil of the northeastern Colorado shortgrass steppe. Using ...

Multi-scale geophysical study to model the distribution and development of fractures in relation to the knickpoint in the Luquillo Critical Zone Observatory (Puerto Rico)

NASA Astrophysics Data System (ADS)

Comas, X.; Wright, W. J.; Hynek, S. A.; Ntarlagiannis, D.; Terry, N.; Job, M. J.; Fletcher, R. C.; Brantley, S.

2017-12-01

Previous studies in the Rio Icacos watershed in the Luquillo Mountains (Puerto Rico) have shown that regolith materials are rapidly developed from the alteration of quartz diorite bedrock, and create a blanket on top of the bedrock with a thickness that decreases with proximity to the knickpoint. The watershed is also characterized by a system of heterogeneous fractures that likely drive bedrock weathering and the formation of corestones and associated spheroidal fracturing and rindlets. Previous efforts to characterize the spatial distribution of fractures were based on aerial images that did not account for the architecture of the critical zone below the subsurface. In this study we use an array of near-surface geophysical methods at multiple scales to better understand how the spatial distribution and density of fractures varies with topography and proximity to the knickpoint. Large km-scale surveys using ground penetrating radar (GPR), terrain conductivity, and capacitively coupled resistivity, were combined with smaller scale surveys (10-100 m) using electrical resistivity imaging (ERI), and shallow seismics, and were directly constrained with boreholes from previous studies. Geophysical results were compared to theoretical models of compressive stress as due to gravity and regional compression, and showed consistency at describing increased dilation of fractures with proximity to the knickpoint. This study shows the potential of multidisciplinary approaches to model critical zone processes at multiple scales of measurement and high spatial resolution. The approach can be particularly efficient at large km-scales when applying geophysical methods that allow for rapid data acquisition (i.e. walking pace) at high spatial resolution (i.e. cm scales).

Laser Doppler velocimeter aerial spray measurements

NASA Technical Reports Server (NTRS)

Zalay, A. D.; Eberle, W. R.; Howle, R. E.; Shrider, K. R.

1978-01-01

An experimental research program for measuring the location, spatial extent, and relative concentration of airborne spray clouds generated by agricultural aircraft is described. The measurements were conducted with a ground-based laser Doppler velocimeter. The remote sensing instrumentation, experimental tests, and the results of the flight tests are discussed. The cross section of the aerial spray cloud and the observed location, extent, and relative concentration of the airborne particulates are presented. It is feasible to use a mobile laser Doppler velocimeter to track and monitor the transport and dispersion of aerial spray generated by an agricultural aircraft.

Determining urban land uses through building-associated element attributes derived from lidar and aerial photographs

NASA Astrophysics Data System (ADS)

Meng, Xuelian

Urban land-use research is a key component in analyzing the interactions between human activities and environmental change. Researchers have conducted many experiments to classify urban or built-up land, forest, water, agriculture, and other land-use and land-cover types. Separating residential land uses from other land uses within urban areas, however, has proven to be surprisingly troublesome. Although high-resolution images have recently become more available for land-use classification, an increase in spatial resolution does not guarantee improved classification accuracy by traditional classifiers due to the increase of class complexity. This research presents an approach to detect and separate residential land uses on a building scale directly from remotely sensed imagery to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then utilizes a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on the seven selected building land-use indicators. Successful execution of this study produces three independent methods, each corresponding to the steps of the methodology: lidar ground filtering, building detection, and building-based object-oriented land-use classification. Furthermore, this research provides a prototype as one of the few early explorations of building-based land-use analysis and successful separation of more than 85% of residential buildings based on an experiment on an 8.25-km2 study site located in Austin, Texas.

Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle.

PubMed

Diaz-Varela, R A; Zarco-Tejada, P J; Angileri, V; Loudjani, P

2014-02-15

Agricultural terraces are features that provide a number of ecosystem services. As a result, their maintenance is supported by measures established by the European Common Agricultural Policy (CAP). In the framework of CAP implementation and monitoring, there is a current and future need for the development of robust, repeatable and cost-effective methodologies for the automatic identification and monitoring of these features at farm scale. This is a complex task, particularly when terraces are associated to complex vegetation cover patterns, as happens with permanent crops (e.g. olive trees). In this study we present a novel methodology for automatic and cost-efficient identification of terraces using only imagery from commercial off-the-shelf (COTS) cameras on board unmanned aerial vehicles (UAVs). Using state-of-the-art computer vision techniques, we generated orthoimagery and digital surface models (DSMs) at 11 cm spatial resolution with low user intervention. In a second stage, these data were used to identify terraces using a multi-scale object-oriented classification method. Results show the potential of this method even in highly complex agricultural areas, both regarding DSM reconstruction and image classification. The UAV-derived DSM had a root mean square error (RMSE) lower than 0.5 m when the height of the terraces was assessed against field GPS data. The subsequent automated terrace classification yielded an overall accuracy of 90% based exclusively on spectral and elevation data derived from the UAV imagery. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Review of Wetland Remote Sensing.

PubMed

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-04-05

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers.

A Review of Wetland Remote Sensing

PubMed Central

Guo, Meng; Li, Jing; Sheng, Chunlei; Xu, Jiawei; Wu, Li

2017-01-01

Wetlands are some of the most important ecosystems on Earth. They play a key role in alleviating floods and filtering polluted water and also provide habitats for many plants and animals. Wetlands also interact with climate change. Over the past 50 years, wetlands have been polluted and declined dramatically as land cover has changed in some regions. Remote sensing has been the most useful tool to acquire spatial and temporal information about wetlands. In this paper, seven types of sensors were reviewed: aerial photos coarse-resolution, medium-resolution, high-resolution, hyperspectral imagery, radar, and Light Detection and Ranging (LiDAR) data. This study also discusses the advantage of each sensor for wetland research. Wetland research themes reviewed in this paper include wetland classification, habitat or biodiversity, biomass estimation, plant leaf chemistry, water quality, mangrove forest, and sea level rise. This study also gives an overview of the methods used in wetland research such as supervised and unsupervised classification and decision tree and object-based classification. Finally, this paper provides some advice on future wetland remote sensing. To our knowledge, this paper is the most comprehensive and detailed review of wetland remote sensing and it will be a good reference for wetland researchers. PMID:28379174

Study to develop improved spacecraft snow survey methods using Skylab/EREP data. [Sierra Nevada Mts., Wasatch Range, central Arizona, and Mississippi and Missouri River basins

NASA Technical Reports Server (NTRS)

Barnes, J. C. (Principal Investigator); Smallwood, M. D.; Cogan, J. L.

1975-01-01

The author has identified the following significant results. Of the four black and white S190A camera stations, snowcover is best defined in the two visible spectral bands, due in part to their better resolution. The overall extent of the snow can be mapped more precisely, and the snow within shadow areas is better defined in the visible bands. Of the two S190A color products, the aerial color photography is the better. Because of the contrast in color between snow and snow-free terrain and the better resolution, this product is concluded to be the best overall of the six camera stations for detecting and mapping snow. Overlapping frames permit stereo viewing, which aids in distinguishing clouds from the underlying snow. Because of the greater spatial resolution of the S190B earth terrain camera, areal snow extent can be mapped in greater detail than from the S190A photographs. The snow line elevation measured from the S190A and S190B photographs is reasonable compared to the meager ground truth data available.

METHODS FOR MULTI-SPATIAL SCALE CHARACTERIZATION OF RIPARIAN CORRIDORS

EPA Science Inventory

This paper describes the application of aerial photography and GIS technology to develop flexible and transferable methods for multi-spatial scale characterization and analysis of riparian corridors. Relationships between structural attributes of riparian corridors and indicator...

Land Cover Mapping using GEOBIA to Estimate Loss of Salacca zalacca Trees in Landslide Area of Clapar, Madukara District of Banjarnegara

NASA Astrophysics Data System (ADS)

Permata, Anggi; Juniansah, Anwar; Nurcahyati, Eka; Dimas Afrizal, Mousafi; Adnan Shafry Untoro, Muhammad; Arifatha, Na'ima; Ramadhani Yudha Adiwijaya, Raden; Farda, Nur Mohammad

2016-11-01

Landslide is an unpredictable natural disaster which commonly happens in highslope area. Aerial photography in small format is one of acquisition method that can reach and obtain high resolution spatial data faster than other methods, and provide data such as orthomosaic and Digital Surface Model (DSM). The study area contained landslide area in Clapar, Madukara District of Banjarnegara. Aerial photographs of landslide area provided advantage in objects visibility. Object's characters such as shape, size, and texture were clearly seen, therefore GEOBIA (Geography Object Based Image Analysis) was compatible as method for classifying land cover in study area. Dissimilar with PPA (PerPixel Analyst) method that used spectral information as base object detection, GEOBIA could use spatial elements as classification basis to establish a land cover map with better accuracy. GEOBIA method used classification hierarchy to divide post disaster land cover into three main objects: vegetation, landslide/soil, and building. Those three were required to obtain more detailed information that can be used in estimating loss caused by landslide and establishing land cover map in landslide area. Estimating loss in landslide area related to damage in Salak (Salacca zalacca) plantations. This estimation towards quantity of Salak tree that were drifted away by landslide was calculated in assumption that every tree damaged by landslide had same age and production class with other tree that weren't damaged. Loss calculation was done by approximating quantity of damaged trees in landslide area with data of trees around area that were acquired from GEOBIA classification method.

Calibration Assessment of Uncooled Thermal Cameras for Deployment on UAV platforms

NASA Astrophysics Data System (ADS)

Aragon, B.; Parkes, S. D.; Lucieer, A.; Turner, D.; McCabe, M.

2017-12-01

In recent years an array of miniaturized sensors have been developed and deployed on Unmanned Aerial Vehicles (UAVs). Prior to gaining useful data from these integrations, it is vitally important to quantify sensor accuracy, precision and cross-sensitivity of retrieved measurements on environmental variables. Small uncooled thermal frame cameras provide a novel solution to monitoring surface temperatures from UAVs with very high spatial resolution, with retrievals being used to investigate heat stress or evapotranspiration. For these studies, accuracies of a few degrees are generally required. Although radiometrically calibrated thermal cameras have recently become commercially available, confirmation of the accuracy of these sensors is required. Here we detail a system for investigating the accuracy and precision, start up stabilisation time, dependence of retrieved temperatures on ambient temperatures and image vignetting. The calibration system uses a relatively inexpensive blackbody source deployed with the sensor inside an environmental chamber to maintain and control the ambient temperature. Calibration of a number of different thermal sensors commonly used for UAV deployment was investigated. Vignetting was shown to be a major limitation on sensor accuracy, requiring characterization through measuring a spatially uniform temperature target such as the blackbody. Our results also showed that a stabilization period is required after powering on the sensors and before conducting an aerial survey. Through use of the environmental chamber it was shown the ambient temperature influenced the temperatures retrieved by the different sensors. This study illustrates the importance of determining the calibration and cross-sensitivities of thermal sensors to obtain accurate thermal maps that can be used to study crop ecosystems.

An Integrated, Observation-based System to Monitor Aboveground Forest Carbon Dynamics in Washington, Oregon, and California

NASA Astrophysics Data System (ADS)

Kennedy, R. E.; Hughes, J.; Neeti, N.; Yang, Z.; Gregory, M.; Roberts, H.; Kane, V. R.; Powell, S. L.; Ohmann, J.

2016-12-01

Because carbon pools and fluxes on wooded landscapes are constrained by their type, age and health, understanding the causes and consequences of carbon change requires frequent observation of forest condition and of disturbance, mortality, and growth processes. As part of USDA and NASA funded efforts, we built empirical monitoring system that integrates time-series Landsat imagery, Forest Inventory and Analysis (FIA) plot data, small-footprint lidar data, and aerial photos to characterize key carbon dynamics in forested ecosystems of Washington, Oregon and California. Here we report yearly biomass estimates for every forested 30 by 30m pixel in the states of Washington, Oregon, and California from 1990 to 2010, including spatially explicit estimates of uncertainty in our yearly predictions. Total biomass at the ecoregion scale agrees well with estimates from FIA plot data alone, currently the only method for reliable monitoring in the forests of the region. Comparisons with estimates of biomass modeled from four small-footprint lidar acquisitions in overlapping portions of our study area show general patterns of agreement between the two types of estimation, but also reveal some disparities in spatial pattern potentially attributable to age and vegetation condition. Using machine-learning techniques based on both Landsat image time series and high resolution aerial photos, we then modeled the agent causing change in biomass for every change event in the region, and report the relative distribution of carbon loss attributable to natural disturbances (primarily fire and insect-related mortality) versus anthropogenic causes (forest management and development).

Vehicle Detection of Aerial Image Using TV-L1 Texture Decomposition

NASA Astrophysics Data System (ADS)

Wang, Y.; Wang, G.; Li, Y.; Huang, Y.

2016-06-01

Vehicle detection from high-resolution aerial image facilitates the study of the public traveling behavior on a large scale. In the context of road, a simple and effective algorithm is proposed to extract the texture-salient vehicle among the pavement surface. Texturally speaking, the majority of pavement surface changes a little except for the neighborhood of vehicles and edges. Within a certain distance away from the given vector of the road network, the aerial image is decomposed into a smoothly-varying cartoon part and an oscillatory details of textural part. The variational model of Total Variation regularization term and L1 fidelity term (TV-L1) is adopted to obtain the salient texture of vehicles and the cartoon surface of pavement. To eliminate the noise of texture decomposition, regions of pavement surface are refined by seed growing and morphological operation. Based on the shape saliency analysis of the central objects in those regions, vehicles are detected as the objects of rectangular shape saliency. The proposed algorithm is tested with a diverse set of aerial images that are acquired at various resolution and scenarios around China. Experimental results demonstrate that the proposed algorithm can detect vehicles at the rate of 71.5% and the false alarm rate of 21.5%, and that the speed is 39.13 seconds for a 4656 x 3496 aerial image. It is promising for large-scale transportation management and planning.

The application of unmanned aerial systems (UAS) in geophysical investigations of geothermal systems

NASA Astrophysics Data System (ADS)

Glen, J. M.; Egger, A. E.; Ippolito, C.; Phelps, G. A.; Berthold, R.; Lee, R.; Spritzer, J. M.; Tchernychev, M.

2012-12-01

Investigations of geothermal systems typically involve ground-based geological and geophysical studies in order to map structures that control and facilitate fluid flow. The spatial extent of ground-based investigations can be limited, however, by surficial hot springs, dense foliage, and roadless or private lands. This can result in data gaps in key areas, particularly around active hydrothermal springs. Manned aircraft can provide access to these areas and can yield broad and uniform data coverage, but high-resolution surveys are costly and relatively inflexible to changes in the survey specifications that may arise as data are collected. Unmanned aerial systems (UAS) are well suited for conducting these surveys, but until recently, various factors (scientific instrumentation requirements, platform limitations, and size of the survey area) have required the use of large UAS platforms, rendering unmanned aerial surveys unsuitable for most investigations. We have developed and tested a new cesium magnetometer system to collect magnetic data using two different small-platform UAS that overcomes many of the challenges described above. We are deploying this new system in Surprise Valley, CA, to study the area's active geothermal field. Surprise Valley is ideally suited to testing UAS due to its low population density, accessible airspace, and broad playa that provides ample opportunity to safely land the aircraft. In combination with gravity and topographic data, magnetic data are particularly useful for identifying buried, intra-basin structures, especially in areas such as Surprise Valley where highly magnetic, dense mafic volcanic rocks are interbedded with and faulted against less magnetic, less dense sedimentary rock. While high-resolution gravity data must be collected at point locations on the ground, high-resolution magnetic data can be obtained by UAS that provide continuous coverage. Once acquired, the magnetic data obtained by the UAS will be combined with high-resolution airborne lidar data in order to correlate subsurface structures with subtle surface features, to identify possible conduits for, or barriers to, geothermal fluid circulation. Our September 2012 mission will deploy NASA's SIERRA UAS platform to perform a reconnaissance survey of the entire valley. Results from ground and flight tests indicate that magnetic "noise" from the SIERRA platform is low, and can be effectively compensated to provide data comparable with high-resolution commercial methods. A second mission will be flown in summer 2013 using the SWIFT platform, which will analyze data from its onboard sensors to continuously optimize its flight path in real-time to autonomously investigate regions of interest such as steep magnetic gradients or abrupt changes in anomaly amplitudes and wavelengths. The SWIFT also has the advantage that it can be flown as a glider, further reducing magnetic noise of the platform arising from the engine. This innovative use of UAS and intelligent automation in geophysical investigations offers the ability to obtain higher-resolution and more comprehensive and targeted data at a lower cost than is presently possible, expanding our ability to explore a wide variety of geothermal systems.

Mapping lightscapes: spatial patterning of artificial lighting in an urban landscape.

PubMed

Hale, James D; Davies, Gemma; Fairbrass, Alison J; Matthews, Thomas J; Rogers, Christopher D F; Sadler, Jon P

2013-01-01

Artificial lighting is strongly associated with urbanisation and is increasing in its extent, brightness and spectral range. Changes in urban lighting have both positive and negative effects on city performance, yet little is known about how its character and magnitude vary across the urban landscape. A major barrier to related research, planning and governance has been the lack of lighting data at the city extent, particularly at a fine spatial resolution. Our aims were therefore to capture such data using aerial night photography and to undertake a case study of urban lighting. We present the finest scale multi-spectral lighting dataset available for an entire city and explore how lighting metrics vary with built density and land-use. We found positive relationships between artificial lighting indicators and built density at coarse spatial scales, whilst at a local level lighting varied with land-use. Manufacturing and housing are the primary land-use zones responsible for the city's brightly lit areas, yet manufacturing sites are relatively rare within the city. Our data suggests that efforts to address light pollution should broaden their focus from residential street lighting to include security lighting within manufacturing areas.

Mapping Lightscapes: Spatial Patterning of Artificial Lighting in an Urban Landscape

PubMed Central

Hale, James D.; Davies, Gemma; Fairbrass, Alison J.; Matthews, Thomas J.; Rogers, Christopher D. F.; Sadler, Jon P.

2013-01-01

Artificial lighting is strongly associated with urbanisation and is increasing in its extent, brightness and spectral range. Changes in urban lighting have both positive and negative effects on city performance, yet little is known about how its character and magnitude vary across the urban landscape. A major barrier to related research, planning and governance has been the lack of lighting data at the city extent, particularly at a fine spatial resolution. Our aims were therefore to capture such data using aerial night photography and to undertake a case study of urban lighting. We present the finest scale multi-spectral lighting dataset available for an entire city and explore how lighting metrics vary with built density and land-use. We found positive relationships between artificial lighting indicators and built density at coarse spatial scales, whilst at a local level lighting varied with land-use. Manufacturing and housing are the primary land-use zones responsible for the city’s brightly lit areas, yet manufacturing sites are relatively rare within the city. Our data suggests that efforts to address light pollution should broaden their focus from residential street lighting to include security lighting within manufacturing areas. PMID:23671566

UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes—A Case Study from the CarboZALF Experimental Area

PubMed Central

Wehrhan, Marc; Rauneker, Philipp; Sommer, Michael

2016-01-01

The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part. PMID:26907284

UAV-Based Estimation of Carbon Exports from Heterogeneous Soil Landscapes--A Case Study from the CarboZALF Experimental Area.

PubMed

Wehrhan, Marc; Rauneker, Philipp; Sommer, Michael

2016-02-19

The advantages of remote sensing using Unmanned Aerial Vehicles (UAVs) are a high spatial resolution of images, temporal flexibility and narrow-band spectral data from different wavelengths domains. This enables the detection of spatio-temporal dynamics of environmental variables, like plant-related carbon dynamics in agricultural landscapes. In this paper, we quantify spatial patterns of fresh phytomass and related carbon (C) export using imagery captured by a 12-band multispectral camera mounted on the fixed wing UAV Carolo P360. The study was performed in 2014 at the experimental area CarboZALF-D in NE Germany. From radiometrically corrected and calibrated images of lucerne (Medicago sativa), the performance of four commonly used vegetation indices (VIs) was tested using band combinations of six near-infrared bands. The highest correlation between ground-based measurements of fresh phytomass of lucerne and VIs was obtained for the Enhanced Vegetation Index (EVI) using near-infrared band b899. The resulting map was transformed into dry phytomass and finally upscaled to total C export by harvest. The observed spatial variability at field- and plot-scale could be attributed to small-scale soil heterogeneity in part.

Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system

NASA Astrophysics Data System (ADS)

Shi, Yeyin; Murray, Seth C.; Rooney, William L.; Valasek, John; Olsenholler, Jeff; Pugh, N. Ace; Henrickson, James; Bowden, Ezekiel; Zhang, Dongyan; Thomasson, J. Alex

2016-05-01

Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.

An integrated photogrammetric and spatial database management system for producing fully structured data using aerial and remote sensing images.

PubMed

Ahmadi, Farshid Farnood; Ebadi, Hamid

2009-01-01

3D spatial data acquired from aerial and remote sensing images by photogrammetric techniques is one of the most accurate and economic data sources for GIS, map production, and spatial data updating. However, there are still many problems concerning storage, structuring and appropriate management of spatial data obtained using these techniques. According to the capabilities of spatial database management systems (SDBMSs); direct integration of photogrammetric and spatial database management systems can save time and cost of producing and updating digital maps. This integration is accomplished by replacing digital maps with a single spatial database. Applying spatial databases overcomes the problem of managing spatial and attributes data in a coupled approach. This management approach is one of the main problems in GISs for using map products of photogrammetric workstations. Also by the means of these integrated systems, providing structured spatial data, based on OGC (Open GIS Consortium) standards and topological relations between different feature classes, is possible at the time of feature digitizing process. In this paper, the integration of photogrammetric systems and SDBMSs is evaluated. Then, different levels of integration are described. Finally design, implementation and test of a software package called Integrated Photogrammetric and Oracle Spatial Systems (IPOSS) is presented.

Accuracy, resolution, and cost comparisons between small format and mapping cameras for environmental mapping

NASA Technical Reports Server (NTRS)

Clegg, R. H.; Scherz, J. P.

1975-01-01

Successful aerial photography depends on aerial cameras providing acceptable photographs within cost restrictions of the job. For topographic mapping where ultimate accuracy is required only large format mapping cameras will suffice. For mapping environmental patterns of vegetation, soils, or water pollution, 9-inch cameras often exceed accuracy and cost requirements, and small formats may be better. In choosing the best camera for environmental mapping, relative capabilities and costs must be understood. This study compares resolution, photo interpretation potential, metric accuracy, and cost of 9-inch, 70mm, and 35mm cameras for obtaining simultaneous color and color infrared photography for environmental mapping purposes.

Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks

NASA Astrophysics Data System (ADS)

Sun, Xiaofeng; Shen, Shuhan; Lin, Xiangguo; Hu, Zhanyi

2017-10-01

High-resolution remote sensing data classification has been a challenging and promising research topic in the community of remote sensing. In recent years, with the rapid advances of deep learning, remarkable progress has been made in this field, which facilitates a transition from hand-crafted features designing to an automatic end-to-end learning. A deep fully convolutional networks (FCNs) based ensemble learning method is proposed to label the high-resolution aerial images. To fully tap the potentials of FCNs, both the Visual Geometry Group network and a deeper residual network, ResNet, are employed. Furthermore, to enlarge training samples with diversity and gain better generalization, in addition to the commonly used data augmentation methods (e.g., rotation, multiscale, and aspect ratio) in the literature, aerial images from other datasets are also collected for cross-scene learning. Finally, we combine these learned models to form an effective FCN ensemble and refine the results using a fully connected conditional random field graph model. Experiments on the ISPRS 2-D Semantic Labeling Contest dataset show that our proposed end-to-end classification method achieves an overall accuracy of 90.7%, a state-of-the-art in the field.

Vernal Pools Detection Using High-Resolution LiDAR Data and Aerial Imagery in Hubbardston, Massachusetts

NASA Astrophysics Data System (ADS)

Jiang, Jiaxin

Vernal pool refers to temporary or semi-permanent pools that occur in surface depressions without permanent inlets or outlets. Because they periodically dry out, vernal pools are free of fish and essential to amphibians, some reptiles, birds, and mammals for breeding habitats. In Massachusetts, vernal pool habitats are found in woodland depressions, swales or kettle holes where water is contained for at least two months in most years. However, vernal pools are delicate ecosystems. These systems are fragile to human activities such as urbanization. Understanding the current situation of vernal pools helps city planners make wiser decisions. This study focuses on identifying vernal pools in the state of Massachusetts with high-resolution light detection and ranging (LiDAR) data and aerial imagery. By using high-resolution light detection and ranging data, aerial imagery, land use data, the MassDEP Hydrography layer and the Soil Survey Geographic Database, the approach located over 1800 potential vernal pools in a 108 km 2 study area in Massachusetts. The assessment of the study result shows the commission rate was 5.6% and omission rate was 7.1%. This method provides an efficient way of locating vernal pools over large areas.

Onboard Algorithms for Data Prioritization and Summarization of Aerial Imagery

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Hayden, David; Thompson, David R.; Castano, Rebecca

2013-01-01

Many current and future NASA missions are capable of collecting enormous amounts of data, of which only a small portion can be transmitted to Earth. Communications are limited due to distance, visibility constraints, and competing mission downlinks. Long missions and high-resolution, multispectral imaging devices easily produce data exceeding the available bandwidth. To address this situation computationally efficient algorithms were developed for analyzing science imagery onboard the spacecraft. These algorithms autonomously cluster the data into classes of similar imagery, enabling selective downlink of representatives of each class, and a map classifying the terrain imaged rather than the full dataset, reducing the volume of the downlinked data. A range of approaches was examined, including k-means clustering using image features based on color, texture, temporal, and spatial arrangement

Simulation of Sentinel-2A Red Edge Bands with RPAS Based Multispectral Data

NASA Astrophysics Data System (ADS)

Davids, Corine; Storvold, Rune; Haarpaintner, Jorg; Arnason, Kolbeinn

2016-08-01

Very high spatial and spectral resolution multispectral data was collected over the Hallormstađur test site in eastern Iceland using a fixed wing remotely piloted aerial system as part of the EU FP7 project North State (www.northstatefp7.eu). The North State project uses forest variable estimates derived from optical and radar satellite data as either input or validation for carbon flux models. The RPAS data from the Hallormsstađur forest test site in Iceland is here used to simulate Landsat and Sentinel-2A data and to explore the advantages of the new Sentinel-2A red edge bands for forest vegetation mapping. Simple supervised classification shows that the inclusion of the red edge bands improves the tree species classification considerably.

Monitoring of Antarctic moss ecosystems using a high spatial resolution imaging spectroscopy

NASA Astrophysics Data System (ADS)

Malenovsky, Zbynek; Lucieer, Arko; Robinson, Sharon; Harwin, Stephen; Turner, Darren; Veness, Tony

2013-04-01

The most abundant photosynthetically active plants growing along the rocky Antarctic shore are mosses of three species: Schistidium antarctici, Ceratodon purpureus, and Bryum pseudotriquetrum. Even though mosses are well adapted to the extreme climate conditions, their existence in Antarctica depends strongly on availability of liquid water from snowmelt during the short summer season. Recent changes in temperature, wind speed and stratospheric ozone are stimulating faster evaporation, which in turn influences moss growing rate, health state and abundance. This makes them an ideal bio-indicator of the Antarctic climate change. Very short growing season, lasting only about three months, requires a time efficient, easily deployable and spatially resolved method for monitoring the Antarctic moss beds. Ground and/or low-altitude airborne imaging spectroscopy (called also hyperspectral remote sensing) offers a fast and spatially explicit approach to investigate an actual spatial extent and physiological state of moss turfs. A dataset of ground-based spectral images was acquired with a mini-Hyperspec imaging spectrometer (Headwall Inc., the USA) during the Antarctic summer 2012 in the surroundings of the Australian Antarctic station Casey (Windmill Islands). The collection of high spatial resolution spectral images, with pixels about 2 cm in size containing from 162 up to 324 narrow spectral bands of wavelengths between 399 and 998 nm, was accompanied with point moss reflectance measurements recorded with the ASD HandHeld-2 spectroradiometer (Analytical Spectral Devices Inc., the USA). The first spectral analysis indicates significant differences in red-edge and near-infrared reflectance of differently watered moss patches. Contrary to high plants, where the Normalized Difference Vegetation Index (NDVI) represents an estimate of green biomass, NDVI of mosses indicates mainly the actual water content. Similarly to high plants, reflectance of visible wavelengths is controlled by the composition and content of various foliar pigments (chlorophylls, xanthophylls, etc.). Additionally, the high spectral resolution reflectance together with the narrow bandwidth allows retrieving the steady state chlorophyll fluorescence, which indicates the actual moss photosynthetic activity. A first airborne imaging spectroscopy acquisition with the mini-Hyperspec sensor on-board a low-flying remote-controlled multi-rotor helicopter (known as micro Unmanned Aerial Systems - UAS) will be performed during the summer 2013. The aim of the UAS observations is to generate high spatial resolution maps of actual physiological state of several moss beds located within the Australian Antarctic Territory. The regular airborne monitoring is expected to reveal spatio-temporal changes in the Antarctic moss ecosystems, indicating the impact of the global climate change in Antarctica.

Characterizing tree canopy temperature heterogeneity using an unmanned aircraft-borne thermal imager

NASA Astrophysics Data System (ADS)

Messinger, M.; Powell, R.; Silman, M.; Wright, M.; Nicholson, W.

2013-12-01

Leaf temperature (Tleaf) is an important control on many physiological processes such as photosynthesis and respiration, is a key variable for characterizing canopy energy fluxes, and is a valuable metric for identifying plant water stress or disease. Traditional methods of Tleaf measurement involve either the use of thermocouples, a time and labor-intensive method that samples sparsely in space, or the use of air temperature (Tair) as a proxy measure, which can introduce inaccuracies due to near constant canopy-atmosphere energy flux. Thermal infrared (TIR) imagery provides an efficient means of collecting Tleaf for large areas. Existing satellite and aircraft-based TIR imagery is, however, limited by low spatial and/or temporal resolution, while crane-mounted camera systems have strictly limited spatial extents. Unmanned aerial systems (UAS) offer new opportunities to acquire high spatial and temporal resolution imagery on demand. Here, we demonstrate the feasibility of collecting tree canopy Tleaf data using a small multirotor UAS fitted with a high spatial resolution TIR imager. The goals of this pilot study were to a) characterize basic patterns of within crown Tleaf for 4 study species and b) identify trends in Tleaf between species with varying leaf morphologies and canopy structures. TIR imagery was acquired for individual tree crowns of 4 species common to the North Carolina Piedmont ecoregion (Quercus phellos, Pinus strobus, Liriodendron tulipifera, Magnolia grandiflora) in an urban park environment. Due to significantly above-average summer precipitation, we assumed that none of the sampled trees was limited by soil water availability. We flew the TIR imaging system over 3-4 individuals of each of the 4 target species on 3 separate days. Imagery of all individuals was collected within the same 2-hour period in the afternoon on all days. There was low wind and partly cloudy skies during imaging. Tair, relative humidity, and wind speed were recorded at each site. Emissivity was assumed to be 0.98 for all species. Acquired images had a pixel resolution of <3 cm and measurement accuracy of ×1° C. We found the UAS-borne TIR imaging system to be an effective tool for collection of high resolution canopy imagery. The system imaged all targeted crowns quickly and reliably, providing a viable alternative to current methods of canopy Tleaf measurement. Analysis of the imagery indicated significant variability in Tleaf both within and between crowns. We identified trends in Tleaf related to average leaf size, shape, and crown structural traits. These data on the heterogeneity of Tleaf can further our understanding of canopy-atmosphere energy exchange. This pilot study demonstrates the promise of UAS-borne TIR sensors for acquiring high spatial resolution imagery at the scale of individual tree crowns.

