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.

Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

NASA Astrophysics Data System (ADS)

Elarab, Manal; Ticlavilca, Andres M.; Torres-Rua, Alfonso F.; Maslova, Inga; McKee, Mac

2015-12-01

Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS-NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm-2, efficiency of 0.76, and 9 relevance vectors.

Application of Geostatistical Simulation to Enhance Satellite Image Products

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer L.; Thirulanambi, Rajkumar; Roy, David

2004-01-01

With the deployment of Earth Observing System (EOS) satellites that provide daily, global imagery, there is increasing interest in defining the limitations of the data and derived products due to its coarse spatial resolution. Much of the detail, i.e. small fragments and notches in boundaries, is lost with coarse resolution imagery such as the EOS MODerate-Resolution Imaging Spectroradiometer (MODIS) data. Higher spatial resolution data such as the EOS Advanced Spaceborn Thermal Emission and Reflection Radiometer (ASTER), Landsat and airborne sensor imagery provide more detailed information but are less frequently available. There are, however, both theoretical and analytical evidence that burn scars and other fragmented types of land covers form self-similar or self-affine patterns, that is, patterns that look similar when viewed at widely differing spatial scales. Therefore small features of the patterns should be predictable, at least in a statistical sense, with knowledge about the large features. Recent developments in fractal modeling for characterizing the spatial distribution of undiscovered petroleum deposits are thus applicable to generating simulations of finer resolution satellite image products. We will present example EOS products, analysis to investigate self-similarity, and simulation results.

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronand; Russell, Jeff; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the foundation of any interoperability or change detection technique. Satellite intercomparisons and accurate vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), require the generation of accurate reflectance maps (NDVI is used to describe or infer a wide variety of biophysical parameters and is defined in terms of near-infrared (NIR) and red band reflectances). Accurate reflectance-map generation from satellite imagery relies on the removal of solar and satellite geometry and of atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance has been widely applied to a few systems only. The ability to obtain atmospherically corrected imagery and products from various satellites is essential to enable widescale use of remotely sensed, multitemporal imagery for a variety of applications. An atmospheric correction approach derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) that can be applied to high-spatial-resolution satellite imagery under many conditions was evaluated to demonstrate a reliable, effective reflectance map generation method. Additional information is included in the original extended abstract.

Use of UAS remote sensing data to estimate crop ET at high spatial resolution

USDA-ARS?s Scientific Manuscript database

Estimation of the spatial distribution of evapotranspiration (ET) based on remotely sensed imagery has become useful for managing water in irrigated agricultural at various spatial scales. However, data acquired by conventional satellites (Landsat, ASTER, etc.) lack the spatial resolution to capture...

Analysis of the impact of spatial resolution on land/water classifications using high-resolution aerial imagery

USGS Publications Warehouse

Enwright, Nicholas M.; Jones, William R.; Garber, Adrienne L.; Keller, Matthew J.

2014-01-01

Long-term monitoring efforts often use remote sensing to track trends in habitat or landscape conditions over time. To most appropriately compare observations over time, long-term monitoring efforts strive for consistency in methods. Thus, advances and changes in technology over time can present a challenge. For instance, modern camera technology has led to an increasing availability of very high-resolution imagery (i.e. submetre and metre) and a shift from analogue to digital photography. While numerous studies have shown that image resolution can impact the accuracy of classifications, most of these studies have focused on the impacts of comparing spatial resolution changes greater than 2 m. Thus, a knowledge gap exists on the impacts of minor changes in spatial resolution (i.e. submetre to about 1.5 m) in very high-resolution aerial imagery (i.e. 2 m resolution or less). This study compared the impact of spatial resolution on land/water classifications of an area dominated by coastal marsh vegetation in Louisiana, USA, using 1:12,000 scale colour-infrared analogue aerial photography (AAP) scanned at four different dot-per-inch resolutions simulating ground sample distances (GSDs) of 0.33, 0.54, 1, and 2 m. Analysis of the impact of spatial resolution on land/water classifications was conducted by exploring various spatial aspects of the classifications including density of waterbodies and frequency distributions in waterbody sizes. This study found that a small-magnitude change (1–1.5 m) in spatial resolution had little to no impact on the amount of water classified (i.e. percentage mapped was less than 1.5%), but had a significant impact on the mapping of very small waterbodies (i.e. waterbodies ≤ 250 m2). These findings should interest those using temporal image classifications derived from very high-resolution aerial photography as a component of long-term monitoring programs.

Coastal habitat mapping in the Aegean Sea using high resolution orthophoto maps

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Papakonstantinou, Apostolos; Doukari, Michaela; Stamatis, Panagiotis; Makri, Despina; Katsanevakis, Stelios

2017-09-01

The significance of coastal habitat mapping lies in the need to prevent from anthropogenic interventions and other factors. Until 2015, Landsat-8 (30m) imagery were used as medium spatial resolution satellite imagery. So far, Sentinel-2 satellite imagery is very useful for more detailed regional scale mapping. However, the use of high resolution orthophoto maps, which are determined from UAV data, is expected to improve the mapping accuracy. This is due to small spatial resolution of the orthophoto maps (30 cm). 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. Additionally, the produced orthophoto maps analyzed through an object-based image analysis (OBIA) and nearest-neighbor classification for mapping the coastal habitats. Classification classes included the main general habitat types, i.e. seagrass, soft bottom, and hard bottom The developed methodology applied at the Koumbara beach (Ios Island - Greece). Results showed that UAS's data revealed the sub-bottom complexity in large shallow areas since they provide such information in the spatial resolution that permits the mapping of seagrass meadows with extreme detail. The produced habitat vectors are ideal as reference data for studies with satellite data of lower spatial resolution.

Improved wetland classification using eight-band high-resolution satellite imagery and a hybrid approach

EPA Science Inventory

Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of derived wetland maps were limited or often unsatisfactory largely due to the relatively coarse spatial resolution of conventional satellite imagery. This re...

Object Based Image Analysis Combining High Spatial Resolution Imagery and Laser Point Clouds for Urban Land Cover

NASA Astrophysics Data System (ADS)

Zou, Xiaoliang; Zhao, Guihua; Li, Jonathan; Yang, Yuanxi; Fang, Yong

2016-06-01

With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.

Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region.

Treesearch

L. Arroyo; S.P. Healey; W.B. Cohen; D. Cocero; J.A. Manzanera

2006-01-01

Knowledge of fuel load and composition is critical in fighting, preventing, and understanding wildfires. Commonly, the generation of fuel maps from remotely sensed imagery has made use of medium-resolution sensors such as Landsat. This paper presents a methodology to generate fuel type maps from high spatial resolution satellite data through object-oriented...

Landsat 7 thermal-IR image sharpening using an artificial neural network and sensor model

USGS Publications Warehouse

Lemeshewsky, G.P.; Schowengerdt, R.A.; ,

2001-01-01

The enhanced thematic mapper (plus) (ETM+) instrument on Landsat 7 shares the same basic design as the TM sensors on Landsats 4 and 5, with some significant improvements. In common are six multispectral bands with a 30-m ground-projected instantaneous field of view (GIFOV). However, the thermaL-IR (TIR) band now has a 60-m GIFOV, instead of 120-m. Also, a 15-m panchromatic band has been added. The artificial neural network (NN) image sharpening method described here uses data from the higher spatial resolution ETM+ bands to enhance (sharpen) the spatial resolution of the TIR imagery. It is based on an assumed correlation over multiple scales of resolution, between image edge contrast patterns in the TIR band and several other spectral bands. A multilayer, feedforward NN is trained to approximate TIR data at 60m, given degraded (from 30-m to 60-m) spatial resolution input from spectral bands 7, 5, and 2. After training, the NN output for full-resolution input generates an approximation of a TIR image at 30-m resolution. Two methods are used to degrade the spatial resolution of the imagery used for NN training, and the corresponding sharpening results are compared. One degradation method uses a published sensor transfer function (TF) for Landsat 5 to simulate sensor coarser resolution imagery from higher resolution imagery. For comparison, the second degradation method is simply Gaussian low pass filtering and subsampling, wherein the Gaussian filter approximates the full width at half maximum amplitude characteristics of the TF-based spatial filter. Two fixed-size NNs (that is, number of weights and processing elements) were trained separately with the degraded resolution data, and the sharpening results compared. The comparison evaluates the relative influence of the degradation technique employed and whether or not it is desirable to incorporate a sensor TF model. Preliminary results indicate some improvements for the sensor model-based technique. Further evaluation using a higher resolution reference image and strict application of sensor model to data is recommended.

Mapping Spatial Variability in Health and Wealth Indicators in Accra, Ghana Using High Spatial Resolution Imagery

NASA Astrophysics Data System (ADS)

Engstrom, R.; Ashcroft, E.

2014-12-01

There has been a tremendous amount of research conducted that examines disparities in health and wealth of persons between urban and rural areas however, relatively little research has been undertaken to examine variations within urban areas. A major limitation to elucidating differences with urban areas is the lack of social and demographic data at a sufficiently high spatial resolution to determine these differences. Generally the only available data that contain this information are census data which are collected at most every ten years and are often difficult to obtain at a high enough spatial resolution to allow for examining in depth variability in health and wealth indicators at high spatial resolutions, especially in developing countries. High spatial resolution satellite imagery may be able to provide timely and synoptic information that is related to health and wealth variability within a city. In this study we use two dates of Quickbird imagery (2003 and 2010) classified into the vegetation-impervious surface-soil (VIS) model introduced by Ridd (1995). For 2003 we only have partial coverage of the city, while for 2010 we have a mosaic, which covers the entire city of Accra, Ghana. Variations in the VIS values represent the physical variations within the city and these are compared to variations in economic, and/or sociodemographic data derived from the 2000 Ghanaian census at two spatial resolutions, the enumeration area (approximately US Census Tract) and the neighborhood for the city. Results indicate a significant correlation between both vegetation and impervious surface to type of cooking fuel used in the household, population density, housing density, availability of sewers, cooking space usage, and other variables. The correlations are generally stronger at the neighborhood level and the relationships are stable through time and space. Overall, the results indicate that information derived from high resolution satellite data is related to indicators of health and wealth within a developing world city and that the even if the imagery is collected 10 years after the census information, the relationships are still significant.

Evaluation of ERTS-1 image sensor spatial resolution in photographic form

NASA Technical Reports Server (NTRS)

Slater, P. N. (Principal Investigator); Schowengerdt, R. A.

1973-01-01

The author has identified the following significant results. A coherent optical system was used to display the spatial frequency content of the amplitude image of one area of the ground as obtained in the four wavelength bands of the multispectral scanner. This enabled a rapid comparison to be made between the four bands, from which it was clear that bands 5 and 7 were preferred to the others in terms of image definition, and thus mapping and acreage estimation, for the particular agricultural area imaged. With suitable scaling it was also possible to compare the modulation, as a function of spatial frequency, of MSS bands 4 and 5 with the green (BB) and red (DD) bands of the same area from the Apollo 9, SO65 experiment. A significant result is that the modulation in the MSS amplitude imagery is 65%-90% of that in the Apollo 9 amplitude imagery. In addition, the ratio of spatial frequencies for the ERTS-1 and Apollo imagery, at which the same modulation occurs, lies between 0.55 and 0.75 for the red band. This ratio is closely related to the ratio of resolutions for the two sensors. These values corroborate statements that the resolution of the MSS imagery is better than anticipated by pre-flight predictions.

Using High Spatial Resolution Digital Imagery

DTIC Science & Technology

2005-02-01

digital base maps were high resolution U.S. Geological Survey (USGS) Digital Orthophoto Quarter Quadrangles (DOQQ). The Root Mean Square Errors (RMSE...next step was to assign real world coordinates to the linear im- age. The mosaics were geometrically registered to the panchromatic orthophotos ...useable thematic map from high-resolution imagery. A more practical approach may be to divide the Refuge into a set of smaller areas, or tiles

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.

High-resolution satellite imagery is an important yet underutilized resource in conservation biology.

PubMed

Boyle, Sarah A; Kennedy, Christina M; Torres, Julio; Colman, Karen; Pérez-Estigarribia, Pastor E; de la Sancha, Noé U

2014-01-01

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.

Using High Resolution Commercial Satellite Imagery to Quantify Spatial Features of Urban Areas and their Relationship to Quality of Life Indicators in Accra, Ghana

NASA Astrophysics Data System (ADS)

Sandborn, A.; Engstrom, R.; Yu, Q.

2014-12-01

Mapping urban areas via satellite imagery is an important task for detecting and anticipating land cover and land use change at multiple scales. As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and quality of life indicators can provide timely and spatially explicit information to researchers and planners working to determine how cities are changing. In this study, we use commercial high spatial resolution satellite imagery in combination with fine resolution census data to determine the ability of using remotely sensed data to reveal the spatial patterns of quality of life in Accra, Ghana. Traditionally, spectral characteristics are used on a per-pixel basis to determine land cover; however, in this study, we test a new methodology that quantifies spatial characteristics using a variety of spatial features observed in the imagery to determine the properties of an urban area. The spatial characteristics used in this study include histograms of oriented gradients, PanTex, Fourier transform, and line support regions. These spatial features focus on extracting structural and textural patterns of built-up areas, such as homogeneous building orientations and straight line indices. Information derived from aggregating the descriptive statistics of the spatial features at both the fine-resolution census unit and the larger neighborhood level are then compared to census derived quality of life indicators including information about housing, education, and population estimates. Preliminary results indicate that there are correlations between straight line indices and census data including available electricity and literacy rates. Results from this study will be used to determine if this methodology provides a new and improved way to measure a city structure in developing cities and differentiate between residential and commercial land use zones, as well as formal versus informal housing areas.

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.

Mapping surface disturbance of energy-related infrastructure in southwest Wyoming--An assessment of methods

USGS Publications Warehouse

Germaine, Stephen S.; O'Donnell, Michael S.; Aldridge, Cameron L.; Baer, Lori; Fancher, Tammy; McBeth, Jamie; McDougal, Robert R.; Waltermire, Robert; Bowen, Zachary H.; Diffendorfer, James; Garman, Steven; Hanson, Leanne

2012-01-01

We evaluated how well three leading information-extraction software programs (eCognition, Feature Analyst, Feature Extraction) and manual hand digitization interpreted information from remotely sensed imagery of a visually complex gas field in Wyoming. Specifically, we compared how each mapped the area of and classified the disturbance features present on each of three remotely sensed images, including 30-meter-resolution Landsat, 10-meter-resolution SPOT (Satellite Pour l'Observation de la Terre), and 0.6-meter resolution pan-sharpened QuickBird scenes. Feature Extraction mapped the spatial area of disturbance features most accurately on the Landsat and QuickBird imagery, while hand digitization was most accurate on the SPOT imagery. Footprint non-overlap error was smallest on the Feature Analyst map of the Landsat imagery, the hand digitization map of the SPOT imagery, and the Feature Extraction map of the QuickBird imagery. When evaluating feature classification success against a set of ground-truthed control points, Feature Analyst, Feature Extraction, and hand digitization classified features with similar success on the QuickBird and SPOT imagery, while eCognition classified features poorly relative to the other methods. All maps derived from Landsat imagery classified disturbance features poorly. Using the hand digitized QuickBird data as a reference and making pixel-by-pixel comparisons, Feature Extraction classified features best overall on the QuickBird imagery, and Feature Analyst classified features best overall on the SPOT and Landsat imagery. Based on the entire suite of tasks we evaluated, Feature Extraction performed best overall on the Landsat and QuickBird imagery, while hand digitization performed best overall on the SPOT imagery, and eCognition performed worst overall on all three images. Error rates for both area measurements and feature classification were prohibitively high on Landsat imagery, while QuickBird was time and cost prohibitive for mapping large spatial extents. The SPOT imagery produced map products that were far more accurate than Landsat and did so at a far lower cost than QuickBird imagery. Consideration of degree of map accuracy required, costs associated with image acquisition, software, operator and computation time, and tradeoffs in the form of spatial extent versus resolution should all be considered when evaluating which combination of imagery and information-extraction method might best serve any given land use mapping project. When resources permit, attaining imagery that supports the highest classification and measurement accuracy possible is recommended.

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.

Multispectral multisensor image fusion using wavelet transforms

USGS Publications Warehouse

Lemeshewsky, George P.

1999-01-01

Fusion techniques can be applied to multispectral and higher spatial resolution panchromatic images to create a composite image that is easier to interpret than the individual images. Wavelet transform-based multisensor, multiresolution fusion (a type of band sharpening) was applied to Landsat thematic mapper (TM) multispectral and coregistered higher resolution SPOT panchromatic images. The objective was to obtain increased spatial resolution, false color composite products to support the interpretation of land cover types wherein the spectral characteristics of the imagery are preserved to provide the spectral clues needed for interpretation. Since the fusion process should not introduce artifacts, a shift invariant implementation of the discrete wavelet transform (SIDWT) was used. These results were compared with those using the shift variant, discrete wavelet transform (DWT). Overall, the process includes a hue, saturation, and value color space transform to minimize color changes, and a reported point-wise maximum selection rule to combine transform coefficients. The performance of fusion based on the SIDWT and DWT was evaluated with a simulated TM 30-m spatial resolution test image and a higher resolution reference. Simulated imagery was made by blurring higher resolution color-infrared photography with the TM sensors' point spread function. The SIDWT based technique produced imagery with fewer artifacts and lower error between fused images and the full resolution reference. Image examples with TM and SPOT 10-m panchromatic illustrate the reduction in artifacts due to the SIDWT based fusion.

Combining Direct Broadcast Polar Hyper-spectral Soundings with Geostationary Multi-spectral Imagery for Producing Low Latency Sounding Products

NASA Astrophysics Data System (ADS)

Smith, W.; Weisz, E.; McNabb, J. M. C.

2017-12-01

A technique is described which enables the combination of high vertical resolution (1 to 2-km) JPSS hyper-spectral soundings (i.e., from AIRS, CrIS, and IASI) with high horizontal (2-km) and temporal (15-min) resolution GOES multi-spectral imagery (i.e., provided by ABI) to produce low latency sounding products with the highest possible spatial and temporal resolution afforded by the instruments.

Evaluating Hyperspectral Imaging of Wetland Vegetation as a Tool for Detecting Estuarine Nutrient Enrichment

DTIC Science & Technology

2008-05-01

the vegetation’s uptake of water column nutrients produces a spectral response; and 3) the spectral and spatial resolutions ...analysis. This allowed us to evaluate these assumptions at the landscape level, by using the high spectral and spatial resolution of the hyperspectral... spatial resolution (2.5 m pixels) HyMap hyperspectral imagery of the entire wetland. After using a hand-held spectrometer to characterize

Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Treesearch

M.D. Nelson; R.E. McRoberts; G.R. Holden; M.E. Bauer

2009-01-01

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We...

Assessing Greater Sage-Grouse Selection of Brood-Rearing Habitat Using Remotely-Sensed Imagery: Can Readily Available High-Resolution Imagery Be Used to Identify Brood-Rearing Habitat Across a Broad Landscape?