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring.

PubMed

Gillan, Jeffrey K; Karl, Jason W; Duniway, Michael; Elaksher, Ahmed

2014-11-01

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring

USGS Publications Warehouse

Gillan, Jeffrey K.; Karl, Jason W.; Duniway, Michael; Elaksher, Ahmed

2014-01-01

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often too costly and time consuming to apply at these broad scales. Most emerging remote sensing techniques capable of measuring surface and vegetation height (e.g., LiDAR or synthetic aperture radar) are often too expensive, and require specialized sensors. An alternative remote sensing approach that is potentially more practical for managers is to measure vegetation heights from digital stereo aerial photographs. As aerial photography is already commonly used for rangeland monitoring, acquiring it in stereo enables three-dimensional modeling and estimation of vegetation height. The purpose of this study was to test the feasibility and accuracy of estimating shrub heights from high-resolution (HR, 3-cm ground sampling distance) digital stereo-pair aerial images. Overlapping HR imagery was taken in March 2009 near Lake Mead, Nevada and 5-cm resolution digital surface models (DSMs) were created by photogrammetric methods (aerial triangulation, digital image matching) for twenty-six test plots. We compared the heights of individual shrubs and plot averages derived from the DSMs to field measurements. We found strong positive correlations between field and image measurements for several metrics. Individual shrub heights tended to be underestimated in the imagery, however, accuracy was higher for dense, compact shrubs compared with shrubs with thin branches. Plot averages of shrub height from DSMs were also strongly correlated to field measurements but consistently underestimated. Grasses and forbs were generally too small to be detected with the resolution of the DSMs. Estimates of vertical structure will be more accurate in plots having low herbaceous cover and high amounts of dense shrubs. Through the use of statistically derived correction factors or choosing field methods that better correlate with the imagery, vegetation heights from HR DSMs could be a valuable technique for broad-scale rangeland monitoring needs.

Positional Quality Assessment of Orthophotos Obtained from Sensors Onboard Multi-Rotor UAV Platforms

PubMed Central

Mesas-Carrascosa, Francisco Javier; Rumbao, Inmaculada Clavero; Berrocal, Juan Alberto Barrera; Porras, Alfonso García-Ferrer

2014-01-01

In this study we explored the positional quality of orthophotos obtained by an unmanned aerial vehicle (UAV). A multi-rotor UAV was used to obtain images using a vertically mounted digital camera. The flight was processed taking into account the photogrammetry workflow: perform the aerial triangulation, generate a digital surface model, orthorectify individual images and finally obtain a mosaic image or final orthophoto. The UAV orthophotos were assessed with various spatial quality tests used by national mapping agencies (NMAs). Results showed that the orthophotos satisfactorily passed the spatial quality tests and are therefore a useful tool for NMAs in their production flowchart. PMID:25587877

Positional quality assessment of orthophotos obtained from sensors onboard multi-rotor UAV platforms.

PubMed

Mesas-Carrascosa, Francisco Javier; Rumbao, Inmaculada Clavero; Berrocal, Juan Alberto Barrera; Porras, Alfonso García-Ferrer

2014-11-26

In this study we explored the positional quality of orthophotos obtained by an unmanned aerial vehicle (UAV). A multi-rotor UAV was used to obtain images using a vertically mounted digital camera. The flight was processed taking into account the photogrammetry workflow: perform the aerial triangulation, generate a digital surface model, orthorectify individual images and finally obtain a mosaic image or final orthophoto. The UAV orthophotos were assessed with various spatial quality tests used by national mapping agencies (NMAs). Results showed that the orthophotos satisfactorily passed the spatial quality tests and are therefore a useful tool for NMAs in their production flowchart.

UAV-based remote sensing surveys of lava flow fields: a case study from Etna's 1974 channel-fed lava flows

NASA Astrophysics Data System (ADS)

Favalli, Massimiliano; Fornaciai, Alessandro; Nannipieri, Luca; Harris, Andrew; Calvari, Sonia; Lormand, Charline

2018-03-01

During an eruption, time scales of topographic change are fast and involve vertical and planimetric evolution of millimeters to meters as the event progresses. Repeat production of high spatial resolution terrain models of lava flow fields over time scales of a few hours is thus a high-value capability in tracking the buildup of the deposit. Among the wide range of terrestrial and aerial methods available to collect such topographic data, the use of an unmanned aerial vehicle (UAV) as an acquisition platform, together with structure from motion (SfM) photogrammetry, has become especially useful. This approach allows high-frequency production of centimeter-scale terrain models over kilometer-scale areas, including dangerous and inaccessible zones, with low cost and minimal hazard to personnel. This study presents the application of such an integrated UAV-SfM method to generate a high spatial resolution digital terrain model and orthomosaic of Mount Etna's January-February 1974 lava flow field. The SfM method, applied to images acquired using a UAV platform, enabled the extraction of a very high spatial resolution (20 cm) digital elevation model and the generation of a 3-cm orthomosaic covering an area of 1.35 km2. This spatial resolution enabled us to analyze the morphology of sub-meter-scale features, such as folds, blocks, and cracks, over kilometer-scale areas. The 3-cm orthomosaic allowed us to further push the analysis to centimeter-scale grain size distribution of the lava surface. Using these data, we define three types of crust structure and relate them to positions within a channel-fed ´áā flow system. These crust structures are (i) flow parallel shear lines, (ii) raft zones, and (iii) folded zones. Flow parallel shear lines are found at the channel edges, and are 2-m-wide and 0.25-m-deep zones running along the levee base and in which cracking is intense. They result from intense shearing between the moving channel lava and the static levee lava. In zones where initial levees are just beginning to form, these subtle features are the only marker that delimits the moving lava from the stagnant marginal lava. Rafts generally form as the system changes from a stable to a transitional channel regime. Over this 170-m-long zone, the channel broadens from 8 to 70 m and rafts are characterized by topographically higher and poorly cracked areas, surrounded by lower, heavily cracked areas. We interpret the rafts as forming due to breakup of crust zones, previously moving in a coherent manner in the narrow proximal channel reach. Folded zones involve arcuate, cross-flow ridges with their apexes pointing down-flow, where ridges have relatively small clasts and depressions are of coarser-grained breccia. Our folds have wavelengths of 10 m and amplitudes of 1 m; are found towards the flow front, down-flow of the raft zones; and are associated with piling up of lava behind a static or slowly moving flow front. The very high spatial resolution topographic data available from UAV-SfM allow us to resolve surfaces where roughness has a vertical and horizontal scale of variation that is less than 1 m. This is the case over pāhoehoe and ´áā flow surfaces, and thus allows us to explore those new structures that are only apparent in the sub-metric data. Moreover, during future eruptions, the possibility to acquire such information in near-real time will allow a prompt analysis of developing lava flow fields and structures therein, such as developing lava channel systems, so as to contribute to timely hazard assessment, modeling, and projections.

Linking innovative measurement technologies (ConMon and Dataflow© systems) for high-resolution temporal and spatial dissolved oxygen criteria assessment.

PubMed

O'Leary, C A; Perry, E; Bayard, A; Wainger, L; Boynton, W R

2015-10-01

One consequence of nutrient-induced eutrophication in shallow estuarine waters is the occurrence of hypoxia and anoxia that has serious impacts on biota, habitats, and biogeochemical cycles of important elements. Because of the important role of dissolved oxygen (DO) on these ecosystem features, a variety of DO criteria have been established as indicators of system condition. However, DO dynamics are complex and vary on time scales ranging from diel to decadal and spatial scales from meters to multiple kilometers. Because of these complexities, determining DO criteria attainment or failure remains difficult. We propose a method for linking two common measurement technologies for shallow water DO criteria assessment using a Chesapeake Bay tributary as a test case. Dataflow© is a spatially intensive (30-60-m collection intervals) system used to map surface water conditions at the whole estuary scale, and ConMon is a high-frequency (15-min collection intervals) fixed station approach. The former technology is effective with spatial descriptions but poor regarding temporal resolution, while the latter provides excellent temporal but very limited spatial resolution. Our methodology for combining the strengths of these measurement technologies involved a sequence of steps. First, a statistical model of surface water DO dynamics, based on temporally intense ConMon data, was developed. The results of this model were used to calculate daily DO minimum concentrations. Second, this model was then inserted into Dataflow©-generated spatial maps of DO conditions and used to adjust measured DO concentrations to daily minimum concentrations. This information was used to assess DO criteria compliance at the full tributary scale. Model results indicated that it is vital to consider the short-term time scale DO criteria across both space and time concurrently. Large fluctuations in DO occurred within a 24-h time period, and DO dynamics varied across the length and width of the tributary. The overall result provided a more detailed and realistic characterization of the shallow water DO minimum conditions that have the potential to be extended to other tributaries and regions. Broader applications of this model include instantaneous DO criteria assessment, utilizing this model in combination with aerial remote sensing, and developing DO amplitude as an indicator of impaired water bodies.

Get the Picture?

NASA Technical Reports Server (NTRS)

1998-01-01

Positive Systems has worked in conjunction with Stennis Space Center to design the ADAR System 5500. This is a four-band airborne digital imaging system used to capture multispectral imagery similar to that available from satellite platforms such as Landsat, SPOT and the new generation of high resolution satellites. Positive Systems has provided remote sensing services for the development of digital aerial camera systems and software for commercial aerial imaging applications.

Individual tree detection from Unmanned Aerial Vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest

Treesearch

Midhun Mohan; Carlos Alberto Silva; Carine Klauberg; Prahlad Jat; Glenn Catts; Adrian Cardil; Andrew Thomas Hudak; Mahendra Dia

2017-01-01

Advances in Unmanned Aerial Vehicle (UAV) technology and data processing capabilities have made it feasible to obtain high-resolution imagery and three dimensional (3D) data which can be used for forest monitoring and assessing tree attributes. This study evaluates the applicability of low consumer grade cameras attached to UAVs and structure-from-motion (SfM)...

Advancing UAS methods for monitoring coastal environments

NASA Astrophysics Data System (ADS)

Ridge, J.; Seymour, A.; Rodriguez, A. B.; Dale, J.; Newton, E.; Johnston, D. W.

2017-12-01

Utilizing fixed-wing Unmanned Aircraft Systems (UAS), we are working to improve coastal monitoring by increasing the accuracy, precision, temporal resolution, and spatial coverage of habitat distribution maps. Generally, multirotor aircraft are preferred for precision imaging, but recent advances in fixed-wing technology have greatly increased their capabilities and application for fine-scale (decimeter-centimeter) measurements. Present mapping methods employed by North Carolina coastal managers involve expensive, time consuming and localized observation of coastal environments, which often lack the necessary frequency to make timely management decisions. For example, it has taken several decades to fully map oyster reefs along the NC coast, making it nearly impossible to track trends in oyster reef populations responding to harvesting pressure and water quality degradation. It is difficult for the state to employ manned flights for collecting aerial imagery to monitor intertidal oyster reefs, because flights are usually conducted after seasonal increases in turbidity. In addition, post-storm monitoring of coastal erosion from manned platforms is often conducted days after the event and collects oblique aerial photographs which are difficult to use for accurately measuring change. Here, we describe how fixed wing UAS and standard RGB sensors can be used to rapidly quantify and assess critical coastal habitats (e.g., barrier islands, oyster reefs, etc.), providing for increased temporal frequency to isolate long-term and event-driven (storms, harvesting) impacts. Furthermore, drone-based approaches can accurately image intertidal habitats as well as resolve information such as vegetation density and bathymetry from shallow submerged areas. We obtain UAS imagery of a barrier island and oyster reefs under ideal conditions (low tide, turbidity, and sun angle) to create high resolution (cm scale) maps and digital elevation models to assess habitat condition. Concurrently, we test the accuracy of UAS platforms and image analysis tools against traditional high-resolution mapping equipment (GPS and terrestrial lidar) and in situ sampling (density quadrats) to conduct error analysis of UAS orthoimagery and data processing.

Quantification of Impervious Surfaces Along the Wasatch Front, Utah: AN Object-Based Image Analysis Approach to Identifying AN Indicator for Wetland Stress

NASA Astrophysics Data System (ADS)

Leydsman-McGinty, E. I.; Ramsey, R. D.; McGinty, C.

2013-12-01

The Remote Sensing/GIS Laboratory at Utah State University, in cooperation with the United States Environmental Protection Agency, is quantifying impervious surfaces for three watershed sub-basins in Utah. The primary objective of developing watershed-scale quantifications of impervious surfaces is to provide an indicator of potential impacts to wetlands that occur within the Wasatch Front and along the Great Salt Lake. A geospatial layer of impervious surfaces can assist state agencies involved with Utah's Wetlands Program Plan (WPP) in understanding the impacts of impervious surfaces on wetlands, as well as support them in carrying out goals and actions identified in the WPP. The three watershed sub-basins, Lower Bear-Malad, Lower Weber, and Jordan, span the highly urbanized Wasatch Front and are consistent with focal areas in need of wetland monitoring and assessment as identified in Utah's WPP. Geospatial layers of impervious surface currently exist in the form of national and regional land cover datasets; however, these datasets are too coarse to be utilized in fine-scale analyses. In addition, the pixel-based image processing techniques used to develop these coarse datasets have proven insufficient in smaller scale or detailed studies, particularly when applied to high-resolution satellite imagery or aerial photography. Therefore, object-based image analysis techniques are being implemented to develop the geospatial layer of impervious surfaces. Object-based image analysis techniques employ a combination of both geospatial and image processing methods to extract meaningful information from high-resolution imagery. Spectral, spatial, textural, and contextual information is used to group pixels into image objects and then subsequently used to develop rule sets for image classification. eCognition, an object-based image analysis software program, is being utilized in conjunction with one-meter resolution National Agriculture Imagery Program (NAIP) aerial photography from 2011.

Using Landsat and a Bayesian hard classifier to study forest change in the Salmon Creek Watershed area from 1972-2013

NASA Astrophysics Data System (ADS)

Mullis, David Stone

The Salmon Creek Watershed in Sonoma County, California, USA, is home to a variety of wildlife, and many of its residents are mindful of their place in its ecology. In the past half century, several of its native and rare species have become threatened, endangered, or extinct, most notably the once common Coho salmon and Chinook salmon. The cause of this decline is believed to be a combination of global climate change, local land use, and land cover change. More specifically, the clearing of forested land to create vineyards, as well as other agricultural and residential uses, has led to a decline in biodiversity and habitat structure. I studied sub-scenes of Landsat data from 1972 to 2013 for the Salmon Creek Watershed area to estimate forest cover over this period. I used a maximum likelihood hard classifier to determine forest area, a Mahalanobis distance soft classifier to show the software's uncertainty in classification, and manually digitized forest cover to test and compare results for the 2013 30 m image. Because the earliest images were lower spatial resolution, I also tested the effects of resolution on these statistics. The images before 1985 are at 60 m spatial resolution while the later images are at 30 m resolution. Each image was processed individually and the training data were based on knowledge of the area and a mosaic of aerial photography. Each sub-scene was classified into five categories: water, forest, pasture, vineyard/orchard, and developed/barren. The research shows a decline in forest area from 1972 to around the mid-1990s, then an increase in forest area from the mid-1990s to present. The forest statistics can be helpful for conservation and restoration purposes, while the study on resolution can be helpful for landscape analysis on many levels.

Monitoring Inflation and Emplacement During the 2014-2015 Kilauea Lava Flow With an Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Perroy, R. L.; Turner, N.; Hon, K. A.; Rasgado, V.

2015-12-01

Unmanned aerial vehicles (UAVs) provide a powerful new tool for collecting high resolution on-demand spatial data over volcanic eruptions and other active geomorphic processes. These data can be used to improve hazard forecasts and emergency response efforts, and also allow users to economically and safely observe and quantify lava flow inflation and emplacement on spatially and temporally useful scales. We used a small fixed-wing UAV with a modified point-and-shoot camera to repeatedly map the active front of the 2014-2015 Kīlauea lava flow over a one-month period in late 2014, at times with a two-hour repeat interval. An additional subsequent flight was added in July, 2015. We used the imagery from these flights to generate a time-series of 5-cm resolution RGB and near-infrared orthoimagery mosaics and associated digital surface models using structure from motion. Survey-grade positional control was provided by ground control points with differential GPS. Two topographic transects were repeatedly surveyed across the flow surface, contemporaneously with UAV flights, to independently confirm topographic changes observed in the UAV-derived surface models. Vertical errors were generally 10 cm. Inside our 50 hectare study site, the flow advanced at a rate of 0.47 hectares/day during the first three weeks of observations before abruptly stalling out <200 m from Pahoa Village road. Over 150,000 m3of lava were added to the study site during our period of observations, with maximum vertical inflation >4 m. New outbreak areas, both on the existing flow surface and along the flow margins, were readily mapped across the study area. We detected sinuous growing inflation ridges within the flow surface that correlated with subsequent outbreaks of new lava, suggesting that repeat UAV flights can provide a means of better predicting pahoehoe lava flow behavior over flat or uneven topography. Our results show that UAVs can generate accurate and digital surface models quickly and inexpensively over rapidly changing active pahoehoe lava flows.

Mapping spatial patterns of stream power and channel change along a gravel-bed river in northern Yellowstone

NASA Astrophysics Data System (ADS)

Lea, Devin M.; Legleiter, Carl J.

2016-01-01

Stream power represents the rate of energy expenditure along a river and can be calculated using topographic data acquired via remote sensing or field surveys. This study sought to quantitatively relate temporal changes in the form of Soda Butte Creek, a gravel-bed river in northeastern Yellowstone National Park, to stream power gradients along an 8-km reach. Aerial photographs from 1994 to 2012 and ground-based surveys were used to develop a locational probability map and morphologic sediment budget to assess lateral channel mobility and changes in net sediment flux. A drainage area-to-discharge relationship and DEM developed from LiDAR data were used to obtain the discharge and slope values needed to calculate stream power. Local and lagged relationships between mean stream power gradient at median peak discharge and volumes of erosion, deposition, and net sediment flux were quantified via spatial cross-correlation analyses. Similarly, autocorrelations of locational probabilities and sediment fluxes were used to examine spatial patterns of sediment sources and sinks. Energy expended above critical stream power was calculated for each time period to relate the magnitude and duration of peak flows to the total volumetric change in each time increment. Collectively, we refer to these methods as the stream power gradient (SPG) framework. The results of this study were compromised by methodological limitations of the SPG framework and revealed some complications likely to arise when applying this framework to small, wandering, gravel-bed rivers. Correlations between stream power gradients and sediment flux were generally weak, highlighting the inability of relatively simple statistical approaches to link sub-budget cell-scale sediment dynamics to larger-scale driving forces such as stream power gradients. Improving the moderate spatial resolution techniques used in this study and acquiring very-high resolution data from recently developed methods in fluvial remote sensing could help improve understanding of the spatial organization of stream power, sediment transport, and channel change in dynamic natural rivers.

Mapping spatial patterns of stream power and channel change along a gravel-bed river in northern Yellowstone

NASA Astrophysics Data System (ADS)

Lea, Devin M.

Stream power represents the rate of energy expenditure along a river and can be calculated using topographic data acquired via remote sensing or field surveys. This study used remote sensing and GIS tools along with field data to quantitatively relate temporal changes in the form of Soda Butte Creek, a gravel-bed river in northeastern Yellowstone National Park, to stream power gradients along an 8 km reach. Aerial photographs from 1994-2012 and cross-section surveys were used to develop a locational probability map and morphologic sediment budget to assess lateral channel mobility and changes in net sediment flux. A drainage area-to-discharge relationship and digital elevation model (DEM) developed from light detection and ranging (LiDAR) data were used to obtain the discharge and slope values needed to calculate stream power. Local and lagged relationships between mean stream power gradient at median peak discharge and volumes of erosion, deposition, and net sediment flux were quantified via spatial cross-correlation analyses. Similarly, autocorrelations of locational probabilities and sediment fluxes were used to examine spatial patterns of sediment sources and sinks. Energy expended above critical stream power was calculated for each time period to relate the magnitude and duration of peak flows to the total volumetric change in each time increment. Results indicated a lack of strong correlation between stream power gradients and sediment response, highlighting the geomorphic complexity of Soda Butte Creek and the inability of relatively simple statistical approaches to link sub-budget cell-scale sediment dynamics to larger-scale driving forces such as stream power gradients. Improving the moderate spatial resolution techniques used in this study and acquiring very-high resolution data from recently developed methods in fluvial remote sensing could help improve understanding of the spatial organization of stream power, sediment transport, and channel change in dynamic natural rivers.

Creating Digital Environments for Multi-Agent Simulation

DTIC Science & Technology

2003-12-01

foliage on a polygon to represent a tree). Tile A spatial partition of a coverage that shares the same set of feature classes with the same... orthophoto datasets can be made from rectified grayscale aerial images. These datasets can support various weapon systems, Command, Control...Raster Product Format (RPF) Standard. This data consists of unclassified seamless orthophotos , made from rectified grayscale aerial images. DOI 10

Development and comparisons of wind retrieval algorithms for small unmanned aerial systems

NASA Astrophysics Data System (ADS)

Bonin, T. A.; Chilson, P. B.; Zielke, B. S.; Klein, P. M.; Leeman, J. R.

2012-12-01

Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.

Comparison and application of wind retrieval algorithms for small unmanned aerial systems

NASA Astrophysics Data System (ADS)

Bonin, T. A.; Chilson, P. B.; Zielke, B. S.; Klein, P. M.; Leeman, J. R.

2013-07-01

Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well-aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.

A Smart Irrigation Approach Aided by Monitoring Surface Soil Moisture using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Wienhold, K. J.; Li, D.; Fang, N. Z.

2017-12-01

Soil moisture is a critical component in the optimization of irrigation scheduling in water resources management. Unmanned Aerial Vehicles (UAV) equipped with multispectral sensors represent an emerging technology capable of detecting and estimating soil moisture for irrigation and crop management. This study demonstrates a method of using a UAV as an optical and thermal remote sensing platform combined with genetic programming to derive high-resolution, surface soil moisture (SSM) estimates. The objective is to evaluate the feasibility of spatially-variable irrigation management for a golf course (about 50 acres) in North Central Texas. Multispectral data is collected over the course of one month in the visible, near infrared and longwave infrared spectrums using a UAV capable of rapid and safe deployment for daily estimates. The accuracy of the model predictions is quantified using a time domain reflectometry (TDR) soil moisture sensor and a holdout validation test set. The model produces reasonable estimates for SSM with an average coefficient of correlation (r) = 0.87 and coefficient of determination of (R2) = 0.76. The study suggests that the derived SSM estimates be used to better inform irrigation scheduling decisions for lightly vegetated areas such as the turf or native roughs found on golf courses.

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

NASA Astrophysics Data System (ADS)

Varela, Sebastian; Assefa, Yared; Vara Prasad, P. V.; Peralta, Nahuel R.; Griffin, Terry W.; Sharda, Ajay; Ferguson, Allison; Ciampitti, Ignacio A.

2017-07-01

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log-log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.

Improving a DSM Obtained by Unmanned Aerial Vehicles for Flood Modelling

NASA Astrophysics Data System (ADS)

Mourato, Sandra; Fernandez, Paulo; Pereira, Luísa; Moreira, Madalena

2017-12-01

According to the EU flood risks directive, flood hazard map must be used to assess the flood risk. These maps can be developed with hydraulic modelling tools using a Digital Surface Runoff Model (DSRM). During the last decade, important evolutions of the spatial data processing has been developed which will certainly improve the hydraulic models results. Currently, images acquired with Red/Green/Blue (RGB) camera transported by Unmanned Aerial Vehicles (UAV) are seen as a good alternative data sources to represent the terrain surface with a high level of resolution and precision. The question is if the digital surface model obtain with this data is adequate enough for a good representation of the hydraulics flood characteristics. For this purpose, the hydraulic model HEC-RAS was run with 4 different DSRM for an 8.5 km reach of the Lis River in Portugal. The computational performance of the 4 modelling implementations is evaluated. Two hydrometric stations water level records were used as boundary conditions of the hydraulic model. The records from a third hydrometric station were used to validate the optimal DSRM. The HEC-RAS results had the best performance during the validation step were the ones where the DSRM with integration of the two altimetry data sources.

An airborne robotic platform for mapping thermal structure in surface water bodies

NASA Astrophysics Data System (ADS)

Thompson, S. E.; Chung, M.; Detweiler, C.; Ore, J. P.

2015-12-01

The significance of thermal heterogeneities in small surface water bodies as drivers of mixing and for habitat provision is increasingly recognized, yet obtaining three-dimensionally resolved observations of the thermal structure of lakes and rivers remains challenging. For relatively shallow water bodies, observations of water temperature from aerial platforms are attractive: they do not require shoreline access, they can be quickly and easily deployed and redeployed, facilitating repeated sampling, and they can rapidly move between measurement locations, allowing multiple measurements to be made during single flights. However, they are also subject to well-known limitations including payload, flight duration and operability, and their effectiveness as a mobile platform for thermal sensing is still poorly characterized. In this talk, I will introduce an aerial thermal sensing platform that enables water temperature measurements to be made and spatially located throughout a water column, and present preliminary results from initial field experiments comparing in-situ temperature observations to those made from the UAS platform. The results highlight the potential scalability of the platform to provide high-resolution 3D thermal mapping of a ~1 ha lake in 2-3 flights (circa 1 hour), sufficient to resolve diurnal variations. Operability constraints and key needs for further development are also identified.

Greenhouse Gas Sensing Using Small Unmanned Aerial Systems - Field Experiment Results and Future Directions

NASA Astrophysics Data System (ADS)

Aubrey, A. D.; Christensen, L. E.; Brockers, R.; Thompson, D. R.

2014-12-01

Requirements for greenhouse gas point source detection and quantification often require high spatial resolution on the order of meters. These applications, which help close the gap in emissions estimate uncertainties, also demand sensing with high sensitivity and in a fashion that accounts for spatiotemporal variability on the order of seconds to minutes. Low-cost vertical takeoff and landing (VTOL) small unmanned aerial systems (sUAS) provide a means to detect and identify the location of point source gas emissions while offering ease of deployment and high maneuverability. Our current fielded gas sensing sUAS platforms are able to provide instantaneous in situ concentration measurements at locations within line of sight of the operator. Recent results from field experiments demonstrating methane detection and plume characterization will be discussed here, including performance assessment conducted via a controlled release experiment in 2013. The logical extension of sUAS gas concentration measurement is quantification of flux rate. We will discuss the preliminary strategy for quantitative flux determination, including intrinsic challenges and heritage from airborne science campaigns, associated with this point source flux quantification. This system approach forms the basis for intelligent autonomous quantitative characterization of gas plumes, which holds great value for applications in commercial, regulatory, and safety environments.

UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Kestur, Ramesh; Farooq, Shariq; Abdal, Rameen; Mehraj, Emad; Narasipura, Omkar; Mudigere, Meenavathi

2018-01-01

Road extraction in imagery acquired by low altitude remote sensing (LARS) carried out using an unmanned aerial vehicle (UAV) is presented. LARS is carried out using a fixed wing UAV with a high spatial resolution vision spectrum (RGB) camera as the payload. Deep learning techniques, particularly fully convolutional network (FCN), are adopted to extract roads by dense semantic segmentation. The proposed model, UFCN (U-shaped FCN) is an FCN architecture, which is comprised of a stack of convolutions followed by corresponding stack of mirrored deconvolutions with the usage of skip connections in between for preserving the local information. The limited dataset (76 images and their ground truths) is subjected to real-time data augmentation during training phase to increase the size effectively. Classification performance is evaluated using precision, recall, accuracy, F1 score, and brier score parameters. The performance is compared with support vector machine (SVM) classifier, a one-dimensional convolutional neural network (1D-CNN) model, and a standard two-dimensional CNN (2D-CNN). The UFCN model outperforms the SVM, 1D-CNN, and 2D-CNN models across all the performance parameters. Further, the prediction time of the proposed UFCN model is comparable with SVM, 1D-CNN, and 2D-CNN models.

Design and Analysis of a Single-Camera Omnistereo Sensor for Quadrotor Micro Aerial Vehicles (MAVs).

PubMed

Jaramillo, Carlos; Valenti, Roberto G; Guo, Ling; Xiao, Jizhong

2016-02-06

We describe the design and 3D sensing performance of an omnidirectional stereo (omnistereo) vision system applied to Micro Aerial Vehicles (MAVs). The proposed omnistereo sensor employs a monocular camera that is co-axially aligned with a pair of hyperboloidal mirrors (a vertically-folded catadioptric configuration). We show that this arrangement provides a compact solution for omnidirectional 3D perception while mounted on top of propeller-based MAVs (not capable of large payloads). The theoretical single viewpoint (SVP) constraint helps us derive analytical solutions for the sensor's projective geometry and generate SVP-compliant panoramic images to compute 3D information from stereo correspondences (in a truly synchronous fashion). We perform an extensive analysis on various system characteristics such as its size, catadioptric spatial resolution, field-of-view. In addition, we pose a probabilistic model for the uncertainty estimation of 3D information from triangulation of back-projected rays. We validate the projection error of the design using both synthetic and real-life images against ground-truth data. Qualitatively, we show 3D point clouds (dense and sparse) resulting out of a single image captured from a real-life experiment. We expect the reproducibility of our sensor as its model parameters can be optimized to satisfy other catadioptric-based omnistereo vision under different circumstances.

The effect of flight altitude to data quality of fixed-wing UAV imagery: case study in Murcia, Spain

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Cammeraat, Erik

2014-05-01

Unmanned Aerial System (UAS) are becoming popular tools in the geosciences due to improving technology and processing techniques. They can potentially fill the gap between spaceborne or manned aircraft remote sensing and terrestrial remote sensing, both in terms of spatial and temporal resolution. In this study we tested a fixed-wing Unmanned Aerial System (UAS) for the application of digital landscape analysis. The focus was to analyze the effect of flight altitude and the effect to accuracy and detail of the produced digital elevation models, derived terrain properties and orthophotos. The aircraft was equipped with a Panasonic GX1 16MP pocket camera with 20 mm lens to capture normal JPEG RGB images. Images were processed using Agisoft Photoscan Pro which includes the structure-from-motion and multiview stereopsis algorithms. The test area consisted of small abandoned agricultural fields in semi-arid Murcia in southeastern Spain. The area was severely damaged after a destructive rainfall event, including damaged check dams, rills, deep gully incisions and piping. Results suggest that careful decisions on flight altitude are essential to find a balance between the area coverage, ground sampling distance, UAS ground speed, camera processing speed and the accurate registration of specific soil erosion features of interest.

Open Skies aerial photography of selected areas in Central America affected by Hurricane Mitch

USGS Publications Warehouse

Molnia, Bruce; Hallam, Cheryl A.

1999-01-01

Between October 27 and November 1, 1998, Central America was devastated by Hurricane Mitch. Following a humanitarian relief effort, one of the first informational needs was complete aerial photographic coverage of the storm ravaged areas so that the governments of the affected countries, the U.S. agencies planning to provide assistance, and the international relief community could come to the aid of the residents of the devastated area. Between December 4 and 19, 1998 an Open Skies aircraft conducted five successful missions and obtained more than 5,000 high-resolution aerial photographs and more than 15,000 video images. The aerial data are being used by the Reconstruction Task Force and many others who are working to begin rebuilding and to help reduce the risk of future destruction.

Mapping Surface Temperatures on a Debris-Covered Glacier with an Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Kraaijenbrink, Philip D. A.; Shea, Joseph M.; Litt, Maxime; Steiner, Jakob F.; Treichler, Désirée; Koch, Inka; Immerzeel, Walter W.

2018-05-01

A mantel of debris cover often accumulates across the surface of glaciers in active mountain ranges with exceptionally steep terrain, such as the Andes, Himalaya and New Zealand Alps. Such a supraglacial debris layer has a major influence on a glacier's surface energy budget, enhancing radiation absorption and melt when the layer is thin, but insulating the ice when thicker than a few cm. Information on spatially distributed debris surface temperature has the potential to provide insight into the properties of the debris, its effects on the ice below and its influence on the near-surface boundary layer. Here, we deploy an unmanned aerial vehicle (UAV) equipped with a thermal infrared sensor on three separate missions over one day to map changing surface temperatures across the debris-covered Lirung Glacier in the Central Himalaya. We present a methodology to georeference and process the acquired thermal imagery, and correct for emissivity and sensor bias. Derived UAV surface temperatures are compared with distributed simultaneous in situ temperature measurements as well as with Landsat 8 thermal satellite imagery. Results show that the UAV-derived surface temperatures vary greatly both spatially and temporally, with -1.4±1.8, 11.0 ±5.2 and 15.3±4.7 °C for the three flights (mean±sd), respectively. The range in surface temperatures over the glacier during the morning is very large with almost 50 °C. Ground-based measurements are generally in agreement with the UAV imagery, but considerable deviations are present that are likely due to differences in measurement technique and approach, and validation is difficult as a result. The difference in spatial and temporal variability captured by the UAV as compared with much coarser satellite imagery is striking and it shows that satellite derived temperature maps should be interpreted with care. We conclude that UAVs provide a suitable means to acquire surface temperature maps of debris-covered glacier surfaces at high spatial and temporal resolution, but that there are caveats with regard to absolute temperature measurement.

Spatio-temporal Variation in Glacier Ice as Habitat for Harbor Seals in an Alaskan Tidewater Glacier Fjord

NASA Astrophysics Data System (ADS)

Womble, J. N.; McNabb, R. W.; Gens, R.; Prakash, A.

2015-12-01

Some of the largest aggregations of harbor seals (Phoca vitulina richardii) in Alaska occur in tidewater glacier fjords where seals rest upon icebergs that are calved from tidewater glaciers into the marine environment. The distribution, amount, and size of floating ice in fjords are likely important factors influencing the spatial distribution and abundance of harbor seals; however, fine-scale characteristics of ice habitat that are used by seals have not been quantified using automated methods. We quantified the seasonal changes in ice habitat for harbor seals in Johns Hopkins Inlet, a tidewater glacier fjord in Glacier Bay National Park, Alaska, using aerial photography, object-based image analysis, and spatial models. Aerial photographic surveys (n = 53) were conducted of seals and ice during the whelping (June) and molting (August) seasons from 2007-2014. Surveys were flown along a grid of 12 transects and high-resolution digital photos were taken directly under the plane using a vertically aimed camera. Seal abundance and spatial distribution was consistently higher during June (range: 1,672-4,340) than August (range: 1,075-2,582) and corresponded to the spatial distribution and amount of ice. Preliminary analyses from 2007 suggest that the average percent of icebergs (ice ≥ than 1.6m2) and brash ice (ice < 1.6m2) per scene were greater in June (icebergs: 1.8% ± 1.6%; brash ice: 43.8% ± 38.9%) than August (icebergs: 0.2% ± 0.7%; brash ice; 15.8% ± 26.4%). Iceberg angularity (an index of iceberg shape) was also greater in June (1.7 ± 0.9) than August (0.9 ± 0.9). Potential factors that may influence the spatio-temporal variation in ice habitat for harbor seals in tidewater glacier fjords include frontal ablation rates of glaciers, fjord circulation, and local winds. Harbor seals exhibit high seasonal fidelity to tidewater glacier fjords, thus understanding the relationships between glacier dynamics and harbor seal distribution will be critical for understanding how future changes in tidewater glaciers may impact harbor seals.

Experimental and rendering-based investigation of laser radar cross sections of small unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Laurenzis, Martin; Bacher, Emmanuel; Christnacher, Frank

2017-12-01

Laser imaging systems are prominent candidates for detection and tracking of small unmanned aerial vehicles (UAVs) in current and future security scenarios. Laser reflection characteristics for laser imaging (e.g., laser gated viewing) of small UAVs are investigated to determine their laser radar cross section (LRCS) by analyzing the intensity distribution of laser reflection in high resolution images. For the first time, LRCSs are determined in a combined experimental and computational approaches by high resolution laser gated viewing and three-dimensional rendering. An optimized simple surface model is calculated taking into account diffuse and specular reflectance properties based on the Oren-Nayar and the Cook-Torrance reflectance models, respectively.

Detecting blind building façades from highly overlapping wide angle aerial imagery

NASA Astrophysics Data System (ADS)

Burochin, Jean-Pascal; Vallet, Bruno; Brédif, Mathieu; Mallet, Clément; Brosset, Thomas; Paparoditis, Nicolas

2014-10-01

This paper deals with the identification of blind building façades, i.e. façades which have no openings, in wide angle aerial images with a decimeter pixel size, acquired by nadir looking cameras. This blindness characterization is in general crucial for real estate estimation and has, at least in France, a particular importance on the evaluation of legal permission of constructing on a parcel due to local urban planning schemes. We assume that we have at our disposal an aerial survey with a relatively high stereo overlap along-track and across-track and a 3D city model of LoD 1, that can have been generated with the input images. The 3D model is textured with the aerial imagery by taking into account the 3D occlusions and by selecting for each façade the best available resolution texture seeing the whole façade. We then parse all 3D façades textures by looking for evidence of openings (windows or doors). This evidence is characterized by a comprehensive set of basic radiometric and geometrical features. The blindness prognostic is then elaborated through an (SVM) supervised classification. Despite the relatively low resolution of the images, we reach a classification accuracy of around 85% on decimeter resolution imagery with 60 × 40 % stereo overlap. On the one hand, we show that the results are very sensitive to the texturing resampling process and to vegetation presence on façade textures. On the other hand, the most relevant features for our classification framework are related to texture uniformity and horizontal aspect and to the maximal contrast of the opening detections. We conclude that standard aerial imagery used to build 3D city models can also be exploited to some extent and at no additional cost for facade blindness characterisation.

Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

NASA Astrophysics Data System (ADS)

Abdelrahim, Mohamed Mahmoud Hosny

2001-11-01

In this research, an "Intelligent Imagery System Prototype" (IISP) was developed. IISP is an integration tool that facilitates the environment for active, direct, and on-the-fly usage of high resolution imagery, internally linked to hidden GIS vector layers, to query the real world phenomena and, consequently, to perform exploratory types of spatial analysis based on a clear/undisturbed image scene. The IISP was designed and implemented using the software components approach to verify the hypothesis that a fully rectified, partially rectified, or even unrectified digital image can be internally linked to a variety of different hidden vector databases/layers covering the end user area of interest, and consequently may be reliably used directly as a base for "on-the-fly" querying of real-world phenomena and for performing exploratory types of spatial analysis. Within IISP, differentially rectified, partially rectified (namely, IKONOS GEOCARTERRA(TM)), and unrectified imagery (namely, scanned aerial photographs and captured video frames) were investigated. The system was designed to handle four types of spatial functions, namely, pointing query, polygon/line-based image query, database query, and buffering. The system was developed using ESRI MapObjects 2.0a as the core spatial component within Visual Basic 6.0. When used to perform the pre-defined spatial queries using different combinations of image and vector data, the IISP provided the same results as those obtained by querying pre-processed vector layers even when the image used was not orthorectified and the vector layers had different parameters. In addition, the real-time pixel location orthorectification technique developed and presented within the IKONOS GEOCARTERRA(TM) case provided a horizontal accuracy (RMSE) of +/- 2.75 metres. This accuracy is very close to the accuracy level obtained when purchasing the orthorectified IKONOS PRECISION products (RMSE of +/- 1.9 metre). The latter cost approximately four times as much as the IKONOS GEOCARTERRA(TM) products. The developed IISP is a step closer towards the direct and active involvement of high-resolution remote sensing imagery in querying the real world and performing exploratory types of spatial analysis. (Abstract shortened by UMI.)

Increasing the UAV data value by an OBIA methodology

NASA Astrophysics Data System (ADS)

García-Pedrero, Angel; Lillo-Saavedra, Mario; Rodriguez-Esparragon, Dionisio; Rodriguez-Gonzalez, Alejandro; Gonzalo-Martin, Consuelo

2017-10-01

Recently, there has been a noteworthy increment of using images registered by unmanned aerial vehicles (UAV) in different remote sensing applications. Sensors boarded on UAVs has lower operational costs and complexity than other remote sensing platforms, quicker turnaround times as well as higher spatial resolution. Concerning this last aspect, particular attention has to be paid on the limitations of classical algorithms based on pixels when they are applied to high resolution images. The objective of this study is to investigate the capability of an OBIA methodology developed for the automatic generation of a digital terrain model of an agricultural area from Digital Elevation Model (DEM) and multispectral images registered by a Parrot Sequoia multispectral sensor board on a eBee SQ agricultural drone. The proposed methodology uses a superpixel approach for obtaining context and elevation information used for merging superpixels and at the same time eliminating objects such as trees in order to generate a Digital Terrain Model (DTM) of the analyzed area. Obtained results show the potential of the approach, in terms of accuracy, when it is compared with a DTM generated by manually eliminating objects.

Defense applications of the CAVE (CAVE automatic virtual environment)

NASA Astrophysics Data System (ADS)

Isabelle, Scott K.; Gilkey, Robert H.; Kenyon, Robert V.; Valentino, George; Flach, John M.; Spenny, Curtis H.; Anderson, Timothy R.

1997-07-01

The CAVE is a multi-person, room-sized, high-resolution, 3D video and auditory environment, which can be used to present very immersive virtual environment experiences. This paper describes the CAVE technology and the capability of the CAVE system as originally developed at the Electronics Visualization Laboratory of the University of Illinois- Chicago and as more recently implemented by Wright State University (WSU) in the Armstrong Laboratory at Wright- Patterson Air Force Base (WPAFB). One planned use of the WSU/WPAFB CAVE is research addressing the appropriate design of display and control interfaces for controlling uninhabited aerial vehicles. The WSU/WPAFB CAVE has a number of features that make it well-suited to this work: (1) 360 degrees surround, plus floor, high resolution visual displays, (2) virtual spatialized audio, (3) the ability to integrate real and virtual objects, and (4) rapid and flexible reconfiguration. However, even though the CAVE is likely to have broad utility for military applications, it does have certain limitations that may make it less well- suited to applications that require 'natural' haptic feedback, vestibular stimulation, or an ability to interact with close detailed objects.

Airborne Thermal Infrared Multispectral Scanner (TIMS) images over disseminated gold deposits, Osgood Mountains, Humboldt County, Nevada

NASA Technical Reports Server (NTRS)

Krohn, M. Dennis

1986-01-01

The U.S. Geological Survey (USGS) acquired airborne Thermal Infrared Multispectral Scanner (TIMS) images over several disseminated gold deposits in northern Nevada in 1983. The aerial surveys were flown to determine whether TIMS data could depict jasperoids (siliceous replacement bodies) associated with the gold deposits. The TIMS data were collected over the Pinson and Getchell Mines in the Osgood Mountains, the Carlin, Maggie Creek, Bootstrap, and other mines in the Tuscarora Mountains, and the Jerritt Canyon Mine in the Independence Mountains. The TIMS data seem to be a useful supplement to conventional geochemical exploration for disseminated gold deposits in the western United States. Siliceous outcrops are readily separable in the TIMS image from other types of host rocks. Different forms of silicification are not readily separable, yet, due to limitations of spatial resolution and spectral dynamic range. Features associated with the disseminated gold deposits, such as the large intrusive bodies and fault structures, are also resolvable on TIMS data. Inclusion of high-resolution thermal inertia data would be a useful supplement to the TIMS data.

Acquision of Geometrical Data of Small Rivers with AN Unmanned Water Vehicle

NASA Astrophysics Data System (ADS)

Sardemann, H.; Eltner, A.; Maas, H.-G.

2018-05-01

Rivers with small- and medium-scaled catchments have been increasingly affected by extreme events, i.e. flash floods, in the last years. New methods to describe and predict these events are developed in the interdisciplinary research project EXTRUSO. Flash flood events happen on small temporal and spatial scales, stressing the necessity of high-resolution input data for hydrological and hydrodynamic modelling. Among others, the benefit of high-resolution digital terrain models (DTMs) will be evaluated in the project. This article introduces a boat-based approach for the acquisition of geometrical and morphological data of small rivers and their banks. An unmanned water vehicle (UWV) is used as a multi-sensor platform to collect 3D-point clouds of the riverbanks, as well as bathymetric measurements of water depth and river morphology. The UWV is equipped with a mobile Lidar, a panorama camera, an echo sounder and a positioning unit. Whole (sub-) catchments of small rivers can be digitalized and provided for hydrological modelling when UWV-based and UAV (unmanned aerial vehicle) based point clouds are fused.

The Unmanned Aerial System SUMO: an alternative measurement tool for polar boundary layer studies

NASA Astrophysics Data System (ADS)

Mayer, S.; Jonassen, M. O.; Reuder, J.

2012-04-01

Numerical weather prediction and climate models face special challenges in particular in the commonly stable conditions in the high-latitude environment. For process studies as well as for model validation purposes in-situ observations in the atmospheric boundary layer are highly required, but difficult to retrieve. We introduce a new measurement system for corresponding observations. The Small Unmanned Meteorological Observer SUMO consists of a small and light-weight auto-piloted model aircraft, equipped with a meteorological sensor package. SUMO has been operated in polar environments, among others during IPY on Spitsbergen in the year 2009 and has proven its capabilities for atmospheric measurements with high spatial and temporal resolution even at temperatures of -30 deg C. A comparison of the SUMO data with radiosondes and tethered balloons shows that SUMO can provide atmospheric profiles with comparable quality to those well-established systems. Its high data quality allowed its utilization for evaluation purposes of high-resolution model runs performed with the Weather Research and Forecasting model WRF and for the detailed investigation of an orographically modified flow during a case study.

A comparison of LANDSAT TM to MSS imagery for detecting submerged aquatic vegetation in lower Chesapeake Bay

NASA Technical Reports Server (NTRS)

Ackleson, S. G.; Klemas, V.

1985-01-01

LANDSAT Thematic Mapper (TM) and Multispectral Scanner (MSS) imagery generated simultaneously over Guinea Marsh, Virginia, are assessed in the ability to detect submerged aquatic, bottom-adhering plant canopies (SAV). An unsupervised clustering algorithm is applied to both image types and the resulting classifications compared to SAV distributions derived from color aerial photography. Class confidence and accuracy are first computed for all water areas and then only shallow areas where water depth is less than 6 feet. In both the TM and MSS imagery, masking water areas deeper than 6 ft. resulted in greater classification accuracy at confidence levels greater than 50%. Both systems perform poorly in detecting SAV with crown cover densities less than 70%. On the basis of the spectral resolution, radiometric sensitivity, and location of visible bands, TM imagery does not offer a significant advantage over MSS data for detecting SAV in Lower Chesapeake Bay. However, because the TM imagery represents a higher spatial resolution, smaller SAV canopies may be detected than is possible with MSS data.

Anthropogenic and geologic influences on subsidence in the vicinity of New Orleans, Louisiana

NASA Astrophysics Data System (ADS)

Jones, Cathleen E.; An, Karen; Blom, Ronald G.; Kent, Joshua D.; Ivins, Erik R.; Bekaert, David

2016-05-01

New measurements of ongoing subsidence of land proximal to the city of New Orleans, Louisiana, and including areas around the communities of Norco and Lutcher upriver along the Mississippi are reported. The rates of vertical motion are derived from interferometric synthetic aperture radar (InSAR) applied to Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data acquired on 16 June 2009 and 2 July 2012. The subsidence trends are similar to those reported for 2002-2004 in parts of New Orleans where observations overlap, in particular in Michoud, the 9th Ward, and Chalmette, but are measured at much higher spatial resolution (6 m). The spatial associations of cumulative surface movements suggest that the most likely drivers of subsidence are groundwater withdrawal and surficial drainage/dewatering activities. High subsidence rates are observed localized around some major industrial facilities and can affect nearby flood control infrastructure. Substantial subsidence is observed to occur rapidly from shallow compaction in highly localized areas, which is why it could be missed in subsidence surveys relying on point measurements at limited locations.

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

Development of an Unmanned Aerial System (UAS) for Scaling Terrestrial Ecosystem Traits

NASA Astrophysics Data System (ADS)

Meng, R.; McMahon, A. M.; Serbin, S.; Rogers, A.

2015-12-01

The next generation of Ecosystem and Earth System Models (EESMs) will require detailed information on ecosystem structure and function, including properties of vegetation related to carbon (C), water, and energy cycling, in order to project the future state of ecosystems. High spatial-temporal resolution measurements of terrestrial ecosystem are also important for EESMs, because they can provide critical inputs and benchmark datasets for evaluation of EESMs simulations across scales. The recent development of high-quality, low-altitude remote sensing platforms or small UAS (< 25 kg) enables measurements of terrestrial ecosystems at unprecedented temporal and spatial scales. Specifically, these new platforms can provide detailed information on patterns and processes of terrestrial ecosystems at a critical intermediate scale between point measurements and suborbital and satellite platforms. Given their potential for sub-decimeter spatial resolution, improved mission safety, high revisit frequency, and reduced operation cost, these platforms are of particular interest in the development of ecological scaling algorithms to parameterize and benchmark EESMs, particularly over complex and remote terrain. Our group is developing a small UAS platform and integrated sensor package focused on measurement needs for scaling and informing ecosystem modeling activities, as well as scaling and mapping plant functional traits. To do this we are developing an integrated software workflow and hardware package using off-the-shelf instrumentation including a high-resolution digital camera for Structure from Motion, spectroradiometer, and a thermal infrared camera. Our workflow includes platform design, measurement, image processing, data management, and information extraction. The fusion of 3D structure information, thermal-infrared imagery, and spectroscopic measurements, will provide a foundation for the development of ecological scaling and mapping algorithms. Our initial focus is in temperate forests but near-term research will expand into the high-arctic and eventually tropical systems. The results of this prototype study show that off-the-shelf technology can be used to develop a low-cost alternative for mapping plant traits and three-dimensional structure for ecological research.

Theme issue ;State-of-the-art in photogrammetry, remote sensing and spatial information science;

NASA Astrophysics Data System (ADS)

Heipke, Christian; Madden, Marguerite; Li, Zhilin; Dowman, Ian

2016-05-01

Over the past few years, photogrammetry, remote sensing and spatial information science have witnessed great changes in virtually every stage of information from imagery. Indeed, we have seen, for example, a sharply increased interest in unmanned aerial vehicles,

Automated high resolution mapping of coffee in Rwanda using an expert Bayesian network

NASA Astrophysics Data System (ADS)

Mukashema, A.; Veldkamp, A.; Vrieling, A.

2014-12-01

African highland agro-ecosystems are dominated by small-scale agricultural fields that often contain a mix of annual and perennial crops. This makes such systems difficult to map by remote sensing. We developed an expert Bayesian network model to extract the small-scale coffee fields of Rwanda from very high resolution data. The model was subsequently applied to aerial orthophotos covering more than 99% of Rwanda and on one QuickBird image for the remaining part. The method consists of a stepwise adjustment of pixel probabilities, which incorporates expert knowledge on size of coffee trees and fields, and on their location. The initial naive Bayesian network, which is a spectral-based classification, yielded a coffee map with an overall accuracy of around 50%. This confirms that standard spectral variables alone cannot accurately identify coffee fields from high resolution images. The combination of spectral and ancillary data (DEM and a forest map) allowed mapping of coffee fields and associated uncertainties with an overall accuracy of 87%. Aggregated to district units, the mapped coffee areas demonstrated a high correlation with the coffee areas reported in the detailed national coffee census of 2009 (R2 = 0.92). Unlike the census data our map provides high spatial resolution of coffee area patterns of Rwanda. The proposed method has potential for mapping other perennial small scale cropping systems in the East African Highlands and elsewhere.

High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates.

PubMed

Madec, Simon; Baret, Fred; de Solan, Benoît; Thomas, Samuel; Dutartre, Dan; Jezequel, Stéphane; Hemmerlé, Matthieu; Colombeau, Gallian; Comar, Alexis

2017-01-01

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z -value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values ( H 2 > 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable ( H 2 > 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed.

Tidewater dynamics at Store Glacier, West Greenland from daily repeat UAV surveys

NASA Astrophysics Data System (ADS)

Ryan, Jonathan; Hubbard, Alun; Toberg, Nick; Box, Jason; Todd, Joe; Christoffersen, Poul; Neal, Snooke

2017-04-01

A significant component of the Greenland ice sheet's mass wasteage to sea level rise is attributed to the acceleration and dynamic thinning at its tidewater margins. To improve understanding of the rapid mass loss processes occurring at large tidewater glaciers, we conducted a suite of daily repeat aerial surveys across the terminus of Store Glacier, a large outlet draining the western Greenland Ice Sheet, from May to July 2014 (https://www.youtube.com/watch?v=-y8kauAVAfE). The unmanned aerial vehicles (UAVs) were equipped with digital cameras, which, in combination with onboard GPS, enabled production of high spatial resolution orthophotos and digital elevation models (DEMs) using standard structure-from-motion techniques. These data provide insight into the short-term dynamics of Store Glacier surrounding the break-up of the sea-ice mélange that occurred between 4 and 7 June. Feature tracking of the orthophotos reveals that mean speed of the terminus is 16 - 18 m per day, which was independently verified against a high temporal resolution time-series derived from an expendable/telemetric GPS deployed at the terminus. Differencing the surface area of successive orthophotos enable quantification of daily calving rates, which significantly increase just after melange break-up. Likewise, by differencing bulk freeboard volume of icebergs through time we could also constrain the magnitude and variation of submarine melt. We calculate a mean submarine melt rate of 0.18 m per day throughout the spring period with relatively little supraglacial runoff and no active meltwater plumes to stimulate fjord circulation and upwelling of deeper, warmer water masses. Finally, we relate calving rates to the zonation and depth of water-filled crevasses, which were prominent across parts of the terminus from June onwards.

High-Throughput Phenotyping of Plant Height: Comparing Unmanned Aerial Vehicles and Ground LiDAR Estimates

PubMed Central

Madec, Simon; Baret, Fred; de Solan, Benoît; Thomas, Samuel; Dutartre, Dan; Jezequel, Stéphane; Hemmerlé, Matthieu; Colombeau, Gallian; Comar, Alexis

2017-01-01

The capacity of LiDAR and Unmanned Aerial Vehicles (UAVs) to provide plant height estimates as a high-throughput plant phenotyping trait was explored. An experiment over wheat genotypes conducted under well watered and water stress modalities was conducted. Frequent LiDAR measurements were performed along the growth cycle using a phénomobile unmanned ground vehicle. UAV equipped with a high resolution RGB camera was flying the experiment several times to retrieve the digital surface model from structure from motion techniques. Both techniques provide a 3D dense point cloud from which the plant height can be estimated. Plant height first defined as the z-value for which 99.5% of the points of the dense cloud are below. This provides good consistency with manual measurements of plant height (RMSE = 3.5 cm) while minimizing the variability along each microplot. Results show that LiDAR and structure from motion plant height values are always consistent. However, a slight under-estimation is observed for structure from motion techniques, in relation with the coarser spatial resolution of UAV imagery and the limited penetration capacity of structure from motion as compared to LiDAR. Very high heritability values (H2> 0.90) were found for both techniques when lodging was not present. The dynamics of plant height shows that it carries pertinent information regarding the period and magnitude of the plant stress. Further, the date when the maximum plant height is reached was found to be very heritable (H2> 0.88) and a good proxy of the flowering stage. Finally, the capacity of plant height as a proxy for total above ground biomass and yield is discussed. PMID:29230229

Unmanned Aerial Vehicles Produce High-Resolution Seasonally-Relevant Imagery for Classifying Wetland Vegetation

NASA Astrophysics Data System (ADS)

Marcaccio, J. V.; Markle, C. E.; Chow-Fraser, P.

2015-08-01

With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < 3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year.

Object-based Image Classification of Arctic Sea Ice and Melt Ponds through Aerial Photos

NASA Astrophysics Data System (ADS)

Miao, X.; Xie, H.; Li, Z.; Lei, R.

2013-12-01

The last six years have marked the lowest Arctic summer sea ice extents in the modern era, with a new record summer minimum (3.4 million km2) set on 13 September 2012. It has been predicted that the Arctic could be free of summer ice within the next 25-30. The loss of Arctic summer ice could have serious consequences, such as higher water temperature due to the positive feedback of albedo, more powerful and frequent storms, rising sea levels, diminished habitats for polar animals, and more pollution due to fossil fuel exploitation and/ or increased traffic through the Northwest/ Northeast Passage. In these processes, melt ponds play an important role in Earth's radiation balance since they strongly absorb solar radiation rather than reflecting it as snow and ice do. Therefore, it is necessary to develop the ability of predicting the sea ice/ melt pond extents and space-time evolution, which is pivotal to prepare for the variation and uncertainty of the future environment, political, economic, and military needs. A lot of efforts have been put into Arctic sea ice modeling to simulate sea ice processes. However, these sea ice models were initiated and developed based on limited field surveys, aircraft or satellite image data. Therefore, it is necessary to collect high resolution sea ice aerial photo in a systematic way to tune up, validate, and improve models. Currently there are many sea ice aerial photos available, such as Chinese Arctic Exploration (CHINARE 2008, 2010, 2012), SHEBA 1998 and HOTRAX 2005. However, manually delineating of sea ice and melt pond from these images is time-consuming and labor-intensive. In this study, we use the object-based remote sensing classification scheme to extract sea ice and melt ponds efficiently from 1,727 aerial photos taken during the CHINARE 2010. The algorithm includes three major steps as follows. (1) Image segmentation groups the neighboring pixels into objects according to the similarity of spectral and texture information; (2) random forest ensemble classifier can distinguish the following objects: water, submerged ice, shadow, and ice/snow; and (3) polygon neighbor analysis can further separate melt ponds from submerged ice according to the spatial neighboring relationship. Our results illustrate the spatial distribution and morphological characters of melt ponds in different latitudes of the Arctic Pacific sector. This method can be applied to massive photos and images taken in past years and future years, in deriving the detailed sea ice and melt pond distribution and changes through years.

Analyzing the Potential for Unmanned Aerial Systems (UAS) Photogrammetry in Estimating Surface Deformations at a Geothermal Fiel

NASA Astrophysics Data System (ADS)

Pai, H.; Burnett, J.; Sladek, C.; Wing, M.; Feigl, K. L.; Selker, J. S.; Tyler, S.; Team, P.

2016-12-01

UAS systems equipped with a variety of spectral imaging devices are increasingly incorporated in spatial environmental assessments of continental surfaces (e.g., digital elevation maps, vegetative coverage classifications, surface temperatures). This presented work performed by the UAS team at the Center for Transformative Environmental Monitoring Programs (AirCTEMPS) examines the potential to measure small (sub-cm) deformation from a geothermal injection experiment at Brady's geothermal field in western Nevada (USA). Areal mapping of the 700 x 270 m area of interest was conducted with a nadir pointing Sony A5100 digital camera onboard an autopiloted quadcopter. A total of 16 ground control points were installed using a TopCon GR3 GPS receiver. Two such mapping campaigns were conducted with one before and one after an anticipated surface deformation event. A digital elevation map (DEM) for each time period was created from over 1500 images having 80% overlap/sidelap by using structure from motion (SfM) via Agisoft Photoscan software. The resulting DEM resolution was 8 mm/pixel with residual aerial triangulation errors was < 5 mm. We present preliminary results from an optimized workflow which achieved errors and average differential DEM heights between campaigns at the cm-scale which is broader than the maximum expected deformation. Despite the disconnect between error and deformation severity, this study presents a unique application of sub-cm UAS-based DEMs and further distinguishes itself by comparing results to concurrent Interferometric Synthetic Radar (InSAR). The intent of our study and presentation of results is to streamline, cross-validate, and share methods to encourage further adoption of UAS imagery into the standard toolkit for environmental surface sensing across spatial scales.

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Harder, Phillip; Schirmer, Michael; Pomeroy, John; Helgason, Warren

2016-11-01

Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land-atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s-1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Classification of Urban Aerial Data Based on Pixel Labelling with Deep Convolutional Neural Networks and Logistic Regression

NASA Astrophysics Data System (ADS)

Yao, W.; Poleswki, P.; Krzystek, P.

2016-06-01

The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN's texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.