PubMed

Westover, Matthew; Baxter, Jared; Baxter, Rick; Day, Casey; Jensen, Ryan; Petersen, Steve; Larsen, Randy

2016-01-01

Greater sage-grouse populations have decreased steadily since European settlement in western North America. Reduced availability of brood-rearing habitat has been identified as a limiting factor for many populations. We used radio-telemetry to acquire locations of sage-grouse broods from 1998 to 2012 in Strawberry Valley, Utah. Using these locations and remotely-sensed NAIP (National Agricultural Imagery Program) imagery, we 1) determined which characteristics of brood-rearing habitat could be used in widely available, high resolution imagery 2) assessed the spatial extent at which sage-grouse selected brood-rearing habitat, and 3) created a predictive habitat model to identify areas of preferred brood-rearing habitat. We used AIC model selection to evaluate support for a list of variables derived from remotely-sensed imagery. We examined the relationship of these explanatory variables at three spatial extents (45, 200, and 795 meter radii). Our top model included 10 variables (percent shrub, percent grass, percent tree, percent paved road, percent riparian, meters of sage/tree edge, meters of riparian/tree edge, distance to tree, distance to transmission lines, and distance to permanent structures). Variables from each spatial extent were represented in our top model with the majority being associated with the larger (795 meter) spatial extent. When applied to our study area, our top model predicted 75% of naïve brood locations suggesting reasonable success using this method and widely available NAIP imagery. We encourage application of our methodology to other sage-grouse populations and species of conservation concern.

Landsat 8 Multispectral and Pansharpened Imagery Processing on the Study of Civil Engineering Issues

NASA Astrophysics Data System (ADS)

Lazaridou, M. A.; Karagianni, A. Ch.

2016-06-01

Scientific and professional interests of civil engineering mainly include structures, hydraulics, geotechnical engineering, environment, and transportation issues. Topics included in the context of the above may concern urban environment issues, urban planning, hydrological modelling, study of hazards and road construction. Land cover information contributes significantly on the study of the above subjects. Land cover information can be acquired effectively by visual image interpretation of satellite imagery or after applying enhancement routines and also by imagery classification. The Landsat Data Continuity Mission (LDCM - Landsat 8) is the latest satellite in Landsat series, launched in February 2013. Landsat 8 medium spatial resolution multispectral imagery presents particular interest in extracting land cover, because of the fine spectral resolution, the radiometric quantization of 12bits, the capability of merging the high resolution panchromatic band of 15 meters with multispectral imagery of 30 meters as well as the policy of free data. In this paper, Landsat 8 multispectral and panchromatic imageries are being used, concerning surroundings of a lake in north-western Greece. Land cover information is extracted, using suitable digital image processing software. The rich spectral context of the multispectral image is combined with the high spatial resolution of the panchromatic image, applying image fusion - pansharpening, facilitating in this way visual image interpretation to delineate land cover. Further processing concerns supervised image classification. The classification of pansharpened image preceded multispectral image classification. Corresponding comparative considerations are also presented.

Toward daily monitoring of vegetation conditions at field scale through fusing data from multiple sensors

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires remote sensing data at fine spatial and temporal resolution. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for crop and rangeland monitoring. The Landsat satellite s...

Daily monitoring of vegetation conditions and evapotranspiration at field scale by fusing multi-satellite images

USDA-ARS?s Scientific Manuscript database

Vegetation monitoring requires frequent remote sensing observations. While imagery from coarse resolution sensors such as MODIS/VIIRS can provide daily observations, they lack spatial detail to capture surface features for vegetation monitoring. The medium spatial resolution (10-100m) sensors are su...

Multispectral image sharpening using wavelet transform techniques and spatial correlation of edges

USGS Publications Warehouse

Lemeshewsky, George P.; Schowengerdt, Robert A.

2000-01-01

Several reported image fusion or sharpening techniques are based on the discrete wavelet transform (DWT). The technique described here uses a pixel-based maximum selection rule to combine respective transform coefficients of lower spatial resolution near-infrared (NIR) and higher spatial resolution panchromatic (pan) imagery to produce a sharpened NIR image. Sharpening assumes a radiometric correlation between the spectral band images. However, there can be poor correlation, including edge contrast reversals (e.g., at soil-vegetation boundaries), between the fused images and, consequently, degraded performance. To improve sharpening, a local area-based correlation technique originally reported for edge comparison with image pyramid fusion is modified for application with the DWT process. Further improvements are obtained by using redundant, shift-invariant implementation of the DWT. Example images demonstrate the improvements in NIR image sharpening with higher resolution pan imagery.

Evaluating satellite imagery for estimating mountain pine beetle-caused lodgepole pine mortality: Current status

Treesearch

B. J. Bentz; D. Endreson

2004-01-01

Spatial accuracy in the detection and monitoring of mountain pine beetle populations is an important aspect of both forest research and management. Using ground-collected data, classification models to predict mountain pine beetle-caused lodgepole pine mortality were developed for Landsat TM, ETM+, and IKONOS imagery. Our results suggest that low-resolution imagery...

Comparative analysis of Worldview-2 and Landsat 8 for coastal saltmarsh mapping accuracy assessment

NASA Astrophysics Data System (ADS)

Rasel, Sikdar M. M.; Chang, Hsing-Chung; Diti, Israt Jahan; Ralph, Tim; Saintilan, Neil

2016-05-01

Coastal saltmarsh and their constituent components and processes are of an interest scientifically due to their ecological function and services. However, heterogeneity and seasonal dynamic of the coastal wetland system makes it challenging to map saltmarshes with remotely sensed data. This study selected four important saltmarsh species Pragmitis australis, Sporobolus virginicus, Ficiona nodosa and Schoeloplectus sp. as well as a Mangrove and Pine tree species, Avecinia and Casuarina sp respectively. High Spatial Resolution Worldview-2 data and Coarse Spatial resolution Landsat 8 imagery were selected in this study. Among the selected vegetation types some patches ware fragmented and close to the spatial resolution of Worldview-2 data while and some patch were larger than the 30 meter resolution of Landsat 8 data. This study aims to test the effectiveness of different classifier for the imagery with various spatial and spectral resolutions. Three different classification algorithm, Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were tested and compared with their mapping accuracy of the results derived from both satellite imagery. For Worldview-2 data SVM was giving the higher overall accuracy (92.12%, kappa =0.90) followed by ANN (90.82%, Kappa 0.89) and MLC (90.55%, kappa = 0.88). For Landsat 8 data, MLC (82.04%) showed the highest classification accuracy comparing to SVM (77.31%) and ANN (75.23%). The producer accuracy of the classification results were also presented in the paper.

Sub-pixel mapping of hyperspectral imagery using super-resolution

NASA Astrophysics Data System (ADS)

Sharma, Shreya; Sharma, Shakti; Buddhiraju, Krishna M.

2016-04-01

With the development of remote sensing technologies, it has become possible to obtain an overview of landscape elements which helps in studying the changes on earth's surface due to climate, geological, geomorphological and human activities. Remote sensing measures the electromagnetic radiations from the earth's surface and match the spectral similarity between the observed signature and the known standard signatures of the various targets. However, problem lies when image classification techniques assume pixels to be pure. In hyperspectral imagery, images have high spectral resolution but poor spatial resolution. Therefore, the spectra obtained is often contaminated due to the presence of mixed pixels and causes misclassification. To utilise this high spectral information, spatial resolution has to be enhanced. Many factors make the spatial resolution one of the most expensive and hardest to improve in imaging systems. To solve this problem, post-processing of hyperspectral images is done to retrieve more information from the already acquired images. The algorithm to enhance spatial resolution of the images by dividing them into sub-pixels is known as super-resolution and several researches have been done in this domain.In this paper, we propose a new method for super-resolution based on ant colony optimization and review the popular methods of sub-pixel mapping of hyperspectral images along with their comparative analysis.

Fusion of UAV photogrammetry and digital optical granulometry for detection of structural changes in floodplains

NASA Astrophysics Data System (ADS)

Langhammer, Jakub; Lendzioch, Theodora; Mirijovsky, Jakub

2016-04-01

Granulometric analysis represents a traditional, important and for the description of sedimentary material substantial method with various applications in sedimentology, hydrology and geomorphology. However, the conventional granulometric field survey methods are time consuming, laborious, costly and are invasive to the surface being sampled, which can be limiting factor for their applicability in protected areas.. The optical granulometry has recently emerged as an image analysis technique, enabling non-invasive survey, employing semi-automated identification of clasts from calibrated digital imagery, taken on site by conventional high resolution digital camera and calibrated frame. The image processing allows detection and measurement of mixed size natural grains, their sorting and quantitative analysis using standard granulometric approaches. Despite known limitations, the technique today presents reliable tool, significantly easing and speeding the field survey in fluvial geomorphology. However, the nature of such survey has still limitations in spatial coverage of the sites and applicability in research at multitemporal scale. In our study, we are presenting novel approach, based on fusion of two image analysis techniques - optical granulometry and UAV-based photogrammetry, allowing to bridge the gap between the needs of high resolution structural information for granulometric analysis and spatially accurate and data coverage. We have developed and tested a workflow that, using UAV imaging platform enabling to deliver seamless, high resolution and spatially accurate imagery of the study site from which can be derived the granulometric properties of the sedimentary material. We have set up a workflow modeling chain, providing (i) the optimum flight parameters for UAV imagery to balance the two key divergent requirements - imagery resolution and seamless spatial coverage, (ii) the workflow for the processing of UAV acquired imagery by means of the optical granulometry and (iii) the workflow for analysis of spatial distribution and temporal changes of granulometric properties across the point bar. The proposed technique was tested on a case study of an active point bar of mid-latitude mountain stream at Sumava mountains, Czech Republic, exposed to repeated flooding. The UAV photogrammetry was used to acquire very high resolution imagery to build high-precision digital terrain models and orthoimage. The orthoimage was then analyzed using the digital optical granulometric tool BaseGrain. This approach allowed us (i) to analyze the spatial distribution of the grain size in a seamless transects over an active point bar and (ii) to assess the multitemporal changes of granulometric properties of the point bar material resulting from flooding. The tested framework prove the applicability of the proposed method for granulometric analysis with accuracy comparable with field optical granulometry. The seamless nature of the data enables to study spatial distribution of granulometric properties across the study sites as well as the analysis of multitemporal changes, resulting from repeated imaging.

Evaluating Climate Causation of Conflict in Darfur Using Multi-temporal, Multi-resolution Satellite Image Datasets With Novel Analyses

NASA Astrophysics Data System (ADS)

Brown, I.; Wennbom, M.

2013-12-01

Climate change, population growth and changes in traditional lifestyles have led to instabilities in traditional demarcations between neighboring ethic and religious groups in the Sahel region. This has resulted in a number of conflicts as groups resort to arms to settle disputes. Such disputes often centre on or are justified by competition for resources. The conflict in Darfur has been controversially explained by resource scarcity resulting from climate change. Here we analyse established methods of using satellite imagery to assess vegetation health in Darfur. Multi-decadal time series of observations are available using low spatial resolution visible-near infrared imagery. Typically normalized difference vegetation index (NDVI) analyses are produced to describe changes in vegetation ';greenness' or ';health'. Such approaches have been widely used to evaluate the long term development of vegetation in relation to climate variations across a wide range of environments from the Arctic to the Sahel. These datasets typically measure peak NDVI observed over a given interval and may introduce bias. It is furthermore unclear how the spatial organization of sparse vegetation may affect low resolution NDVI products. We develop and assess alternative measures of vegetation including descriptors of the growing season, wetness and resource availability. Expanding the range of parameters used in the analysis reduces our dependence on peak NDVI. Furthermore, these descriptors provide a better characterization of the growing season than the single NDVI measure. Using multi-sensor data we combine high temporal/moderate spatial resolution data with low temporal/high spatial resolution data to improve the spatial representativity of the observations and to provide improved spatial analysis of vegetation patterns. The approach places the high resolution observations in the NDVI context space using a longer time series of lower resolution imagery. The vegetation descriptors derived are evaluated using independent high spatial resolution datasets that reveal the pattern and health of vegetation at metre scales. We also use climate variables to support the interpretation of these data. We conclude that the spatio-temporal patterns in Darfur vegetation and climate datasets suggest that labelling the conflict a climate-change conflict is inaccurate and premature.

The Use of Coarse Resolution Satellite Imagery to Predict Human Puumala Virus Epidemics in Sweden.

DTIC Science & Technology

1992-09-11

the adverse effects on NDVI data quality can occur in both the spatial and temporal dimension. In other words, a specific pixel value recorded in...are compared to the land-oriented systems.22 On the other hand, the very course spatial resolution has the advantage of greatly reducing the volume...necessary on the scale of individual fields, in which case LANDSAT-TM has higher spatial resolution ; and secondly, when specific

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.

How Much Can Remotely-Sensed Natural Resource Inventories Benefit from Finer Spatial Resolutions?

NASA Astrophysics Data System (ADS)

Hou, Z.; Xu, Q.; McRoberts, R. E.; Ståhl, G.; Greenberg, J. A.

2017-12-01

For remote sensing facilitated natural resource inventories, the effects of spatial resolution in the form of pixel size and the effects of subpixel information on estimates of population parameters were evaluated by comparing results obtained using Landsat 8 and RapidEye auxiliary imagery. The study area was in Burkina Faso, and the variable of interest was the stem volume (m3/ha) convertible to the woodland aboveground biomass. A sample consisting of 160 field plots was selected and measured from the population following a two-stage sampling design. Models were fit using weighted least squares; the population mean, mu, and the variance of the estimator of the population mean, Var(mu.hat), were estimated in two inferential frameworks, model-based and model-assisted, and compared; for each framework, Var(mu.hat) was estimated both analytically and empirically. Empirical variances were estimated with bootstrapping that for resampling takes clustering effects into account. The primary results were twofold. First, for the effects of spatial resolution and subpixel information, four conclusions are relevant: (1) finer spatial resolution imagery indeed contributes to greater precision for estimators of population parameter, but this increase is slight at a maximum rate of 20% considering that RapidEye data are 36 times finer resolution than Landsat 8 data; (2) subpixel information on texture is marginally beneficial when it comes to making inference for population of large areas; (3) cost-effectiveness is more favorable for the free of charge Landsat 8 imagery than RapidEye imagery; and (4) for a given plot size, candidate remote sensing auxiliary datasets are more cost-effective when their spatial resolutions are similar to the plot size than with much finer alternatives. Second, for the comparison between estimators, three conclusions are relevant: (1) model-based variance estimates are consistent with each other and about half as large as stabilized model-assisted estimates, suggesting superior effectiveness of model-based inference to model-assisted inference; (2) bootstrapping is an effective alternative to analytical variance estimators; and (3) prediction accuracy expressed by RMSE is useful for screening candidate models to be used for population inferences.

A Comparison of Spatial and Spectral Image Resolution for Mapping Invasive Plants in Coastal California

NASA Astrophysics Data System (ADS)

Underwood, Emma C.; Ustin, Susan L.; Ramirez, Carlos M.

2007-01-01

We explored the potential of detecting three target invasive species: iceplant ( Carpobrotus edulis), jubata grass ( Cortaderia jubata), and blue gum ( Eucalyptus globulus) at Vandenberg Air Force Base, California. We compared the accuracy of mapping six communities (intact coastal scrub, iceplant invaded coastal scrub, iceplant invaded chaparral, jubata grass invaded chaparral, blue gum invaded chaparral, and intact chaparral) using four images with different combinations of spatial and spectral resolution: hyperspectral AVIRIS imagery (174 wavebands, 4 m spatial resolution), spatially degraded AVIRIS (174 bands, 30 m), spectrally degraded AVIRIS (6 bands, 4 m), and both spatially and spectrally degraded AVIRIS (6 bands, 30 m, i.e., simulated Landsat ETM data). Overall success rates for classifying the six classes was 75% (kappa 0.7) using full resolution AVIRIS, 58% (kappa 0.5) for the spatially degraded AVIRIS, 42% (kappa 0.3) for the spectrally degraded AVIRIS, and 37% (kappa 0.3) for the spatially and spectrally degraded AVIRIS. A true Landsat ETM image was also classified to illustrate that the results from the simulated ETM data were representative, which provided an accuracy of 50% (kappa 0.4). Mapping accuracies using different resolution images are evaluated in the context of community heterogeneity (species richness, diversity, and percent species cover). Findings illustrate that higher mapping accuracies are achieved with images possessing high spectral resolution, thus capturing information across the visible and reflected infrared solar spectrum. Understanding the tradeoffs in spectral and spatial resolution can assist land managers in deciding the most appropriate imagery with respect to target invasives and community characteristics.

Optimal attributes for the object based detection of giant reed in riparian habitats: A comparative study between Airborne High Spatial Resolution and WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Fernandes, Maria Rosário; Aguiar, Francisca C.; Silva, João M. N.; Ferreira, Maria Teresa; Pereira, José M. C.

2014-10-01

Giant reed is an aggressive invasive plant of riparian ecosystems in many sub-tropical and warm-temperate regions, including Mediterranean Europe. In this study we tested a set of geometric, spectral and textural attributes in an object based image analysis (OBIA) approach to map giant reed invasions in riparian habitats. Bagging Classification and Regression Tree were used to select the optimal attributes and to build the classification rules sets. Mapping accuracy was performed using landscape metrics and the Kappa coefficient to compare the topographical and geometric similarity between the giant reed patches obtained with the OBIA map and with a validation map derived from on-screen digitizing. The methodology was applied in two high spatial resolution images: an airborne multispectral imagery and the newly WorldView-2 imagery. A temporal coverage of the airborne multispectral images was radiometrically calibrated with the IR-Mad transformation and used to assess the influence of the phenological variability of the invader. We found that optimal attributes for giant reed OBIA detection are a combination of spectral, geometric and textural information, with different scoring selection depending on the spectral and spatial characteristics of the imagery. WorldView-2 showed higher mapping accuracy (Kappa coefficient of 77%) and spectral attributes, including the newly yellow band, were preferentially selected, although a tendency to overestimate the total invaded area, due to the low spatial resolution (2 m of pixel size vs. 50 cm) was observed. When airborne images were used, geometric attributes were primarily selected and a higher spatial detail of the invasive patches was obtained, due to the higher spatial resolution. However, in highly heterogeneous landscapes, the low spectral resolution of the airborne images (4 bands instead of the 8 of WorldView-2) reduces the capability to detect giant reed patches. Giant reed displays peculiar spectral and geometric traits, at leaf, canopy and stand level, which makes the OBIA approach a very suitable technique for management purposes.

Rapid mapping of hurricane damage to forests

Treesearch

Erik M. Nielsen

2009-01-01

The prospects for producing rapid, accurate delineations of the spatial extent of forest wind damage were evaluated using Hurricane Katrina as a test case. A damage map covering the full spatial extent of Katrina?s impact was produced from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery using higher resolution training data. Forest damage...

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

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.

Classification of High Spatial Resolution, Hyperspectral ...

EPA Pesticide Factsheets

EPA announced the availability of the final report,

Producing fractional rangeland component predictions in a sagebrush ecosystem, a Wyoming sensitivity analysis

USGS Publications Warehouse

Xian, George; Homer, Collin G.; Granneman, Brian; Meyer, Debra K.