The evolution of active Lavina di Roncovetro landslides by multi-temporal high-resolution topographic data

NASA Astrophysics Data System (ADS)

Isola, Ilaria; Fornaciai, Alessandro; Favalli, Massimiliano; Gigli, Giovanni; Nannipieri, Luca; Mucchi, Lorenzo; Intrieri, Emanuele; Pizziolo, Marco; Bertolini, Giovanni; Trippi, Federico; Casagli, Nicola; Schina, Rosa; Carnevale, Ennio

2017-04-01

High-resolution topographic data has been collected over the Lavina di Roncovetro active landslide (Reggio Emilia, Italy) for about 3 years by using various methods and technologies. Tha Lavina di Roncovetro landslide can be considered as a fluid-viscous mudflow, which can reach a down flow maximum rate of 10 m/day. The landslide started between the middle and the end of the XIX century and since then it has had a rapid evolution mainly characterized by the rapid retrogression of the crown to the extent that now reaches the top of Mount Staffola. In the frame of EU Wireless Sensor Network for Ground Instability Monitoring - Wi-GIM project (LIFE12ENV/IT/001033) the Lavina di Roncovetro landslide has been periodically tracked using technologies that span from the LiDAR, both terrestrial and aerial, to the Structure from Motion (SfM) photogrammetry method based on Unmanned Aerial Vehicle (UAV) and aerial survey. These data are used to create six high-resolution Digital Terrain Models (DEMs), which imaged the landslide surface on March 2014, October 2014, June 2015, July 2015, January 2016 and December 2016. Multi-temporal high-resolution topographic data have been used for qualitative and quantitative morphometric analysis and topographic change detection of the landslide with the aim to estimate and map the volume of removed and/or accumulated material, the average rates of vertical and horizontal displacement and the deformation structures affecting the landslide over the investigated period.

Delineation of marsh types and marsh-type change in coastal Louisiana for 2007 and 2013

USGS Publications Warehouse

Hartley, Stephen B.; Couvillion, Brady R.; Enwright, Nicholas M.

2017-05-30

The Bureau of Ocean Energy Management researchers often require detailed information regarding emergent marsh vegetation types (such as fresh, intermediate, brackish, and saline) for modeling habitat capacities and mitigation. In response, the U.S. Geological Survey in cooperation with the Bureau of Ocean Energy Management produced a detailed change classification of emergent marsh vegetation types in coastal Louisiana from 2007 and 2013. This study incorporates two existing vegetation surveys and independent variables such as Landsat Thematic Mapper multispectral satellite imagery, high-resolution airborne imagery from 2007 and 2013, bare-earth digital elevation models based on airborne light detection and ranging, alternative contemporary land-cover classifications, and other spatially explicit variables. An image classification based on image objects was created from 2007 and 2013 National Agriculture Imagery Program color-infrared aerial photography. The final products consisted of two 10-meter raster datasets. Each image object from the 2007 and 2013 spatial datasets was assigned a vegetation classification by using a simple majority filter. In addition to those spatial datasets, we also conducted a change analysis between the datasets to produce a 10-meter change raster product. This analysis identified how much change has taken place and where change has occurred. The spatial data products show dynamic areas where marsh loss is occurring or where marsh type is changing. This information can be used to assist and advance conservation efforts for priority natural resources.

Measuring cloud thermodynamic phase with shortwave infrared imaging spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thompson, David R.; McCubbin, Ian; Gao, Bo Cai

Shortwave Infrared imaging spectroscopy enables accurate remote mapping of cloud thermodynamic phase at high spatial resolution. We describe a measurement strategy to exploit signatures of liquid and ice absorption in cloud top apparent reflectance spectra from 1.4 to 1.8 μm. This signal is generally insensitive to confounding factors such as solar angles, view angles, and surface albedo. We first evaluate the approach in simulation and then apply it to airborne data acquired in the Calwater-2/ACAPEX campaign of Winter 2015. Here NASA’s “Classic” Airborne Visible Infrared Imaging Spectrometer (AVIRIS-C) remotely observed diverse cloud formations while the U.S. Department of Energy ARMmore » Aerial Facility G-1 aircraft measured cloud integral and microphysical properties in situ. Finally, the coincident measurements demonstrate good separation of the thermodynamic phases for relatively homogeneous clouds.« less

UNMANNED AERIAL VEHICLE (UAV) HYPERSPECTRAL REMOTE SENSING FOR DRYLAND VEGETATION MONITORING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nancy F. Glenn; Jessica J. Mitchell; Matthew O. Anderson

2012-06-01

UAV-based hyperspectral remote sensing capabilities developed by the Idaho National Lab and Idaho State University, Boise Center Aerospace Lab, were recently tested via demonstration flights that explored the influence of altitude on geometric error, image mosaicking, and dryland vegetation classification. The test flights successfully acquired usable flightline data capable of supporting classifiable composite images. Unsupervised classification results support vegetation management objectives that rely on mapping shrub cover and distribution patterns. Overall, supervised classifications performed poorly despite spectral separability in the image-derived endmember pixels. Future mapping efforts that leverage ground reference data, ultra-high spatial resolution photos and time series analysis shouldmore » be able to effectively distinguish native grasses such as Sandberg bluegrass (Poa secunda), from invasives such as burr buttercup (Ranunculus testiculatus) and cheatgrass (Bromus tectorum).« less

Estimating evaporation with thermal UAV data and two-source energy balance models

NASA Astrophysics Data System (ADS)

Hoffmann, H.; Nieto, H.; Jensen, R.; Guzinski, R.; Zarco-Tejada, P.; Friborg, T.

2016-02-01

Estimating evaporation is important when managing water resources and cultivating crops. Evaporation can be estimated using land surface heat flux models and remotely sensed land surface temperatures (LST), which have recently become obtainable in very high resolution using lightweight thermal cameras and Unmanned Aerial Vehicles (UAVs). In this study a thermal camera was mounted on a UAV and applied into the field of heat fluxes and hydrology by concatenating thermal images into mosaics of LST and using these as input for the two-source energy balance (TSEB) modelling scheme. Thermal images are obtained with a fixed-wing UAV overflying a barley field in western Denmark during the growing season of 2014 and a spatial resolution of 0.20 m is obtained in final LST mosaics. Two models are used: the original TSEB model (TSEB-PT) and a dual-temperature-difference (DTD) model. In contrast to the TSEB-PT model, the DTD model accounts for the bias that is likely present in remotely sensed LST. TSEB-PT and DTD have already been well tested, however only during sunny weather conditions and with satellite images serving as thermal input. The aim of this study is to assess whether a lightweight thermal camera mounted on a UAV is able to provide data of sufficient quality to constitute as model input and thus attain accurate and high spatial and temporal resolution surface energy heat fluxes, with special focus on latent heat flux (evaporation). Furthermore, this study evaluates the performance of the TSEB scheme during cloudy and overcast weather conditions, which is feasible due to the low data retrieval altitude (due to low UAV flying altitude) compared to satellite thermal data that are only available during clear-sky conditions. TSEB-PT and DTD fluxes are compared and validated against eddy covariance measurements and the comparison shows that both TSEB-PT and DTD simulations are in good agreement with eddy covariance measurements, with DTD obtaining the best results. The DTD model provides results comparable to studies estimating evaporation with similar experimental setups, but with LST retrieved from satellites instead of a UAV. Further, systematic irrigation patterns on the barley field provide confidence in the veracity of the spatially distributed evaporation revealed by model output maps. Lastly, this study outlines and discusses the thermal UAV image processing that results in mosaics suited for model input. This study shows that the UAV platform and the lightweight thermal camera provide high spatial and temporal resolution data valid for model input and for other potential applications requiring high-resolution and consistent LST.

Orthorectification, mosaicking, and analysis of sub-decimeter resolution UAV imagery for rangeland monitoring

USDA-ARS?s Scientific Manuscript database

Unmanned aerial vehicles (UAVs) offer an attractive platform for acquiring imagery for rangeland monitoring. UAVs can be deployed quickly and repeatedly, and they can obtain sub-decimeter resolution imagery at lower image acquisition costs than with piloted aircraft. Low flying heights result in ima...

Low-cost camera modifications and methodologies for very-high-resolution digital images

USDA-ARS?s Scientific Manuscript database

Aerial color and color-infrared photography are usually acquired at high altitude so the ground resolution of the photographs is < 1 m. Moreover, current color-infrared cameras and manned aircraft flight time are expensive, so the objective is the development of alternative methods for obtaining ve...

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

USDA-ARS?s Scientific Manuscript database

Ultra high resolution digital aerial photography has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands...

A HYBRID HIGH RESOLUTION IMAGE CLASSIFICATION METHOD FOR MAPPING EELGRASS DISTRIBUTIONS IN YAQUINA BAY ESTUARY, OREGON

EPA Science Inventory

False-color infrared aerial photography of the Yaquina Bay Estuary, Oregon was acquired at extreme low tides and digitally orthorectified with a ground pixel resolution of 20 cm to provide data for intertidal vegetation mapping. Submerged, semi-exposed and exposed eelgrass mead...

High Density Aerial Image Matching: State-Of and Future Prospects

NASA Astrophysics Data System (ADS)

Haala, N.; Cavegn, S.

2016-06-01

Ongoing innovations in matching algorithms are continuously improving the quality of geometric surface representations generated automatically from aerial images. This development motivated the launch of the joint ISPRS/EuroSDR project "Benchmark on High Density Aerial Image Matching", which aims on the evaluation of photogrammetric 3D data capture in view of the current developments in dense multi-view stereo-image matching. Originally, the test aimed on image based DSM computation from conventional aerial image flights for different landuse and image block configurations. The second phase then put an additional focus on high quality, high resolution 3D geometric data capture in complex urban areas. This includes both the extension of the test scenario to oblique aerial image flights as well as the generation of filtered point clouds as additional output of the respective multi-view reconstruction. The paper uses the preliminary outcomes of the benchmark to demonstrate the state-of-the-art in airborne image matching with a special focus of high quality geometric data capture in urban scenarios.

Comparison of SLAR images and small-scale, low-sun aerial photographs.

NASA Technical Reports Server (NTRS)

Clark, M. M.

1971-01-01

A comparison of side-looking airborne radar (SLAR) images and black and white aerial photos of similar scale and illumination of an area in the Mojave Desert of California shows that aerial photos yield far more information about geology than do SLAR images because of greater resolution, tonal range, and geometric fidelity, and easier use in stereo. Nevertheless, radar can differentiate some materials or surfaces that aerial photos cannot; thus, they should be considered as complementary, rather than competing tools in geologic investigations. The most significant advantage of SLAR, however, is its freedom from the stringent conditions of weather, date, and time that are required by small-scale aerial photos taken with a specified direction and angle of illumination. Indeed, in low latitudes, SLAR is the only way to obtain small-scale images with low illumination from certain directions; moreover, in areas of nearly continuous cloudiness, radar may be the only practical source of small-scale images.

Creating a water depth map from Earth Observation-derived flood extent and topography data

NASA Astrophysics Data System (ADS)

Matgen, Patrick; Giustarini, Laura; Chini, Marco; Hostache, Renaud; Pelich, Ramona; Schlaffer, Stefan

2017-04-01

Enhanced methods for monitoring temporal and spatial variations of water depth in rivers and floodplains are very important in operational water management. Currently, variations of water elevation can be estimated indirectly at the land-water interface using sequences of satellite EO imagery in combination with topographic data. In recent years high-resolution digital elevation models (DEM) and satellite EO data have become more readily available at global scale. This study introduces an approach for efficiently converting remote sensing-derived flood extent maps into water depth maps using a floodplain's topography information. For this we make the assumption of uniform flow, that is the depth of flow with respect to the drainage network is considered to be the same at every section of the floodplain. In other words, the depth of water above the nearest drainage is expected to be constant for a given river reach. To determine this value we first need the Height Above Nearest Drainage (HAND) raster obtained by using the area of interest's DEM as source topography and a shapefile of the river network. The HAND model normalizes the topography with respect to the drainage network. Next, the HAND raster is thresholded in order to generate a binary mask that optimally fits, over the entire region of study, the flood extent map obtained from SAR or any other remote sensing product, including aerial photographs. The optimal threshold value corresponds to the height of the water line above the nearest drainage, termed HANDWATER, and is considered constant for a given subreach. Once the HANDWATER has been optimized, a water depth map can be generated by subtracting the value of the HAND raster at the each location from this parameter value. These developments enable large scale and near real-time applications and only require readily available EO data, a DEM and the river network as input data. The approach is based on a hierarchical split-based approach that subdivides a drainage network into segments of variable length with evidence of uniform flow. The method has been tested with remote sensing data and DEM data that differ in terms of spatial resolution and accuracy. A comprehensive evaluation of the obtained water depth maps with hydrodynamic modelling results and in situ measured water level recordings was carried out on a reach of the river Severn located in the United Kingdom. First results show that the obtained root mean squared difference is 10 cm when using high resolution high precision data sets (i.e. aerial photographs of flood extent and a LiDAR-derived DEM) and amount to 50 cm when using as inputs moderate resolution SAR imagery from ENVISAT and a SRTM-derived DEM.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A

2017-01-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

High Resolution Airborne Laser Scanning and Hyperspectral Imaging with a Small Uav Platform

NASA Astrophysics Data System (ADS)

Gallay, Michal; Eck, Christoph; Zgraggen, Carlo; Kaňuk, Ján; Dvorný, Eduard

2016-06-01

The capabilities of unmanned airborne systems (UAS) have become diverse with the recent development of lightweight remote sensing instruments. In this paper, we demonstrate our custom integration of the state-of-the-art technologies within an unmanned aerial platform capable of high-resolution and high-accuracy laser scanning, hyperspectral imaging, and photographic imaging. The technological solution comprises the latest development of a completely autonomous, unmanned helicopter by Aeroscout, the Scout B1-100 UAV helicopter. The helicopter is powered by a gasoline two-stroke engine and it allows for integrating 18 kg of a customized payload unit. The whole system is modular providing flexibility of payload options, which comprises the main advantage of the UAS. The UAS integrates two kinds of payloads which can be altered. Both payloads integrate a GPS/IMU with a dual GPS antenna configuration provided by OXTS for accurate navigation and position measurements during the data acquisition. The first payload comprises a VUX-1 laser scanner by RIEGL and a Sony A6000 E-Mount photo camera. The second payload for hyperspectral scanning integrates a push-broom imager AISA KESTREL 10 by SPECIM. The UAS was designed for research of various aspects of landscape dynamics (landslides, erosion, flooding, or phenology) in high spectral and spatial resolution.

Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data

NASA Astrophysics Data System (ADS)

Teodoro, Ana C.; Araujo, Ricardo

2016-01-01

The use of unmanned aerial vehicles (UAVs) for remote sensing applications is becoming more frequent. However, this type of information can result in several software problems related to the huge amount of data available. Object-based image analysis (OBIA) has proven to be superior to pixel-based analysis for very high-resolution images. The main objective of this work was to explore the potentialities of the OBIA methods available in two different open source software applications, Spring and OTB/Monteverdi, in order to generate an urban land cover map. An orthomosaic derived from UAVs was considered, 10 different regions of interest were selected, and two different approaches were followed. The first one (Spring) uses the region growing segmentation algorithm followed by the Bhattacharya classifier. The second approach (OTB/Monteverdi) uses the mean shift segmentation algorithm followed by the support vector machine (SVM) classifier. Two strategies were followed: four classes were considered using Spring and thereafter seven classes were considered for OTB/Monteverdi. The SVM classifier produces slightly better results and presents a shorter processing time. However, the poor spectral resolution of the data (only RGB bands) is an important factor that limits the performance of the classifiers applied.

Methods Used in EnviroAtlas to Assess Urban Natural ...

EPA Pesticide Factsheets

Previous studies have positively correlated human exposures to natural features with health promoting outcomes such as increased physical activity, improved cognitive function, increased social engagement, and reduced ambient air pollution. When using remotely-sensed data to investigate these relationships, researchers must first identify an appropriate spatial resolution to characterize exposures. However, metric development has often been limited by the lack of fine-scale land cover data, especially across multiple communities. As a result, researchers commonly use coarse resolution imagery. EnviroAtlas, a U.S. Environmental Protection Agency web-based ecosystem services mapping tool, has developed 1-meter resolution land cover data across 16 diverse U.S. Census Urban Areas using aerial photography and supplemental data. Research maps derived from these foundational data include percent tree cover along busy roads, percent tree cover and green space along walkable streets, and percent natural vegetation bordering water bodies. EnviroAtlas has also developed multiple smoothed “heat maps” of proximity to specific types of features at every 1m point; these include total green space, tree cover, and water within 50m, 500m, and 1,000m buffers; walking distance to the nearest park entrance; and intersection density as an indicator of neighborhood walkability.EnviroAtlas variables are available to external researchers, public health professionals and planners t

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.

2017-12-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

Using aerial images for establishing a workflow for the quantification of water management measures

NASA Astrophysics Data System (ADS)

Leuschner, Annette; Merz, Christoph; van Gasselt, Stephan; Steidl, Jörg

2017-04-01

Quantified landscape characteristics, such as morphology, land use or hydrological conditions, play an important role for hydrological investigations as landscape parameters directly control the overall water balance. A powerful assimilation and geospatial analysis of remote sensing datasets in combination with hydrological modeling allows to quantify landscape parameters and water balances efficiently. This study focuses on the development of a workflow to extract hydrologically relevant data from aerial image datasets and derived products in order to allow an effective parametrization of a hydrological model. Consistent and self-contained data source are indispensable for achieving reasonable modeling results. In order to minimize uncertainties and inconsistencies, input parameters for modeling should be extracted from one remote-sensing dataset mainly if possbile. Here, aerial images have been chosen because of their high spatial and spectral resolution that permits the extraction of various model relevant parameters, like morphology, land-use or artificial drainage-systems. The methodological repertoire to extract environmental parameters range from analyses of digital terrain models, multispectral classification and segmentation of land use distribution maps and mapping of artificial drainage-systems based on spectral and visual inspection. The workflow has been tested for a mesoscale catchment area which forms a characteristic hydrological system of a young moraine landscape located in the state of Brandenburg, Germany. These dataset were used as input-dataset for multi-temporal hydrological modelling of water balances to detect and quantify anthropogenic and meteorological impacts. ArcSWAT, as a GIS-implemented extension and graphical user input interface for the Soil Water Assessment Tool (SWAT) was chosen. The results of this modeling approach provide the basis for anticipating future development of the hydrological system, and regarding system changes for the adaption of water resource management decisions.

Melt pond fraction and spectral sea ice albedo retrieval from MERIS data - Part 1: Validation against in situ, aerial, and ship cruise data

NASA Astrophysics Data System (ADS)

Istomina, L.; Heygster, G.; Huntemann, M.; Schwarz, P.; Birnbaum, G.; Scharien, R.; Polashenski, C.; Perovich, D.; Zege, E.; Malinka, A.; Prikhach, A.; Katsev, I.

2015-08-01

The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

Feeding the hungry river: Fluvial morphodynamics and the entrainment of artificially inserted sediment at the dammed river Isar, Eastern Alps, Germany

NASA Astrophysics Data System (ADS)

Heckmann, Tobias; Haas, Florian; Abel, Judith; Rimböck, Andreas; Becht, Michael

2017-08-01

Dams interrupt the sediment continuum in rivers by retaining the bedload; combined with flow diversion, bedload retention in tributaries and river engineering measures, this causes a bedload deficit leading to changes in river planform and morphodynamics, with potentially detrimental downstream effects. As part of the SedAlp joint project (Sediment management in Alpine basins: integrating sediment continuum, risk mitigation and hydropower), this study investigates changes within a section of the dammed river Isar between the Sylvenstein reservoir and the city of Bad Tölz. We use a multi-method approach on a range of spatial and temporal scales. First, we analysed historical maps and aerial photos to analyse river planform and landcover changes within the river corridor of the whole study area on a temporal scale of over 100 years. Results show that major changes occurred before the construction of the Sylvenstein reservoir, suggesting that present morphodynamics represent the reaction to different disturbances on different time scales. Second, changes in mean bed elevation of cross profiles regularly surveyed by the water authorities are analysed in light of artificial sediment insertion and floods; they are also used to estimate the sediment budget of river reaches between consecutive cross profiles. Results suggest stability and a slight tendency towards incision, especially near the Sylvenstein reservoir; further downstream, the sediment balance was positive. Third, we acquired multitemporal aerial photos using an unmanned aerial vehicle and generated high-resolution digital elevation models to show how sediment artificially inserted in the river corridor is entrained. Depending on the position of the artificial deposits in relation to the channel, the deposits are entrained during floods of different return periods.

Toward Automatic Georeferencing of Archival Aerial Photogrammetric Surveys

NASA Astrophysics Data System (ADS)

Giordano, S.; Le Bris, A.; Mallet, C.

2018-05-01

Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes over the past 100 years. They provide a relatively dense temporal sampling of the territories with very high spatial resolution. Such time series image analysis is a mandatory baseline for a large variety of long-term environmental monitoring studies. The current bottleneck for accurate comparison between epochs is their fine georeferencing step. No fully automatic method has been proposed yet and existing studies are rather limited in terms of area and number of dates. State-of-the art shows that the major challenge is the identification of ground references: cartographic coordinates and their position in the archival images. This task is manually performed, and extremely time-consuming. This paper proposes to use a photogrammetric approach, and states that the 3D information that can be computed is the key to full automation. Its original idea lies in a 2-step approach: (i) the computation of a coarse absolute image orientation; (ii) the use of the coarse Digital Surface Model (DSM) information for automatic absolute image orientation. It only relies on a recent orthoimage+DSM, used as master reference for all epochs. The coarse orthoimage, compared with such a reference, allows the identification of dense ground references and the coarse DSM provides their position in the archival images. Results on two areas and 5 dates show that this method is compatible with long and dense archival aerial image series. Satisfactory planimetric and altimetric accuracies are reported, with variations depending on the ground sampling distance of the images and the location of the Ground Control Points.

The potential of unmanned aerial systems for sea turtle research and conservation: a review and future directions

USGS Publications Warehouse

Rees, Alan F.; Avens, Larisa; Ballorain, Katia; Bevan, Elizabeth; Broderick, Annette C.; Carthy, Raymond R.; Christianen, Marjolijn J. A.; Duclos, Gwénaël; Heithaus, Michael R.; Johnston, David W.; Mangel, Jeffrey C.; Paladino, Frank V.; Pendoley, Kellie; Reina, Richard D.; Robinson, Nathan J.; Ryan, Robert; Sykora-Bodie, Seth T.; Tilley, Dominic; Varela, Miguel R.; Whitman, Elizabeth R.; Whittock, Paul A.; Wibbels, Thane; Godley, Brendan J.

2018-01-01

The use of satellite systems and manned aircraft surveys for remote data collection has been shown to be transformative for sea turtle conservation and research by enabling the collection of data on turtles and their habitats over larger areas than can be achieved by surveys on foot or by boat. Unmanned aerial vehicles (UAVs) or drones are increasingly being adopted to gather data, at previously unprecedented spatial and temporal resolutions in diverse geographic locations. This easily accessible, low-cost tool is improving existing research methods and enabling novel approaches in marine turtle ecology and conservation. Here we review the diverse ways in which incorporating inexpensive UAVs may reduce costs and field time while improving safety and data quality and quantity over existing methods for studies on turtle nesting, at-sea distribution and behaviour surveys, as well as expanding into new avenues such as surveillance against illegal take. Furthermore, we highlight the impact that high-quality aerial imagery captured by UAVs can have for public outreach and engagement. This technology does not come without challenges. We discuss the potential constraints of these systems within the ethical and legal frameworks which researchers must operate and the difficulties that can result with regard to storage and analysis of large amounts of imagery. We then suggest areas where technological development could further expand the utility of UAVs as data-gathering tools; for example, functioning as downloading nodes for data collected by sensors placed on turtles. Development of methods for the use of UAVs in sea turtle research will serve as case studies for use with other marine and terrestrial taxa.

An Aerial Radiological Survey of Selected Areas of Area 18 - Nevada Test Site

DOE Office of Scientific and Technical Information (OSTI.GOV)

Craig Lyons

As part of the proficiency training for the Radiological Mapping mission of the Aerial Measuring System (AMS), a survey team from the Remote Sensing Laboratory-Nellis (RSL-Nellis) conducted an aerial radiological survey of selected areas of Area 18 of the Nevada Test Site (NTS) for the purpose of mapping man-made radiation deposited as a result of the Johnnie Boy and Little Feller I tests. The survey area centered over the Johnnie Boy ground zero but also included the ground zero and deposition area of the Little Feller I test, approximately 7,000 feet (2133 meters) southeast of the Johnnie Boy site. Themore » survey was conducted in one flight. The completed survey covered a total of 4.0 square miles. The flight lines (with the turns) over the surveyed areas are presented in Figure 1. One 2.5-hour-long flight was performed at an altitude of 100 ft above ground level (AGL) with 200 foot flight-line spacing. A test-line flight was conducted near the Desert Rock Airstrip to ensure quality control of the data. The test line is not shown in Figure 1. However, Figure 1 does include the flight lines for a ''perimeter'' flight. The path traced by the helicopter flying over distinct roads within the survey area can be used to overlay the survey data on a base map or image. The flight survey lines were flown in an east-west orientation perpendicular to the deposition patterns for both sites. This technique provides better spatial resolution when contouring the data. The data were collected by the AMS data acquisition system (REDAR V) using an array of twelve 2-inch x 4-inch x 16-inch sodium iodide (NaI) detectors flown on-board a twin-engine Bell 412 helicopter. Data, in the form of gamma energy spectra, were collected every second over the course of the survey and were geo-referenced using a differential Global Positioning System. Spectral data allows the system to distinguish between ordinary fluctuations in natural background radiation levels and the signature produced by man-made radioisotopes. Spectral data can also identify specific radioactive isotopes. Based on the results of the RSL NTS 1994 surveys, this area was chosen for a resurvey to improve the spatial resolution of the reported depositions for the Johnnie Boy and Little Feller I events. In addition, the survey was expected to confirm the absence of detectable concentrations of Americium-241 (Am-241) at the Johnnie Boy site and attempt to confirm the presence of Uranium-235 (U-235).« less

High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery

NASA Astrophysics Data System (ADS)

Tokarczyk, P.; Leitao, J. P.; Rieckermann, J.; Schindler, K.; Blumensaat, F.

2015-01-01

Modelling rainfall-runoff in urban areas is increasingly applied to support flood risk assessment particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the area. While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increase as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data is unavailable. Modern unmanned air vehicles (UAVs) allow acquiring high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements, and obtaining a degree of detail tailored for the purpose of the study. In this study, we investigate for the first time the possibility to derive high-resolution imperviousness maps for urban areas from UAV imagery and to use this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is tested and applied in a state-of-the-art urban drainage modelling exercise. In a real-life case study in the area of Lucerne, Switzerland, we compare imperviousness maps generated from a consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their correctness, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyze the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet. We show that imperviousness maps generated using UAV imagery processed with modern classification methods achieve accuracy comparable with standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on modelled surface runoff and pipe flows. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility to flexibly acquire up-to-date aerial images at a superior quality and a competitive price. Our analyses furthermore suggest that spatially more detailed urban drainage models can even better benefit from the full detail of UAV imagery.

Quantifying post-fire ponderosa pine snags using GIS techniques on scanned aerial photographs

NASA Astrophysics Data System (ADS)

Kent, Kevin

Snags are an important component of forest ecosystems because of their utility in forest-nutrient cycling and provision of critical wildlife habitat, as well as associated fuel management concerns relating to coarse woody debris (CWD). Knowledge of snag and CWD trajectories are needed for land managers to plan for long-term ecosystem change in post-fire regimes. This need will likely be exacerbated by increasingly warm and dry climatic conditions projected for the U.S. Southwest. One of the best prospects for studying fire-induced landscape change beyond the plot scale, but still at a resolution sufficient to resolve individual snags, is to utilize the available aerial photography record. Previous field-based studies of snag and CWD loads in the Southwest have relied on regional chronosequences to judge the recovery dynamic of ponderosa pine (Pinus ponderosa) burns. This previous research has been spatially and temporally restricted because of field survey extent limitations and uncertainty associated with the chronosequence approach (i.e., space-for-time substitution), which does not consider differences between specific site conditions and histories. This study develops highly automated methods for remotely quantifying and characterizing the spatial and temporal distribution of large snags associated with severe forest fires from very high resolution (VHR) landscape imagery I obtained from scans of aerial photos. Associated algorithms utilize the sharp edges, shape, shadow, and contrast characteristics of snags to enable feature recognition. Additionally, using snag shadow length, image acquisition time, and location information, heights were estimated for each identified snag. Furthermore, a novel solution was developed for extracting individual snags from areas of high snag density by overlaying parallel lines in the direction of the snag shadows and extracting local maxima lines contained by each snag polygon. Field survey data coincident to imagery coverage for post-fire ponderosa pine forests allowed calibration and accuracy assessment of these new tools. These new methods may allow for broader estimation of snag dynamics in post fire landscapes while significantly lowering the human and material costs of conducting such surveys. Outcomes for these methods were mixed. Both the snag count and snag height values were chronically underestimated using a feature extraction method and an edge detection method; so, an adjustment constant was developed for the categories of each method. Average accuracies ranged from 54 to 46% lower than the field-based values for the count attribute and 2-12% over the ground values for the snag height attribute using these two approaches. These methods show much promise, and are less resource intensive than field surveys, but more research is needed to improve overall accuracy.

Integration of Heterogenous Digital Surface Models

NASA Astrophysics Data System (ADS)

Boesch, R.; Ginzler, C.

2011-08-01

The application of extended digital surface models often reveals, that despite an acceptable global accuracy for a given dataset, the local accuracy of the model can vary in a wide range. For high resolution applications which cover the spatial extent of a whole country, this can be a major drawback. Within the Swiss National Forest Inventory (NFI), two digital surface models are available, one derived from LiDAR point data and the other from aerial images. Automatic photogrammetric image matching with ADS80 aerial infrared images with 25cm and 50cm resolution is used to generate a surface model (ADS-DSM) with 1m resolution covering whole switzerland (approx. 41000 km2). The spatially corresponding LiDAR dataset has a global point density of 0.5 points per m2 and is mainly used in applications as interpolated grid with 2m resolution (LiDAR-DSM). Although both surface models seem to offer a comparable accuracy from a global view, local analysis shows significant differences. Both datasets have been acquired over several years. Concerning LiDAR-DSM, different flight patterns and inconsistent quality control result in a significantly varying point density. The image acquisition of the ADS-DSM is also stretched over several years and the model generation is hampered by clouds, varying illumination and shadow effects. Nevertheless many classification and feature extraction applications requiring high resolution data depend on the local accuracy of the used surface model, therefore precise knowledge of the local data quality is essential. The commercial photogrammetric software NGATE (part of SOCET SET) generates the image based surface model (ADS-DSM) and delivers also a map with figures of merit (FOM) of the matching process for each calculated height pixel. The FOM-map contains matching codes like high slope, excessive shift or low correlation. For the generation of the LiDAR-DSM only first- and last-pulse data was available. Therefore only the point distribution can be used to derive a local accuracy measure. For the calculation of a robust point distribution measure, a constrained triangulation of local points (within an area of 100m2) has been implemented using the Open Source project CGAL. The area of each triangle is a measure for the spatial distribution of raw points in this local area. Combining the FOM-map with the local evaluation of LiDAR points allows an appropriate local accuracy evaluation of both surface models. The currently implemented strategy ("partial replacement") uses the hypothesis, that the ADS-DSM is superior due to its better global accuracy of 1m. If the local analysis of the FOM-map within the 100m2 area shows significant matching errors, the corresponding area of the triangulated LiDAR points is analyzed. If the point density and distribution is sufficient, the LiDAR-DSM will be used in favor of the ADS-DSM at this location. If the local triangulation reflects low point density or the variance of triangle areas exceeds a threshold, the investigated location will be marked as NODATA area. In a future implementation ("anisotropic fusion") an anisotropic inverse distance weighting (IDW) will be used, which merges both surface models in the point data space by using FOM-map and local triangulation to derive a quality weight for each of the interpolation points. The "partial replacement" implementation and the "fusion" prototype for the anisotropic IDW make use of the Open Source projects CGAL (Computational Geometry Algorithms Library), GDAL (Geospatial Data Abstraction Library) and OpenCV (Open Source Computer Vision).