2012-01-01

Remote sensing information has been widely used to monitor vegetation condition and variations in a variety of ecosystems, including shrublands. Careful application of remotely sensed imagery can provide additional spatially explicit, continuous, and extensive data on the composition and condition of shrubland ecosystems. Historically, the most widely available remote sensing information has been collected by Landsat, which has offered large spatial coverage and moderate spatial resolution data globally for nearly three decades. Such medium-resolution satellite remote sensing information can quantify the distribution and variation of terrestrial ecosystems. Landsat imagery has been frequently used with other high-resolution remote sensing data to classify sagebrush components and quantify their spatial distributions (Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow and others, 2008; Underwood and others, 2007). Modeling algorithms have been developed to use field measurements and satellite remote sensing data to quantify the extent and evaluate the quality of shrub ecosystem components in large geographic areas (Homer and others, 2009). The percent cover of sagebrush ecosystem components, including bare-ground, herbaceous, litter, sagebrush, and shrub, have been quantified for entire western states (Homer and others, 2012). Furthermore, research has demonstrated the use of current measurements with historical archives of Landsat imagery to quantify the variations of these components for the last two decades (Xian and others, 2012). The modeling method used to quantify the extent and spatial distribution of sagebrush components over a large area also has required considerable amounts of training data to meet targeted accuracy requirements. These training data have maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate ecosystem phenology and subsequently maximized by extrapolation on high-resolution remote sensing data (Homer and others, 2012). This method has proven its utility; however, to develop these products across even larger areas will require additional cost efficiencies to ensure that an adequate product can be developed for the lowest cost possible. Given the vast geographic extent of shrubland ecosystems in the western United States, identifying cost efficiencies with optimal training data development and subsequent application to medium resolution satellite imagery provide the most likely areas for methodological efficiency gains. The primary objective of this research was to conduct a series of sensitivity tests to evaluate the most optimal and practical way to develop Landsat scale information for estimating the extent and distribution of sagebrush ecosystem components over large areas in the conterminous United States. An existing dataset of sagebrush components developed from extensive field measurements, high-resolution satellite imagery, and medium resolution Landsat imagery in Wyoming was used as the reference database (Homer and others, 2012). Statistical analysis was performed to analyze the relation between the accuracy of sagebrush components and the amount and distribution of training data on Landsat scenes needed to obtain accurate predictions.

Operational data fusion framework for building frequent Landsat-like imagery in a cloudy region

USDA-ARS?s Scientific Manuscript database

An operational data fusion framework is built to generate dense time-series Landsat-like images for a cloudy region by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) data products and Landsat imagery. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is integrated in ...

A TECHNIQUE FOR ASSESSING THE ACCURACY OF SUB-PIXEL IMPERVIOUS SURFACE ESTIMATES DERIVED FROM LANDSAT TM IMAGERY

EPA Science Inventory

We developed a technique for assessing the accuracy of sub-pixel derived estimates of impervious surface extracted from LANDSAT TM imagery. We utilized spatially coincident
sub-pixel derived impervious surface estimates, high-resolution planimetric GIS data, vector--to-
r...

Improving urban land use and land cover classification from high-spatial-resolution hyperspectral imagery using contextual information

NASA Astrophysics Data System (ADS)

Yang, He; Ma, Ben; Du, Qian; Yang, Chenghai

2010-08-01

In this paper, we propose approaches to improve the pixel-based support vector machine (SVM) classification for urban land use and land cover (LULC) mapping from airborne hyperspectral imagery with high spatial resolution. Class spatial neighborhood relationship is used to correct the misclassified class pairs, such as roof and trail, road and roof. These classes may be difficult to be separated because they may have similar spectral signatures and their spatial features are not distinct enough to help their discrimination. In addition, misclassification incurred from within-class trivial spectral variation can be corrected by using pixel connectivity information in a local window so that spectrally homogeneous regions can be well preserved. Our experimental results demonstrate the efficiency of the proposed approaches in classification accuracy improvement. The overall performance is competitive to the object-based SVM classification.

[Extraction of buildings three-dimensional information from high-resolution satellite imagery based on Barista software].

PubMed

Zhang, Pei-feng; Hu, Yuan-man; He, Hong-shi

2010-05-01

The demand for accurate and up-to-date spatial information of urban buildings is becoming more and more important for urban planning, environmental protection, and other vocations. Today's commercial high-resolution satellite imagery offers the potential to extract the three-dimensional information of urban buildings. This paper extracted the three-dimensional information of urban buildings from QuickBird imagery, and validated the precision of the extraction based on Barista software. It was shown that the extraction of three-dimensional information of the buildings from high-resolution satellite imagery based on Barista software had the advantages of low professional level demand, powerful universality, simple operation, and high precision. One pixel level of point positioning and height determination accuracy could be achieved if the digital elevation model (DEM) and sensor orientation model had higher precision and the off-Nadir View Angle was relatively perfect.

Monitoring Termite-Mediated Ecosystem Processes Using Moderate and High Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Lind, B. M.; Hanan, N. P.

2016-12-01

Termites are considered dominant decomposers and prominent ecosystem engineers in the global tropics and they build some of the largest and architecturally most complex non-human-made structures in the world. Termite mounds significantly alter soil texture, structure, and nutrients, and have major implications for local hydrological dynamics, vegetation characteristics, and biological diversity. An understanding of how these processes change across large scales has been limited by our ability to detect termite mounds at high spatial resolutions. Our research develops methods to detect large termite mounds in savannas across extensive geographic areas using moderate and high resolution satellite imagery. We also investigate the effect of termite mounds on vegetation productivity using Landsat-8 maximum composite NDVI data as a proxy for production. Large termite mounds in arid and semi-arid Senegal generate highly reflective `mound scars' with diameters ranging from 10 m at minimum to greater than 30 m. As Sentinel-2 has several bands with 10 m resolution and Landsat-8 has improved calibration, higher radiometric resolution, 15 m spatial resolution (pansharpened), and improved contrast between vegetated and bare surfaces compared to previous Landsat missions, we found that the largest and most influential mounds in the landscape can be detected. Because mounds as small as 4 m in diameter are easily detected in high resolution imagery we used these data to validate detection results and quantify omission errors for smaller mounds.

A non-parametric, supervised classification of vegetation types on the Kaibab National Forest using decision trees

Treesearch

Suzanne M. Joy; R. M. Reich; Richard T. Reynolds

2003-01-01

Traditional land classification techniques for large areas that use Landsat Thematic Mapper (TM) imagery are typically limited to the fixed spatial resolution of the sensors (30m). However, the study of some ecological processes requires land cover classifications at finer spatial resolutions. We model forest vegetation types on the Kaibab National Forest (KNF) in...

Defining habitat covariates in camera-trap based occupancy studies

PubMed Central

Niedballa, Jürgen; Sollmann, Rahel; Mohamed, Azlan bin; Bender, Johannes; Wilting, Andreas

2015-01-01

In species-habitat association studies, both the type and spatial scale of habitat covariates need to match the ecology of the focal species. We assessed the potential of high-resolution satellite imagery for generating habitat covariates using camera-trapping data from Sabah, Malaysian Borneo, within an occupancy framework. We tested the predictive power of covariates generated from satellite imagery at different resolutions and extents (focal patch sizes, 10–500 m around sample points) on estimates of occupancy patterns of six small to medium sized mammal species/species groups. High-resolution land cover information had considerably more model support for small, patchily distributed habitat features, whereas it had no advantage for large, homogeneous habitat features. A comparison of different focal patch sizes including remote sensing data and an in-situ measure showed that patches with a 50-m radius had most support for the target species. Thus, high-resolution satellite imagery proved to be particularly useful in heterogeneous landscapes, and can be used as a surrogate for certain in-situ measures, reducing field effort in logistically challenging environments. Additionally, remote sensed data provide more flexibility in defining appropriate spatial scales, which we show to impact estimates of wildlife-habitat associations. PMID:26596779

Very high resolution UV and X-ray spectroscopy and imagery of solar active regions

NASA Technical Reports Server (NTRS)

Bruner, M.; Brown, W. A.; Haisch, B. M.

1987-01-01

A scientific investigation of the physics of the solar atmosphere, which uses the techniques of high resolution soft X-ray spectroscopy and high resolution UV imagery, is described. The experiments were conducted during a series of three sounding rocket flights. All three flights yielded excellent images in the UV range, showing unprecedented spatial resolution. The second flight recorded the X-ray spectrum of a solar flare, and the third that of an active region. A normal incidence multi-layer mirror was used during the third flight to make the first astronomical X-ray observations using this new technique.

Forest cover type analysis of New England forests using innovative WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Kovacs, Jenna M.

For many years, remote sensing has been used to generate land cover type maps to create a visual representation of what is occurring on the ground. One significant use of remote sensing is the identification of forest cover types. New England forests are notorious for their especially complex forest structure and as a result have been, and continue to be, a challenge when classifying forest cover types. To most accurately depict forest cover types occurring on the ground, it is essential to utilize image data that have a suitable combination of both spectral and spatial resolution. The WorldView-2 (WV2) commercial satellite, launched in 2009, is the first of its kind, having both high spectral and spatial resolutions. WV2 records eight bands of multispectral imagery, four more than the usual high spatial resolution sensors, and has a pixel size of 1.85 meters at the nadir. These additional bands have the potential to improve classification detail and classification accuracy of forest cover type maps. For this reason, WV2 imagery was utilized on its own, and in combination with Landsat 5 TM (LS5) multispectral imagery, to evaluate whether these image data could more accurately classify forest cover types. In keeping with recent developments in image analysis, an Object-Based Image Analysis (OBIA) approach was used to segment images of Pawtuckaway State Park and nearby private lands, an area representative of the typical complex forest structure found in the New England region. A Classification and Regression Tree (CART) analysis was then used to classify image segments at two levels of classification detail. Accuracies for each forest cover type map produced were generated using traditional and area-based error matrices, and additional standard accuracy measures (i.e., KAPPA) were generated. The results from this study show that there is value in analyzing imagery with both high spectral and spatial resolutions, and that WV2's new and innovative bands can be useful for the classification of complex forest structures.

Multi-scale investigation of shrub encroachment in southern Africa

NASA Astrophysics Data System (ADS)

Aplin, Paul; Marston, Christopher; Wilkinson, David; Field, Richard; O'Regan, Hannah

2016-04-01

There is growing speculation that savannah environments throughout Africa have been subject to shrub encroachment in recent years, whereby grassland is lost to woody vegetation cover. Changes in the relative proportions of grassland and woodland are important in the context of conservation of savannah systems, with implications for faunal distributions, environmental management and tourism. Here, we focus on southern Kruger National Park, South Africa, and investigate whether or not shrub encroachment has occurred over the last decade and a half. We use a multi-scale approach, examining the complementarity of medium (e.g. Landsat TM and OLI) and fine (e.g. QuickBird and WorldView-2) spatial resolution satellite sensor imagery, supported by intensive field survey in 2002 and 2014. We employ semi-automated land cover classification, involving a hybrid unsupervised clustering approach with manual class grouping and checking, followed by change detection post-classification comparison analysis. The results show that shrub encroachment is indeed occurring, a finding evidenced through three fine resolution replicate images plus medium resolution imagery. The results also demonstrate the complementarity of medium and fine resolution imagery, though some thematic information must be sacrificed to maintain high medium resolution classification accuracy. Finally, the findings have broader implications for issues such as vegetation seasonality, spatial transferability and management practices.

Mapping evapotranspiration with high resolution aircraft imagery over vineyards using one and two source modeling schemes

USDA-ARS?s Scientific Manuscript database

Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (= 10 m) and plant canopy (= 1 m) scale evapotranspiration (ET) monitoring. In this study, high resolution aircraft sub-meter scale thermal infrared and multispectral...

Atmospheric Correction of High-Spatial-Resolution Commercial Satellite Imagery Products Using MODIS Atmospheric Products

NASA Technical Reports Server (NTRS)

Pagnutti, Mary; Holekamp, Kara; Ryan, Robert E.; Vaughan, Ronald; Russell, Jeffrey A.; Prados, Don; Stanley, Thomas

2005-01-01

Remotely sensed ground reflectance is the basis for many inter-sensor interoperability or change detection techniques. Satellite inter-comparisons and accurate vegetation indices such as the Normalized Difference Vegetation Index, which is used to describe or to imply a wide variety of biophysical parameters and is defined in terms of near-infrared and redband reflectance, require the generation of accurate reflectance maps. This generation relies upon the removal of solar illumination, satellite geometry, and atmospheric effects and is generally referred to as atmospheric correction. Atmospheric correction of remotely sensed imagery to ground reflectance, however, has been widely applied to only a few systems. In this study, we atmospherically corrected commercially available, high spatial resolution IKONOS and QuickBird imagery using several methods to determine the accuracy of the resulting reflectance maps. We used extensive ground measurement datasets for nine IKONOS and QuickBird scenes acquired over a two-year period to establish reflectance map accuracies. A correction approach using atmospheric products derived from Moderate Resolution Imaging Spectrometer data created excellent reflectance maps and demonstrated a reliable, effective method for reflectance map generation.

Forest Stand Canopy Structure Attribute Estimation from High Resolution Digital Airborne Imagery

Treesearch

Demetrios Gatziolis

2006-01-01

A study of forest stand canopy variable assessment using digital, airborne, multispectral imagery is presented. Variable estimation involves stem density, canopy closure, and mean crown diameter, and it is based on quantification of spatial autocorrelation among pixel digital numbers (DN) using variogram analysis and an alternative, non-parametric approach known as...

Estimation of Cirrus and Stratus Cloud Heights Using Landsat Imagery

NASA Technical Reports Server (NTRS)

Inomata, Yasushi; Feind, R. E.; Welch, R. M.

1996-01-01

A new method based upon high-spatial-resolution imagery is presented that matches cloud and shadow regions to estimate cirrus and stratus cloud heights. The distance between the cloud and the matching shadow pattern is accomplished using the 2D cross-correlation function from which the cloud height is derived. The distance between the matching cloud-shadow patterns is verified manually. The derived heights also are validated through comparison with a temperature-based retrieval of cloud height. It is also demonstrated that an estimate of cloud thickness can be retrieved if both the sunside and anti-sunside of the cloud-shadow pair are apparent. The technique requires some intepretation to determine the cloud height level retrieved (i.e., the top, base, or mid-level). It is concluded that the method is accurate to within several pixels, equivalent to cloud height variations of about +/- 250 m. The results show that precise placement of the templates is unnecessary, so that the development of a semi-automated procedure is possible. Cloud templates of about 64 pixels on a side or larger produce consistent results. The procedure was repeated for imagery degraded to simulate lower spatial resolutions. The results suggest that spatial resolution of 150-200 m or better is necessary in order to obtain stable cloud height retrievals.

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.

Solutions Network Formulation Report. Visible/Infrared Imager/Radiometer Suite and Landsat Data Continuity Mission Simulated Data Products for the Great Lakes Basin Ecological Team

NASA Technical Reports Server (NTRS)

Estep, Leland

2007-01-01

The proposed solution would simulate VIIRS and LDCM sensor data for use in the USGS/USFWS GLBET DST. The VIIRS sensor possesses a spectral range that provides water-penetrating bands that could be used to assess water clarity on a regional spatial scale. The LDCM sensor possesses suitable spectral bands in a range of wavelengths that could be used to map water quality at finer spatial scales relative to VIIRS. Water quality, alongshore sediment transport and pollutant discharge tracking into the Great Lakes system are targeted as the primary products to be developed. A principal benefit of water quality monitoring via satellite imagery is its economy compared to field-data collection methods. Additionally, higher resolution satellite imagery provides a baseline dataset(s) against which later imagery can be overlaid in GIS-based DST programs. Further, information derived from higher resolution satellite imagery can be used to address public concerns and to confirm environmental compliance. The candidate solution supports the Public Health, Coastal Management, and Water Management National Applications.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

Snow Pattern Delineation, Scaling, Fidelity, and Landscape Factors

NASA Astrophysics Data System (ADS)

Hiemstra, C. A.; Wagner, A. M.; Deeb, E. J.; Morriss, B. F.; Sturm, M.

2014-12-01

In many snow-covered landscapes, snow tends to be shallow or deep in the same locations year after year. As snowmelt progresses in spring, areas of shallow snow become snow-free earlier than areas with deep snow. This pattern (Sturm and Wagner 2010) could likely be used to inform or improve modeled snow depth estimates where ground measurements are not collected; however, we must be certain of their utility before ingesting them into model calculations. Do patterns, as we detect them, have a relationship with earlier measured snow distributions? Second, are certain areas on the landscape likely to yield patterns that are influenced too highly by melting to be useful? Our Imnavait Creek Study Area (11 by 19 km) is on Alaska's North Slope, where we have examined a vast library of spring satellite imagery (ranging from mostly snow-covered to mostly snow-free). Landsat TM Imagery has been collected from the early 1980s-present, and the temporal and spatial resolution is roughly two weeks and 30 m, respectively. High resolution satellite imagery (WorldView 1, WorldView 2, IKONOS) has been obtained from 2010-2013 for the same area with almost daily- to monthly-temporal and at 2.5 m spatial resolutions, respectively. We found that there is a striking similarity among patterns from year to year across the span of decades and resolutions. However, the relationship of pattern with observed snow depths was strong in some areas and less clear in others. Overall, we suspect spatial scaling, spatial mismatch, sampling errors, and melt patterns explain most of the areas of pattern and depth disparity.

Automated, per pixel Cloud Detection from High-Resolution VNIR Data

NASA Technical Reports Server (NTRS)

Varlyguin, Dmitry L.

2007-01-01

CASA is a fully automated software program for the per-pixel detection of clouds and cloud shadows from medium- (e.g., Landsat, SPOT, AWiFS) and high- (e.g., IKONOS, QuickBird, OrbView) resolution imagery without the use of thermal data. CASA is an object-based feature extraction program which utilizes a complex combination of spectral, spatial, and contextual information available in the imagery and the hierarchical self-learning logic for accurate detection of clouds and their shadows.

Infrared Imagery of Shuttle (IRIS). Task 1, summary report

NASA Technical Reports Server (NTRS)

Chocol, C. J.

1977-01-01

The feasibility of remote, high-resolution infrared imagery of the Shuttle Orbiter lower surface during entry to obtain accurate measurements of aerodynamic heat transfer was demonstrated. Using available technology, such images can be taken from an existing aircraft/telescope system (the C141 AIRO) with minimum modification or addition of systems. Images with a spatial resolution of 1 m or better and a temperature resolution of 2.5% between temperatures of 800 and 1900 K can be obtained. Data reconstruction techniques can provide a geometrically and radiometrically corrected array on addressable magnetic tape ready for display by NASA.

The Early Detection of the Emerald Ash Borer (EAB) Using Advanced Geospacial Technologies

NASA Astrophysics Data System (ADS)

Hu, B.; Li, J.; Wang, J.; Hall, B.

2014-11-01

The objectives of this study were to exploit Light Detection And Ranging (LiDAR) and very high spatial resolution (VHR) data and their synergy with hyperspectral imagery in the early detection of the EAB presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, individual trees were first extracted and then classified into different species based on their spectral information derived from hyperspectral imagery, spatial information from VHR imagery, and for each ash tree its health state and EAB infestation stage were determined based on hyperspectral imagery. The developed framework and methods were demonstrated to be effective according to the results obtained on two study sites in the city of Toronto, Ontario Canada. The individual tree delineation method provided satisfactory results with an overall accuracy of 78 % and 19 % commission and 23 % omission errors when used on the combined very high-spatial resolution imagery and LiDAR data. In terms of the identification of ash trees, given sufficient representative training data, our classification model was able to predict tree species with above 75 % overall accuracy, and mis-classification occurred mainly between ash and maple trees. The hypothesis that a strong correlation exists between general tree stress and EAB infestation was confirmed. Vegetation indices sensitive to leaf chlorophyll content derived from hyperspectral imagery can be used to predict the EAB infestation levels for each ash tree.