Evaluation of factors affecting resolution of shallow water bottom features

NASA Technical Reports Server (NTRS)

Mason, C. C.; Norris, D. R.; Browne, I. D.

1972-01-01

To ensure good aerial photography, the effects that factors such as submergence depth, sun angle, film and filter type, exposure, aircraft altitude, and polarization have on the photographic resolution of an underwater object must be determined. Various subjects were photographed, such as the deck of a small submersible, colored and gray scale panels, and natural bottom features. No underwater resolution target was used.

Evaluation of a Change Detection Methodology by Means of Binary Thresholding Algorithms and Informational Fusion Processes

PubMed Central

Molina, Iñigo; Martinez, Estibaliz; Arquero, Agueda; Pajares, Gonzalo; Sanchez, Javier

2012-01-01

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth’s resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution. PMID:22737023

Evaluation of a change detection methodology by means of binary thresholding algorithms and informational fusion processes.

PubMed

Molina, Iñigo; Martinez, Estibaliz; Arquero, Agueda; Pajares, Gonzalo; Sanchez, Javier

2012-01-01

Landcover is subject to continuous changes on a wide variety of temporal and spatial scales. Those changes produce significant effects in human and natural activities. Maintaining an updated spatial database with the occurred changes allows a better monitoring of the Earth's resources and management of the environment. Change detection (CD) techniques using images from different sensors, such as satellite imagery, aerial photographs, etc., have proven to be suitable and secure data sources from which updated information can be extracted efficiently, so that changes can also be inventoried and monitored. In this paper, a multisource CD methodology for multiresolution datasets is applied. First, different change indices are processed, then different thresholding algorithms for change/no_change are applied to these indices in order to better estimate the statistical parameters of these categories, finally the indices are integrated into a change detection multisource fusion process, which allows generating a single CD result from several combination of indices. This methodology has been applied to datasets with different spectral and spatial resolution properties. Then, the obtained results are evaluated by means of a quality control analysis, as well as with complementary graphical representations. The suggested methodology has also been proved efficiently for identifying the change detection index with the higher contribution.

Using Satellite Images for Wireless Network Planing in Baku City

NASA Astrophysics Data System (ADS)

Gojamanov, M.; Ismayilov, J.

2013-04-01

It is a well known fact that the Information-Telecommunication and Space research technologies are the fields getting much more benefits from the achievements of the scientific and technical progress. In many cases, these areas supporting each other have improved the conditions for their further development. For instance, the intensive development in the field of the mobile communication has caused the rapid progress of the Space research technologies and vice versa.Today it is impossible to solve one of the most important tasks of the mobile communication as Radio Frecance planning without the 2D and 3D digital maps. The compiling of such maps is much more efficient by means of the space images. Because the quality of the space images has been improved and developed, especially at the both spectral and spatial resolution points. It has been possible to to use 8 Band images with the spatial resolution of 50 sm. At present, in relation to the function 3G of mobile communications one of the main issues facing mobile operator companies is a high-precision 3D digital maps. It should be noted that the number of mobile phone users in the Republic of Azerbaijan went forward other Community of Independent States Countries. Of course, using of aerial images for 3D mapping would be optimal. However, depending on a number of technical and administrative problems aerial photography cannot be used. Therefore, the experience of many countries shows that it will be more effective to use the space images with the higher resolution for these issues. Concerning the fact that the mobile communication within the city of Baku has included 3G function there were ordered stereo images wih the spatial resolution of 50 cm for the 150 sq.km territory occupying the central part of the city in order to compile 3D digital maps. The images collected from the WorldView-2 satellite are 4-Band Bundle(Pan+MS1) stereo images. Such kind of imagery enable to automatically classificate some required clutter classes.Meanwhile, there were created 12 GPS points in the territory and there have been held some appropriate observations in these points for the geodesic reference of the space images in the territory. Moreover, it would like to mention that there have been constructed 37 permanently acting GPS stations in the territory of Azerbaijan at present. It significantly facilitates the process of the geodesic reference of the space images in order to accomplish such kind of mentioned projects. The processing of the collected space images was accomplished by means of Erdas LPS 10 program. In the first stage there was created the main component of the 3D maps- Digital Elevevation Model. In this model the following clutter classes are presented: Open; Open areas in urban; Airport, Sea, Inland water; Forest; Parks in urban; Semi Open Area; Open Wet Area; Urban/Urban Mean; Dense urban, Villages, Industrial/Commercial, Residential/Suburban; Dense residential/Suburban; Block of BUILDINGS; Dense Urban High; Buildings, Urban Mixed, Mixed dense urban

Object-based methods for individual tree identification and tree species classification from high-spatial resolution imagery

NASA Astrophysics Data System (ADS)

Wang, Le

2003-10-01

Modern forest management poses an increasing need for detailed knowledge of forest information at different spatial scales. At the forest level, the information for tree species assemblage is desired whereas at or below the stand level, individual tree related information is preferred. Remote Sensing provides an effective tool to extract the above information at multiple spatial scales in the continuous time domain. To date, the increasing volume and readily availability of high-spatial-resolution data have lead to a much wider application of remotely sensed products. Nevertheless, to make effective use of the improving spatial resolution, conventional pixel-based classification methods are far from satisfactory. Correspondingly, developing object-based methods becomes a central challenge for researchers in the field of Remote Sensing. This thesis focuses on the development of methods for accurate individual tree identification and tree species classification. We develop a method in which individual tree crown boundaries and treetop locations are derived under a unified framework. We apply a two-stage approach with edge detection followed by marker-controlled watershed segmentation. Treetops are modeled from radiometry and geometry aspects. Specifically, treetops are assumed to be represented by local radiation maxima and to be located near the center of the tree-crown. As a result, a marker image was created from the derived treetop to guide a watershed segmentation to further differentiate overlapping trees and to produce a segmented image comprised of individual tree crowns. The image segmentation method developed achieves a promising result for a 256 x 256 CASI image. Then further effort is made to extend our methods to the multiscales which are constructed from a wavelet decomposition. A scale consistency and geometric consistency are designed to examine the gradients along the scale-space for the purpose of separating true crown boundary from unwanted textures occurring due to branches and twigs. As a result from the inverse wavelet transform, the tree crown boundary is enhanced while the unwanted textures are suppressed. Based on the enhanced image, an improvement is achieved when applying the two-stage methods to a high resolution aerial photograph. To improve tree species classification, we develop a new method to choose the optimal scale parameter with the aid of Bhattacharya Distance (BD), a well-known index of class separability in traditional pixel-based classification. The optimal scale parameter is then fed in the process of a region-growing-based segmentation as a break-off value. Our object classification achieves a better accuracy in separating tree species when compared to the conventional Maximum Likelihood Classification (MLC). In summary, we develop two object-based methods for identifying individual trees and classifying tree species from high-spatial resolution imagery. Both methods achieve promising results and will promote integration of Remote Sensing and GIS in forest applications.

Temporal and Spatial Regulation of Gene Expression during Asexual Development of Neurospora crassa

USDA-ARS?s Scientific Manuscript database

In this study we profiled spatial and temporal transcriptional changes during asexual sporulation in the filamentous fungus Neurospora crassa. Aerial tissue was separated from the mycelium to allow detection of genes specific to each tissue. We identified 2641 genes that were differentially expresse...

L-band HIgh Spatial Resolution Soil Moisture Mapping using SMALL UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.; Venkitasubramony, A.; Gasiewski, A. J.; Stachura, M.; Elston, J. S.; Walter, B.; Lankford, D.; Corey, C.

2017-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 provided new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of 36 km. However, there exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters. Compared with other methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site and Yuma Colorado Irrigation Research Foundation (IRF) site in 2015 and 2016, respectively, using LDCR Revision A and Tempest sUAS. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. LDCR Revision B has been built and integrated into SuperSwift sUAS and additional field experiments will be performed at IRF in 2017. In Revision B the IF signal is sampled at 80 MS/s to enable digital correlation and RFI mitigation capabilities, in addition to analog correlation. [1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C-Band Microwave Soil Moisture Retrieval During CLASIC 2007," Proc. IGARSS, 2008. [2] Robock, A., S. Steele-Dunne, J. Basara, W. Crow, and M. Moghaddam M, "In Situ Network and Scaling," SMAP Algorithm and Cal/Val Workshop, 2009. [3] Walker, A., "Airborne Microwave Radiometer Measurements During CanEx-SM10," Second SMAP Cal/Val Workshop, 2011.

High-Resolution Monitoring of Himalayan Glacier Dynamics Using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Immerzeel, W.; Kraaijenbrink, P. D. A.; Shea, J.; Shrestha, A. B.; Pellicciotti, F.; Bierkens, M. F.; de Jong, S. M.

2014-12-01

Himalayan glacier tongues are commonly debris covered and play an important role in modulating the glacier response to climate . However, they remain relatively unstudied because of the inaccessibility of the terrain and the difficulties in field work caused by the thick debris mantles. Observations of debris-covered glaciers are therefore limited to point locations and airborne remote sensing may bridge the gap between scarce, point field observations and coarse resolution space-borne remote sensing. In this study we deploy an Unmanned Airborne Vehicle (UAV) on two debris covered glaciers in the Nepalese Himalayas: the Lirung and Langtang glacier during four field campaigns in 2013 and 2014. Based on stereo-imaging and the structure for motion algorithm we derive highly detailed ortho-mosaics and digital elevation models (DEMs), which we geometrically correct using differential GPS observations collected in the field. Based on DEM differencing and manual feature tracking we derive the mass loss and the surface velocity of the glacier at a high spatial resolution and accuracy. We also assess spatiotemporal changes in supra-glacial lakes and ice cliffs based on the imagery. On average, mass loss is limited and the surface velocity is very small. However, the spatial variability of melt rates is very high, and ice cliffs and supra-glacial ponds show mass losses that can be an order of magnitude higher than the average. We suggest that future research should focus on the interaction between supra-glacial ponds, ice cliffs and englacial hydrology to further understand the dynamics of debris-covered glaciers. Finally, we conclude that UAV deployment has large potential in glaciology and it represents a substantial advancement over methods currently applied in studying glacier surface features.

Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information

NASA Astrophysics Data System (ADS)

Avbelj, Janja; Iwaszczuk, Dorota; Müller, Rupert; Reinartz, Peter; Stilla, Uwe

2015-02-01

For image fusion in remote sensing applications the georeferencing accuracy using position, attitude, and camera calibration measurements can be insufficient. Thus, image processing techniques should be employed for precise coregistration of images. In this article a method for multimodal object-based image coregistration refinement between hyperspectral images (HSI) and digital surface models (DSM) is presented. The method is divided in three parts: object outline detection in HSI and DSM, matching, and determination of transformation parameters. The novelty of our proposed coregistration refinement method is the use of material properties and height information of urban objects from HSI and DSM, respectively. We refer to urban objects as objects which are typical in urban environments and focus on buildings by describing them with 2D outlines. Furthermore, the geometric accuracy of these detected building outlines is taken into account in the matching step and for the determination of transformation parameters. Hence, a stochastic model is introduced to compute optimal transformation parameters. The feasibility of the method is shown by testing it on two aerial HSI of different spatial and spectral resolution, and two DSM of different spatial resolution. The evaluation is carried out by comparing the accuracies of the transformations parameters to the reference parameters, determined by considering object outlines at much higher resolution, and also by computing the correctness and the quality rate of the extracted outlines before and after coregistration refinement. Results indicate that using outlines of objects instead of only line segments is advantageous for coregistration of HSI and DSM. The extraction of building outlines in comparison to the line cue extraction provides a larger amount of assigned lines between the images and is more robust to outliers, i.e. false matches.

Quantifying the Functionality of Ephemeral Streams at the Watershed Scale for Land Management Applications

NASA Astrophysics Data System (ADS)

O'Connor, B. L.; Hamada, Y.; Bowen, E. E.; Wuthrich, K. K.; Grippo, M. A.

2013-12-01

Land development and associated disturbances in arid environments can adversely affect the ecological functionality of ephemeral stream channels. Land use managers have limited methodologies available for assessing low-impact development plans, or for monitoring changes in stream functionality as land use changes are implemented. The development of utility-scale solar energy facilities is underway in the southwestern United States. Federal and state agencies have developed plans to concentrate facilities in specific regions to minimize transmission limitations (e.g., the Bureau of Land Management's Solar Energy Zones cover 1,100 km2). However, multiple facility footprints in a single desert valley have the potential to drastically alter the natural pattern of ephemeral stream networks. This study focuses on quantifying the sensitivity of ephemeral streams with respect to land disturbance impacts on flow and sediment conveyance, groundwater recharge, and the loss of soil and vegetative habitats. An initial assessment used publicly-available geospatial data (typically 10- to 30-m resolution) on topography, surficial geology, and soil characteristics, as well as data on historical peak discharges and aerial photographs. These datasets were used to inform a professional judgment, score-based ranking of potential land disturbance impacts on the functionality of ephemeral streams. The results were limited to mapped stream channels in the National Hydrography Dataset, but suggested that hydrological and geomorphic impacts were a greater concern in valley piedmont regions, and that habitat concerns were greater in the valley regions where vegetation is sparsely distributed. Current efforts are focused on using a remote sensing approach to obtain high-resolution information on topography, soil, and vegetation in order to map detailed ephemeral stream networks, measure channel bathymetry characteristics, and use spectral indices of soil and vegetation to develop surrogate measures of stream ecological functionality. The initial results for a small watershed (110 km2) using stereoscopic, sub-meter resolution aerial images, detected an increase of more than 100% in identified ephemeral stream channels and habitat patterns were more spatially correlated with ephemeral stream networks than was observed for the initial assessment approach. The eventual goal of these efforts is to refine the methodology for quantifying the disturbance sensitivity of ephemeral streams, from professional judgment rankings to spectral indices of stream functionality, and to close the spatial gap between the need for large-scale assessments for land management planning and the small-scale analyses and data requirements for quantifying ephemeral stream functionality.

Net Changes in Above Ground Woody Carbon Stock in Western Juniper Woodlands using Wavelet Techniques and Multi-temporal Aerial Photography

NASA Astrophysics Data System (ADS)

Strand, E. K.; Bunting, S. C.; Smith, A. M.

2006-12-01

Expansion of woody plant cover in semi-arid ecosystems previously occupied primarily by grasses and forbs has been identified as an important land cover change process affecting the global carbon budget. Although woody encroachment occurs worldwide, quantifying changes in carbon pools and fluxes related to this phenomenon via remote sensing is challenging because large areas are affected at a fine spatial resolution (1- 10 m) and, in many cases, at slow temporal rates. Two-dimensional spatial wavelet analysis (SWA) represents a novel image processing technique that has been successful in automatically and objectively quantifying ecologically relevant features at multiple scales. We apply SWA to current and historic 1-m resolution black and white aerial photography to quantify changes in above ground woody biomass and carbon stock of western juniper (Juniperus occidentalis subsp. occidentalis) expanding into sagebrush (Artemisia spp.) steppe on the Owyhee Plateau in southwestern Idaho. Due to the large land area (330,000 ha) and variable availability of historical photography, we sampled forty-eight 100-ha blocks situated across the area, stratified using topographic, soil, and land stewardship variables. The average juniper plant cover increased one-fold (from 5.3% to 10.4% total cover) at the site during the time period of 1939-1946 to 1998-2004. Juniper plant density has increased by 128% with a higher percentage of the plant population in the smaller size classes compared to the size distribution 60 years ago. After image-based SWA delineation of tree crown sizes, we computed the change in above ground woody plant biomass and carbon stock between the two time periods using allometry. Areas where the shrub steppe is dominated by low sagebrush (Artemisia arbuscula) has experienced little to no expansion of western juniper. However, on deeper, more well drained soils capable of supporting mountain big sagebrush (Artemisia tridentata subsp. vaseyana), the above ground carbon stock has increased on average 4.1 ±0.6 gCm-2yr-1, with rates varying spatially from 0.3 to 9.9 gCm- 2yr-1. Within the sampled elevation range (1500-1900 m) we found no significant effect of altitude or topographic position on the accumulation of biomass and carbon over the time period of investigation. The research presented here demonstrates the considerable potential for this remote sensing technique in decadal- scale monitoring of change in vegetation structure, cover, and biogeochemical properties from the scale of the plant to the landscape.

Enhancement of spectral quality of archival aerial photographs using satellite imagery for detection of land cover

NASA Astrophysics Data System (ADS)

Siok, Katarzyna; Jenerowicz, Agnieszka; Woroszkiewicz, Małgorzata

2017-07-01

Archival aerial photographs are often the only reliable source of information about the area. However, these data are single-band data that do not allow unambiguous detection of particular forms of land cover. Thus, the authors of this article seek to develop a method of coloring panchromatic aerial photographs, which enable increasing the spectral information of such images. The study used data integration algorithms based on pansharpening, implemented in commonly used remote sensing programs: ERDAS, ENVI, and PCI. Aerial photos and Landsat multispectral data recorded in 1987 and 2016 were chosen. This study proposes the use of modified intensity-hue-saturation and Brovey methods. The use of these methods enabled the addition of red-green-blue (RGB) components to monochrome images, thus enhancing their interpretability and spectral quality. The limitations of the proposed method relate to the availability of RGB satellite imagery, the accuracy of mutual orientation of the aerial and the satellite data, and the imperfection of archival aerial photographs. Therefore, it should be expected that the results of coloring will not be perfect compared to the results of the fusion of recent data with a similar ground sampling resolution, but still, they will allow a more accurate and efficient classification of land cover registered on archival aerial photographs.

Assessing a potential solution for spatially referencing of historical aerial photography in South Africa

NASA Astrophysics Data System (ADS)

Denner, Michele; Raubenheimer, Jacobus H.

2018-05-01

Historical aerial photography has become a valuable commodity in any country, as it provides a precise record of historical land management over time. In a developing country, such as South Africa, that has undergone enormous political and social change over the last years, such photography is invaluable as it provides a clear indication of past injustices and serves as an aid to addressing post-apartheid issues such as land reform and land redistribution. National mapping organisations throughout the world have vast repositories of such historical aerial photography. In order to effectively use these datasets in today's digital environment requires that it be georeferenced to an accuracy that is suitable for the intended purpose. Using image-to-image georeferencing techniques, this research sought to determine the accuracies achievable for ortho-rectifying large volumes of historical aerial imagery, against the national standard for ortho-rectification in South Africa, using two different types of scanning equipment. The research conducted four tests using aerial photography from different time epochs over a period of sixty years, where the ortho-rectification matched each test to an already ortho-rectified mosaic of a developed area of mixed land use. The results of each test were assessed in terms of visual accuracy, spatial accuracy and conformance to the national standard for ortho-rectification in South Africa. The results showed a decrease in the overall accuracy of the image as the epoch range between the historical image and the reference image increased. Recommendations on the applications possible given the different epoch ranges and scanning equipment used are provided.

Surface Temperature Mapping of the University of Northern Iowa Campus Using High Resolution Thermal Infrared Aerial Imageries

PubMed Central

Savelyev, Alexander; Sugumaran, Ramanathan

2008-01-01

The goal of this project was to map the surface temperature of the University of Northern Iowa campus using high-resolution thermal infrared aerial imageries. A thermal camera with a spectral bandwidth of 3.0-5.0 μm was flown at the average altitude of 600 m, achieving ground resolution of 29 cm. Ground control data was used to construct the pixel- to-temperature conversion model, which was later used to produce temperature maps of the entire campus and also for validation of the model. The temperature map then was used to assess the building rooftop conditions and steam line faults in the study area. Assessment of the temperature map revealed a number of building structures that may be subject to insulation improvement due to their high surface temperatures leaks. Several hot spots were also identified on the campus for steam pipelines faults. High-resolution thermal infrared imagery proved highly effective tool for precise heat anomaly detection on the campus, and it can be used by university facility services for effective future maintenance of buildings and grounds. PMID:27873800

Exploration of the Use of Unmanned Aerial Vehicles along with Other Assets to Enhance Border Protection

DTIC Science & Technology

2009-06-01

Border Initiative SUAV Small Unmanned Aerial Vehicle SAR Synthetic Aperture Radar TTPs Tactics, Techniques, And Procedures TRVS Trailer Remote...2008). 4 3. Overview of Illegal Activities According to the CBP, 178,770 pounds of cocaine, 2,178 pounds of heroin, 2,471,931 pounds of marijuana ...Raytheon Company Web Site) Another component of Predator B is the high- resolution Lynx Synthetic Aperture Radar (SAR). In their study, Tsunoda, et

Earth Explorer

USGS Publications Warehouse

,

2000-01-01

The U.S. Geological Survey's (USGS) Earth Explorer Web site provides access to millions of land-related products, including the following: Satellite images from Landsat, advanced very high resolution radiometer (AVHRR), and Corona data sets. Aerial photographs from the National Aerial Photography Program, NASA, and USGS data sets.  Digital cartographic data from digital elevation models, digital line graphs, digital raster graphics, and digital orthophoto quadrangles. USGS paper maps Digital, film, and paper products are available, and many products can be previewed before ordering.

Integration of aerial remote sensing imaging data in a 3D-GIS environment

NASA Astrophysics Data System (ADS)

Moeller, Matthias S.

2003-03-01

For some years sensor systems have been available providing digital images of a new quality. Especially aerial stereo scanners acquire digital multispectral images with an extremely high ground resolution of about 0.10 - 0.15m and provide in addition a Digital Surface Models (DSM). These imaging products both can be used for a detailed monitoring at scales up to 1:500. The processed georeferenced multispectral orthoimages can be readily integrated into GIS making them useful for a number of applications. The DSM, derived from forward and backward facing sensors of an aerial imaging system provides a ground resolution of 0.5 m and can be used for 3D visualization purposes. In some cases it is essential, to store the ground elevation as a Digital Terrain Model (DTM) and also the height of 3-dimensional objects in a separated database. Existing automated algorithms do not work precise for the extraction of DTM from aerial scanner DSM. This paper presents a new approach which combines the visible image data and the DSM data for the generation of DTM with a reliable geometric accuracy. Already existing cadastral data can be used as a knowledge base for the extraction of building heights in cities. These elevation data is the essential source for a GIS based urban information system with a 3D visualization component.

Development of a regionally consistent geospatial dataset of agricultural lands in the Upper Colorado River Basin, 2007-10

USGS Publications Warehouse

Buto, Susan G.; Gold, Brittany L.; Jones, Kimberly A.

2014-01-01

Irrigation in arid environments can alter the natural rate at which salts are dissolved and transported to streams. Irrigated agricultural lands are the major anthropogenic source of dissolved solids in the Upper Colorado River Basin (UCRB). Understanding the location, spatial distribution, and irrigation status of agricultural lands and the method used to deliver water to agricultural lands are important to help improve the understanding of agriculturally derived dissolved-solids loading to surface water in the UCRB. Irrigation status is the presence or absence of irrigation on an agricultural field during the selected growing season or seasons. Irrigation method is the system used to irrigate a field. Irrigation method can broadly be grouped into sprinkler or flood methods, although other techniques such as drip irrigation are used in the UCRB. Flood irrigation generally causes greater dissolved-solids loading to streams than sprinkler irrigation. Agricultural lands in the UCRB mapped by state agencies at varying spatial and temporal resolutions were assembled and edited to represent conditions in the UCRB between 2007 and 2010. Edits were based on examination of 1-meter resolution aerial imagery collected between 2009 and 2011. Remote sensing classification techniques were used to classify irrigation status for the June to September growing seasons between 2007 and 2010. The final dataset contains polygons representing approximately 1,759,900 acres of agricultural lands in the UCRB. Approximately 66 percent of the mapped agricultural lands were likely irrigated during the study period.

Visibility through the gaseous smoke in airborne remote sensing using a DSLR camera

NASA Astrophysics Data System (ADS)

Chabok, Mirahmad; Millington, Andrew; Hacker, Jorg M.; McGrath, Andrew J.

2016-08-01

Visibility and clarity of remotely sensed images acquired by consumer grade DSLR cameras, mounted on an unmanned aerial vehicle or a manned aircraft, are critical factors in obtaining accurate and detailed information from any area of interest. The presence of substantial haze, fog or gaseous smoke particles; caused, for example, by an active bushfire at the time of data capture, will dramatically reduce image visibility and quality. Although most modern hyperspectral imaging sensors are capable of capturing a large number of narrow range bands of the shortwave and thermal infrared spectral range, which have the potential to penetrate smoke and haze, the resulting images do not contain sufficient spatial detail to enable locating important objects or assist search and rescue or similar applications which require high resolution information. We introduce a new method for penetrating gaseous smoke without compromising spatial resolution using a single modified DSLR camera in conjunction with image processing techniques which effectively improves the visibility of objects in the captured images. This is achieved by modifying a DSLR camera and adding a custom optical filter to enable it to capture wavelengths from 480-1200nm (R, G and Near Infrared) instead of the standard RGB bands (400-700nm). With this modified camera mounted on an aircraft, images were acquired over an area polluted by gaseous smoke from an active bushfire. Processed data using our proposed method shows significant visibility improvements compared with other existing solutions.

Visualization of spatial patterns and temporal trends for aerial surveillance of illegal oil discharges in western Canadian marine waters.

PubMed

Serra-Sogas, Norma; O'Hara, Patrick D; Canessa, Rosaline; Keller, Peter; Pelot, Ronald

2008-05-01

This paper examines the use of exploratory spatial analysis for identifying hotspots of shipping-based oil pollution in the Pacific Region of Canada's Exclusive Economic Zone. It makes use of data collected from fiscal years 1997/1998 to 2005/2006 by the National Aerial Surveillance Program, the primary tool for monitoring and enforcing the provisions imposed by MARPOL 73/78. First, we present oil spill data as points in a "dot map" relative to coastlines, harbors and the aerial surveillance distribution. Then, we explore the intensity of oil spill events using the Quadrat Count method, and the Kernel Density Estimation methods with both fixed and adaptive bandwidths. We found that oil spill hotspots where more clearly defined using Kernel Density Estimation with an adaptive bandwidth, probably because of the "clustered" distribution of oil spill occurrences. Finally, we discuss the importance of standardizing oil spill data by controlling for surveillance effort to provide a better understanding of the distribution of illegal oil spills, and how these results can ultimately benefit a monitoring program.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Integrating Landsat Data and High-Resolution Imagery for Applied Conservation Assessment of Forest Cover in Latin American Heterogenous Landscapes

NASA Astrophysics Data System (ADS)

Thomas, N.; Rueda, X.; Lambin, E.; Mendenhall, C. D.

2012-12-01

Large intact forested regions of the world are known to be critical to maintaining Earth's climate, ecosystem health, and human livelihoods. Remote sensing has been successfully implemented as a tool to monitor forest cover and landscape dynamics over broad regions. Much of this work has been done using coarse resolution sensors such as AVHRR and MODIS in combination with moderate resolution sensors, particularly Landsat. Finer scale analysis of heterogeneous and fragmented landscapes is commonly performed with medium resolution data and has had varying success depending on many factors including the level of fragmentation, variability of land cover types, patch size, and image availability. Fine scale tree cover in mixed agricultural areas can have a major impact on biodiversity and ecosystem sustainability but may often be inadequately captured with the global to regional (coarse resolution and moderate resolution) satellite sensors and processing techniques widely used to detect land use and land cover changes. This study investigates whether advanced remote sensing methods are able to assess and monitor percent tree canopy cover in spatially complex human-dominated agricultural landscapes that prove challenging for traditional mapping techniques. Our study areas are in high altitude, mixed agricultural coffee-growing regions in Costa Rica and the Colombian Andes. We applied Random Forests regression tree analysis to Landsat data along with additional spectral, environmental, and spatial variables to predict percent tree canopy cover at 30m resolution. Image object-based texture, shape, and neighborhood metrics were generated at the Landsat scale using eCognition and included in the variable suite. Training and validation data was generated using high resolution imagery from digital aerial photography at 1m to 2.5 m resolution. Our results are promising with Pearson's correlation coefficients between observed and predicted percent tree canopy cover of .86 (Costa Rica) and .83 (Colombia). The tree cover mapping developed here supports two distinct projects on sustaining biodiversity and natural and human capital: in Costa Rica the tree canopy cover map is utilized to predict bird community composition; and in Colombia the mapping is performed for two time periods and used to assess the impact of coffee eco-certification programs on the landscape. This research identifies ways to leverage readily available, high quality, and cost-free Landsat data or other medium resolution satellite data sources in combination with high resolution data, such as that frequently available through Google Earth, to monitor and support sustainability efforts in fragmented and heterogeneous landscapes.

Mapping Spatial Distributions of Stream Power and Channel Change along a Gravel-Bed River in Northern Yellowstone

NASA Astrophysics Data System (ADS)

Lea, D. M.; Legleiter, C. J.

2014-12-01

Stream power represents the rate of energy expenditure along a river and can be calculated using topographic data acquired via remote sensing. This study used remotely sensed data and field measurements to quantitatively relate temporal changes in the form of Soda Butte Creek, a gravel-bed river in northeastern Yellowstone National Park, to stream power gradients along an 8 km reach. Aerial photographs from 1994-2012 and cross-section surveys were used to assess lateral channel mobility and develop a morphologic sediment budget for quantifying net sediment flux for a series of budget cells. A drainage area-to-discharge relationship and digital elevation model (DEM) developed from LiDAR data were used to obtain the discharge and slope values, respectively, needed to calculate stream power. Local and lagged relationships between mean stream power gradient at median peak discharge and volumes of erosion, deposition, and net sediment flux were quantified via spatial cross-correlation analyses. Similarly, autocorrelations of locational probabilities and sediment fluxes were used to examine spatial patterns of channel mobility and sediment transfer. Energy expended above critical stream power was calculated for each time period to relate the magnitude and duration of peak flows to the total volume of sediment eroded or deposited during each time increment. Our results indicated a lack of strong correlation between stream power gradients and sediment flux, which we attributed to the geomorphic complexity of the Soda Butte Creek watershed and the inability of our relatively simple statistical approach to link sediment dynamics expressed at a sub-budget cell scale to larger-scale driving forces such as stream power gradients. Future studies should compare the moderate spatial resolution techniques used in this study to very-high resolution data acquired from new fluvial remote sensing technologies to better understand the amount of error associated with stream power, sediment transport, and channel change calculated from historical datasets.