Development of Fire Emissions Inventory Using Satellite Data

EPA Science Inventory

There are multiple satellites observing and reporting fire imagery at various spatial and temporal resolutions and each system has inherent merits and deficiencies. In our study, data are acquired from the Moderate Resolution Imaging Spectro-radiometer (MODIS) aboard the Nationa...

Advanced Ecosystem Mapping Techniques for Large Arctic Study Domains Using Calibrated High-Resolution Imagery

NASA Astrophysics Data System (ADS)

Macander, M. J.; Frost, G. V., Jr.

2015-12-01

Regional-scale mapping of vegetation and other ecosystem properties has traditionally relied on medium-resolution remote sensing such as Landsat (30 m) and MODIS (250 m). Yet, the burgeoning availability of high-resolution (<=2 m) imagery and ongoing advances in computing power and analysis tools raises the prospect of performing ecosystem mapping at fine spatial scales over large study domains. Here we demonstrate cutting-edge mapping approaches over a ~35,000 km² study area on Alaska's North Slope using calibrated and atmospherically-corrected mosaics of high-resolution WorldView-2 and GeoEye-1 imagery: (1) an a priori spectral approach incorporating the Satellite Imagery Automatic Mapper (SIAM) algorithms; (2) image segmentation techniques; and (3) texture metrics. The SIAM spectral approach classifies radiometrically-calibrated imagery to general vegetation density categories and non-vegetated classes. The SIAM classes were developed globally and their applicability in arctic tundra environments has not been previously evaluated. Image segmentation, or object-based image analysis, automatically partitions high-resolution imagery into homogeneous image regions that can then be analyzed based on spectral, textural, and contextual information. We applied eCognition software to delineate waterbodies and vegetation classes, in combination with other techniques. Texture metrics were evaluated to determine the feasibility of using high-resolution imagery to algorithmically characterize periglacial surface forms (e.g., ice-wedge polygons), which are an important physical characteristic of permafrost-dominated regions but which cannot be distinguished by medium-resolution remote sensing. These advanced mapping techniques provide products which can provide essential information supporting a broad range of ecosystem science and land-use planning applications in northern Alaska and elsewhere in the circumpolar Arctic.

Application of QuickBird imagery in fuel load estimation in the Daxinganling region, China.

Treesearch

Sen Jin; Shyh-Chin Chen

2012-01-01

A high spatial resolution QuickBird satellite image and a low spatial but high spectral resolution Landsat Thermatic Mapper image were used to linearly regress fuel loads of 70 plots with size 30X30m over the Daxinganling region of north-east China. The results were compared with loads from field surveys and from regression estimations by surveyed stand characteristics...

Production of Landsat ETM+ reference imagery of burned areas within Southern African savannahs: comparison of methods and application to MODIS

Treesearch

A. M. S. Smith; N. A. Drake; M. J. Wooster; A. T. Hudak; Z. A. Holden; C. J. Gibbons

2007-01-01

Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution...

Applications of Fractal Analytical Techniques in the Estimation of Operational Scale

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Quattrochi, Dale A.

2000-01-01

The observational scale and the resolution of remotely sensed imagery are essential considerations in the interpretation process. Many atmospheric, hydrologic, and other natural and human-influenced spatial phenomena are inherently scale dependent and are governed by different physical processes at different spatial domains. This spatial and operational heterogeneity constrains the ability to compare interpretations of phenomena and processes observed in higher spatial resolution imagery to similar interpretations obtained from lower resolution imagery. This is a particularly acute problem, since longterm global change investigations will require high spatial resolution Earth Observing System (EOS), Landsat 7, or commercial satellite data to be combined with lower resolution imagery from older sensors such as Landsat TM and MSS. Fractal analysis is a useful technique for identifying the effects of scale changes on remotely sensed imagery. The fractal dimension of an image is a non-integer value between two and three which indicates the degree of complexity in the texture and shapes depicted in the image. A true fractal surface exhibits self-similarity, a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution, and the slope of the fractal dimension-resolution relationship would be zero. Most geographical phenomena, however, are not self-similar at all scales, but they can be modeled by a stochastic fractal in which the scaling properties of the image exhibit patterns that can be described by statistics such as area-perimeter ratios and autocovariances. Stochastic fractal sets relax the self-similarity assumption and measure many scales and resolutions to represent the varying form of a phenomenon as the pixel size is increased in a convolution process. We have observed that for images of homogeneous land covers, the fractal dimension varies linearly with changes in resolution or pixel size over the range of past, current, and planned space-borne sensors. This relationship differs significantly in images of agricultural, urban, and forest land covers, with urban areas retaining the same level of complexity, forested areas growing smoother, and agricultural areas growing more complex as small pixels are aggregated into larger, mixed pixels. Images of scenes having a mixture of land covers have fractal dimensions that exhibit a non-linear, complex relationship to pixel size. Measuring the fractal dimension of a difference image derived from two images of the same area obtained on different dates showed that the fractal dimension increased steadily, then exhibited a sharp decrease at increasing levels of pixel aggregation. This breakpoint of the fractal dimension/resolution plot is related to the spatial domain or operational scale of the phenomenon exhibiting the predominant visible difference between the two images (in this case, mountain snow cover). The degree to which an image departs from a theoretical ideal fractal surface provides clues as to how much information is altered or lost in the processes of rescaling and rectification. The measured fractal dimension of complex, composite land covers such as urban areas also provides a useful textural index that can assist image classification of complex scenes.

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.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact.

PubMed

Khanna, Shruti; Santos, Maria J; Ustin, Susan L; Shapiro, Kristen; Haverkamp, Paul J; Lay, Mui

2018-02-12

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills.

Comparing the Potential of Multispectral and Hyperspectral Data for Monitoring Oil Spill Impact

PubMed Central

Santos, Maria J.; Ustin, Susan L.; Haverkamp, Paul J.; Lay, Mui

2018-01-01

Oil spills from offshore drilling and coastal refineries often cause significant degradation of coastal environments. Early oil detection may prevent losses and speed up recovery if monitoring of the initial oil extent, oil impact, and recovery are in place. Satellite imagery data can provide a cost-effective alternative to expensive airborne imagery or labor intensive field campaigns for monitoring effects of oil spills on wetlands. However, these satellite data may be restricted in their ability to detect and map ecosystem recovery post-spill given their spectral measurement properties and temporal frequency. In this study, we assessed whether spatial and spectral resolution, and other sensor characteristics influence the ability to detect and map vegetation stress and mortality due to oil. We compared how well three satellite multispectral sensors: WorldView2, RapidEye and Landsat EMT+, match the ability of the airborne hyperspectral AVIRIS sensor to map oil-induced vegetation stress, recovery, and mortality after the DeepWater Horizon oil spill in the Gulf of Mexico in 2010. We found that finer spatial resolution (3.5 m) provided better delineation of the oil-impacted wetlands and better detection of vegetation stress along oiled shorelines in saltmarsh wetland ecosystems. As spatial resolution become coarser (3.5 m to 30 m) the ability to accurately detect and map stressed vegetation decreased. Spectral resolution did improve the detection and mapping of oil-impacted wetlands but less strongly than spatial resolution, suggesting that broad-band data may be sufficient to detect and map oil-impacted wetlands. AVIRIS narrow-band data performs better detecting vegetation stress, followed by WorldView2, RapidEye and then Landsat 15 m (pan sharpened) data. Higher quality sensor optics and higher signal-to-noise ratio (SNR) may also improve detection and mapping of oil-impacted wetlands; we found that resampled coarser resolution AVIRIS data with higher SNR performed better than either of the three satellite sensors. The ability to acquire imagery during certain times (midday, low tide, etc.) or a certain date (cloud-free, etc.) is also important in these tidal wetlands; WorldView2 imagery captured at high-tide detected a narrower band of shoreline affected by oil likely because some of the impacted wetland was below the tideline. These results suggest that while multispectral data may be sufficient for detecting the extent of oil-impacted wetlands, high spectral and spatial resolution, high-quality sensor characteristics, and the ability to control time of image acquisition may improve assessment and monitoring of vegetation stress and recovery post oil spills. PMID:29439504

Multisensor data fusion across time and space

NASA Astrophysics Data System (ADS)

Villeneuve, Pierre V.; Beaven, Scott G.; Reed, Robert A.

2014-06-01

Field measurement campaigns typically deploy numerous sensors having different sampling characteristics for spatial, temporal, and spectral domains. Data analysis and exploitation is made more difficult and time consuming as the sample data grids between sensors do not align. This report summarizes our recent effort to demonstrate feasibility of a processing chain capable of "fusing" image data from multiple independent and asynchronous sensors into a form amenable to analysis and exploitation using commercially-available tools. Two important technical issues were addressed in this work: 1) Image spatial registration onto a common pixel grid, 2) Image temporal interpolation onto a common time base. The first step leverages existing image matching and registration algorithms. The second step relies upon a new and innovative use of optical flow algorithms to perform accurate temporal upsampling of slower frame rate imagery. Optical flow field vectors were first derived from high-frame rate, high-resolution imagery, and then finally used as a basis for temporal upsampling of the slower frame rate sensor's imagery. Optical flow field values are computed using a multi-scale image pyramid, thus allowing for more extreme object motion. This involves preprocessing imagery to varying resolution scales and initializing new vector flow estimates using that from the previous coarser-resolution image. Overall performance of this processing chain is demonstrated using sample data involving complex too motion observed by multiple sensors mounted to the same base. Multiple sensors were included, including a high-speed visible camera, up to a coarser resolution LWIR camera.

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)...

Spectral Mixture Analysis to map burned areas in Brazil's deforestation arc from 1992 to 2011

NASA Astrophysics Data System (ADS)

Antunes Daldegan, G.; Ribeiro, F.; Roberts, D. A.

2017-12-01

The two most extensive biomes in South America, the Amazon and the Cerrado, are subject to several fire events every dry season. Both are known for their ecological and environmental importance. However, due to the intensive human occupation over the last four decades, they have been facing high deforestation rates. The Cerrado biome is adapted to fire and is considered a fire-dependent landscape. In contrast, the Amazon as a tropical moist broadleaf forest does not display similar characteristics and is classified as a fire-sensitive landscape. Nonetheless, studies have shown that forest areas that have already been burned become more prone to experience recurrent burns. Remote sensing has been extensively used by a large number of researchers studying fire occurrence at a global scale, as well as in both landscapes aforementioned. Digital image processing aiming to map fire activity has been applied to a number of imagery from sensors of various spatial, temporal, and spectral resolutions. More specifically, several studies have used Landsat data to map fire scars in the Amazon forest and in the Cerrado. An advantage of using Landsat data is the potential to map fire scars at a finer spatial resolution, when compared to products derived from imagery of sensors featuring better temporal resolution but coarser spatial resolution, such as MODIS (Moderate Resolution Imaging Spectrometer) and GOES (Geostationary Operational Environmental Satellite). This study aimed to map burned areas present in the Amazon-Cerrado transition zone by applying Spectral Mixture Analysis on Landsat imagery for a period of 20 years (1992-2011). The study area is a subset of this ecotone, centered at the State of Mato Grosso. By taking advantage of the Landsat 5TM and Landsat 7ETM+ imagery collections available in Google Earth Engine platform and applying Spectral Mixture Analysis (SMA) techniques over them permitted to model fire scar fractions and delimitate burned areas. Overlaying yearly burned areas allowed to identify areas with high fire recurrence.

Lessons Learned From Large-Scale Evapotranspiration and Root Zone Soil Moisture Mapping Using Ground Measurements (meteorological, LAS, EC) and Remote Sensing (METRIC)

NASA Astrophysics Data System (ADS)

Hendrickx, J. M. H.; Allen, R. G.; Myint, S. W.; Ogden, F. L.

2015-12-01

Large scale mapping of evapotranspiration and root zone soil moisture is only possible when satellite images are used. The spatial resolution of this imagery typically depends on its temporal resolution or the satellite overpass time. For example, the Landsat satellite acquires images at 30 m resolution every 16 days while the MODIS satellite acquires images at 250 m resolution every day. In this study we deal with optical/thermal imagery that is impacted by cloudiness contrary to radar imagery that penetrates through clouds. Due to cloudiness, the temporal resolution of Landsat drops from 16 days to about one clear sky Landsat image per month in the southwestern USA and about one every ten years in the humid tropics of Panama. Only by launching additional satellites can the temporal resolution be improved. Since this is too costly, an alternative is found by using ground measurements with high temporal resolution (from minutes to days) but poor spatial resolution. The challenge for large-scale evapotranspiration and root zone soil moisture mapping is to construct a layer stack consisting of N time layers covering the period of interest each containing M pixels covering the region of interest. We will present examples of the Phoenix Active Management Area in AZ (14,600 km2), Green River Basin in WY (44,000 km2), the Kishwaukee Watershed in IL (3,150 km2), the area covered by Landsat Path 28/Row 35 in OK (30,000 km2) and the Agua Salud Watershed in Panama (200 km2). In these regions we used Landsat or MODIS imagery for mapping evapotranspiration and root zone soil moisture by the algorithm Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC) together with meteorological measurements and sometimes either Large Aperture Scintillometers (LAS) or Eddy Covariance (EC). We conclude with lessons learned for future large-scale hydrological studies.

Assessing the spatial distribution of coral bleaching using small unmanned aerial systems

NASA Astrophysics Data System (ADS)

Levy, Joshua; Hunter, Cynthia; Lukacazyk, Trent; Franklin, Erik C.

2018-06-01

Small unmanned aerial systems (sUAS) are an affordable, effective complement to existing coral reef monitoring and assessment tools. sUAS provide repeatable low-altitude, high-resolution photogrammetry to address fundamental questions of spatial ecology and community dynamics for shallow coral reef ecosystems. Here, we qualitatively describe the use of sUAS to survey the spatial characteristics of coral cover and the distribution of coral bleaching across patch reefs in Kānéohe Bay, Hawaii, and address limitations and anticipated technology advancements within the field of UAS. Overlapping sub-decimeter low-altitude aerial reef imagery collected during the 2015 coral bleaching event was used to construct high-resolution reef image mosaics of coral bleaching responses on four Kānéohe Bay patch reefs, totaling 60,000 m2. Using sUAS imagery, we determined that paled, bleached and healthy corals on all four reefs were spatially clustered. Comparative analyses of data from sUAS imagery and in situ diver surveys found as much as 14% difference in coral cover values between survey methods, depending on the size of the reef and area surveyed. When comparing the abundance of unhealthy coral (paled and bleached) between sUAS and in situ diver surveys, we found differences in cover from 1 to 49%, depending on the depth of in situ surveys, the percent of reef area covered with sUAS surveys and patchiness of the bleaching response. This study demonstrates the effective use of sUAS surveys for assessing the spatial dynamics of coral bleaching at colony-scale resolutions across entire patch reefs and evaluates the complementarity of data from both sUAS and in situ diver surveys to more accurately characterize the spatial ecology of coral communities on reef flats and slopes.

Ground mapping resolution accuracy of a scanning radiometer from a geostationary satellite.

PubMed

Stremler, F G; Khalil, M A; Parent, R J

1977-06-01

Measures of the spatial and spatial rate (frequency) mapping of scanned visual imagery from an earth reference system to a spin-scan geostationary satellite are examined. Mapping distortions and coordinate inversions to correct for these distortions are formulated in terms of geometric transformations between earth and satellite frames of reference. Probabilistic methods are used to develop relations for obtainable mapping resolution when coordinate inversions are employed.

Where can pixel counting area estimates meet user-defined accuracy requirements?

NASA Astrophysics Data System (ADS)

Waldner, François; Defourny, Pierre

2017-08-01

Pixel counting is probably the most popular way to estimate class areas from satellite-derived maps. It involves determining the number of pixels allocated to a specific thematic class and multiplying it by the pixel area. In the presence of asymmetric classification errors, the pixel counting estimator is biased. The overarching objective of this article is to define the applicability conditions of pixel counting so that the estimates are below a user-defined accuracy target. By reasoning in terms of landscape fragmentation and spatial resolution, the proposed framework decouples the resolution bias and the classifier bias from the overall classification bias. The consequence is that prior to any classification, part of the tolerated bias is already committed due to the choice of the spatial resolution of the imagery. How much classification bias is affordable depends on the joint interaction of spatial resolution and fragmentation. The method was implemented over South Africa for cropland mapping, demonstrating its operational applicability. Particular attention was paid to modeling a realistic sensor's spatial response by explicitly accounting for the effect of its point spread function. The diagnostic capabilities offered by this framework have multiple potential domains of application such as guiding users in their choice of imagery and providing guidelines for space agencies to elaborate the design specifications of future instruments.

Determining seagrass abundance in southern New England waters using high resolution remotely sensed imagery

EPA Science Inventory

Advances in understanding the optics of shallow water environments, submerged vegetation canopies and seagrass physiology, combined with improved spatial resolution of remote sensing platforms, now enable eelgrass ecosystems to be monitored at a variety of time scales from earth-...

SkySat-1: very high-resolution imagery from a small satellite

NASA Astrophysics Data System (ADS)

Murthy, Kiran; Shearn, Michael; Smiley, Byron D.; Chau, Alexandra H.; Levine, Josh; Robinson, M. Dirk

2014-10-01

This paper presents details of the SkySat-1 mission, which is the first microsatellite-class commercial earth- observation system to generate sub-meter resolution panchromatic imagery, in addition to sub-meter resolution 4-band pan-sharpened imagery. SkySat-1 was built and launched for an order of magnitude lower cost than similarly performing missions. The low-cost design enables the deployment of a large imaging constellation that can provide imagery with both high temporal resolution and high spatial resolution. One key enabler of the SkySat-1 mission was simplifying the spacecraft design and instead relying on ground- based image processing to achieve high-performance at the system level. The imaging instrument consists of a custom-designed high-quality optical telescope and commercially-available high frame rate CMOS image sen- sors. While each individually captured raw image frame shows moderate quality, ground-based image processing algorithms improve the raw data by combining data from multiple frames to boost image signal-to-noise ratio (SNR) and decrease the ground sample distance (GSD) in a process Skybox calls "digital TDI". Careful qual-ity assessment and tuning of the spacecraft, payload, and algorithms was necessary to generate high-quality panchromatic, multispectral, and pan-sharpened imagery. Furthermore, the framing sensor configuration en- abled the first commercial High-Definition full-frame rate panchromatic video to be captured from space, with approximately 1 meter ground sample distance. Details of the SkySat-1 imaging instrument and ground-based image processing system are presented, as well as an overview of the work involved with calibrating and validating the system. Examples of raw and processed imagery are shown, and the raw imagery is compared to pre-launch simulated imagery used to tune the image processing algorithms.

Comparison of satellite reflectance algorithms for estimating ...

EPA Pesticide Factsheets

We analyzed 10 established and 4 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense water truth collected within one hour of image acquisition to develop simple proxies for algal blooms and to facilitate portability between multispectral satellite imagers for regional algal bloom monitoring. Narrow band hyperspectral aircraft images were upscaled spectrally and spatially to simulate 5 current and near future satellite imaging systems. Established and new Chl-a algorithms were then applied to the synthetic satellite images and then compared to calibrated Chl-a water truth measurements collected from 44 sites within one hour of aircraft acquisition of the imagery. Masks based on the spatial resolution of the synthetic satellite imagery were then applied to eliminate mixed pixels including vegetated shorelines. Medium-resolution Landsat and finer resolution data were evaluated against 29 coincident water truth sites. Coarse-resolution MODIS and MERIS-like data were evaluated against 9 coincident water truth sites. Each synthetic satellite data set was then evaluated for the performance of a variety of spectrally appropriate algorithms with regard to the estimation of Chl-a concentrations against the water truth data set. The goal is to inform water resource decisions on the appropriate satellite data acquisition and processing for the es

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

USDA-ARS?s Scientific Manuscript database

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...

A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska

USGS Publications Warehouse

Selkowitz, D.J.

2010-01-01

Shrub cover appears to be increasing across many areas of the Arctic tundra biome, and increasing shrub cover in the Arctic has the potential to significantly impact global carbon budgets and the global climate system. For most of the Arctic, however, there is no existing baseline inventory of shrub canopy cover, as existing maps of Arctic vegetation provide little information about the density of shrub cover at a moderate spatial resolution across the region. Remotely-sensed fractional shrub canopy maps can provide this necessary baseline inventory of shrub cover. In this study, we compare the accuracy of fractional shrub canopy (> 0.5 m tall) maps derived from multi-spectral, multi-angular, and multi-temporal datasets from Landsat imagery at 30 m spatial resolution, Moderate Resolution Imaging SpectroRadiometer (MODIS) imagery at 250 m and 500 m spatial resolution, and MultiAngle Imaging Spectroradiometer (MISR) imagery at 275 m spatial resolution for a 1067 km2 study area in Arctic Alaska. The study area is centered at 69 ??N, ranges in elevation from 130 to 770 m, is composed primarily of rolling topography with gentle slopes less than 10??, and is free of glaciers and perennial snow cover. Shrubs > 0.5 m in height cover 2.9% of the study area and are primarily confined to patches associated with specific landscape features. Reference fractional shrub canopy is determined from in situ shrub canopy measurements and a high spatial resolution IKONOS image swath. Regression tree models are constructed to estimate fractional canopy cover at 250 m using different combinations of input data from Landsat, MODIS, and MISR. Results indicate that multi-spectral data provide substantially more accurate estimates of fractional shrub canopy cover than multi-angular or multi-temporal data. Higher spatial resolution datasets also provide more accurate estimates of fractional shrub canopy cover (aggregated to moderate spatial resolutions) than lower spatial resolution datasets, an expected result for a study area where most shrub cover is concentrated in narrow patches associated with rivers, drainages, and slopes. Including the middle infrared bands available from Landsat and MODIS in the regression tree models (in addition to the four standard visible and near-infrared spectral bands) typically results in a slight boost in accuracy. Including the multi-angular red band data available from MISR in the regression tree models, however, typically boosts accuracy more substantially, resulting in moderate resolution fractional shrub canopy estimates approaching the accuracy of estimates derived from the much higher spatial resolution Landsat sensor. Given the poor availability of snow and cloud-free Landsat scenes in many areas of the Arctic and the promising results demonstrated here by the MISR sensor, MISR may be the best choice for large area fractional shrub canopy mapping in the Alaskan Arctic for the period 2000-2009.

Spatial Metadata for Global Change Investigations Using Remote Sensing

NASA Technical Reports Server (NTRS)

Emerson, Charles W.; Quattrochi, Dale A.; Lam, Nina Siu-Ngan; Arnold, James E. (Technical Monitor)

2002-01-01

Satellite and aircraft-borne remote sensors have gathered petabytes of data over the past 30+ years. These images are an important resource for establishing cause and effect relationships between human-induced land cover changes and alterations in climate and other biophysical patterns at local to global scales. However, the spatial, temporal, and spectral characteristics of these datasets vary, thus complicating long-term studies involving several types of imagery. As the geographical and temporal coverage, the spectral and spatial resolution, and the number of individual sensors increase, the sheer volume and complexity of available data sets will complicate management and use of the rapidly growing archive of earth imagery. Mining this vast data resource for images that provide the necessary information for climate change studies becomes more difficult as more sensors are launched and more imagery is obtained.

Correlation between high-resolution remote-sensing imagery and detailed field mapping in Cordilleran Miogeocline

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

Feldman, S.C.; Taranik, J.V.

1986-05-01

Selected areas were mapped at a scale of 1:6000 in the southern hot Creek Range (south-central Nevada), which is underlain by Paleozoic autochthonous limestone, shale, and sandstone, Paleozoic allochthonous chert and siltstone, and Tertiary rhyolitic to dactitic ash flow tuff. The mapping was compared with computer-processed Airborne Imaging Spectrometer (AIS) data and Landsat Thematic Mapper (TM) imagery. The AIS imagery of the Hot Creek Range was acquired in 1984 by a NASA C-130 aircraft; it has a spatial resolution of 12 m, and swath width of 380 m. The sensor was developed by the Jet Propulsion Laboratory and is themore » first in a series of NASA imaging spectrometers. The AIS collects 128 spectral bands, having a bandwidth of approximately 9 nm, in the short-wave infrared between 1.2 and 2.4 ..mu..m. This part of the spectrum contains important narrow spectral absorption features for the carbonate ion, hydroxyl ion, and water of hydration. Using computer-processed AIS imagery, therefore, the authors can separate calcite from dolomite, and kaolinite from illite and montmorillonite as well as differentiate geologic units containing these minerals. On the AIS imagery, the Upper Mississippian Tripon Pass Limestone shows a distinctive calcite absorption feature at 2.34 ..mu..m; this feature is not as pronounced in Cambrian and Ordovician limestones. The dolomitized Nevada Formation exhibits the dolomite absorption feature at 2.32 ..mu..m. Clay mineral absorption features near 2.2 ..mu..m can be distinguished in altered volcanics. Mineralogic identification was confirmed with field and laboratory spectroradiometer measurements, thin-section examination, and x-ray analysis. AIS results and field mapping were also compared to computer-processed Landsat TM imagery, the highest spectral and spatial resolution worldwide data set currently available.« less

Quantitative assessment of urban wetland dynamics using high spatial resolution satellite imagery between 2000 and 2013.

PubMed

Hu, Tangao; Liu, Jiahong; Zheng, Gang; Li, Yao; Xie, Bin

2018-05-09

Accurate and timely information describing urban wetland resources and their changes over time, especially in rapidly urbanizing areas, is becoming more important. We applied an object-based image analysis and nearest neighbour classifier to map and monitor changes in land use/cover using multi-temporal high spatial resolution satellite imagery in an urban wetland area (Hangzhou Xixi Wetland) from 2000, 2005, 2007, 2009 and 2013. The overall eight-class classification accuracies averaged 84.47% for the five years. The maps showed that between 2000 and 2013 the amount of non-wetland (urban) area increased by approximately 100%. Herbaceous (32.22%), forest (29.57%) and pond (23.85%) are the main land-cover types that changed to non-wetland, followed by cropland (6.97%), marsh (4.04%) and river (3.35%). In addition, the maps of change patterns showed that urban wetland loss is mainly distributed west and southeast of the study area due to real estate development, and the greatest loss of urban wetlands occurred from 2007 to 2013. The results demonstrate the advantages of using multi-temporal high spatial resolution satellite imagery to provide an accurate, economical means to map and analyse changes in land use/cover over time and the ability to use the results as inputs to urban wetland management and policy decisions.

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.

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

Sea-Ice Feature Mapping using JERS-1 Imagery

NASA Technical Reports Server (NTRS)

Maslanik, James; Heinrichs, John

1994-01-01

JERS-1 SAR and OPS imagery are examined in combination with other data sets to investigate the utility of the JERS-1 sensors for mapping fine-scale sea ice conditions. Combining ERS-1 C band and JERS-1 L band SAR aids in discriminating multiyear and first-year ice. Analysis of OPS imagery for a field site in the Canadian Archipelago highlights the advantages of OPS's high spatial and spectral resolution for mapping ice structure, melt pond distribution, and surface albedo.

Use of airborne hyperspectral imagery to map soil parameters in tilled agricultural fields

USGS Publications Warehouse

Hively, W. Dean; McCarty, Gregory W.; Reeves, James B.; Lang, Megan W.; Oesterling, Robert A.; Delwiche, Stephen R.

2011-01-01

Soil hyperspectral reflectance imagery was obtained for six tilled (soil) agricultural fields using an airborne imaging spectrometer (400–2450 nm, ~10 nm resolution, 2.5 m spatial resolution). Surface soil samples (n = 315) were analyzed for carbon content, particle size distribution, and 15 agronomically important elements (Mehlich-III extraction). When partial least squares (PLS) regression of imagery-derived reflectance spectra was used to predict analyte concentrations, 13 of the 19 analytes were predicted with R2 > 0.50, including carbon (0.65), aluminum (0.76), iron (0.75), and silt content (0.79). Comparison of 15 spectral math preprocessing treatments showed that a simple first derivative worked well for nearly all analytes. The resulting PLS factors were exported as a vector of coefficients and used to calculate predicted maps of soil properties for each field. Image smoothing with a 3 × 3 low-pass filter prior to spectral data extraction improved prediction accuracy. The resulting raster maps showed variation associated with topographic factors, indicating the effect of soil redistribution and moisture regime on in-field spatial variability. High-resolution maps of soil analyte concentrations can be used to improve precision environmental management of farmlands.

Evaluating ASTER satellite imagery and gradient modeling for mapping and characterizing wildland fire fuels

Treesearch

Michael J. Falkowski; Paul Gessler; Penelope Morgan; Alistair M. S. Smith; Andrew T. Hudak

2004-01-01

Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection...

Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Treesearch

Michael J. Falkowski; Paul E. Gessler; Penelope Morgan; Andrew T. Hudak; Alistair M. S. Smith

2005-01-01

Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and...

Use of ultra-high spatial resolution aerial imagery in the estimation of chaparral wildfire fuel loads

Treesearch

Ian T. Schmidt; John F. O' Leary; Douglas A. Stow; Kellie A. Uyeda; Philip Riggan

2016-01-01

Development of methods that more accurately estimate spatial distributions of fuel loads in shrublands allows for improved understanding of ecological processes such as wildfire behavior and postburn recovery. The goal of this study is to develop and test

Earthquake Damage Assessment Using Very High Resolution Satelliteimagery

NASA Astrophysics Data System (ADS)

Chiroiu, L.; André, G.; Bahoken, F.; Guillande, R.

Various studies using satellite imagery were applied in the last years in order to assess natural hazard damages, most of them analyzing the case of floods, hurricanes or landslides. For the case of earthquakes, the medium or small spatial resolution data available in the recent past did not allow a reliable identification of damages, due to the size of the elements (e.g. buildings or other structures), too small compared with the pixel size. The recent progresses of remote sensing in terms of spatial resolution and data processing makes possible a reliable damage detection to the elements at risk. Remote sensing techniques applied to IKONOS (1 meter resolution) and IRS (5 meters resolution) imagery were used in order to evaluate seismic vulnerability and post earthquake damages. A fast estimation of losses was performed using a multidisciplinary approach based on earthquake engineering and geospatial analysis. The results, integrated into a GIS database, could be transferred via satellite networks to the rescue teams deployed on the affected zone, in order to better coordinate the emergency operations. The methodology was applied to the city of Bhuj and Anjar after the 2001 Gujarat (India) Earthquake.

On the creation of high spatial resolution imaging spectroscopy data from multi-temporal low spatial resolution imagery

NASA Astrophysics Data System (ADS)

Yao, Wei; van Aardt, Jan; Messinger, David

2017-05-01

The Hyperspectral Infrared Imager (HyspIRI) mission aims to provide global imaging spectroscopy data to the benefit of especially ecosystem studies. The onboard spectrometer will collect radiance spectra from the visible to short wave infrared (VSWIR) regions (400-2500 nm). The mission calls for fine spectral resolution (10 nm band width) and as such will enable scientists to perform material characterization, species classification, and even sub-pixel mapping. However, the global coverage requirement results in a relatively low spatial resolution (GSD 30m), which restricts applications to objects of similar scales. We therefore have focused on the assessment of sub-pixel vegetation structure from spectroscopy data in past studies. In this study, we investigate the development or reconstruction of higher spatial resolution imaging spectroscopy data via fusion of multi-temporal data sets to address the drawbacks implicit in low spatial resolution imagery. The projected temporal resolution of the HyspIRI VSWIR instrument is 15 days, which implies that we have access to as many as six data sets for an area over the course of a growth season. Previous studies have shown that select vegetation structural parameters, e.g., leaf area index (LAI) and gross ecosystem production (GEP), are relatively constant in summer and winter for temperate forests; we therefore consider the data sets collected in summer to be from a similar, stable forest structure. The first step, prior to fusion, involves registration of the multi-temporal data. A data fusion algorithm then can be applied to the pre-processed data sets. The approach hinges on an algorithm that has been widely applied to fuse RGB images. Ideally, if we have four images of a scene which all meet the following requirements - i) they are captured with the same camera configurations; ii) the pixel size of each image is x; and iii) at least r2 images are aligned on a grid of x/r - then a high-resolution image, with a pixel size of x/r, can be reconstructed from the multi-temporal set. The algorithm was applied to data from NASA's classic Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-C; GSD 18m), collected between 2013-2015 (summer and fall) over our study area (NEON's Southwest Pacific Domain; Fresno, CA) to generate higher spatial resolution imagery (GSD 9m). The reconstructed data set was validated via comparison to NEON's imaging spectrometer (NIS) data (GSD 1m). The results showed that algorithm worked well with the AVIRIS-C data and could be applied to the HyspIRI data.

Multispectral image enhancement processing for microsat-borne imager

NASA Astrophysics Data System (ADS)

Sun, Jianying; Tan, Zheng; Lv, Qunbo; Pei, Linlin

2017-10-01

With the rapid development of remote sensing imaging technology, the micro satellite, one kind of tiny spacecraft, appears during the past few years. A good many studies contribute to dwarfing satellites for imaging purpose. Generally speaking, micro satellites weigh less than 100 kilograms, even less than 50 kilograms, which are slightly larger or smaller than the common miniature refrigerators. However, the optical system design is hard to be perfect due to the satellite room and weight limitation. In most cases, the unprocessed data captured by the imager on the microsatellite cannot meet the application need. Spatial resolution is the key problem. As for remote sensing applications, the higher spatial resolution of images we gain, the wider fields we can apply them. Consequently, how to utilize super resolution (SR) and image fusion to enhance the quality of imagery deserves studying. Our team, the Key Laboratory of Computational Optical Imaging Technology, Academy Opto-Electronics, is devoted to designing high-performance microsat-borne imagers and high-efficiency image processing algorithms. This paper addresses a multispectral image enhancement framework for space-borne imagery, jointing the pan-sharpening and super resolution techniques to deal with the spatial resolution shortcoming of microsatellites. We test the remote sensing images acquired by CX6-02 satellite and give the SR performance. The experiments illustrate the proposed approach provides high-quality images.

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.)

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Monitoring Forest Degradation for a Case Study in Cambodia: Comparison of Landsat 8 and Sentinel-2 Imagery

NASA Astrophysics Data System (ADS)

Langner, Andreas; Miettinen, Jukka; Stibig, Hans-Jurgen

2016-08-01

We use a Normalized Burned Ratio (NBR) differential approach for detecting forest canopy disturbance caused by selective logging in evergreen tropical moist forests of central Cambodia. The general disturbance pattern obtained from Landsat 8 (30 m) imagery is largely compatible to Sentinel-2 (10 m), showing good conformity to high resolution RapidEye reference data. However, the 10 m spatial resolution of Sentinel-2 provides notably higher spatial detail and purer pixel values, increasing the potential for detecting fine and subtle forest canopy changes as indicators for potential forest degradation. We can expect further improvement for detecting short-lived disturbance signals in tropical forest canopies due to an increased revisit frequency (5 days) after the Sentinel-2B launch.

Individual differences in brain activity during visuo-spatial processing assessed by slow cortical potentials and LORETA.

PubMed

Lamm, Claus; Fischmeister, Florian Ph S; Bauer, Herbert

2005-12-01

Using slow-cortical potentials (SCPs), Vitouch et al. demonstrated that subjects with low ability to solve a complex visuo-spatial imagery task show higher activity in occipital, parietal and frontal cortex during task processing than subjects with high ability. This finding has been interpreted in the sense of the so-called "neural efficiency" hypothesis, which assumes that the central nervous system of individuals with higher intellectual abilities is functioning in a more efficient way than the one of individuals with lower abilities. Using a higher spatial resolution of SCP recordings, and by employing the source localization method of LORETA (low-resolution electromagnetic tomography), we investigated this hypothesis by performing an extended replication of Vitouch et al.'s study. SCPs during processing of a visuo-spatial imagery task were recorded in pre-selected subjects with either high or low abilities in solving the imagery task. Topographic and LORETA analyses of SCPs revealed that a distributed network of extrastriate occipital, superior parietal, temporal, medial frontal and prefrontal areas was active during task solving. This network is well in line with former studies of the functional neuroanatomy of visuo-spatial imagery. Contrary to our expectations, however, the results of Vitouch et al. as well as of other studies supporting the neural efficiency hypothesis could not be confirmed since no difference in brain activity between groups was observed. This inconsistency between studies might be due to differing task processing strategies. While subjects with high abilities in the Vitouch et al. study seemed to use a visuo-perceptual task solving approach, all other subjects relied upon a visuo-motor task processing strategy.

Image interpreter tool: An ArcGIS tool for estimating vegetation cover from high-resolution imagery

USDA-ARS?s Scientific Manuscript database

Land managers need increased temporal and spatial resolution of rangeland assessment and monitoring data. However, with flat or declining land management and monitoring agency budgets, such increases in sampling intensity are unlikely unless new methods can be developed that capture data of key rang...

Appalachian Mountains

Atmospheric Science Data Center

2014-05-15

... Radiance Ellipsoid Product. MISR uses this enhanced sensitivity along with the angular variation in signal to monitor particulate ... of MISR's unique capability of providing moderately high spatial resolution, calibrated imagery at very oblique angles. Gradations ...

Small Fire Detection Algorithm Development using VIIRS 375m Imagery: Application to Agricultural Fires in Eastern China

NASA Astrophysics Data System (ADS)

Zhang, Tianran; Wooster, Martin

2016-04-01

Until recently, crop residues have been the second largest industrial waste product produced in China and field-based burning of crop residues is considered to remain extremely widespread, with impacts on air quality and potential negative effects on health, public transportation. However, due to the small size and perhaps short-lived nature of the individual burns, the extent of the activity and its spatial variability remains somewhat unclear. Satellite EO data has been used to gauge the timing and magnitude of Chinese crop burning, but current approaches very likely miss significant amounts of the activity because the individual burned areas are either too small to detect with frequently acquired moderate spatial resolution data such as MODIS. The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi-NPP (National Polar-orbiting Partnership) satellite launched on October, 2011 has one set of multi-spectral channels providing full global coverage at 375 m nadir spatial resolutions. It is expected that the 375 m spatial resolution "I-band" imagery provided by VIIRS will allow active fires to be detected that are ~ 10× smaller than those that can be detected by MODIS. In this study the new small fire detection algorithm is built based on VIIRS-I band global fire detection algorithm and hot spot detection algorithm for the BIRD satellite mission. VIIRS-I band imagery data will be used to identify agricultural fire activity across Eastern China. A 30 m spatial resolution global land cover data map is used for false alarm masking. The ground-based validation is performed using images taken from UAV. The fire detection result is been compared with active fire product from the long-standing MODIS sensor onboard the TERRA and AQUA satellites, which shows small fires missed from traditional MODIS fire product may count for over 1/3 of total fire energy in Eastern China.