Making riverscapes real

NASA Astrophysics Data System (ADS)

Carbonneau, Patrice; Fonstad, Mark A.; Marcus, W. Andrew; Dugdale, Stephen J.

2012-01-01

The structure and function of rivers have long been characterized either by: (1) qualitative models such as the River Continuum Concept or Serial Discontinuity Concept which paint broad descriptive portraits of how river habitats and communities vary, or (2) quantitative models, such as downstream hydraulic geometry, which rely on a limited number of measurements spread widely throughout a river basin. In contrast, authors such as Fausch et al. (2002) and Wiens (2002) proposed applying existing quantitative, spatially comprehensive ecology and landscape ecology methods to rivers. This new framework for river sciences which preserves variability and spatial relationships is called a riverine landscape or a 'riverscape'. Application of this riverscape concept requires information on the spatial distribution of organism-scale habitats throughout entire river systems. This article examines the ways in which recent technical and methodological developments can allow us to quantitatively implement and realize the riverscape concept. Using 3-cm true color aerial photos and 5-m resolution elevation data from the River Tromie, Scotland, we apply the newly developed Fluvial Information System which integrates a suite of cutting edge, high resolution, remote sensing methods in a spatially explicit framework. This new integrated approach allows for the extraction of primary fluvial variables such as width, depth, particle size, and elevation. From these first-order variables, we derive second-order geomorphic and hydraulic variables including velocity, stream power, Froude number and shear stress. Channel slope can be approximated from available topographic data. Based on these first and second-order variables, we produce riverscape metrics that begin to explore how geomorphic structures may influence river habitats, including connectivity, patchiness of habitat, and habitat distributions. The results show a complex interplay of geomorphic variable and habitat patchiness that is not predicted by existing fluvial theory. Riverscapes, thus, challenge the existing understanding of how rivers structure themselves and will force development of new paradigms.

The development of a performance assessment methodology for activity based intelligence: A study of spatial, temporal, and multimodal considerations

NASA Astrophysics Data System (ADS)

Lewis, Christian M.

Activity Based Intelligence (ABI) is the derivation of information from a series of in- dividual actions, interactions, and transactions being recorded over a period of time. This usually occurs in Motion imagery and/or Full Motion Video. Due to the growth of unmanned aerial systems technology and the preponderance of mobile video devices, more interest has developed in analyzing people's actions and interactions in these video streams. Currently only visually subjective quality metrics exist for determining the utility of these data in detecting specific activities. One common misconception is that ABI boils down to a simple resolution problem; more pixels and higher frame rates are better. Increasing resolution simply provides more data, not necessary more informa- tion. As part of this research, an experiment was designed and performed to address this assumption. Nine sensors consisting of four modalities were place on top of the Chester F. Carlson Center for Imaging Science in order to record a group of participants executing a scripted set of activities. The multimodal characteristics include data from the visible, long-wave infrared, multispectral, and polarimetric regimes. The activities the participants were scripted to cover a wide range of spatial and temporal interactions (i.e. walking, jogging, and a group sporting event). As with any large data acquisition, only a subset of this data was analyzed for this research. Specifically, a walking object exchange scenario and simulated RPG. In order to analyze this data, several steps of preparation occurred. The data were spatially and temporally registered; the individual modalities were fused; a tracking algorithm was implemented, and an activity detection algorithm was applied. To develop a performance assessment for these activities a series of spatial and temporal degradations were performed. Upon completion of this work, the ground truth ABI dataset will be released to the community for further analysis.

Identification and extraction of the seaward edge of terrestrial vegetation using digital aerial photography

USGS Publications Warehouse

Harris, Melanie; Brock, John C.; Nayegandhi, A.; Duffy, M.; Wright, C.W.

2006-01-01

This report is created as part of the Aerial Data Collection and Creation of Products for Park Vital Signs Monitoring within the Northeast Region Coastal and Barrier Network project, which is a joint project between the National Park Service Inventory and Monitoring Program (NPS-IM), the National Aeronautics and Space Administration (NASA) Observational Sciences Branch, and the U.S. Geological Survey (USGS) Center for Coastal and Watershed Studies (CCWS). This report is one of a series that discusses methods for extracting topographic features from aerial survey data. It details step-by-step methods used to extract a spatially referenced digital line from aerial photography that represents the seaward edge of terrestrial vegetation along the coast of Assateague Island National Seashore (ASIS). One component of the NPS-IM/USGS/NASA project includes the collection of NASA aerial surveys over various NPS barrier islands and coastal parks throughout the National Park Service's Northeast Region. These aerial surveys consist of collecting optical remote sensing data from a variety of sensors, including the NASA Airborne Topographic Mapper (ATM), the NASA Experimental Advanced Airborne Research Lidar (EAARL), and down-looking digital mapping cameras.

Geology, tectonics, and the 2002-2003 eruption of the Semeru volcano, Indonesia: Interpreted from high-spatial resolution satellite imagery

NASA Astrophysics Data System (ADS)

Solikhin, Akhmad; Thouret, Jean-Claude; Gupta, Avijit; Harris, Andy J. L.; Liew, Soo Chin

2012-02-01

The paper illustrates the application of high-spatial resolution satellite images in interpreting volcanic structures and eruption impacts in the Tengger-Semeru massif in east Java, Indonesia. We use high-spatial resolution images (IKONOS and SPOT 5) and aerial photos in order to analyze the structures of Semeru volcano and map the deposits. Geological and tectonic mapping is based on two DEMs and on the interpretation of aerial photos and four SPOT and IKONOS optical satellite images acquired between 1996 and 2002. We also compared two thermal Surface Kinetic Temperature ASTER images before and after the 2002-2003 eruption in order to delineate and evaluate the impacts of the pyroclastic density currents. Semeru's principal structural features are probably due to the tectonic setting of the volcano. A structural map of the Tengger-Semeru massif shows four groups of faults orientated N40, N160, N75, and N105 to N140. Conspicuous structures, such as the SE-trending horseshoe-shaped scar on Semeru's summit cone, coincide with the N160-trending faults. The direction of minor scars on the east flank parallels the first and second groups of faults. The Semeru composite cone hosts the currently active Jonggring-Seloko vent. This is located on, and buttressed against, the Mahameru edifice at the head of a large scar that may reflect a failure plane at shallow depth. Dipping 35° towards the SE, this failure plane may correspond to a weak basal layer of weathered volcaniclastic rocks of Tertiary age. We suggest that the deformation pattern of Semeru and its large scar may be induced by flank spreading over the weak basal layer of the volcano. It is therefore necessary to consider the potential for flank and summit collapse in the future. The last major eruption took place in December 2002-January 2003, and involved emplacement of block-and-ash flows. We have used the 2003 ASTER Surface Kinetic Temperature image to map the 2002-2003 pyroclastic density current deposits. We have also compared two 10 m-pixel images acquired before and after the event to describe the extent and impact of an estimated volume of 5.45 × 10 6 m 3 of block-and-ash flow deposits. An ash-rich pyroclastic surge escaped from one of the valley-confined block-and ash flows at 5 to 8 km distance from the crater and swept across the forest and tilled land on the SW side of the Bang River Valley. Downvalley, the temperature of the pyroclastic surge decreased and a mud-rich deposit coated the banks of the Bang River Valley. Thus, hazard mitigation at Semeru should combine: (1) continuous monitoring of the eruptive activity through an early-warning system, and (2) continuous remote sensing of the morphological changes in the drainage system due to the impact of frequent pyroclastic density currents and lahars.

Demography and population status of polar bears in western Hudson Bay

USGS Publications Warehouse

Lunn, Nicholas J.; Regher, Eric V; Servanty, Sabrina; Converse, Sarah J.; Richardson, Evan S.; Stirling, Ian

2013-01-01

The 2011 abundance estimate from this analysis was 806 bears with a 95% Bayesian credible interval of 653-984. This is lower than, but broadly consistent with, the abundance estimate of 1,030 (95% confidence interval = 745-1406) from a 2011 aerial survey (Stapleton et al. 2014). The capture-recapture and aerial survey approaches have different spatial and temporal coverage of the WH subpopulation and, consequently, the effective study population considered by each approach is different.

Back to the Basics: Lake Tahoe, California/Nevada--Spatial Measurement

ERIC Educational Resources Information Center

Handley, Lawrence R.; Lockwood, Catherine M.; Handley, Nathan

2006-01-01

"Back to the Basics: South Lake Tahoe, California/Nevada" continues the series of exercises on teaching foundational map reading and spatial differentiation skills. It is the third published exercise from the Back to the Basics series developed by the Wetland Education through Maps and Aerial Photography (WETMAAP) Program. The current…

Quality Test Various Existing dem in Indonesia Toward 10 Meter National dem

NASA Astrophysics Data System (ADS)

Amhar, Fahmi

2016-06-01

Indonesia has various DEM from many sources and various acquisition date spreaded in the past two decades. There are DEM from spaceborne system (Radarsat, TerraSAR-X, ALOS, ASTER-GDEM, SRTM), airborne system (IFSAR, Lidar, aerial photos) and also terrestrial one. The research objective is the quality test and how to extract best DEM in particular area. The method is using differential GPS levelling using geodetic GPS equipment on places which is ensured not changed during past 20 years. The result has shown that DEM from TerraSAR-X and SRTM30 have the best quality (rmse 3.1 m and 3.5 m respectively). Based on this research, it was inferred that these parameters are still positively correlated with the basic concept, namely that the lower and the higher the spatial resolution of a DEM data, the more imprecise the resulting vertical height.

Mapping the Active Vents of Stromboli Volcano with an Unmanned Aerial Vehicle

NASA Astrophysics Data System (ADS)

Turner, N.; Houghton, B. F.; von der Lieth, J.; Hort, M. K.; Taddeucci, J.; Kueppers, U.; Ricci, T.; Gaudin, D.

2016-12-01

We present a new detailed map of the active vents of Stromboli volcano obtained from UAV flights in May 2016, when the active NE and SW craters were repeatedly mapped. Due to high levels of gas emissions and frequent explosions, fine-scale measurements of vent geometry from single flights were challenging. However, the compilation of data acquired over 12 flights used with Structure from Motion software allowed us to create a 10 cm Digital Elevation Model (DEM) offering a non-obstructed view into the active craters. Such direct observations permits us to constrain parameters such as vent geometry and depth with an unprecedented precision, thus potentially reducing the uncertainty of models depending on such inputs (e.g. conduit and acoustic models). Furthermore, the low-cost and safety of UAVs allows mapping changes at small temporal and spatial resolutions, making this technique complementary to monitoring efforts at active volcanoes.

Wide area methane emissions mapping with airborne IPDA lidar

NASA Astrophysics Data System (ADS)

Bartholomew, Jarett; Lyman, Philip; Weimer, Carl; Tandy, William

2017-08-01

Methane emissions from natural gas production, storage, and transportation are potential sources of greenhouse gas emissions. Methane leaks also constitute revenue loss potential from operations. Since 2013, Ball Aerospace has been developing advanced airborne sensors using integrated path differential absorption (IPDA) LIDAR instrumentation to identify methane, propane, and longer-chain alkanes in the lowest region of the atmosphere. Additional funding has come from the U.S. Department of Transportation, Pipeline and Hazardous Materials Administration (PHMSA) to upgrade instrumentation to a broader swath coverage of up to 400 meters while maintaining high spatial sampling resolution and geolocation accuracy. Wide area coverage allows efficient mapping of emissions from gathering and distribution networks, processing facilities, landfills, natural seeps, and other distributed methane sources. This paper summarizes the benefits of advanced instrumentation for aerial methane emission mapping, describes the operating characteristics and design of this upgraded IPDA instrumentation, and reviews technical challenges encountered during development and deployment.

Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance

PubMed Central

Hammoud, Riad I.; Sahin, Cem S.; Blasch, Erik P.; Rhodes, Bradley J.; Wang, Tao

2014-01-01

We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports. PMID:25340453

Method of transmission of dynamic multibit digital images from micro-unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Petrov, E. P.; Kharina, N. L.

2018-01-01

In connection with successful usage of nanotechnologies in remote sensing great attention is paid to the systems in micro-unmanned aerial vehicles (MUAVs) capable to provide high spatial resolution of dynamic multibit digital images (MDI). Limited energy resources on board the MUAV do not allow transferring a large amount of video information in the shortest possible time. It keeps back the broad development of MUAV. The search for methods to shorten the transmission time of dynamic MDIs from MUAV over the radio channel leads to the methods of MDI compression without computational operations onboard the MUAV. The known compression codecs of video information can not be applied because of the limited energy resources. In this paper we propose a method for reducing the transmission time of dynamic MDIs without computational operations and distortions onboard the MUAV. To develop the method a mathematical apparatus of the theory of conditional Markov processes with discrete arguments was used. On its basis a mathematical model for the transformation of the MDI represented by binary images (BI) in the MDI, consisting of groups of neighboring BIs (GBI) transmitted by multiphase (MP) signals, is constructed. The algorithm for multidimensional nonlinear filtering of MP signals is synthesized, realizing the statistical redundancy of the MDI to compensate for the noise stability losses caused by the use of MP signals.

Design and Analysis of a Single-Camera Omnistereo Sensor for Quadrotor Micro Aerial Vehicles (MAVs) †

PubMed Central

Jaramillo, Carlos; Valenti, Roberto G.; Guo, Ling; Xiao, Jizhong

2016-01-01

We describe the design and 3D sensing performance of an omnidirectional stereo (omnistereo) vision system applied to Micro Aerial Vehicles (MAVs). The proposed omnistereo sensor employs a monocular camera that is co-axially aligned with a pair of hyperboloidal mirrors (a vertically-folded catadioptric configuration). We show that this arrangement provides a compact solution for omnidirectional 3D perception while mounted on top of propeller-based MAVs (not capable of large payloads). The theoretical single viewpoint (SVP) constraint helps us derive analytical solutions for the sensor’s projective geometry and generate SVP-compliant panoramic images to compute 3D information from stereo correspondences (in a truly synchronous fashion). We perform an extensive analysis on various system characteristics such as its size, catadioptric spatial resolution, field-of-view. In addition, we pose a probabilistic model for the uncertainty estimation of 3D information from triangulation of back-projected rays. We validate the projection error of the design using both synthetic and real-life images against ground-truth data. Qualitatively, we show 3D point clouds (dense and sparse) resulting out of a single image captured from a real-life experiment. We expect the reproducibility of our sensor as its model parameters can be optimized to satisfy other catadioptric-based omnistereo vision under different circumstances. PMID:26861351

Mapping rock forming minerals at Boundary Canyon, Death Valey National Park, California, using aerial SEBASS thermal infrared hyperspectral image data

NASA Astrophysics Data System (ADS)

Aslett, Zan; Taranik, James V.; Riley, Dean N.

2018-02-01

Aerial spatially enhanced broadband array spectrograph system (SEBASS) long-wave infrared (LWIR) hyperspectral image data were used to map the distribution of rock-forming minerals indicative of sedimentary and meta-sedimentary lithologies around Boundary Canyon, Death Valley, California, USA. Collection of data over the Boundary Canyon detachment fault (BCDF) facilitated measurement of numerous lithologies representing a contact between the relatively unmetamorphosed Grapevine Mountains allochthon and the metamorphosed core complex of the Funeral Mountains autochthon. These included quartz-rich sandstone, quartzite, conglomerate, and alluvium; muscovite-rich schist, siltstone, and slate; and carbonate-rich dolomite, limestone, and marble, ranging in age from late Precambrian to Quaternary. Hyperspectral data were reduced in dimensionality and processed to statistically identify and map unique emissivity spectra endmembers. Some minerals (e.g., quartz and muscovite) dominate multiple lithologies, resulting in a limited ability to differentiate them. Abrupt variations in image data emissivity amongst pelitic schists corresponded to amphibolite; these rocks represent gradation from greenschist- to amphibolite-metamorphic facies lithologies. Although the full potential of LWIR hyperspectral image data may not be fully utilized within this study area due to lack of measurable spectral distinction between rocks of similar bulk mineralogy, the high spectral resolution of the image data was useful in characterizing silicate- and carbonate-based sedimentary and meta-sedimentary rocks in proximity to fault contacts, as well as for interpreting some mineral mixtures.

Automatic association of chats and video tracks for activity learning and recognition in aerial video surveillance.

PubMed

Hammoud, Riad I; Sahin, Cem S; Blasch, Erik P; Rhodes, Bradley J; Wang, Tao

2014-10-22

We describe two advanced video analysis techniques, including video-indexed by voice annotations (VIVA) and multi-media indexing and explorer (MINER). VIVA utilizes analyst call-outs (ACOs) in the form of chat messages (voice-to-text) to associate labels with video target tracks, to designate spatial-temporal activity boundaries and to augment video tracking in challenging scenarios. Challenging scenarios include low-resolution sensors, moving targets and target trajectories obscured by natural and man-made clutter. MINER includes: (1) a fusion of graphical track and text data using probabilistic methods; (2) an activity pattern learning framework to support querying an index of activities of interest (AOIs) and targets of interest (TOIs) by movement type and geolocation; and (3) a user interface to support streaming multi-intelligence data processing. We also present an activity pattern learning framework that uses the multi-source associated data as training to index a large archive of full-motion videos (FMV). VIVA and MINER examples are demonstrated for wide aerial/overhead imagery over common data sets affording an improvement in tracking from video data alone, leading to 84% detection with modest misdetection/false alarm results due to the complexity of the scenario. The novel use of ACOs and chat Sensors 2014, 14 19844 messages in video tracking paves the way for user interaction, correction and preparation of situation awareness reports.

Health monitoring of unmanned aerial vehicle based on optical fiber sensor array

NASA Astrophysics Data System (ADS)

Luo, Yuxiang; Shen, Jingshi; Shao, Fei; Guo, Chunhui; Yang, Ning; Zhang, Jiande

2017-10-01

The unmanned aerial vehicle (UAV) in flight needs to face the complicated environment, especially to withstand harsh weather conditions, such as the temperature and pressure. Compared with conventional sensors, fiber Bragg grating (FBG) sensor has the advantages of small size, light weight, high reliability, high precision, anti-electromagnetic interference, long lift-span, moistureproof and good resistance to causticity. It's easy to be embedded in composite structural components of UAVs. In the paper, over 1000 FBG sensors distribute regularly on a wide range of UAVs body, combining wavelength division multiplexing (WDM), time division multiplexing (TDM) and multichannel parallel architecture. WDM has the advantage of high spatial resolution. TDM has the advantage of large capacity and wide range. It is worthful to constitute a sensor network by different technologies. For the signal demodulation of FBG sensor array, WDM works by means of wavelength scanning light sources and F-P etalon. TDM adopts the technology of optical time-domain reflectometry. In order to demodulate efficiently, the most proper sensor multiplex number with some reflectivity is given by the curves fitting. Due to the regular array arrangement of FBG sensors on the UAVs, we can acquire the health state of UAVs in the form of 3D visualization. It is helpful to master the information of health status rapidly and give a real-time health evaluation.

Integration of aerial and satellite remote sensing for archaeological investigations: a case study of the Etruscan site of San Giovenale

NASA Astrophysics Data System (ADS)

Lasaponara, R.; Masini, N.; Holmgren, R.; Backe Forsberg, Y.

2012-08-01

The objective of this research is to detect and extract traces of past human activities on the Etruscan site of San Giovenale (Blera) in Northern Lazio, Italy. Investigations have been conducted by integrating high-resolution satellite data with digital models derived from LiDAR survey and multisensory aerial prospection (traditional, thermal and near infrared pictures). The use of different sensor technologies is requested to cope with (i) different types of surface covers, i.e. vegetated and non-vegetated areas (trees, bushes, agricultural uses, etc), (ii) variety of archaeological marks (micro-relief, crop marks, etc) and (iii) different types of expected spatial/spectral feature patterns linked to past human activities (urban necropoleis, palaeorivers, etc). Field surveys enabled us to confirm remotely sensed features which were detected in both densely and sparsely vegetated areas, thus revealing a large variety of cultural transformations, ritual and infrastructural remains such as roads, tombs and water installations. Our findings clearly point out a connection between the Vignale plateau and the main acropolis (San Giovenale) as well as with the surrounding burial grounds. Our results suggest that the synergic use of multisensory/multisource data sets, including ancillary information, provides a comprehensive overview of new findings. This facilitates the interpretation of various results obtained from different sensors when studied in a larger prospective.

Constraining the Sulfur Dioxide Degassing Flux from Turrialba Volcano, Costa Rica Using Unmanned Aerial System Measurements

NASA Technical Reports Server (NTRS)

Xi, Xin; Johnson, Matthew S.; Jeong, Seongeun; Fladeland, Matthew; Pieri, David; Diaz, Jorge Andres; Bland, Geoffrey L.

2016-01-01

Observed sulfur dioxide (SO2)mixing ratios onboard unmanned aerial systems (UAS) duringMarch 11-13, 2013 are used to constrain the three-day averaged SO2 degassing flux fromTurrialba volcanowithin a Bayesian inverse modeling framework. A mesoscale model coupled with Lagrangian stochastic particle backward trajectories is used to quantify the source-receptor relationships at very high spatial resolutions (i.e., b1 km). The model shows better performance in reproducing the near-surface meteorological properties and observed SO2 variations when using a first-order closure non-local planetary boundary layer (PBL) scheme. The optimized SO2 degassing fluxes vary from 0.59 +/- 0.37 to 0.83 +/- 0.33 kt d-1 depending on the PBL scheme used. These fluxes are in good agreement with ground-based gas flux measurements, and correspond to corrective scale factors of 8-12 to the posteruptive SO2 degassing rate in the AeroCom emission inventory. The maximum a posteriori solution for the SO2 flux is highly sensitive to the specification of prior and observational errors, and relatively insensitive to the SO2 loss term and temporal averaging of observations. Our results indicate relatively low degassing activity but sustained sulfur emissions from Turrialba volcano to the troposphere during March 2013. This study demonstrates the utility of low-cost small UAS platforms for volcanic gas composition and flux analysis.

Extraction of Dems and Orthoimages from Archive Aerial Imagery to Support Project Planning in Civil Engineering

NASA Astrophysics Data System (ADS)

Cogliati, M.; Tonelli, E.; Battaglia, D.; Scaioni, M.

2017-12-01

Archive aerial photos represent a valuable heritage to provide information about land content and topography in the past years. Today, the availability of low-cost and open-source solutions for photogrammetric processing of close-range and drone images offers the chance to provide outputs such as DEM's and orthoimages in easy way. This paper is aimed at demonstrating somehow and to which level of accuracy digitized archive aerial photos may be used within a such kind of low-cost software (Agisoft Photoscan Professional®) to generate photogrammetric outputs. Different steps of the photogrammetric processing workflow are presented and discussed. The main conclusion is that this procedure may come to provide some final products, which however do not feature the high accuracy and resolution that may be obtained using high-end photogrammetric software packages specifically designed for aerial survey projects. In the last part a case study is presented about the use of four-epoch archive of aerial images to analyze the area where a tunnel has to be excavated.

Detection of Aspens Using High Resolution Aerial Laser Scanning Data and Digital Aerial Images

PubMed Central

Säynäjoki, Raita; Packalén, Petteri; Maltamo, Matti; Vehmas, Mikko; Eerikäinen, Kalle

2008-01-01

The aim was to use high resolution Aerial Laser Scanning (ALS) data and aerial images to detect European aspen (Populus tremula L.) from among other deciduous trees. The field data consisted of 14 sample plots of 30 m × 30 m size located in the Koli National Park in the North Karelia, Eastern Finland. A Canopy Height Model (CHM) was interpolated from the ALS data with a pulse density of 3.86/m2, low-pass filtered using Height-Based Filtering (HBF) and binarized to create the mask needed to separate the ground pixels from the canopy pixels within individual areas. Watershed segmentation was applied to the low-pass filtered CHM in order to create preliminary canopy segments, from which the non-canopy elements were extracted to obtain the final canopy segmentation, i.e. the ground mask was analysed against the canopy mask. A manual classification of aerial images was employed to separate the canopy segments of deciduous trees from those of coniferous trees. Finally, linear discriminant analysis was applied to the correctly classified canopy segments of deciduous trees to classify them into segments belonging to aspen and those belonging to other deciduous trees. The independent variables used in the classification were obtained from the first pulse ALS point data. The accuracy of discrimination between aspen and other deciduous trees was 78.6%. The independent variables in the classification function were the proportion of vegetation hits, the standard deviation of in pulse heights, accumulated intensity at the 90th percentile and the proportion of laser points reflected at the 60th height percentile. The accuracy of classification corresponded to the validation results of earlier ALS-based studies on the classification of individual deciduous trees to tree species. PMID:27873799

Possible methods for distinguishing icebergs from ships by aerial remote sensing

NASA Technical Reports Server (NTRS)

Howes, W. L.

1979-01-01

The simplest methods for aerial remote sensing which are least affected by atmospheric opacities are summarized. Radar is preferred for targets off the flight path, and microwave radiometry for targets along the flight path. Radar methods are classified by ability to resolve targets. Techniques which do not require target resolution are preferred. Among these techniques, polarization methods appear most promising, specifically those which differentiate the expected relatively greater depolarization by icebergs from that by ships or which detect doubly-reversed circular polarization.

Wetland mapping from digitized aerial photography. [Sheboygen Marsh, Sheboygen County, Wisconsin

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Quirk, B. K.; Kiefer, R. W.; Wynn, S. L.

1981-01-01

Computer assisted interpretation of small scale aerial imagery was found to be a cost effective and accurate method of mapping complex vegetation patterns if high resolution information is desired. This type of technique is suited for problems such as monitoring changes in species composition due to environmental factors and is a feasible method of monitoring and mapping large areas of wetlands. The technique has the added advantage of being in a computer compatible form which can be transformed into any georeference system of interest.

Estimating occupancy and abundance using aerial images with imperfect detection

USGS Publications Warehouse

Williams, Perry J.; Hooten, Mevin B.; Womble, Jamie N.; Bower, Michael R.

2017-01-01

Species distribution and abundance are critical population characteristics for efficient management, conservation, and ecological insight. Point process models are a powerful tool for modelling distribution and abundance, and can incorporate many data types, including count data, presence-absence data, and presence-only data. Aerial photographic images are a natural tool for collecting data to fit point process models, but aerial images do not always capture all animals that are present at a site. Methods for estimating detection probability for aerial surveys usually include collecting auxiliary data to estimate the proportion of time animals are available to be detected.We developed an approach for fitting point process models using an N-mixture model framework to estimate detection probability for aerial occupancy and abundance surveys. Our method uses multiple aerial images taken of animals at the same spatial location to provide temporal replication of sample sites. The intersection of the images provide multiple counts of individuals at different times. We examined this approach using both simulated and real data of sea otters (Enhydra lutris kenyoni) in Glacier Bay National Park, southeastern Alaska.Using our proposed methods, we estimated detection probability of sea otters to be 0.76, the same as visual aerial surveys that have been used in the past. Further, simulations demonstrated that our approach is a promising tool for estimating occupancy, abundance, and detection probability from aerial photographic surveys.Our methods can be readily extended to data collected using unmanned aerial vehicles, as technology and regulations permit. The generality of our methods for other aerial surveys depends on how well surveys can be designed to meet the assumptions of N-mixture models.

Spatial analysis of soybean canopy response to soybean cyst nematodes (Heterodera glycines) in eastern Arkansas: An approach to future precision agriculture technology application

NASA Astrophysics Data System (ADS)

Kulkarni, Subodh

2008-10-01

Heterodera glycines Ichinohe, commonly known as soybean cyst nematode (SCN) is a serious widespread pathogen of soybean in the US. Present research primarily investigated feasibility of detecting SCN infestation in the field using aerial images and ground level spectrometric sensing. Non-spatial and spatial linear regression analyses were performed to correlate SCN population densities with Normalized Difference Vegetation Index (NDVI) and Green NDVI (GNDVI) derived from soybean canopy spectra. Field data were obtained from two fields; Field A and B under different nematode control strategies in 2003 and 2004. Analysis of aerial image data from July 18, 2004 from the Field A showed a significant relationship between SCN population at planting and the GNDVI (R2=0.17 at p=0.0006). Linear regression analysis revealed that SCN had a little effect on yield (R2 =0.14, at p=0.0001, RMSEP=1052.42 kg ha-1) and GNDVI (R 2=0.17 at p=0.0006, RMSEP=0.087) derived from the aerial imagery on a single date. However, the spatial regression analysis based on spherical semivariogram showed that the RMSEP was 0.037 for the GNDVI on July 18, 2004 and 427.32 kg ha-1 for yield on October 14, 2003 indicating better model performance. For July 18, 2004 data from Field B, a relationship between NDVI and the cyst counts at planting was significant (R2=0.5 at p=0.0468). Non-spatial analyses of the ground level spectrometric data for the first field showed that NDVI and GNDVI were correlated with cyst counts at planting (R 2=0.34 and 0.27 at p=0.0015 and 0.0127, respectively), and GNDVI was correlated with eggs count at planting (R2= 0.27 at p=0.0118). Both NDVI and GNDVI were correlated with egg counts at flowering (R 2=0.34 and 0.27 at p=0.0013 and 0.0018, respectively). However, paired T test to validate the above relationships showed that, predicted values of NDVI and GNDVI were significantly different. The statistical evidences suggested that variability in vegetation indices was caused by SCN infestation. Comparison of estimators such as -2 RLL, AIC, and BIC of non-spatial and spatial models affirmed that incorporating spatial covariance structure of observations improved model performances. These results demonstrated a limited potential of aerial imaging and ground level spectrometry for detecting nematode infestation in the field. However, it is strongly recommended that more multisite-multiyear trials must be performed to establish and validate empirical models to quantify SCN population densities and their impact on soybean canopy reflectance.