Technical parameters for specifying imagery requirements

NASA Technical Reports Server (NTRS)

Coan, Paul P.; Dunnette, Sheri J.

1994-01-01

Providing visual information acquired from remote events to various operators, researchers, and practitioners has become progressively more important as the application of special skills in alien or hazardous situations increases. To provide an understanding of the technical parameters required to specify imagery, we have identified, defined, and discussed seven salient characteristics of images: spatial resolution, linearity, luminance resolution, spectral discrimination, temporal discrimination, edge definition, and signal-to-noise ratio. We then describe a generalizing imaging system and identified how various parts of the system affect the image data. To emphasize the different applications of imagery, we have constrasted the common television system with the significant parameters of a televisual imaging system for technical applications. Finally, we have established a method by which the required visual information can be specified by describing certain technical parameters which are directly related to the information content of the imagery. This method requires the user to complete a form listing all pertinent data requirements for the imagery.

Delineating Tree Types in a Complex Tropical Forest Setting Using High Resolution Multispectral Satellite Imagery

NASA Astrophysics Data System (ADS)

Cross, M.

2016-12-01

An improved process for the identification of tree types from satellite imagery for tropical forests is needed for more accurate assessments of the impact of forests on the global climate. La Selva Biological Station in Costa Rica was the tropical forest area selected for this particular study. WorldView-3 imagery was utilized because of its high spatial, spectral and radiometric resolution, its availability, and its potential to differentiate species in a complex forest setting. The first-step was to establish confidence in the high spatial and high radiometric resolution imagery from WorldView-3 in delineating tree types within a complex forest setting. In achieving this goal, ASD field spectrometer data were collected of specific tree species to establish solid ground control within the study site. The spectrometer data were collected from the top of each specific tree canopy utilizing established towers located at La Selva Biological Station so as to match the near-nadir view of the WorldView-3 imagery. The ASD data was processed utilizing the spectral response functions for each of the WorldView-3 bands to convert the ASD data into a band specific reflectivity. This allowed direct comparison of the ASD spectrometer reflectance data to the WorldView-3 multispectral imagery. The WorldView-3 imagery was processed to surface reflectance using two standard atmospheric correction procedures and the proprietary DigitalGlobe Atmospheric Compensation (AComp) product. The most accurate correction process was identified through comparison to the spectrometer data collected. A series of statistical measures were then utilized to access the accuracy of the processed imagery and which imagery bands are best suited for tree type identification. From this analysis, a segmentation/classification process was performed to identify individual tree type locations within the study area. It is envisioned the results of this study will improve traditional forest classification processes, provide more accurate assessments of species density and distribution, facilitate a more accurate biomass estimate of the tropical forest which will impact the accuracy of tree carbon storage estimates, and ultimately assist in developing a better overall characterization of tropical rainforest dynamics.

Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

NASA Astrophysics Data System (ADS)

Onojeghuo, Alex Okiemute; Onojeghuo, Ajoke Ruth

2017-07-01

This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.

Vegetation Mapping in a Dryland Ecosystem Using Multi-temporal Sentinel-2 Imagery and Ensemble Learning

NASA Astrophysics Data System (ADS)

Enterkine, J.; Spaete, L.; Glenn, N. F.; Gallagher, M.

2017-12-01

Remote sensing and mapping of dryland ecosystem vegetation is notably problematic due to the low canopy cover and fugacious growing seasons. Recent improvements in available satellite imagery and machine learning techniques have enabled enhanced approaches to mapping and monitoring vegetation across dryland ecosystems. The Sentinel-2 satellites (launched June 2015 and March 2017) of ESA's Copernicus Programme offer promising developments from existing multispectral satellite systems such as Landsat. Freely-available, Sentinel-2 imagery offers a five-day revisit frequency, thirteen spectral bands (in the visible, near infrared, and shortwave infrared), and high spatial resolution (from 10m to 60m). Three narrow spectral bands located between the visible and the near infrared are designed to observe changes in photosynthesis. The high temporal, spatial, and spectral resolution of this imagery makes it ideal for monitoring vegetation in dryland ecosystems. In this study, we calculated a large number of vegetation and spectral indices from Sentinel-2 imagery spanning a growing season. This data was leveraged with robust field data of canopy cover at precise geolocations. We then used a Random Forests ensemble learning model to identify the most predictive variables for each landcover class, which were then used to impute landcover over the study area. The resulting vegetation map product will be used by land managers, and the mapping approaches will serve as a basis for future remote sensing projects using Sentinel-2 imagery and machine learning.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Validation of Suomi-NPP VIIRS sea ice concentration with very high-resolution satellite and airborne camera imagery

NASA Astrophysics Data System (ADS)

Baldwin, Daniel; Tschudi, Mark; Pacifici, Fabio; Liu, Yinghui

2017-08-01

Two independent VIIRS-based Sea Ice Concentration (SIC) products are validated against SIC as estimated from Very High Spatial Resolution Imagery for several VIIRS overpasses. The 375 m resolution VIIRS SIC from the Interface Data Processing Segment (IDPS) SIC algorithm is compared against estimates made from 2 m DigitalGlobe (DG) WorldView-2 imagery and also against estimates created from 10 cm Digital Mapping System (DMS) camera imagery. The 750 m VIIRS SIC from the Enterprise SIC algorithm is compared against DG imagery. The IDPS vs. DG comparisons reveal that, due to algorithm issues, many of the IDPS SIC retrievals were falsely assigned ice-free values when the pixel was clearly over ice. These false values increased the validation bias and RMS statistics. The IDPS vs. DMS comparisons were largely over ice-covered regions and did not demonstrate the false retrieval issue. The validation results show that products from both the IDPS and Enterprise algorithms were within or very close to the 10% accuracy (bias) specifications in both the non-melting and melting conditions, but only products from the Enterprise algorithm met the 25% specifications for the uncertainty (RMS).

Main Road Extraction from ZY-3 Grayscale Imagery Based on Directional Mathematical Morphology and VGI Prior Knowledge in Urban Areas

PubMed Central

Liu, Bo; Wu, Huayi; Wang, Yandong; Liu, Wenming

2015-01-01

Main road features extracted from remotely sensed imagery play an important role in many civilian and military applications, such as updating Geographic Information System (GIS) databases, urban structure analysis, spatial data matching and road navigation. Current methods for road feature extraction from high-resolution imagery are typically based on threshold value segmentation. It is difficult however, to completely separate road features from the background. We present a new method for extracting main roads from high-resolution grayscale imagery based on directional mathematical morphology and prior knowledge obtained from the Volunteered Geographic Information found in the OpenStreetMap. The two salient steps in this strategy are: (1) using directional mathematical morphology to enhance the contrast between roads and non-roads; (2) using OpenStreetMap roads as prior knowledge to segment the remotely sensed imagery. Experiments were conducted on two ZiYuan-3 images and one QuickBird high-resolution grayscale image to compare our proposed method to other commonly used techniques for road feature extraction. The results demonstrated the validity and better performance of the proposed method for urban main road feature extraction. PMID:26397832

Using hyperspectral imagery to assist federal forest monitoring and restoration projects in the Southern Rocky Mountains, Colorado

NASA Astrophysics Data System (ADS)

Wamser, Kyle

Hyperspectral imagery and the corresponding ability to conduct analysis below the pixel level have tremendous potential to aid in landcover monitoring. During large ecosystem restoration projects, being able to monitor specific aspects of the recovery over large and often inaccessible areas under constrained finances are major challenges. The Civil Air Patrol's Airborne Real-time Cueing Hyperspectral Enhanced Reconnaissance (ARCHER) can provide hyperspectral data in most parts of the United States at relatively low cost. Although designed specifically for use in locating downed aircraft, the imagery holds the potential to identify specific aspects of landcover at far greater fidelity than traditional multispectral means. The goals of this research were to improve the use of ARCHER hyperspectral imagery to classify sub-canopy and open-area vegetation in coniferous forests located in the Southern Rockies and to determine how much fidelity might be lost from a baseline of 1 meter spatial resolution resampled to 2 and 5 meter pixel size to simulate higher altitude collection. Based on analysis comparing linear spectral unmixing with a traditional supervised classification, the linear spectral unmixing proved to be statistically superior. More importantly, however, linear spectral unmixing provided additional sub-pixel information that was unavailable using other techniques. The second goal of determining fidelity loss based on spatial resolution was more difficult to determine due to how the data are represented. Furthermore, the 2 and 5 meter imagery were obtained by resampling the 1 meter imagery and therefore may not be representative of the quality of actual 2 or 5 meter imagery. Ultimately, the information derived from this research may be useful in better utilizing hyperspectral imagery to conduct forest monitoring and assessment.

The use of Sentinel-2 imagery for seagrass mapping: Kalloni Gulf (Lesvos Island, Greece) case study

NASA Astrophysics Data System (ADS)

Topouzelis, Konstantinos; Charalampis Spondylidis, Spyridon; Papakonstantinou, Apostolos; Soulakellis, Nikolaos

2016-08-01

Seagrass meadows play a significant role in ecosystems by stabilizing sediment and improving water clarity, which enhances seagrass growing conditions. It is high on the priority of EU legislation to map and protect them. The traditional use of medium spatial resolution satellite imagery e.g. Landsat-8 (30m) is very useful for mapping seagrass meadows on a regional scale. However, the availability of Sentinel-2 data, the recent ESA's satellite with its payload Multi-Spectral Instrument (MSI) is expected to improve the mapping accuracy. MSI designed to improve coastline studies due to its enhanced spatial and spectral capabilities e.g. optical bands with 10m spatial resolution. The present work examines the quality of Sentinel-2 images for seagrass mapping, the ability of each band in detection and discrimination of different habitats and estimates the accuracy of seagrass mapping. After pre-processing steps, e.g. radiometric calibration and atmospheric correction, image classified into four classes. Classification classes included sub-bottom composition e.g. seagrass, soft bottom, and hard bottom. Concrete vectors describing the areas covered by seagrass extracted from the high-resolution satellite image and used as in situ measurements. The developed methodology applied in the Gulf of Kalloni, (Lesvos Island - Greece). Results showed that Sentinel-2 images can be robustly used for seagrass mapping due to their spatial resolution, band availability and radiometric accuracy.

High temporal resolution aerosol retrieval using Geostationary Ocean Color Imager: application and initial validation

NASA Astrophysics Data System (ADS)

Zhang, Yuhuan; Li, Zhengqiang; Zhang, Ying; Hou, Weizhen; Xu, Hua; Chen, Cheng; Ma, Yan

2014-01-01

The Geostationary Ocean Color Imager (GOCI) provides multispectral imagery of the East Asia region hourly from 9:00 to 16:00 local time (GMT+9) and collects multispectral imagery at eight spectral channels (412, 443, 490, 555, 660, 680, 745, and 865 nm) with a spatial resolution of 500 m. Thus, this technology brings significant advantages to high temporal resolution environmental monitoring. We present the retrieval of aerosol optical depth (AOD) in northern China based on GOCI data. Cross-calibration was performed against Moderate Resolution Imaging Spectrometer (MODIS) data in order to correct the land calibration bias of the GOCI sensor. AOD retrievals were then accomplished using a look-up table (LUT) strategy with assumptions of a quickly varying aerosol and a slowly varying surface with time. The AOD retrieval algorithm calculates AOD by minimizing the surface reflectance variations of a series of observations in a short period of time, such as several days. The monitoring of hourly AOD variations was implemented, and the retrieved AOD agreed well with AErosol RObotic NETwork (AERONET) ground-based measurements with a good R2 of approximately 0.74 at validation sites at the cities of Beijing and Xianghe, although intercept bias may be high in specific cases. The comparisons with MODIS products also show a good agreement in AOD spatial distribution. This work suggests that GOCI imagery can provide high temporal resolution monitoring of atmospheric aerosols over land, which is of great interest in climate change studies and environmental monitoring.

Supersampling multiframe blind deconvolution resolution enhancement of adaptive-optics-compensated imagery of LEO satellites

NASA Astrophysics Data System (ADS)

Gerwe, David R.; Lee, David J.; Barchers, Jeffrey D.

2000-10-01

A post-processing methodology for reconstructing undersampled image sequences with randomly varying blur is described which can provide image enhancement beyond the sampling resolution of the sensor. This method is demonstrated on simulated imagery and on adaptive optics compensated imagery taken by the Starfire Optical Range 3.5 meter telescope that has been artificially undersampled. Also shown are the results of multiframe blind deconvolution of some of the highest quality optical imagery of low earth orbit satellites collected with a ground based telescope to date. The algorithm used is a generalization of multiframe blind deconvolution techniques which includes a representation of spatial sampling by the focal plane array elements in the forward stochastic model of the imaging system. This generalization enables the random shifts and shape of the adaptive compensated PSF to be used to partially eliminate the aliasing effects associated with sub- Nyquist sampling of the image by the focal plane array. The method could be used to reduce resolution loss which occurs when imaging in wide FOV modes.

Supersampling multiframe blind deconvolution resolution enhancement of adaptive optics compensated imagery of low earth orbit satellites

NASA Astrophysics Data System (ADS)

Gerwe, David R.; Lee, David J.; Barchers, Jeffrey D.

2002-09-01

We describe a postprocessing methodology for reconstructing undersampled image sequences with randomly varying blur that can provide image enhancement beyond the sampling resolution of the sensor. This method is demonstrated on simulated imagery and on adaptive-optics-(AO)-compensated imagery taken by the Starfire Optical Range 3.5-m telescope that has been artificially undersampled. Also shown are the results of multiframe blind deconvolution of some of the highest quality optical imagery of low earth orbit satellites collected with a ground-based telescope to date. The algorithm used is a generalization of multiframe blind deconvolution techniques that include a representation of spatial sampling by the focal plane array elements based on a forward stochastic model. This generalization enables the random shifts and shape of the AO- compensated point spread function (PSF) to be used to partially eliminate the aliasing effects associated with sub-Nyquist sampling of the image by the focal plane array. The method could be used to reduce resolution loss that occurs when imaging in wide- field-of-view (FOV) modes.

Land cover mapping at sub-pixel scales

NASA Astrophysics Data System (ADS)

Makido, Yasuyo Kato

One of the biggest drawbacks of land cover mapping from remotely sensed images relates to spatial resolution, which determines the level of spatial details depicted in an image. Fine spatial resolution images from satellite sensors such as IKONOS and QuickBird are now available. However, these images are not suitable for large-area studies, since a single image is very small and therefore it is costly for large area studies. Much research has focused on attempting to extract land cover types at sub-pixel scale, and little research has been conducted concerning the spatial allocation of land cover types within a pixel. This study is devoted to the development of new algorithms for predicting land cover distribution using remote sensory imagery at sub-pixel level. The "pixel-swapping" optimization algorithm, which was proposed by Atkinson for predicting sub-pixel land cover distribution, is investigated in this study. Two limitations of this method, the arbitrary spatial range value and the arbitrary exponential model of spatial autocorrelation, are assessed. Various weighting functions, as alternatives to the exponential model, are evaluated in order to derive the optimum weighting function. Two different simulation models were employed to develop spatially autocorrelated binary class maps. In all tested models, Gaussian, Exponential, and IDW, the pixel swapping method improved classification accuracy compared with the initial random allocation of sub-pixels. However the results suggested that equal weight could be used to increase accuracy and sub-pixel spatial autocorrelation instead of using these more complex models of spatial structure. New algorithms for modeling the spatial distribution of multiple land cover classes at sub-pixel scales are developed and evaluated. Three methods are examined: sequential categorical swapping, simultaneous categorical swapping, and simulated annealing. These three methods are applied to classified Landsat ETM+ data that has been resampled to 210 meters. The result suggested that the simultaneous method can be considered as the optimum method in terms of accuracy performance and computation time. The case study employs remote sensing imagery at the following sites: tropical forests in Brazil and temperate multiple land mosaic in East China. Sub-areas for both sites are used to examine how the characteristics of the landscape affect the ability of the optimum technique. Three types of measurement: Moran's I, mean patch size (MPS), and patch size standard deviation (STDEV), are used to characterize the landscape. All results suggested that this technique could increase the classification accuracy more than traditional hard classification. The methods developed in this study can benefit researchers who employ coarse remote sensing imagery but are interested in detailed landscape information. In many cases, the satellite sensor that provides large spatial coverage has insufficient spatial detail to identify landscape patterns. Application of the super-resolution technique described in this dissertation could potentially solve this problem by providing detailed land cover predictions from the coarse resolution satellite sensor imagery.

The application of satellite data in monitoring strip mines

NASA Technical Reports Server (NTRS)

Sharber, L. A.; Shahrokhi, F.

1977-01-01

Strip mines in the New River Drainage Basin of Tennessee were studied through use of Landsat-1 imagery and aircraft photography. A multilevel analysis, involving conventional photo interpretation techniques, densitometric methods, multispectral analysis and statistical testing was applied to the data. The Landsat imagery proved adequate for monitoring large-scale change resulting from active mining and land-reclamation projects. However, the spatial resolution of the satellite imagery rendered it inadequate for assessment of many smaller strip mines, in the region which may be as small as a few hectares.

Evaluation of MODIS NPP and GPP products across multiple biomes.

Treesearch

David P. Turner; William D. Ritts; Warren B. Cohen; Stith T. Gower; Steve W. Running; Maosheng Zhao; Marcos H. Costa; Al A. Kirschbaum; Jay M. Ham; Scott R. Saleska; Douglas E. Ahl

2006-01-01

Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of...

Building Change Detection in Very High Resolution Satellite Stereo Image Time Series

NASA Astrophysics Data System (ADS)

Tian, J.; Qin, R.; Cerra, D.; Reinartz, P.

2016-06-01

There is an increasing demand for robust methods on urban sprawl monitoring. The steadily increasing number of high resolution and multi-view sensors allows producing datasets with high temporal and spatial resolution; however, less effort has been dedicated to employ very high resolution (VHR) satellite image time series (SITS) to monitor the changes in buildings with higher accuracy. In addition, these VHR data are often acquired from different sensors. The objective of this research is to propose a robust time-series data analysis method for VHR stereo imagery. Firstly, the spatial-temporal information of the stereo imagery and the Digital Surface Models (DSMs) generated from them are combined, and building probability maps (BPM) are calculated for all acquisition dates. In the second step, an object-based change analysis is performed based on the derivative features of the BPM sets. The change consistence between object-level and pixel-level are checked to remove any outlier pixels. Results are assessed on six pairs of VHR satellite images acquired within a time span of 7 years. The evaluation results have proved the efficiency of the proposed method.

A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei

2016-06-01

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes.

Estimating Vegetation Structure in African Savannas using High Spatial Resolution Imagery

NASA Astrophysics Data System (ADS)

Axelsson, C.; Hanan, N. P.

2016-12-01

High spatial resolution satellite imagery allows for detailed mapping of trees in savanna landscapes, including estimates of woody cover, tree densities, crown sizes, and the spatial pattern of trees. By linking these vegetation parameters to rainfall and soil properties we gain knowledge of how the local environment influences vegetation. A thorough understanding of the underlying ecosystem processes is key to assessing the future productivity and stability of these ecosystems. In this study, we have processed and analyzed hundreds of sites sampled from African savannas across a wide range of rainfall and soil conditions. The vegetation at each site is classified using unsupervised classification with manual assignment into woody, herbaceous and bare cover classes. A crown delineation method further divides the woody areas into individual tree crowns. The results show that rainfall, soil, and topography interactively influence vegetation structure. We see that both total rainfall and rainfall seasonality play important roles and that soil type influences woody cover and the sizes of tree crowns.