Systems engineering analysis of five 'as-manufactured' SXI telescopes

NASA Astrophysics Data System (ADS)

Harvey, James E.; Atanassova, Martina; Krywonos, Andrey

2005-09-01

Four flight models and a spare of the Solar X-ray Imager (SXI) telescope mirrors have been fabricated. The first of these is scheduled to be launched on the NOAA GOES- N satellite on July 29, 2005. A complete systems engineering analysis of the "as-manufactured" telescope mirrors has been performed that includes diffraction effects, residual design errors (aberrations), surface scatter effects, and all of the miscellaneous errors in the mirror manufacturer's error budget tree. Finally, a rigorous analysis of mosaic detector effects has been included. SXI is a staring telescope providing full solar disc images at X-ray wavelengths. For wide-field applications such as this, a field-weighted-average measure of resolution has been modeled. Our performance predictions have allowed us to use metrology data to model the "as-manufactured" performance of the X-ray telescopes and to adjust the final focal plane location to optimize the number of spatial resolution elements in a given operational field-of-view (OFOV) for either the aerial image or the detected image. The resulting performance predictions from five separate mirrors allow us to evaluate and quantify the optical fabrication process for producing these very challenging grazing incidence X-ray optics.

Delineating wetland catchments and modeling hydrologic ...

EPA Pesticide Factsheets

In traditional watershed delineation and topographic modeling, surface depressions are generally treated as spurious features and simply removed from a digital elevation model (DEM) to enforce flow continuity of water across the topographic surface to the watershed outlets. In reality, however, many depressions in the DEM are actual wetland landscape features with seasonal to permanent inundation patterning characterized by nested hierarchical structures and dynamic filling–spilling–merging surface-water hydrological processes. Differentiating and appropriately processing such ecohydrologically meaningful features remains a major technical terrain-processing challenge, particularly as high-resolution spatial data are increasingly used to support modeling and geographic analysis needs. The objectives of this study were to delineate hierarchical wetland catchments and model their hydrologic connectivity using high-resolution lidar data and aerial imagery. The graph-theory-based contour tree method was used to delineate the hierarchical wetland catchments and characterize their geometric and topological properties. Potential hydrologic connectivity between wetlands and streams were simulated using the least-cost-path algorithm. The resulting flow network delineated potential flow paths connecting wetland depressions to each other or to the river network on scales finer than those available through the National Hydrography Dataset. The results demonstrated that

An all-optronic synthetic aperture lidar

NASA Astrophysics Data System (ADS)

Turbide, Simon; Marchese, Linda; Terroux, Marc; Babin, François; Bergeron, Alain

2012-09-01

Synthetic Aperture Radar (SAR) is a mature technology that overcomes the diffraction limit of an imaging system's real aperture by taking advantage of the platform motion to coherently sample multiple sections of an aperture much larger than the physical one. Synthetic Aperture Lidar (SAL) is the extension of SAR to much shorter wavelengths (1.5 μm vs 5 cm). This new technology can offer higher resolution images in day or night time as well as in certain adverse conditions. It could be a powerful tool for Earth monitoring (ship detection, frontier surveillance, ocean monitoring) from aircraft, unattended aerial vehicle (UAV) or spatial platforms. A continuous flow of high-resolution images covering large areas would however produce a large amount of data involving a high cost in term of post-processing computational time. This paper presents a laboratory demonstration of a SAL system complete with image reconstruction based on optronic processing. This differs from the more traditional digital approach by its real-time processing capability. The SAL system is discussed and images obtained from a non-metallic diffuse target at ranges up to 3m are shown, these images being processed by a real-time optronic SAR processor origiinally designed to reconstruct SAR images from ENVISAT/ASAR data.

Wide field of view spectroscopy using solid Fabry-Perot interferometers

NASA Astrophysics Data System (ADS)

Nikoleyczik, Jonathan; Kutyrev, Alexander; Moseley, Harvey; Veilleux, Sylvain

2016-08-01

We present a high resolution spectrometer consisting of dual solid Fabry-Perot Interferometers (FPI). Each FPI is made of a single piece of L-BBH2 glass which has a high index of refraction n 2.07. Each is then coated with partially reflective mirrors to achieve a spectral resolution of R 30,000. Running the FPIs in tandem reduces the overlapping orders and allows for a much wider free spectral range and higher contrast. Tuning of the FPIs is achieved by adjusting the temperature and thus changing the FPI gap and the refractive index of the material. The spectrometer then moves spatially in order to get spectral information at every point in the field of view. We select spectral lines for further analysis and create maps of the line depths across the field. Using this technique we are able to measure the fluorescence of chlorophyll in plants and observe zodiacal light. In the chlorophyll analysis we are able to detect chlorophyll fluorescence using the line depth in a plant using the sky as a reference solar spectrum. This instrument has possible applications in either a cubesat or aerial observations to measure bulk plant activity over large areas.

Classifying spatially heterogeneous wetland communities using machine learning algorithms and spectral and textural features.

PubMed

Szantoi, Zoltan; Escobedo, Francisco J; Abd-Elrahman, Amr; Pearlstine, Leonard; Dewitt, Bon; Smith, Scot

2015-05-01

Mapping of wetlands (marsh vs. swamp vs. upland) is a common remote sensing application.Yet, discriminating between similar freshwater communities such as graminoid/sedge fromremotely sensed imagery is more difficult. Most of this activity has been performed using medium to low resolution imagery. There are only a few studies using highspatial resolutionimagery and machine learning image classification algorithms for mapping heterogeneouswetland plantcommunities. This study addresses this void by analyzing whether machine learning classifierssuch as decisiontrees (DT) and artificial neural networks (ANN) can accurately classify graminoid/sedgecommunities usinghigh resolution aerial imagery and image texture data in the Everglades National Park, Florida.In addition tospectral bands, the normalized difference vegetation index, and first- and second-order texturefeatures derivedfrom the near-infrared band were analyzed. Classifier accuracies were assessed using confusiontablesand the calculated kappa coefficients of the resulting maps. The results indicated that an ANN(multilayerperceptron based on backpropagation) algorithm produced a statistically significantly higheraccuracy(82.04%) than the DT (QUEST) algorithm (80.48%) or the maximum likelihood (80.56%)classifier (α<0.05). Findings show that using multiple window sizes provided the best results. First-ordertexture featuresalso provided computational advantages and results that were not significantly different fromthose usingsecond-order texture features.

Airborne Hydromapping - How high-resolution bathymetric surveys will change the research and work focused on waterbody-related topics

NASA Astrophysics Data System (ADS)

Steinbacher, Frank; Baran, Ramona; Dobler, Wolfgang; Aufleger, Markus

2013-04-01

Repetitive surveying of inshore waters and coastal zones is becoming more and more essential in order to evaluate water-level dynamics, structural and zonal variations of rivers and riparian areas, river degradation, water flow, reservoir sedimentation, delta growth, as well as coastal processes. This can only be achieved in an effective manner by employing hydrographic airborne laser scanning (hydromapping). A new laser scanner is introduced, which has been specifically designed for the acquisition of high-resolution hydrographic data in order to survey and monitor inland waters and shallow coastal zones. Recently, this scanner has been developed within the framework of an Austrian research cooperation between Riegl LMS and the Unit of Hydraulic Engineering at the University of Innsbruck. We present exemplary measurement results obtained with the compact airborne laser-scanning system during our project work. Along the Baltic Sea coast northeast of Kiel city, northern Germany, we obtained measurement depths up to 8 m under clear-water conditions. Moreover, we detect underwater dune-structures and the accumulation of sediment within groin structures. In contrast, under turbid water conditions we obtained depths of approximately 3 m along the Rhine River at Rheinfelden, German-Swiss border east of Basel city. Nevertheless, we were able to map small-scale and complex morphologic features within a fish ramp or bedrock cliffs. The laser data had been combined with sonar measurements displaying the bathymetry at depths of ca. 2-25 m in order to document comprehensively the actual hydrographic setting after the new construction of the hydropower plant Rheinfelden. In summary, a high-resolution spatial view on the ground of various waterbodies is now possible for the first time with point densities in the usual range of approximately 10-20 points/m². However, the combination of these data with high-resolution aerial (approximately < 5 cm/pixel) or spectral images offers a variety of new opportunities for further analysis. Lastly, the combined datasets - all of them captured during a single flight including topography, bathymetry, aerial and spectral pictures - provide a comprehensive and homogeneous database for the detailed and precise description of river- or coastal-bed hydraulic, morphologic and ecohydraulic processes. The high density and accuracy (less than 10 cm) of information offer the extended possibility for monitoring and supervisory purposes.

Submarine melt rates under Greenland's ice tongues

NASA Astrophysics Data System (ADS)

Wilson, Nat; Straneo, Fiametta; Heimbach, Patrick; Cenedese, Claudia

2017-04-01

The few remaining ice tongues (ice-shelf like extensions) of Greenland's glaciers are undergoing rapid changes with potential implications for the stability of the ice sheet. Submarine melting is recognized as a major contributor to mass loss, yet the magnitude and spatial distribution of melt are poorly known or understood. Here, we use high resolution satellite imagery to infer the magnitude and spatial variability of melt rates under Greenland's largest remaining ice tongues: Ryder Glacier, Petermann Glacier and Nioghalvfjerdsbræ (79 North Glacier). We find that submarine plus aerial melt approximately balance the ice flux from the grounded ice sheet for the first two while at Nioghalvfjerdsbræ the total melt flux exceeds the inflow of ice indicating thinning of the ice tongue. We also show that melt rates under the ice tongues vary considerably, exceeding 60 m yr-1 near the grounding zone and decaying rapidly downstream. Channels, likely originating from upstream subglacial channels, give rise to large melt variations across the ice tongues. Using derived melt rates, we test simplified melt parameterizations appropriate for ice sheet models and find the best agreement with those that incorporate ice tongue geometry in the form of depth and slope.

Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada

NASA Astrophysics Data System (ADS)

Zhang, Kongwen; Hu, Baoxin; Robinson, Justin

2014-01-01

The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyperspectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors.

Mapping forest canopy gaps using air-photo interpretation and ground surveys

USGS Publications Warehouse

Fox, T.J.; Knutson, M.G.; Hines, R.K.

2000-01-01

Canopy gaps are important structural components of forested habitats for many wildlife species. Recent improvements in the spatial accuracy of geographic information system tools facilitate accurate mapping of small canopy features such as gaps. We compared canopy-gap maps generated using ground survey methods with those derived from air-photo interpretation. We found that maps created from high-resolution air photos were more accurate than those created from ground surveys. Errors of omission were 25.6% for the ground-survey method and 4.7% for the air-photo method. One variable of inter est in songbird research is the distance from nests to gap edges. Distances from real and simulated nests to gap edges were longer using the ground-survey maps versus the air-photo maps, indicating that gap omission could potentially bias the assessment of spatial relationships. If research or management goals require location and size of canopy gaps and specific information about vegetation structure, we recommend a 2-fold approach. First, canopy gaps can be located and the perimeters defined using 1:15,000-scale or larger aerial photographs and the methods we describe. Mapped gaps can then be field-surveyed to obtain detailed vegetation data.

Tree biomass in the Swiss landscape: nationwide modelling for improved accounting for forest and non-forest trees.

PubMed

Price, B; Gomez, A; Mathys, L; Gardi, O; Schellenberger, A; Ginzler, C; Thürig, E

2017-03-01

Trees outside forest (TOF) can perform a variety of social, economic and ecological functions including carbon sequestration. However, detailed quantification of tree biomass is usually limited to forest areas. Taking advantage of structural information available from stereo aerial imagery and airborne laser scanning (ALS), this research models tree biomass using national forest inventory data and linear least-square regression and applies the model both inside and outside of forest to create a nationwide model for tree biomass (above ground and below ground). Validation of the tree biomass model against TOF data within settlement areas shows relatively low model performance (R 2 of 0.44) but still a considerable improvement on current biomass estimates used for greenhouse gas inventory and carbon accounting. We demonstrate an efficient and easily implementable approach to modelling tree biomass across a large heterogeneous nationwide area. The model offers significant opportunity for improved estimates on land use combination categories (CC) where tree biomass has either not been included or only roughly estimated until now. The ALS biomass model also offers the advantage of providing greater spatial resolution and greater within CC spatial variability compared to the current nationwide estimates.

Determination of Exterior Orientation Parameters Through Direct Geo-Referencing in a Real-Time Aerial Monitoring System

NASA Astrophysics Data System (ADS)

Kim, H.; Lee, J.; Choi, K.; Lee, I.

2012-07-01

Rapid responses for emergency situations such as natural disasters or accidents often require geo-spatial information describing the on-going status of the affected area. Such geo-spatial information can be promptly acquired by a manned or unmanned aerial vehicle based multi-sensor system that can monitor the emergent situations in near real-time from the air using several kinds of sensors. Thus, we are in progress of developing such a real-time aerial monitoring system (RAMS) consisting of both aerial and ground segments. The aerial segment acquires the sensory data about the target areas by a low-altitude helicopter system equipped with sensors such as a digital camera and a GPS/IMU system and transmits them to the ground segment through a RF link in real-time. The ground segment, which is a deployable ground station installed on a truck, receives the sensory data and rapidly processes them to generate ortho-images, DEMs, etc. In order to generate geo-spatial information, in this system, exterior orientation parameters (EOP) of the acquired images are obtained through direct geo-referencing because it is difficult to acquire coordinates of ground points in disaster area. The main process, since the data acquisition stage until the measurement of EOP, is discussed as follows. First, at the time of data acquisition, image acquisition time synchronized by GPS time is recorded as part of image file name. Second, the acquired data are then transmitted to the ground segment in real-time. Third, by processing software for ground segment, positions/attitudes of acquired images are calculated through a linear interpolation using the GPS time of the received position/attitude data and images. Finally, the EOPs of images are obtained from position/attitude data by deriving the relationships between a camera coordinate system and a GPS/IMU coordinate system. In this study, we evaluated the accuracy of the EOP decided by direct geo-referencing in our system. To perform this, we used the precisely calculated EOP through the digital photogrammetry workstation (DPW) as reference data. The results of the evaluation indicate that the accuracy of the EOP acquired by our system is reasonable in comparison with the performance of GPS/IMU system. Also our system can acquire precise multi-sensory data to generate the geo-spatial information in emergency situations. In the near future, we plan to complete the development of the rapid generation system of the ground segment. Our system is expected to be able to acquire the ortho-image and DEM on the damaged area in near real-time. Its performance along with the accuracy of the generated geo-spatial information will also be evaluated and reported in the future work.

Controls on morphometry and morphology of alluvial and colluvial fans in the high-Arctic setting, Petuniabukta, Svalbard.

NASA Astrophysics Data System (ADS)

Tomczyk, Aleksandra; Ewertowski, Marek

2016-04-01

The Petuniabukta (78o42' N, 16o32') is a bay in the northern part of Billefjorden in the central part of Spitsbergen Island, Svalbard. The bay is surrounded by six major, partly glaciated valleys. A numerous alluvial and colluvial fans have developed within valleys as well as along the fiord margins. Distribution and characterization of morphometric parameters of fans were investigated using time-series of orthophotos and digital elevation models (generated based on 1961, 1990, 2009 aerial photographs) and high resolution satellite imagery from 2013. In addition, a very detailed DEM and orthophoto (5 cm resolution) have been produced from unmanned aerial vehicle (UAV) imagery from 2014 and 2015, covering three fans characterised by different types of surface morphology. A 1:40,000 map showing the distribution of almost 300 alluvial and colluvial fans (ranging in area from 325 km2 to 451 275 km2), together with time-series of 1:5,000 geomorphological maps of sample fans enabled an assessment of the spatial and temporal evolution of processes responsible for delivery and erosion of sediments from the fans. The relationship between terrain parameters (e.g. slope, exposition) as well as geology was also investigated. Many of the studied alluvial fans were at least partly coupled and sediments were transferred from the upstream zone to the downstream zone, either due to debris-flow or channelized stream flow. In other cases, coarse sediments were stored within fans, and fines were transported downstream by sheet flows or sub-surface flows. In most of smaller colluvial fans and debris cones, sediments were delivered by mass movement processes (mainly rockfalls and snowfalls) and did not reach lower margin of landforms. Analysis of historical aerial photographs indicated recent increase in the activity of debris-flow modification of surface morphology of fans. Fans located outside limits of the Little Ice Age (LIA) glaciation are dominated by the secondary processes, which do not cause significant aggradation, but can substantially modified surface morphology. In contrary, surface morphology of fans located inside the limits of the LIA glaciation and along contemporary glaciers is dominated by the primary processes of deposition. The research was founded by the Polish National Science Centre.

Evaluating UAV and LiDAR Retrieval of Snow Depth in a Coniferous Forest in Arizona

NASA Astrophysics Data System (ADS)

Van Leeuwen, W. J. D.; Broxton, P.; Biederman, J. A.

2017-12-01

Remote sensing of snow depth and cover in forested environments is challenging. Trees interfere with the remote sensing of snowpack below the canopy and cause large variations in the spatial distribution of the snowpack itself (e.g. between below canopy environments to shaded gaps to open clearings). The distribution of trees and topographic variation make it challenging to monitor the snowpack with in-situ observations. Airborne LiDAR has improved our ability to monitor snowpack over large areas in montane and forested environments because of its high sampling rate and ability to penetrate the canopy. However, these LiDAR flights can be too expensive and time-consuming to process, making it hard to use them for real-time snow monitoring. In this research, we evaluate Structure from Motion (SfM) as an alternative to Airborne LiDAR to generate high-resolution snow depth data in forested environments. This past winter, we conducted a snow field campaign over Arizona's Mogollon Rim where we acquired aerial LiDAR, multi-angle aerial photography from a UAV, and extensive field observations of snow depth at two sites. LiDAR and SFM derived snow depth maps were generated by comparing "snow-on" and "snow-off" LiDAR and SfM data. The SfM- and LiDAR-generated snow depth maps were similar at a site with fewer trees, though there were more discrepancies at a site with more trees. Both compared reasonably well with the field observations at the sparser forested site, with poorer agreement at the denser forested site. Finally, although the SfM produced point clouds with much higher point densities than the aerial LiDAR, the SfM was not able to produce meaningful snow depth estimates directly underneath trees and had trouble in areas with deep shadows. Based on these findings, we are optimizing our UAV data acquisition strategies for this upcoming field season. We are using these data, along with high-resolution hydrological modeling, to gain a better understanding of how different forest structural, climatic and topographic conditions affect the snowpack and consequently the water resources available to the Salt River Project, a water utility providing power and water to millions of customers in the Phoenix area

Can DEM time series produced by UAV be used to quantify diffuse erosion in an agricultural watershed?

NASA Astrophysics Data System (ADS)

Pineux, N.; Lisein, J.; Swerts, G.; Bielders, C. L.; Lejeune, P.; Colinet, G.; Degré, A.

2017-03-01

Erosion and deposition modelling should rely on field data. Currently these data are seldom available at large spatial scales and/or at high spatial resolution. In addition, conventional erosion monitoring approaches are labour intensive and costly. This calls for the development of new approaches for field erosion data acquisition. As a result of rapid technological developments and low cost, unmanned aerial vehicles (UAV) have recently become an attractive means of generating high resolution digital elevation models (DEMs). The use of UAV to observe and quantify gully erosion is now widely established. However, in some agro-pedological contexts, soil erosion results from multiple processes, including sheet and rill erosion, tillage erosion and erosion due to harvest of root crops. These diffuse erosion processes often represent a particular challenge because of the limited elevation changes they induce. In this study, we propose to assess the reliability and development perspectives of UAV to locate and quantify erosion and deposition in a context of an agricultural watershed with silt loam soils and a smooth relief. Erosion and deposition rates derived from high resolution DEM time series are compared to field measurements. The UAV technique demonstrates a high level of flexibility and can be used, for instance, after a major erosive event. It delivers a very high resolution DEM (pixel size: 6 cm) which allows us to compute high resolution runoff pathways. This could enable us to precisely locate runoff management practices such as fascines. Furthermore, the DEMs can be used diachronically to extract elevation differences before and after a strongly erosive rainfall and be validated by field measurements. While the analysis for this study was carried out over 2 years, we observed a tendency along the slope from erosion to deposition. Erosion and deposition patterns detected at the watershed scale are also promising. Nevertheless, further development in the processing workflow of UAV data is required in order to make this technique accurate and robust enough for detecting sediment movements in an agricultural watershed affected by diffuse erosion. This area of investigation holds much potential as the images processing is relatively new and expanding.

Investigating lava flows at Quizapu Volcano, on the ground and in the air

NASA Astrophysics Data System (ADS)

Lev, E.; Ruprecht, P.; Moon, R. S.

2017-12-01

The emplacement of silicic and intermediate lava flows is not often witnessed directly, and thus quantitative assessment of existing flows is a critical step in the interpretation of flow dynamics and eruption conditions. Two key parameters - lava rheology and effusion rate - are both difficult to assess many years after the eruption ended. Yet both are reflected in observables such as flow morphology (including roughness, folding and inflation structures), and micro-texture (including vesicularity, crystallinity, and microlite content). Therefore, it is important to collect data sets of high spatial resolution of both samples and topography of a target flow. We present a case study from Quizapu volcano (Chile), where an 1846 effusive eruption emplaced a suite of large lava flows, spanning composition from silicis andesitic to dacite. We focus on two major flow lobes, which, despite originating from the same eruption, and traversing similar topography, exhibit different large-scale structure: The southern flow (SF) has a uniform, smooth, almost straight geometry, while the northern flow (NF) has undulating boundaries and irregular width and thickness. We collected and utilized two sets of data: 1) thousands of aerial photos collected during 12 UAV flights, and 2) 68 hand samples which covered both the main channels and the levees of both flows in a systematic grid pattern. We present outcomes from analysis of samples for 3D structure, crystallinity, and vesicularity using X-ray microtomography, for micrstructure using thin sections and SEM, and for major and trace element composition using XRF. The aerial photographs were used to construct high-resolution (few cm) digital elevation models (DEMs) of several segments of each flow. From the DEMs we extracted along- and across-flow profiles which reveal morphological differences between NF and SF, with pressure ridges at NF wider and taller than those of SF. However, both flows share a common trend line in the relationship between the height and width of surface pressure ridges, suggesting a shared controlling process. Our results highlight the importance of collecting high-resolution data sets. In particular, we demonstrate the capability of UAVs to collect, at a low cost, the high-quality topography data critical for volcanology and geoscience in general.

Spatial configuration and distribution of forest patches in Champaign County, Illinois: 1940 to 1993

Treesearch

J. Danilo Chinea

1997-01-01

Spatial configuration and distribution of landscape elements have implications for the dynamics of forest ecosystems, and, therefore, for the management of these resources. The forest cover of Champaign County, in east-central Illinois, was mapped from 1940 and 1993 aerial photography and entered in a geographical information system database. In 1940, 208 forest...

The use of historical imagery in the remediation of an urban hazardous waste site

USGS Publications Warehouse

Slonecker, E.T.

2011-01-01

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity. ?? 2010 IEEE.

The use of historical imagery in the remediation of an urban hazardous waste site

USGS Publications Warehouse

Slonecker, E.T.

2011-01-01

The information derived from the interpretation of historical aerial photographs is perhaps the most basic multitemporal application of remote-sensing data. Aerial photographs dating back to the early 20th century can be extremely valuable sources of historical landscape activity. In this application, imagery from 1918 to 1927 provided a wealth of information about chemical weapons testing, storage, handling, and disposal of these hazardous materials. When analyzed by a trained photo-analyst, the 1918 aerial photographs resulted in 42 features of potential interest. When compared with current remedial activities and known areas of contamination, 33 of 42 or 78.5% of the features were spatially correlated with areas of known contamination or other remedial hazardous waste cleanup activity.

Understanding Multifunctional Agricultural Land by Using Low Cost and Open Source Solutions to Quantify Ecosystem Function and Services

NASA Astrophysics Data System (ADS)

Forsmoo, Joel; Anderson, Karen; Brazier, Richard; Macleod, Kit; Wilkinson, Mark

2016-04-01

There is a need to advance our understanding of how the spatial structure of farmed landscapes contributes to the provision of functions and services. Agricultural land is of critical importance in NW Europe, covering large parts of NW Europe's temperate land. Moreover, these agricultural areas are primarily intensively managed, with a focus on maximizing food and fibre production. Such landscapes therefore can provide a wealth of ecosystem goods and services (ESs) including regulation of climate, erosion regulation, hydrology, water quality, nutrient cycling and biodiversity conservation. However, it has been shown they are key sources of sediment, phosphorous, nitrogen and storm runoff contributing to flooding, and therefore it is likely that most agricultural landscapes do not maximize the services or benefits that they might provide. The focus of this study is the spatio-temporal assessment of carbon sequestration (particularly through proxies such as above-ground biomass) and hydrological processes on agricultural land. Understanding and quantifying both of these is important to (a) inform payments for ecosystem services frameworks, (b) evaluate and improve carbon sequestration models, (c) manage the flood risk, (d) downstream water security and (e) water quality. Quantifying both of these ESs is dependent on data describing the fine spatial and temporal structure and function of the landscape. Common practice has been to use remote sensing techniques, e.g. satellites, providing coarse spatial resolution (around 30cm at 20° off nadir) and/or temporal resolution (around 5 days revisit time at <20° off nadir). In this paper we will explain how imaging data from lightweight and easily deployed unmanned aerial vehicles (UAVs) can be used to generate structure from motion (SFM) products describing the very fine detailed (<3 cm pixel resolution) structure of the agricultural environment. We will demonstrate how these products can be delivered using advanced free and open source post-processing alternatives and low cost sensors (digital cameras) and platforms (UAVs). We furthermore draw attention to the influence post-processing solutions have on the accuracy of the final product, the digital surface model (DSM), by using recently acquired data. Specifically, when applied in a structurally complex field site with irregular surface roughness patterns, over a land use gradient, from livestock grazing to agricultural crops. We will demonstrate the added value of using very fine detail data, highlighting important structural properties and patterns overlooked with coarser spatial resolution data.

Implications of different digital elevation models and preprocessing techniques to delineate debris flow inundation hazard zones in El Salvador

NASA Astrophysics Data System (ADS)

Anderson, E. R.; Griffin, R.; Irwin, D.

2013-12-01

Heavy rains and steep, volcanic slopes in El Salvador cause numerous landslides every year, posing a persistent threat to the population, economy and environment. Although potential debris inundation hazard zones have been delineated using digital elevation models (DEMs), some disparities exist between the simulated zones and actual affected areas. Moreover, these hazard zones have only been identified for volcanic lahars and not the shallow landslides that occur nearly every year. This is despite the availability of tools to delineate a variety of landslide types (e.g., the USGS-developed LAHARZ software). Limitations in DEM spatial resolution, age of the data, and hydrological preprocessing techniques can contribute to inaccurate hazard zone definitions. This study investigates the impacts of using different elevation models and pit filling techniques in the final debris hazard zone delineations, in an effort to determine which combination of methods most closely agrees with observed landslide events. In particular, a national DEM digitized from topographic sheets from the 1970s and 1980s provide an elevation product at a 10 meter resolution. Both natural and anthropogenic modifications of the terrain limit the accuracy of current landslide hazard assessments derived from this source. Global products from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM) offer more recent data but at the cost of spatial resolution. New data derived from the NASA Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in 2013 provides the opportunity to update hazard zones at a higher spatial resolution (approximately 6 meters). Hydrological filling of sinks or pits for current hazard zone simulation has previously been achieved through ArcInfo spatial analyst. Such hydrological processing typically only fills pits and can lead to drastic modifications of original elevation values. Optimized pit filling techniques use both cut and fill operations to minimize modifications of the original DEM. Satellite image interpretation and field surveying provide the baseline upon which to test the accuracy of each model simulation. By outlining areas that could potentially be inundated by debris flows, these efforts can be used to more accurately identify the places and assets immediately exposed to landslide hazards. We contextualize the results of the previous and ongoing efforts into how they may be incorporated into decision support systems. We also discuss if and how these analyses would have provided additional knowledge in the past, and identify specific recommendations as to how they could contribute to a more robust decision support system in the future.

Comparison of a Fixed-Wing and Multi-Rotor Uav for Environmental Mapping Applications: a Case Study

NASA Astrophysics Data System (ADS)

Boon, M. A.; Drijfhout, A. P.; Tesfamichael, S.

2017-08-01

The advent and evolution of Unmanned Aerial Vehicles (UAVs) and photogrammetric techniques has provided the possibility for on-demand high-resolution environmental mapping. Orthoimages and three dimensional products such as Digital Surface Models (DSMs) are derived from the UAV imagery which is amongst the most important spatial information tools for environmental planning. The two main types of UAVs in the commercial market are fixed-wing and multi-rotor. Both have their advantages and disadvantages including their suitability for certain applications. Fixed-wing UAVs normally have longer flight endurance capabilities while multi-rotors can provide for stable image capturing and easy vertical take-off and landing. Therefore, the objective of this study is to assess the performance of a fixed-wing versus a multi-rotor UAV for environmental mapping applications by conducting a specific case study. The aerial mapping of the Cors-Air model aircraft field which includes a wetland ecosystem was undertaken on the same day with a Skywalker fixed-wing UAV and a Raven X8 multi-rotor UAV equipped with similar sensor specifications (digital RGB camera) under the same weather conditions. We compared the derived datasets by applying the DTMs for basic environmental mapping purposes such as slope and contour mapping including utilising the orthoimages for identification of anthropogenic disturbances. The ground spatial resolution obtained was slightly higher for the multi-rotor probably due to a slower flight speed and more images. The results in terms of the overall precision of the data was noticeably less accurate for the fixed-wing. In contrast, orthoimages derived from the two systems showed small variations. The multi-rotor imagery provided better representation of vegetation although the fixed-wing data was sufficient for the identification of environmental factors such as anthropogenic disturbances. Differences were observed utilising the respective DTMs for the mapping of the wetland slope and contour mapping including the representation of hydrological features within the wetland. Factors such as cost, maintenance and flight time is in favour of the Skywalker fixed-wing. The multi-rotor on the other hand is more favourable in terms of data accuracy including for precision environmental planning purposes although the quality of the data of the fixed-wing is satisfactory for most environmental mapping applications.

Modeling background radiation in Southern Nevada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Haber, Daniel A.; Burnley, Pamela C.; Adcock, Christopher T.

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials by creating a high resolution background model. The intention is for this method to be used in an emergency response scenario where the background radiation envi-ronment is unknown. Two studymore » areas in Southern Nevada have been modeled using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas that are homogenous in terms of K, U, and Th, referred to as background radiation units, are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by the Department of Energy's Remote Sensing Lab - Nellis, allowing for the refinement of the technique. By using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide and define radiation background units within alluvium, successful models have been produced for Government Wash, north of Lake Mead, and for the western shore of Lake Mohave, east of Searchlight, NV.« less

Film cameras or digital sensors? The challenge ahead for aerial imaging

USGS Publications Warehouse

Light, D.L.