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

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

The effect of spatial resolution upon cloud optical property retrievals. I - Optical thickness

NASA Technical Reports Server (NTRS)

Feind, Rand E.; Christopher, Sundar A.; Welch, Ronald M.

1992-01-01

High spectral and spatial resolution Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery is used to study the effects of spatial resolution upon fair weather cumulus cloud optical thickness retrievals. As a preprocessing step, a variation of the Gao and Goetz three-band ratio technique is used to discriminate clouds from the background. The combination of the elimination of cloud shadow pixels and using the first derivative of the histogram allows for accurate cloud edge discrimination. The data are progressively degraded from 20 m to 960 m spatial resolution. The results show that retrieved cloud area increases with decreasing spatial resolution. The results also show that there is a monotonic decrease in retrieved cloud optical thickness with decreasing spatial resolution. It is also demonstrated that the use of a single, monospectral reflectance threshold is inadequate for identifying cloud pixels in fair weather cumulus scenes and presumably in any inhomogeneous cloud field. Cloud edges have a distribution of reflectance thresholds. The incorrect identification of cloud edges significantly impacts the accurate retrieval of cloud optical thickness values.

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.

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

Radiometric and Spatial Characterization of High-Spatial Resolution Sensors

NASA Technical Reports Server (NTRS)

Thome, Kurtis; Zanoni, Vicki (Technical Monitor)

2002-01-01

The development and improvement of commercial hyperspatial sensors in recent years has increased the breadth of information that can be retrieved from spaceborne and airborne imagery. NASA, through it's Scientific Data Purchases, has successfully provided such data sets to its user community. A key element to the usefulness of these data are an understanding of the radiometric and spatial response quality of the imagery. This proposal seeks funding to examine the absolute radiometric calibration of the Ikonos sensor operated by Space Imaging and the recently-launched Quickbird sensor from DigitalGlobe. In addition, we propose to evaluate the spatial response of the two sensors. The proposed methods rely on well-understood, ground-based targets that have been used by the University of Arizona for more than a decade.

Bridging the Past with Today's Microwave Remote Sensing: A Case Study of Long Term Inundation Patterns in Two River Deltas

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Jensen, K.; Schroeder, R.; Tessler, Z. D.

2016-12-01

Surface inundation extent and its predictability vary tremendously across the globe. This dynamic is being and has been captured by three general categories of satellite imagery: a) low-spatial-resolution microwave sensors with global coverage and a long record of observations (e.g., SSM/I), b) optical sensors with high spatial and temporal resolution and global coverage as well, but with cloud contamination (e.g. MODIS), and also c) less frequently in ``snapshot'' form by high-resolution synthetic aperture radar (SAR) sensors. We explore the ability to bridge techniques that can exploit the higher spatial resolution of more recent data products back in time with the help of the temporal evolution of lower resolution products. We present a study of long term (20+ yrs) inundation patterns in two river deltas: (1) the Mekong, and (2) the Ganges-Brahmaputra. This research utilizes baseline observations from the Surface Water Microwave Product Series (SWAMPS), an inundation area fraction product derived at 25km scale from active and passive microwave instruments (ERS, QuikSCAT, ASCAT, and SSM/I) that spans from Jan 1992 to the present. Every hydrological basin has unique characteristics - such as its topography, land cover / land use, and spatio-temporal variability - thus, a downscaling algorithm needs to take into account these idiosyncrasies. We merge SWAMPS with topographical information derived from 30m SRTM DEM, river networks from USGS HydroSHEDS, and train a downscaling algorithm to learn from two sets of classified SAR data: (1) L-band imaging radar from ALOS PALSAR, 2007-2010, and (2) more recent C-band imagery from the Sentinel-1 mission (2014 to present). We present an accuracy assessment of retrospective downscaled flood extent with Landsat imagery and address potential sources of biases. With a higher spatial resolution of past flooding extent, we can improve our understanding of how delta surface hydrology has responded to climate events and human activities. This is important both in the short-term for accurate flood prediction, as well as on longer-term planning horizons.

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data

PubMed Central

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-01-01

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97. PMID:26393607

Generating Daily Synthetic Landsat Imagery by Combining Landsat and MODIS Data.

PubMed

Wu, Mingquan; Huang, Wenjiang; Niu, Zheng; Wang, Changyao

2015-09-18

Owing to low temporal resolution and cloud interference, there is a shortage of high spatial resolution remote sensing data. To address this problem, this study introduces a modified spatial and temporal data fusion approach (MSTDFA) to generate daily synthetic Landsat imagery. This algorithm was designed to avoid the limitations of the conditional spatial temporal data fusion approach (STDFA) including the constant window for disaggregation and the sensor difference. An adaptive window size selection method is proposed in this study to select the best window size and moving steps for the disaggregation of coarse pixels. The linear regression method is used to remove the influence of differences in sensor systems using disaggregated mean coarse reflectance by testing and validation in two study areas located in Xinjiang Province, China. The results show that the MSTDFA algorithm can generate daily synthetic Landsat imagery with a high correlation coefficient (R) ranged from 0.646 to 0.986 between synthetic images and the actual observations. We further show that MSTDFA can be applied to 250 m 16-day MODIS MOD13Q1 products and the Landsat Normalized Different Vegetation Index (NDVI) data by generating a synthetic NDVI image highly similar to actual Landsat NDVI observation with a high R of 0.97.

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

Graesser, Jordan B; Cheriyadat, Anil M; Vatsavai, Raju

The high rate of global urbanization has resulted in a rapid increase in informal settlements, which can be de ned as unplanned, unauthorized, and/or unstructured housing. Techniques for ef ciently mapping these settlement boundaries can bene t various decision making bodies. From a remote sensing perspective, informal settlements share unique spatial characteristics that distinguish them from other types of structures (e.g., industrial, commercial, and formal residential). These spatial characteristics are often captured in high spatial resolution satellite imagery. We analyzed the role of spatial, structural, and contextual features (e.g., GLCM, Histogram of Oriented Gradients, Line Support Regions, Lacunarity) for urbanmore » neighborhood mapping, and computed several low-level image features at multiple scales to characterize local neighborhoods. The decision parameters to classify formal-, informal-, and non-settlement classes were learned under Decision Trees and a supervised classi cation framework. Experiments were conducted on high-resolution satellite imagery from the CitySphere collection, and four different cities (i.e., Caracas, Kabul, Kandahar, and La Paz) with varying spatial characteristics were represented. Overall accuracy ranged from 85% in La Paz, Bolivia, to 92% in Kandahar, Afghanistan. While the disparities between formal and informal neighborhoods varied greatly, many of the image statistics tested proved robust.« less

Surface Properties Associated With Dust Storm Plume's Point-Source Locations In The Border Region Of The US And Mexico

NASA Astrophysics Data System (ADS)

Bleiweiss, M. P.; DuBois, D. W.; Flores, M. I.

2013-12-01

Dust storms in the border region of the Southwest US and Northern Mexico are a serious problem for air quality (PM10 exceedances), health (Valley Fever is pandemic in the region) and transportation (road closures and deadly traffic accidents). In order to better understand the phenomena, we are attempting to identify critical characteristics of dust storm sources so that, possibly, one can perform more accurate predictions of events and, thus, mitigate some of the deleterious effects. Besides the emission mechanisms for dust storm production that are tied to atmospheric dynamics, one must know those locations whose source characteristics can be tied to dust production and, therefore, identify locations where a dust storm is eminent under favorable atmospheric dynamics. During the past 13 years, we have observed, on satellite imagery, more than 500 dust events in the region and are in the process of identifying the source regions for the dust plumes that make up an event. Where satellite imagery exists with high spatial resolution (less than or equal to 250m), dust 'plumes' appear to be made up of individual and merged plumes that are emitted from a 'point source' (smaller than the resolution of the imagery). In particular, we have observed events from the ASTER sensor whose spatial resolution is 15m as well as Landsat whose spatial resolution is 30m. Tying these source locations to surface properties such as NDVI, albedo, and soil properties (percent sand, silt, clay, and gravel; soil moisture; etc.) will identify regions with enhanced capability to produce a dust storm. This, along with atmospheric dynamics, will allow the forecast of dust events. The analysis of 10 events from the period 2004-2013, for which we have identified 1124 individual plumes, will be presented.

Automatic Extraction of Small Spatial Plots from Geo-Registered UAS Imagery

NASA Astrophysics Data System (ADS)

Cherkauer, Keith; Hearst, Anthony

2015-04-01

Accurate extraction of spatial plots from high-resolution imagery acquired by Unmanned Aircraft Systems (UAS), is a prerequisite for accurate assessment of experimental plots in many geoscience fields. If the imagery is correctly geo-registered, then it may be possible to accurately extract plots from the imagery based on their map coordinates. To test this approach, a UAS was used to acquire visual imagery of 5 ha of soybean fields containing 6.0 m2 plots in a complex planting scheme. Sixteen artificial targets were setup in the fields before flights and different spatial configurations of 0 to 6 targets were used as Ground Control Points (GCPs) for geo-registration, resulting in a total of 175 geo-registered image mosaics with a broad range of geo-registration accuracies. Geo-registration accuracy was quantified based on the horizontal Root Mean Squared Error (RMSE) of targets used as checkpoints. Twenty test plots were extracted from the geo-registered imagery. Plot extraction accuracy was quantified based on the percentage of the desired plot area that was extracted. It was found that using 4 GCPs along the perimeter of the field minimized the horizontal RMSE and enabled a plot extraction accuracy of at least 70%, with a mean plot extraction accuracy of 92%. The methods developed are suitable for work in many fields where replicates across time and space are necessary to quantify variability.

Landsat continuity: issues and opportunities for land cover monitoring

Treesearch

Michael A. Wulder; Joanne C. White; Samuel N. Goward; Jeffrey G. Masek; James R. Irons; Martin Herold; Warren B. Cohen; Thomas R. Loveland; Curtis E. Woodcock

2008-01-01

Initiated in 1972, the Landsat program has provided a continuous record of Earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and...

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.

Identification of lake trout Salvelinus namaycush spawning habitat in northern Lake Huron using high-resolution satellite imagery

USGS Publications Warehouse

Grimm, Amanda G.; Brooks, Colin N.; Binder, Thomas R.; Riley, Stephen C.; Farha, Steve A.; Shuchman, Robert A.; Krueger, Charles C.

2016-01-01

The availability and quality of spawning habitat may limit lake trout recovery in the Great Lakes, but little is known about the location and characteristics of current spawning habitats. Current methods used to identify lake trout spawning locations are time- and labor-intensive and spatially limited. Due to the observation that some lake trout spawning sites are relatively clean of overlaying algae compared to areas not used for spawning, we suspected that spawning sites could be identified using satellite imagery. Satellite imagery collected just before and after the spawning season in 2013 was used to assess whether lake trout spawning habitat could be identified based on its spectral characteristics. Results indicated that Pléiades high-resolution multispectral satellite imagery can be successfully used to estimate algal coverage of substrates and temporal changes in algal coverage, and that models developed from processed imagery can be used to identify potential lake trout spawning sites based on comparison of sites where lake trout eggs were and were not observed after spawning. Satellite imagery is a potential new tool for identifying lake trout spawning habitat at large scales in shallow nearshore areas of the Great Lakes.

Roi-Orientated Sensor Correction Based on Virtual Steady Reimaging Model for Wide Swath High Resolution Optical Satellite Imagery

NASA Astrophysics Data System (ADS)

Zhu, Y.; Jin, S.; Tian, Y.; Wang, M.

2017-09-01

To meet the requirement of high accuracy and high speed processing for wide swath high resolution optical satellite imagery under emergency situation in both ground processing system and on-board processing system. This paper proposed a ROI-orientated sensor correction algorithm based on virtual steady reimaging model for wide swath high resolution optical satellite imagery. Firstly, the imaging time and spatial window of the ROI is determined by a dynamic search method. Then, the dynamic ROI sensor correction model based on virtual steady reimaging model is constructed. Finally, the corrected image corresponding to the ROI is generated based on the coordinates mapping relationship which is established by the dynamic sensor correction model for corrected image and rigours imaging model for original image. Two experimental results show that the image registration between panchromatic and multispectral images can be well achieved and the image distortion caused by satellite jitter can be also corrected efficiently.

Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system

PubMed Central

Brenner, Claire; Thiem, Christina Elisabeth; Wizemann, Hans-Dieter; Bernhardt, Matthias; Schulz, Karsten

2017-01-01

ABSTRACT In this study, high-resolution thermal imagery acquired with a small unmanned aerial vehicle (UAV) is used to map evapotranspiration (ET) at a grassland site in Luxembourg. The land surface temperature (LST) information from the thermal imagery is the key input to a one-source and two-source energy balance model. While the one-source model treats the surface as a single uniform layer, the two-source model partitions the surface temperature and fluxes into soil and vegetation components. It thus explicitly accounts for the different contributions of both components to surface temperature as well as turbulent flux exchange with the atmosphere. Contrary to the two-source model, the one-source model requires an empirical adjustment parameter in order to account for the effect of the two components. Turbulent heat flux estimates of both modelling approaches are compared to eddy covariance (EC) measurements using the high-resolution input imagery UAVs provide. In this comparison, the effect of different methods for energy balance closure of the EC data on the agreement between modelled and measured fluxes is also analysed. Additionally, the sensitivity of the one-source model to the derivation of the empirical adjustment parameter is tested. Due to the very dry and hot conditions during the experiment, pronounced thermal patterns developed over the grassland site. These patterns result in spatially variable turbulent heat fluxes. The model comparison indicates that both models are able to derive ET estimates that compare well with EC measurements under these conditions. However, the two-source model, with a more complex treatment of the energy and surface temperature partitioning between the soil and vegetation, outperformed the simpler one-source model in estimating sensible and latent heat fluxes. This is consistent with findings from prior studies. For the one-source model, a time-variant expression of the adjustment parameter (to account for the difference between aerodynamic and radiometric temperature) that depends on the surface-to-air temperature gradient yielded the best agreement with EC measurements. This study showed that the applied UAV system equipped with a dual-camera set-up allows for the acquisition of thermal imagery with high spatial and temporal resolution that illustrates the small-scale heterogeneity of thermal surface properties. The UAV-based thermal imagery therefore provides the means for analysing patterns of LST and other surface properties with a high level of detail that cannot be obtained by traditional remote sensing methods. PMID:28515537

Estimating spatially distributed turbulent heat fluxes from high-resolution thermal imagery acquired with a UAV system.

PubMed

Brenner, Claire; Thiem, Christina Elisabeth; Wizemann, Hans-Dieter; Bernhardt, Matthias; Schulz, Karsten

2017-05-19

In this study, high-resolution thermal imagery acquired with a small unmanned aerial vehicle (UAV) is used to map evapotranspiration (ET) at a grassland site in Luxembourg. The land surface temperature (LST) information from the thermal imagery is the key input to a one-source and two-source energy balance model. While the one-source model treats the surface as a single uniform layer, the two-source model partitions the surface temperature and fluxes into soil and vegetation components. It thus explicitly accounts for the different contributions of both components to surface temperature as well as turbulent flux exchange with the atmosphere. Contrary to the two-source model, the one-source model requires an empirical adjustment parameter in order to account for the effect of the two components. Turbulent heat flux estimates of both modelling approaches are compared to eddy covariance (EC) measurements using the high-resolution input imagery UAVs provide. In this comparison, the effect of different methods for energy balance closure of the EC data on the agreement between modelled and measured fluxes is also analysed. Additionally, the sensitivity of the one-source model to the derivation of the empirical adjustment parameter is tested. Due to the very dry and hot conditions during the experiment, pronounced thermal patterns developed over the grassland site. These patterns result in spatially variable turbulent heat fluxes. The model comparison indicates that both models are able to derive ET estimates that compare well with EC measurements under these conditions. However, the two-source model, with a more complex treatment of the energy and surface temperature partitioning between the soil and vegetation, outperformed the simpler one-source model in estimating sensible and latent heat fluxes. This is consistent with findings from prior studies. For the one-source model, a time-variant expression of the adjustment parameter (to account for the difference between aerodynamic and radiometric temperature) that depends on the surface-to-air temperature gradient yielded the best agreement with EC measurements. This study showed that the applied UAV system equipped with a dual-camera set-up allows for the acquisition of thermal imagery with high spatial and temporal resolution that illustrates the small-scale heterogeneity of thermal surface properties. The UAV-based thermal imagery therefore provides the means for analysing patterns of LST and other surface properties with a high level of detail that cannot be obtained by traditional remote sensing methods.

Partial Least Square Discriminant Analysis Based on Normalized Two-Stage Vegetation Indices for Mapping Damage from Rice Diseases Using PlanetScope Datasets.

PubMed

Shi, Yue; Huang, Wenjiang; Ye, Huichun; Ruan, Chao; Xing, Naichen; Geng, Yun; Dong, Yingying; Peng, Dailiang

2018-06-11

In recent decades, rice disease co-epidemics have caused tremendous damage to crop production in both China and Southeast Asia. A variety of remote sensing based approaches have been developed and applied to map diseases distribution using coarse- to moderate-resolution imagery. However, the detection and discrimination of various disease species infecting rice were seldom assessed using high spatial resolution data. The aims of this study were (1) to develop a set of normalized two-stage vegetation indices (VIs) for characterizing the progressive development of different diseases with rice; (2) to explore the performance of combined normalized two-stage VIs in partial least square discriminant analysis (PLS-DA); and (3) to map and evaluate the damage caused by rice diseases at fine spatial scales, for the first time using bi-temporal, high spatial resolution imagery from PlanetScope datasets at a 3 m spatial resolution. Our findings suggest that the primary biophysical parameters caused by different disease (e.g., changes in leaf area, pigment contents, or canopy morphology) can be captured using combined normalized two-stage VIs. PLS-DA was able to classify rice diseases at a sub-field scale, with an overall accuracy of 75.62% and a Kappa value of 0.47. The approach was successfully applied during a typical co-epidemic outbreak of rice dwarf (Rice dwarf virus, RDV), rice blast ( Magnaporthe oryzae ), and glume blight ( Phyllosticta glumarum ) in Guangxi Province, China. Furthermore, our approach highlighted the feasibility of the method in capturing heterogeneous disease patterns at fine spatial scales over the large spatial extents.

Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011.

PubMed

Shields, Timothy; Pinchoff, Jessie; Lubinda, Jailos; Hamapumbu, Harry; Searle, Kelly; Kobayashi, Tamaki; Thuma, Philip E; Moss, William J; Curriero, Frank C

2016-05-31

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

Automated detection of slum area change in Hyderabad, India using multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Kit, Oleksandr; Lüdeke, Matthias

2013-09-01

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.