1996-01-01

Cartographic aerial cameras continue to play the key role in producing quality products for the aerial photography business, and specifically for the National Aerial Photography Program (NAPP). One NAPP photograph taken with cameras capable of 39 lp/mm system resolution can contain the equivalent of 432 million pixels at 11 ??m spot size, and the cost is less than $75 per photograph to scan and output the pixels on a magnetic storage medium. On the digital side, solid state charge coupled device linear and area arrays can yield quality resolution (7 to 12 ??m detector size) and a broader dynamic range. If linear arrays are to compete with film cameras, they will require precise attitude and positioning of the aircraft so that the lines of pixels can be unscrambled and put into a suitable homogeneous scene that is acceptable to an interpreter. Area arrays need to be much larger than currently available to image scenes competitive in size with film cameras. Analysis of the relative advantages and disadvantages of the two systems show that the analog approach is more economical at present. However, as arrays become larger, attitude sensors become more refined, global positioning system coordinate readouts become commonplace, and storage capacity becomes more affordable, the digital camera may emerge as the imaging system for the future. Several technical challenges must be overcome if digital sensors are to advance to where they can support mapping, charting, and geographic information system applications.

Modeling background radiation in Southern Nevada

DOE PAGES

Haber, Daniel A.; Burnley, Pamela C.; Adcock, Christopher T.; ...

2017-02-06

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials by creating a high resolution background model. The intention is for this method to be used in an emergency response scenario where the background radiation envi-ronment is unknown. Two studymore » areas in Southern Nevada have been modeled using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas that are homogenous in terms of K, U, and Th, referred to as background radiation units, are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by the Department of Energy's Remote Sensing Lab - Nellis, allowing for the refinement of the technique. By using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide and define radiation background units within alluvium, successful models have been produced for Government Wash, north of Lake Mead, and for the western shore of Lake Mohave, east of Searchlight, NV.« less

Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

NASA Astrophysics Data System (ADS)

Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

2017-12-01

Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

Optical quality of the living cat eye

PubMed Central

Bonds, A. B.

1974-01-01

1. The optical quality of the living cat eye was measured under conditions similar to those of cat retinal ganglion cell experiments by recording the aerial image of a nearly monochromatic thin line of light. 2. Experiments were performed to assess the nature of the fundal reflexion of the cat eye, which was found to behave essentially as a diffuser. 3. The optical Modulation Transfer Function (MTF) was calculated from the measured aerial linespread using Fourier mathematics; the MTF of a `typical' cat eye was averaged from data collected from ten eyes. 4. The state of focus of the optical system, the pupil size and the angle of the light incident on the eye were all varied to determine their effect on image quality. 5. By using an image rotator, the aerial linespread was measured for several orientations of the line; these measurements yielded an approximation of the two-dimensional pointspread completely characterizing the optical system. 6. Evidence is reviewed to show that the optical resolution of the cat, albeit some 3-5 times worse than that of human, appears to be better than the neural resolution of its retina and its visual system as a whole. PMID:4449081

Optical quality of the living cat eye.

PubMed

Bonds, A B

1974-12-01

1. The optical quality of the living cat eye was measured under conditions similar to those of cat retinal ganglion cell experiments by recording the aerial image of a nearly monochromatic thin line of light.2. Experiments were performed to assess the nature of the fundal reflexion of the cat eye, which was found to behave essentially as a diffuser.3. The optical Modulation Transfer Function (MTF) was calculated from the measured aerial linespread using Fourier mathematics; the MTF of a ;typical' cat eye was averaged from data collected from ten eyes.4. The state of focus of the optical system, the pupil size and the angle of the light incident on the eye were all varied to determine their effect on image quality.5. By using an image rotator, the aerial linespread was measured for several orientations of the line; these measurements yielded an approximation of the two-dimensional pointspread completely characterizing the optical system.6. Evidence is reviewed to show that the optical resolution of the cat, albeit some 3-5 times worse than that of human, appears to be better than the neural resolution of its retina and its visual system as a whole.

USGS aerial resolution targets.

USGS Publications Warehouse

Salamonowicz, P.H.

1982-01-01

It is necessary to measure the achievable resolution of any airborne sensor that is to be used for metric purposes. Laboratory calibration facilities may be inadequate or inappropriate for determining the resolution of non-photographic sensors such as optical-mechanical scanners, television imaging tubes, and linear arrays. However, large target arrays imaged in the field can be used in testing such systems. The USGS has constructed an array of resolution targets in order to permit field testing of a variety of airborne sensing systems. The target array permits any interested organization with an airborne sensing system to accurately determine the operational resolution of its system. -from Author

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

USGS Publications Warehouse

Stow, Douglas A.; Hope, Allen; McGuire, David; Verbyla, David; Gamon, John A.; Huemmrich, Fred; Houston, Stan; Racine, Charles H.; Sturm, Matthew; Tape, Ken D.; Hinzman, Larry D.; Yoshikawa, Kenji; Tweedie, Craig E.; Noyle, Brian; Silapaswan, Cherie; Douglas, David C.; Griffith, Brad; Jia, Gensuo; Howard E. Epstein,; Walker, Donald A.; Daeschner, Scott; Petersen, Aaron; Zhou, Liming; Myneni, Ranga B.

2004-01-01

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images

NASA Astrophysics Data System (ADS)

Keyport, Ren N.; Oommen, Thomas; Martha, Tapas R.; Sajinkumar, K. S.; Gierke, John S.

2018-02-01

A comparative analysis of landslides detected by pixel-based and object-oriented analysis (OOA) methods was performed using very high-resolution (VHR) remotely sensed aerial images for the San Juan La Laguna, Guatemala, which witnessed widespread devastation during the 2005 Hurricane Stan. A 3-band orthophoto of 0.5 m spatial resolution together with a 115 field-based landslide inventory were used for the analysis. A binary reference was assigned with a zero value for landslide and unity for non-landslide pixels. The pixel-based analysis was performed using unsupervised classification, which resulted in 11 different trial classes. Detection of landslides using OOA includes 2-step K-means clustering to eliminate regions based on brightness; elimination of false positives using object properties such as rectangular fit, compactness, length/width ratio, mean difference of objects, and slope angle. Both overall accuracy and F-score for OOA methods outperformed pixel-based unsupervised classification methods in both landslide and non-landslide classes. The overall accuracy for OOA and pixel-based unsupervised classification was 96.5% and 94.3%, respectively, whereas the best F-score for landslide identification for OOA and pixel-based unsupervised methods: were 84.3% and 77.9%, respectively.Results indicate that the OOA is able to identify the majority of landslides with a few false positive when compared to pixel-based unsupervised classification.

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

USGS Publications Warehouse

Sturdivant, Emily; Lentz, Erika; Thieler, E. Robert; Farris, Amy; Weber, Kathryn; Remsen, David P.; Miner, Simon; Henderson, Rachel

2017-01-01

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

Can reliable sage-grouse lek counts be obtained using aerial infrared technology

USGS Publications Warehouse

Gillette, Gifford L.; Coates, Peter S.; Petersen, Steven; Romero, John P.

2013-01-01

More effective methods for counting greater sage-grouse (Centrocercus urophasianus) are needed to better assess population trends through enumeration or location of new leks. We describe an aerial infrared technique for conducting sage-grouse lek counts and compare this method with conventional ground-based lek count methods. During the breeding period in 2010 and 2011, we surveyed leks from fixed-winged aircraft using cryogenically cooled mid-wave infrared cameras and surveyed the same leks on the same day from the ground following a standard lek count protocol. We did not detect significant differences in lek counts between surveying techniques. These findings suggest that using a cryogenically cooled mid-wave infrared camera from an aerial platform to conduct lek surveys is an effective alternative technique to conventional ground-based methods, but further research is needed. We discuss multiple advantages to aerial infrared surveys, including counting in remote areas, representing greater spatial variation, and increasing the number of counted leks per season. Aerial infrared lek counts may be a valuable wildlife management tool that releases time and resources for other conservation efforts. Opportunities exist for wildlife professionals to refine and apply aerial infrared techniques to wildlife monitoring programs because of the increasing reliability and affordability of this technology.

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

The National Map - Orthoimagery

USGS Publications Warehouse

Mauck, James; Brown, Kim; Carswell, William J.

2009-01-01

Orthorectified digital aerial photographs and satellite images of 1-meter (m) pixel resolution or finer make up the orthoimagery component of The National Map. The process of orthorectification removes feature displacements and scale variations caused by terrain relief and sensor geometry. The result is a combination of the image characteristics of an aerial photograph or satellite image and the geometric qualities of a map. These attributes allow users to: *Measure distance *Calculate areas *Determine shapes of features *Calculate directions *Determine accurate coordinates *Determine land cover and use *Perform change detection *Update maps The standard digital orthoimage is a 1-m or finer resolution, natural color or color infra-red product. Most are now produced as GeoTIFFs and accompanied by a Federal Geographic Data Committee (FGDC)-compliant metadata file. The primary source for 1-m data is the National Agriculture Imagery Program (NAIP) leaf-on imagery. The U.S. Geological Survey (USGS) utilizes NAIP imagery as the image layer on its 'Digital- Map' - a new generation of USGS topographic maps (http://nationalmap.gov/digital_map). However, many Federal, State, and local governments and organizations require finer resolutions to meet a myriad of needs. Most of these images are leaf-off, natural-color products at resolutions of 1-foot (ft) or finer.

Applications systems verification and transfer project. Volume 2: Operational applications of satellite snow-cover observations and data-collection systems in the Arizona test site

NASA Technical Reports Server (NTRS)

Schumann, H. H.

1981-01-01

Ground surveys and aerial observations were used to monitor rapidly changing moisture conditions in the Salt-Verde watershed. Repetitive satellite snow cover observations greatly reduce the necessity for routine aerial snow reconnaissance flights over the mountains. High resolution, multispectral imagery provided by LANDSAT satellite series enabled rapid and accurate mapping of snow-cover distributions for small- to medium-sized subwatersheds; however, the imagery provided only one observation every 9 days of about a third of the watershed. Low resolution imagery acquired by the ITOSa dn SMS/GOES meteorological satellite series provides the daily synoptic observation necessary to monitor the rapid changes in snow-covered area in the entire watershed. Short term runoff volumes can be predicted from daily sequential snow cover observations.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application

PubMed Central

Maxwell, Susan K.

2010-01-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. PMID:21135917

Adaptive Texture Synthesis for Large Scale City Modeling

NASA Astrophysics Data System (ADS)

Despine, G.; Colleu, T.

2015-02-01

Large scale city models textured with aerial images are well suited for bird-eye navigation but generally the image resolution does not allow pedestrian navigation. One solution to face this problem is to use high resolution terrestrial photos but it requires huge amount of manual work to remove occlusions. Another solution is to synthesize generic textures with a set of procedural rules and elementary patterns like bricks, roof tiles, doors and windows. This solution may give realistic textures but with no correlation to the ground truth. Instead of using pure procedural modelling we present a method to extract information from aerial images and adapt the texture synthesis to each building. We describe a workflow allowing the user to drive the information extraction and to select the appropriate texture patterns. We also emphasize the importance to organize the knowledge about elementary pattern in a texture catalogue allowing attaching physical information, semantic attributes and to execute selection requests. Roofs are processed according to the detected building material. Façades are first described in terms of principal colours, then opening positions are detected and some window features are computed. These features allow selecting the most appropriate patterns from the texture catalogue. We experimented this workflow on two samples with 20 cm and 5 cm resolution images. The roof texture synthesis and opening detection were successfully conducted on hundreds of buildings. The window characterization is still sensitive to the distortions inherent to the projection of aerial images onto the facades.

Ecological Energetics of an Abundant Aerial Insectivore, the Purple Martin

PubMed Central

Kelly, Jeffrey F.; Bridge, Eli S.; Frick, Winifred F.; Chilson, Phillip B.

2013-01-01

The atmospheric boundary layer and lower free atmosphere, or aerosphere, is increasingly important for human transportation, communication, environmental monitoring, and energy production. The impacts of anthropogenic encroachment into aerial habitats are not well understood. Insectivorous birds and bats are inherently valuable components of biodiversity and play an integral role in aerial trophic dynamics. Many of these insectivores are experiencing range-wide population declines. As a first step toward gaging the potential impacts of these declines on the aerosphere’s trophic system, estimates of the biomass and energy consumed by aerial insectivores are needed. We developed a suite of energetics models for one of the largest and most common avian aerial insectivores in North America, the Purple Martin ( Progne subis ). The base model estimated that Purple Martins consumed 412 (± 104) billion insects*y-1 with a biomass of 115,860 (± 29,192) metric tonnes*y-1. During the breeding season Purple Martins consume 10.3 (+ 3.0) kg of prey biomass per km3 of aerial habitat, equal to about 36,000 individual insects*km-3. Based on these calculations, the cumulative seasonal consumption of insects*km-3 is greater in North America during the breeding season than during other phases of the annual cycle, however the maximum daily insect consumption*km-3 occurs during fall migration. This analysis provides the first range-wide quantitative estimate of the magnitude of the trophic impact of this large and common aerial insectivore. Future studies could use a similar modeling approach to estimate impacts of the entire guild of aerial insectivores at a variety of temporal and spatial scales. These analyses would inform our understanding of the impact of population declines among aerial insectivores on the aerosphere’s trophic dynamics. PMID:24086755

Integrated remotely sensed datasets for disaster management

NASA Astrophysics Data System (ADS)

McCarthy, Timothy; Farrell, Ronan; Curtis, Andrew; Fotheringham, A. Stewart

2008-10-01

Video imagery can be acquired from aerial, terrestrial and marine based platforms and has been exploited for a range of remote sensing applications over the past two decades. Examples include coastal surveys using aerial video, routecorridor infrastructures surveys using vehicle mounted video cameras, aerial surveys over forestry and agriculture, underwater habitat mapping and disaster management. Many of these video systems are based on interlaced, television standards such as North America's NTSC and European SECAM and PAL television systems that are then recorded using various video formats. This technology has recently being employed as a front-line, remote sensing technology for damage assessment post-disaster. This paper traces the development of spatial video as a remote sensing tool from the early 1980s to the present day. The background to a new spatial-video research initiative based at National University of Ireland, Maynooth, (NUIM) is described. New improvements are proposed and include; low-cost encoders, easy to use software decoders, timing issues and interoperability. These developments will enable specialists and non-specialists collect, process and integrate these datasets within minimal support. This integrated approach will enable decision makers to access relevant remotely sensed datasets quickly and so, carry out rapid damage assessment during and post-disaster.

Predictive spatial modeling of narcotic crop growth patterns

USGS Publications Warehouse

Waltz, Frederick A.; Moore, D.G.

1986-01-01

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement. The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization. The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

Multispectral data processing from unmanned aerial vehicles: application in precision agriculture using different sensors and platforms

NASA Astrophysics Data System (ADS)

Piermattei, Livia; Bozzi, Carlo Alberto; Mancini, Adriano; Tassetti, Anna Nora; Karel, Wilfried; Pfeifer, Norbert

2017-04-01

Unmanned aerial vehicles (UAVs) in combination with consumer grade cameras have become standard tools for photogrammetric applications and surveying. The recent generation of multispectral, cost-efficient and lightweight cameras has fostered a breakthrough in the practical application of UAVs for precision agriculture. For this application, multispectral cameras typically use Green, Red, Red-Edge (RE) and Near Infrared (NIR) wavebands to capture both visible and invisible images of crops and vegetation. These bands are very effective for deriving characteristics like soil productivity, plant health and overall growth. However, the quality of results is affected by the sensor architecture, the spatial and spectral resolutions, the pattern of image collection, and the processing of the multispectral images. In particular, collecting data with multiple sensors requires an accurate spatial co-registration of the various UAV image datasets. Multispectral processed data in precision agriculture are mainly presented as orthorectified mosaics used to export information maps and vegetation indices. This work aims to investigate the acquisition parameters and processing approaches of this new type of image data in order to generate orthoimages using different sensors and UAV platforms. Within our experimental area we placed a grid of artificial targets, whose position was determined with differential global positioning system (dGPS) measurements. Targets were used as ground control points to georeference the images and as checkpoints to verify the accuracy of the georeferenced mosaics. The primary aim is to present a method for the spatial co-registration of visible, Red-Edge, and NIR image sets. To demonstrate the applicability and accuracy of our methodology, multi-sensor datasets were collected over the same area and approximately at the same time using the fixed-wing UAV senseFly "eBee". The images were acquired with the camera Canon S110 RGB, the multispectral cameras Canon S110 NIR and S110 RE and with the multi-camera system Parrot Sequoia, which is composed of single-band cameras (Green, Red, Red Edge, NIR and RGB). Imagery from each sensor was georeferenced and mosaicked with the commercial software Agisoft PhotoScan Pro and different approaches for image orientation were compared. To assess the overall spatial accuracy of each dataset the root mean square error was computed between check point coordinates measured with dGPS and coordinates retrieved from georeferenced image mosaics. Additionally, image datasets from different UAV platforms (i.e. DJI Phantom 4Pro, DJI Phantom 3 professional, and DJI Inspire 1 Pro) were acquired over the same area and the spatial accuracy of the orthoimages was evaluated.

Integration of contributed data with HEC-RAS hydrodynamic model for flood inundation and damage assessment: 2015 Dallas Texas Case Study

NASA Astrophysics Data System (ADS)

Sava, E.; Thornton, J. C.; Kalyanapu, A. J.; Cervone, G.

2016-12-01

Transportation infrastructure networks in urban areas are highly sensitive to natural disasters, yet are a very critical source for the success of rescue, recovery, and renovation operations. Therefore, prompt restoration of such networks is of high importance for disaster relief services. Satellite and aerial images provide data with high spatial and temporal resolution and are a powerful tool for monitoring the environment and mapping the spatio-temporal variability of the Earth's surface. They provide a synoptic overview and give useful environmental information for a wide range of scales, from entire continents to urban areas, with spatial pixel resolutions ranging from kilometers to centimeters. However, sensor limitations are often a serious drawback since no single sensor offers the optimal spectral, spatial, and temporal resolution at the same time. Specific data may not be collected in the time and space most urgently required and/or may it contain gaps as a result of the satellite revisit time, atmospheric opacity, or other obstructions. In this study, the feasibility of integrating multiple sources of contributed data including remotely sensed datasets and open-source geospatial datasets, into hydrodynamic models for flood inundation simulations is assessed. The 2015 Dallas floods that caused up to $61 million dollars in damage was selected for this study. A Hydraulic Engineering Center - River Analysis System (HEC-RAS) model was developed for the study area, using reservoir surcharge releases and geometry provided by the U.S. Army Corps of Engineers Fort Worth District. The simulated flood inundation is compared with the "contributed data" for the location (such as Civil Air Patrol data and WorldView 3 dataset) which indicated the model's lack of representing lateral inflows near the upstream section. An Artificial Neural Network (ANN) model is developed that used local precipitation and discharge values in the vicinity to estimate the lateral flows. This addition of estimated lateral inflows is expected to improve the model performance to match with the observed flows. Future work will focus on extending this preliminary work to assess the model performance after integrating these additional data sources.

Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest

NASA Astrophysics Data System (ADS)

Tian, Jinyan; Wang, Le; Li, Xiaojuan; Gong, Huili; Shi, Chen; Zhong, Ruofei; Liu, Xiaomeng

2017-09-01

Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2). In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation.

Effects of spatial resolution ratio in image fusion

USGS Publications Warehouse

Ling, Y.; Ehlers, M.; Usery, E.L.; Madden, M.

2008-01-01

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1:10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1:10 to 1:30). However, even with a spatial resolution ratio as small as 1:30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1:30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image.

Analysis of the possibilities of using aerial photographs to determine the bathymetry in shallow coastal zone of the selected section of the Baltic Sea

NASA Astrophysics Data System (ADS)

Cieszynski, Lukasz; Furmanczyk, Kazimierz

2017-04-01

Bathymetry data for the coastal zone of the Baltic Sea are usually created in profiles based on echo sounding measurements. However, in the shallow coastal zone (up to 4 m depth), the quality and accuracy of data is insufficient because of the spatial variability of the seabed. The green laser - LIDAR - can comprise a solution for studies of such shallow areas. However, this method is still an expensive one and that is why we have decided to use the RGB digital aerial photographs to create a model for mapping the seabed of the shallow coastal zone. So far, in the 60's, researchers in the USA (Musgrove, 1969) and Russia (Zdanowicz, 1963) developed the first method of bathymetry determining from aerial panchromatic (black-white) photographs. This method was adapted for the polish conditions by Furmanczyk in 1975 and in 2014 we have returned to his concept using more advanced techniques of recording and image processing. In our study, we propose to determine the bathymetry in shallow coastal zone of the Baltic Sea by using the digital vertical aerial photographs (both single and multi-channel spectral). These photos are the high-resolution matrix (10 cm per pixel) containing values of the grey level in the individual spectral bands (RGB). This gives great possibilities to determine the bathymetry in order to analyze the changes in the marine coastal zone. Comparing the digital bathymetry maps - obtained by proposed method - in the following periods, you can develop differential maps, which reflect the movements of sea-bottom sediments. This can be used to indicate the most dynamic regions in the examined area. The model is based on the image pixel values and relative depths measured in situ (in selected checkpoints). As a result, the relation of the pixel brightness and sea depth (the algorithm) was defined. Using the algorithm, depth calculations for the whole scene were done and high resolution bathymetric map created. However, the algorithm requires numbers of adjustments resulting from, e.g., the phenomenon of vignetting, distribution of light, or the collapse of the rays of light at the atmosphere - sea interface. We have developed the algorithm with correction formulas and created a final model in MATLAB. It allows one to obtain three-dimensional bathymetry visualization for a specific region from a digital color aerial photograph. This model enables to determine the bathymetry of the most dynamic areas in the marine coastal zone up to 3-4 meters depth with a relatively good accuracy. In addition, the possibility to take pictures from the drone instead of a plane, significantly reduces the cost of the process. In the poster presentation, we will present the model and its results for the area of the Polish west coast. 1. Musgrove R,G., 1969. Photometry for interpretation. Photogrametric Engineering No. 10. 2. Furmańczyk K., 1975. Możliwości praktycznego zastosowania metody fotogrametrycznej do określania głębokości w strefie brzegowej morza. Gdańsk. 3. Zdanowicz W.G., 1963. Primienienije aerometodow dlia issledowanija moria. Leningrad.

Use of 35-mm color aerial photography to acquire mallard sex ratio data

USGS Publications Warehouse

Ferguson, Edgar L.; Jorde, Dennis G.; Sease, John L.

1981-01-01

A conventional 35-mm camera equipped with an f2.8 135-mm lens and ASA 64 color film was used to acquire sex ratio data on mallards (Anas platyrhynchos) wintering in the Platte River Valley of south-central Nebraska. Prelight focusing for a distance of 30.5 metres and setting of shutter speed at 1/2000 of a second eliminated focusing and reduced image motion problems and resulted in high-resolution, large-scale aerial photography of small targets. This technique has broad application to the problem of determining sex ratios of various species of waterfowl concentrated on wintering and staging areas. The aerial photographic method was cheaper than the ground ocular method when costs were compared on a per-100 bird basis.

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Treesearch

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

Estimates of underwater and aerial visual acuity in the European beaver Castor fiber L. based on morphological data.

PubMed

Mass, A M; Supin, A Ya

2017-03-01

The eye optics and topographic distribution of ganglion cells were studied using whole mount preparations from European beaver Castor fiber L. The beaver eye optics provides emmetropia in air and hypermetropia in water. The optometrical measurements predict retinal resolution of the beaver eye around 17' in air and 9' in water. In air, retinal resolution corresponds to the real visual acuity, whereas in water, visual acuity is below the retinal resolution because of the non-precise focusing.

Cloud-free resolution element statistics program

NASA Technical Reports Server (NTRS)

Liley, B.; Martin, C. D.

1971-01-01

Computer program computes number of cloud-free elements in field-of-view and percentage of total field-of-view occupied by clouds. Human error is eliminated by using visual estimation to compute cloud statistics from aerial photographs.

Cogongrass inventory and management.

DOT National Transportation Integrated Search

2007-08-01

A field study was conducted from 2005-2006 to test broad scale classification of cogongrass (Imperata cylindrica (L.) Beauv.) on Mississippi highway rights of ways with aerial imagery. Four mosaics of high resolution multispectral images of median an...

[Limitations of landscape pattern analysis based on landscape indices: a case study of Lize-jian wetland in Yilan of Taiwan Province, China].

PubMed

Lin, Meng-lung; Cao, Yu; Wang, Shin

2008-01-01

In this paper, the Lizejian wetland landscape patterns in northeastern Taiwan of China were established by landscape indices and aerial photo interpretation, and a parallel analysis was made on them. The results showed that landscape indices could only indicate the landscape geometric characteristics of the wetland at patch and landscape levels, but could not present its spatial and functional characteristics observed from aerial photos. Combining aerial photo interpretation with landscape indices could be helpful to the holistic understanding of Lizejian wetland' s landscape structure and function, and improve the landscape pattern analysis. The new method for assessing landscape structure from a holistic point of view would play an important role in future landscape ecology research.

Detecting lost persons using the k-mean method applied to aerial photographs taken by unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Niedzielski, Tomasz; Stec, Magdalena; Wieczorek, Malgorzata; Slopek, Jacek; Jurecka, Miroslawa

2016-04-01

The objective of this work is to discuss the usefulness of the k-mean method in the process of detecting persons on oblique aerial photographs acquired by unmanned aerial vehicles (UAVs). The detection based on the k-mean procedure belongs to one of the modules of a larger Search and Rescue (SAR) system which is being developed at the University of Wroclaw, Poland (research project no. IP2014 032773 financed by the Ministry of Science and Higher Education of Poland). The module automatically processes individual geotagged visual-light UAV-taken photographs or their orthorectified versions. Firstly, we separate red (R), green (G) and blue (B) channels, express raster data as numeric matrices and acquire coordinates of centres of images using the exchangeable image file format (EXIF). Subsequently, we divide the matrices into matrices of smaller dimensions, the latter being associated with the size of spatial window which is suitable for discriminating between human and terrain. Each triplet of the smaller matrices (R, G and B) serves as input spatial data for the k-mean classification. We found that, in several configurations of the k-mean parameters, it is possible to distinguish a separate class which characterizes a person. We compare the skills of this approach by performing two experiments, based on UAV-taken RGB photographs and their orthorectified versions. This allows us to verify the hypothesis that the two exercises lead to similar classifications. In addition, we discuss the performance of the approach for dissimilar spatial windows, hence various dimensions of the above-mentioned matrices, and we do so in order to find the one which offers the most adequate classification. The numerical experiment is carried out using the data acquired during a dedicated observational UAV campaign carried out in the Izerskie Mountains (SW Poland).

Integration of aerial oblique imagery and terrestrial imagery for optimized 3D modeling in urban areas

NASA Astrophysics Data System (ADS)

Wu, Bo; Xie, Linfu; Hu, Han; Zhu, Qing; Yau, Eric

2018-05-01

Photorealistic three-dimensional (3D) models are fundamental to the spatial data infrastructure of a digital city, and have numerous potential applications in areas such as urban planning, urban management, urban monitoring, and urban environmental studies. Recent developments in aerial oblique photogrammetry based on aircraft or unmanned aerial vehicles (UAVs) offer promising techniques for 3D modeling. However, 3D models generated from aerial oblique imagery in urban areas with densely distributed high-rise buildings may show geometric defects and blurred textures, especially on building façades, due to problems such as occlusion and large camera tilt angles. Meanwhile, mobile mapping systems (MMSs) can capture terrestrial images of close-range objects from a complementary view on the ground at a high level of detail, but do not offer full coverage. The integration of aerial oblique imagery with terrestrial imagery offers promising opportunities to optimize 3D modeling in urban areas. This paper presents a novel method of integrating these two image types through automatic feature matching and combined bundle adjustment between them, and based on the integrated results to optimize the geometry and texture of the 3D models generated from aerial oblique imagery. Experimental analyses were conducted on two datasets of aerial and terrestrial images collected in Dortmund, Germany and in Hong Kong. The results indicate that the proposed approach effectively integrates images from the two platforms and thereby improves 3D modeling in urban areas.

Detecting and connecting agricultural ditches using LiDAR data

NASA Astrophysics Data System (ADS)

Roelens, Jennifer; Dondeyne, Stefaan; Van Orshoven, Jos; Diels, Jan

2017-04-01

High-resolution hydrological data are essential for spatially-targeted water resource management decisions and future modelling efforts. For Flanders, small water courses like agricultural ditches and their connection to the river network are incomplete in the official digital atlas. High-resolution LiDAR data offer the prospect for automated detection of ditches, but there is no established method or software to do so nor to predict how these are connected to each other and the wider hydrographic network. An aerial LiDAR database encompassing at least 16 points per square meter linked with simultaneously collected digital RGB aerial images, is available for Flanders. The potential of detecting agricultural ditches and their connectivity based on point LiDAR data was investigated in a 1.9 km2 study area located in the alluvial valley of the river Demer. The area consists of agricultural parcels and woodland with a ditch network of approximately 17 km. The entire network of open ditches, and the location of culverts were mapped during a field survey to test the effectiveness of the proposed method. In the first step of the proposed method, the LiDAR point data were transformed into a raster DEM with a 1-m resolution to reduce the amount of data to be analyzed. This was done by interpolating the bare earth points using the nearest neighborhood method. In a next step, a morphological approach was used for detecting a preliminary network as traditional flow algorithms are not suitable for detecting small water courses in low-lying areas. This resulted in a preliminary classified raster image with ditch and non-ditch cells. After eliminating small details that are the result of background noise, the resulting classified raster image was vectorized to match the format of the digital watercourse network. As the vectorisation does not always adequately represent the shape of linear features, the results did not meet the high-quality cartographic needs. The spatial accuracy of the derived ditches was improved by referring to the original LiDAR point cloud data. In each 1-m buffer of the preliminary detected network vertices, the lowest LiDAR point was taken as the vertex of an improved network, shifting the preliminary network into the lowest 'depressions' of the study area. A drawback of a morphological approach is that the connectivity of the network is usually poor since the pixels are processed separately. Therefore, the field observations on connectivity (including culverts) were used to develop an empirical model to estimate the probability of connectivity between LiDAR-derived ditch segments from auxiliary datasets and hydrological and morphological properties of the preliminary network. This allowed deriving a connectivity probability map. The underlying model was tested by cross-validating the field observations on connectivity.

Comparison of LiDAR-derived data and high resolution true color imagery for extracting urban forest cover

Treesearch

Aaron E. Maxwell; Adam C. Riley; Paul Kinder

2013-01-01

Remote sensing has many applications in forestry. Light detection and ranging (LiDAR) and high resolution aerial photography have been investigated as means to extract forest data, such as biomass, timber volume, stand dynamics, and gap characteristics. LiDAR return intensity data are often overlooked as a source of input raster data for thematic map creation. We...