Spectral Dimensionality and Scale of Urban Radiance

NASA Technical Reports Server (NTRS)

Small, Christopher

2001-01-01

Characterization of urban radiance and reflectance is important for understanding the effects of solar energy flux on the urban environment as well as for satellite mapping of urban settlement patterns. Spectral mixture analyses of Landsat and Ikonos imagery suggest that the urban radiance field can very often be described with combinations of three or four spectral endmembers. Dimensionality estimates of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) radiance measurements of urban areas reveal the existence of 30 to 60 spectral dimensions. The extent to which broadband imagery collected by operational satellites can represent the higher dimensional mixing space is a function of both the spatial and spectral resolution of the sensor. AVIRIS imagery offers the spatial and spectral resolution necessary to investigate the scale dependence of the spectral dimensionality. Dimensionality estimates derived from Minimum Noise Fraction (MNF) eigenvalue distributions show a distinct scale dependence for AVIRIS radiance measurements of Milpitas, California. Apparent dimensionality diminishes from almost 40 to less than 10 spectral dimensions between scales of 8000 m and 300 m. The 10 to 30 m scale of most features in urban mosaics results in substantial spectral mixing at the 20 m scale of high altitude AVIRIS pixels. Much of the variance at pixel scales is therefore likely to result from actual differences in surface reflectance at pixel scales. Spatial smoothing and spectral subsampling of AVIRIS spectra both result in substantial loss of information and reduction of apparent dimensionality, but the primary spectral endmembers in all cases are analogous to those found in global analyses of Landsat and Ikonos imagery of other urban areas.

A multi-temporal fusion-based approach for land cover mapping in support of nuclear incident response

NASA Astrophysics Data System (ADS)

Sah, Shagan

An increasingly important application of remote sensing is to provide decision support during emergency response and disaster management efforts. Land cover maps constitute one such useful application product during disaster events; if generated rapidly after any disaster, such map products can contribute to the efficacy of the response effort. In light of recent nuclear incidents, e.g., after the earthquake/tsunami in Japan (2011), our research focuses on constructing rapid and accurate land cover maps of the impacted area in case of an accidental nuclear release. The methodology involves integration of results from two different approaches, namely coarse spatial resolution multi-temporal and fine spatial resolution imagery, to increase classification accuracy. Although advanced methods have been developed for classification using high spatial or temporal resolution imagery, only a limited amount of work has been done on fusion of these two remote sensing approaches. The presented methodology thus involves integration of classification results from two different remote sensing modalities in order to improve classification accuracy. The data used included RapidEye and MODIS scenes over the Nine Mile Point Nuclear Power Station in Oswego (New York, USA). The first step in the process was the construction of land cover maps from freely available, high temporal resolution, low spatial resolution MODIS imagery using a time-series approach. We used the variability in the temporal signatures among different land cover classes for classification. The time series-specific features were defined by various physical properties of a pixel, such as variation in vegetation cover and water content over time. The pixels were classified into four land cover classes - forest, urban, water, and vegetation - using Euclidean and Mahalanobis distance metrics. On the other hand, a high spatial resolution commercial satellite, such as RapidEye, can be tasked to capture images over the affected area in the case of a nuclear event. This imagery served as a second source of data to augment results from the time series approach. The classifications from the two approaches were integrated using an a posteriori probability-based fusion approach. This was done by establishing a relationship between the classes, obtained after classification of the two data sources. Despite the coarse spatial resolution of MODIS pixels, acceptable accuracies were obtained using time series features. The overall accuracies using the fusion-based approach were in the neighborhood of 80%, when compared with GIS data sets from New York State. This fusion thus contributed to classification accuracy refinement, with a few additional advantages, such as correction for cloud cover and providing for an approach that is robust against point-in-time seasonal anomalies, due to the inclusion of multi-temporal data. We concluded that this approach is capable of generating land cover maps of acceptable accuracy and rapid turnaround, which in turn can yield reliable estimates of crop acreage of a region. The final algorithm is part of an automated software tool, which can be used by emergency response personnel to generate a nuclear ingestion pathway information product within a few hours of data collection.

The spatial resolving power of earth resources satellites: A review

NASA Technical Reports Server (NTRS)

Townshend, J. R. G.

1980-01-01

The significance of spatial resolving power on the utility of current and future Earth resources satellites is critically discussed and the relative merits of different approaches in defining and estimating spatial resolution are outlined. It is shown that choice of a particular measure of spatial resolution depends strongly on the particular needs of the user. Several experiments have simulated the capabilities of future satellite systems by degradation of aircraft images. Surprisingly, many of these indicated that improvements in resolution may lead to a reduction in the classification accuracy of land cover types using computer assisted methods. However, where the frequency of boundary pixels is high, the converse relationship is found. Use of imagery dependent upon visual interpretation is likely to benefit more consistently from higher resolutions. Extraction of information from images will depend upon several other factors apart from spatial resolving power: these include characteristics of the terrain being sensed, the image processing methods that are applied as well as certain sensor characteristics.

Geometric Positioning for Satellite Imagery without Ground Control Points by Exploiting Repeated Observation.

PubMed

Ma, Zhenling; Wu, Xiaoliang; Yan, Li; Xu, Zhenliang

2017-01-26

With the development of space technology and the performance of remote sensors, high-resolution satellites are continuously launched by countries around the world. Due to high efficiency, large coverage and not being limited by the spatial regulation, satellite imagery becomes one of the important means to acquire geospatial information. This paper explores geometric processing using satellite imagery without ground control points (GCPs). The outcome of spatial triangulation is introduced for geo-positioning as repeated observation. Results from combining block adjustment with non-oriented new images indicate the feasibility of geometric positioning with the repeated observation. GCPs are a must when high accuracy is demanded in conventional block adjustment; the accuracy of direct georeferencing with repeated observation without GCPs is superior to conventional forward intersection and even approximate to conventional block adjustment with GCPs. The conclusion is drawn that taking the existing oriented imagery as repeated observation enhances the effective utilization of previous spatial triangulation achievement, which makes the breakthrough for repeated observation to improve accuracy by increasing the base-height ratio and redundant observation. Georeferencing tests using data from multiple sensors and platforms with the repeated observation will be carried out in the follow-up research.

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure and increase the robustness of the proposed algorithm. The proposed algorithm is validated with a publicly available 10-class object detection dataset.

Remote Sensing and Wetland Ecology: a South African Case Study.

PubMed

De Roeck, Els R; Verhoest, Niko E C; Miya, Mtemi H; Lievens, Hans; Batelaan, Okke; Thomas, Abraham; Brendonck, Luc

2008-05-26

Remote sensing offers a cost efficient means for identifying and monitoring wetlands over a large area and at different moments in time. In this study, we aim at providing ecologically relevant information on characteristics of temporary and permanent isolated open water wetlands, obtained by standard techniques and relatively cheap imagery. The number, surface area, nearest distance, and dynamics of isolated temporary and permanent wetlands were determined for the Western Cape, South Africa. Open water bodies (wetlands) were mapped from seven Landsat images (acquired during 1987 - 2002) using supervised maximum likelihood classification. The number of wetlands fluctuated over time. Most wetlands were detected in the winter of 2000 and 2002, probably related to road constructions. Imagery acquired in summer contained fewer wetlands than in winter. Most wetlands identified from Landsat images were smaller than one hectare. The average distance to the nearest wetland was larger in summer. In comparison to temporary wetlands, fewer, but larger permanent wetlands were detected. In addition, classification of non-vegetated wetlands on an Envisat ASAR radar image (acquired in June 2005) was evaluated. The number of detected small wetlands was lower for radar imagery than optical imagery (acquired in June 2002), probably because of deterioration of the spatial information content due the extensive pre-processing requirements of the radar image. Both optical and radar classifications allow to assess wetland characteristics that potentially influence plant and animal metacommunity structure. Envisat imagery, however, was less suitable than Landsat imagery for the extraction of detailed ecological information, as only large wetlands can be detected. This study has indicated that ecologically relevant data can be generated for the larger wetlands through relatively cheap imagery and standard techniques, despite the relatively low resolution of Landsat and Envisat imagery. For the characterisation of very small wetlands, high spatial resolution optical or radar images are needed. This study exemplifies the benefits of integrating remote sensing and ecology and hence stimulates interdisciplinary research of isolated wetlands.

Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index

NASA Astrophysics Data System (ADS)

Yang, Dedi; Chen, Jin; Zhou, Yuan; Chen, Xiang; Chen, Xuehong; Cao, Xin

2017-06-01

Plastic greenhouses (PGs) are an important agriculture development technique to protect and control the growing environment for food crops. The extensive use of PGs can change the agriculture landscape and affects the local environment. Accurately mapping and estimating the coverage of PGs is a necessity to the strategic planning of modern agriculture. Unfortunately, PG mapping over large areas is methodologically challenging, as the medium spatial resolution satellite imagery (such as Landsat data) used for analysis lacks spatial details and spectral variations. To fill the gap, the paper proposes a new plastic greenhouse index (PGI) based on the spectral, sensitivity, and separability analysis of PGs using medium spatial resolution images. In the context of the Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, the paper examines the effectiveness and capability of the proposed PGI. The results indicate that PGs in Landsat ETM+ image can be successfully detected by the PGI if the PG fraction is greater than 12% in a mixed pixel. A kappa coefficient of 0.83 and overall accuracy of 91.2% were achieved when applying the proposed PGI in the case of Weifang District, Shandong, China. These results show that the proposed index can be applied to identifying transparent PGs in atmospheric corrected Landsat image and has the potential for the digital mapping of plastic greenhouse coverage over a large area.

Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

NASA Astrophysics Data System (ADS)

Broich, Mark

Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single best Landsat images. Such an approach does not provide timely results, and cloud cover reduces the utility of map outputs. In a second study, I develop a method to exhaustively mine the recently opened Landsat archive for cloud-free observations and automatically map forest cover loss for Sumatra and Kalimantan for the 2000-2005 interval. In a comparison with a reference dataset consisting of 64 Landsat sample blocks, I show that my method, using per pixel time-series, provides more accurate forest cover loss maps for multiyear intervals than approaches using image composites. In a third study, I disaggregate Landsat-mapped forest cover loss, mapped over a multiyear interval, by year using annual forest cover loss maps generated from coarse spatial, high temporal resolution MODIS imagery. I further disaggregate and analyze forest cover loss by forest land use, and provinces. Forest cover loss trends show high spatial and temporal variability. These results underline the importance of annual mapping for the quantification of forest cover loss in Indonesia, specifically in the light of the developing Reducing Emissions from Deforestation and Forest Degradation in Developing Countries policy framework (REDD). All three studies highlight the advances in quantifying forest cover loss in the humid tropics made by integrating coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data. The three methods presented can be combined into an integrated monitoring strategy.

Detecting tents to estimate the displaced populations for post-disaster relief using high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Wang, Shifeng; So, Emily; Smith, Pete

2015-04-01

Estimating the number of refugees and internally displaced persons is important for planning and managing an efficient relief operation following disasters and conflicts. Accurate estimates of refugee numbers can be inferred from the number of tents. Extracting tents from high-resolution satellite imagery has recently been suggested. However, it is still a significant challenge to extract tents automatically and reliably from remote sensing imagery. This paper describes a novel automated method, which is based on mathematical morphology, to generate a camp map to estimate the refugee numbers by counting tents on the camp map. The method is especially useful in detecting objects with a clear shape, size, and significant spectral contrast with their surroundings. Results for two study sites with different satellite sensors and different spatial resolutions demonstrate that the method achieves good performance in detecting tents. The overall accuracy can be up to 81% in this study. Further improvements should be possible if over-identified isolated single pixel objects can be filtered. The performance of the method is impacted by spectral characteristics of satellite sensors and image scenes, such as the extent of area of interest and the spatial arrangement of tents. It is expected that the image scene would have a much higher influence on the performance of the method than the sensor characteristics.

The use of ERTS imagery in reservoir management and operation. [New England

NASA Technical Reports Server (NTRS)

Cooper, S. (Principal Investigator); Bock, P.; Horowitz, J.; Foran, D.

1975-01-01

The author has identified the following significant results. Real time data collection by orbiting satellite relay was found to be both reliable and feasible. ERTS imagery was assessed and it was shown that in most cases better spatial resolution and/or additional spectral bands would be required to satisfy NED's needs. A man-computer interactive system, using cathode ray tube display could solve the problem of an unwieldy mass of data for interpretation.

Super-Resolution Reconstruction of Remote Sensing Images Using Multifractal Analysis

PubMed Central

Hu, Mao-Gui; Wang, Jin-Feng; Ge, Yong

2009-01-01

Satellite remote sensing (RS) is an important contributor to Earth observation, providing various kinds of imagery every day, but low spatial resolution remains a critical bottleneck in a lot of applications, restricting higher spatial resolution analysis (e.g., intra-urban). In this study, a multifractal-based super-resolution reconstruction method is proposed to alleviate this problem. The multifractal characteristic is common in Nature. The self-similarity or self-affinity presented in the image is useful to estimate details at larger and smaller scales than the original. We first look for the presence of multifractal characteristics in the images. Then we estimate parameters of the information transfer function and noise of the low resolution image. Finally, a noise-free, spatial resolution-enhanced image is generated by a fractal coding-based denoising and downscaling method. The empirical case shows that the reconstructed super-resolution image performs well in detail enhancement. This method is not only useful for remote sensing in investigating Earth, but also for other images with multifractal characteristics. PMID:22291530

A Modeling Approach to Global Land Surface Monitoring with Low Resolution Satellite Imaging

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Dungan, Jennifer; Livingston, Gerry P.; Gore, Warren J. (Technical Monitor)

1998-01-01

The effects of changing land use/land cover on global climate and ecosystems due to greenhouse gas emissions and changing energy and nutrient exchange rates are being addressed by federal programs such as NASA's Mission to Planet Earth (MTPE) and by international efforts such as the International Geosphere-Biosphere Program (IGBP). The quantification of these effects depends on accurate estimates of the global extent of critical land cover types such as fire scars in tropical savannas and ponds in Arctic tundra. To address the requirement for accurate areal estimates, methods for producing regional to global maps with satellite imagery are being developed. The only practical way to produce maps over large regions of the globe is with data of coarse spatial resolution, such as Advanced Very High Resolution Radiometer (AVHRR) weather satellite imagery at 1.1 km resolution or European Remote-Sensing Satellite (ERS) radar imagery at 100 m resolution. The accuracy of pixel counts as areal estimates is in doubt, especially for highly fragmented cover types such as fire scars and ponds. Efforts to improve areal estimates from coarse resolution maps have involved regression of apparent area from coarse data versus that from fine resolution in sample areas, but it has proven difficult to acquire sufficient fine scale data to develop the regression. A method for computing accurate estimates from coarse resolution maps using little or no fine data is therefore needed.

Spectro-spatial relationship between UAV derived high resolution DEM and SWIR hyperspectral data: application to an ombrotrophic peatland

NASA Astrophysics Data System (ADS)

Arroyo-Mora, J. Pablo; Kalacska, Margaret; Lucanus, Oliver; Soffer, Raymond; Leblanc, George

2017-10-01

Peatlands cover 3% of the globe and are key ecosystems for climate regulation. To better understand the potential effects of climate change in peatlands, a major challenge is to determine the complex relationship between hydrology, microtopography, vegetation patterns, and gas exchange. Here we study the spectral and spatial relationship of microtopographic features (e.g. hollows and hummocks) and near-surface water through narrow-band spectral indices derived from hyperspectral imagery. We used a very high resolution digital elevation model (2.5 cm horizontal, 2.2 cm vertical resolution) derived from an UAV based Structure from Motion photogrammetry to map hollows and hummocks in the peatland area. We also created a 2 cm spatial resolution orthophoto mosaic to enhance the visual identification of these hollows and hummocks. Furthermore, we collected SWIR airborne hyperspectral (880-2450 nm) imagery at 1 m pixel resolution over four time periods, from April to June 2016 (phenological gradient: vegetation greening). Our results revealed an increase in the water indices values (NDWI1640 and NDWI2130) and a decrease in the moisture stress index (MSI) between April and June. In addition, for the same period the NDWI2130 shows a bimodal distribution indicating potential to quantitatively assess moisture differences between mosses and vascular plants. Our results, using the digital surface model to extract NDWI2130 values, showed significant differences between hollows and hummocks for each time period, with higher moisture values for hollows (i.e. moss dominated). However, for June, the water index for hummocks approximated the values found in hollows. Our study shows the advantages of using fine spatial and spectral scales to detect temporal trends in near surface water in a peatland.

An Evaluation of Population Density Mapping and Built up Area Estimates in Sri Lanka Using Multiple Methodologies

NASA Astrophysics Data System (ADS)

Engstrom, R.; Soundararajan, V.; Newhouse, D.

2017-12-01

In this study we examine how well multiple population density and built up estimates that utilize satellite data compare in Sri Lanka. The population relationship is examined at the Gram Niladhari (GN) level, the lowest administrative unit in Sri Lanka from the 2011 census. For this study we have two spatial domains, the whole country and a 3,500km2 sub-sample, for which we have complete high spatial resolution imagery coverage. For both the entire country and the sub-sample we examine how consistent are the existing publicly available measures of population constructed from satellite imagery at predicting population density? For just the sub-sample we examine how well do a suite of values derived from high spatial resolution satellite imagery predict population density and how does our built up area estimate compare to other publicly available estimates. Population measures were obtained from the Sri Lankan census, and were downloaded from Facebook, WorldPoP, GPW, and Landscan. Percentage built-up area at the GN level was calculated from three sources: Facebook, Global Urban Footprint (GUF), and the Global Human Settlement Layer (GHSL). For the sub-sample we have derived a variety of indicators from the high spatial resolution imagery. Using deep learning convolutional neural networks, an object oriented, and a non-overlapping block, spatial feature approach. Variables calculated include: cars, shadows (a proxy for building height), built up area, and buildings, roof types, roads, type of agriculture, NDVI, Pantex, and Histogram of Oriented Gradients (HOG) and others. Results indicate that population estimates are accurate at the higher, DS Division level but not necessarily at the GN level. Estimates from Facebook correlated well with census population (GN correlation of 0.91) but measures from GPW and WorldPop are more weakly correlated (0.64 and 0.34). Estimates of built-up area appear to be reliable. In the 32 DSD-subsample, Facebook's built- up area measure is highly correlated with our built-up measure (correlation of 0.9). Preliminary regression results based on variables selected from Lasso-regressions indicate that satellite indicators have exceptionally strong predictive power in predicting GN level population level and density with an out of sample r-squared of 0.75 and 0.72 respectively.

KOLAM: a cross-platform architecture for scalable visualization and tracking in wide-area imagery

NASA Astrophysics Data System (ADS)

Fraser, Joshua; Haridas, Anoop; Seetharaman, Guna; Rao, Raghuveer M.; Palaniappan, Kannappan

2013-05-01

KOLAM is an open, cross-platform, interoperable, scalable and extensible framework supporting a novel multi- scale spatiotemporal dual-cache data structure for big data visualization and visual analytics. This paper focuses on the use of KOLAM for target tracking in high-resolution, high throughput wide format video also known as wide-area motion imagery (WAMI). It was originally developed for the interactive visualization of extremely large geospatial imagery of high spatial and spectral resolution. KOLAM is platform, operating system and (graphics) hardware independent, and supports embedded datasets scalable from hundreds of gigabytes to feasibly petabytes in size on clusters, workstations, desktops and mobile computers. In addition to rapid roam, zoom and hyper- jump spatial operations, a large number of simultaneously viewable embedded pyramid layers (also referred to as multiscale or sparse imagery), interactive colormap and histogram enhancement, spherical projection and terrain maps are supported. The KOLAM software architecture was extended to support airborne wide-area motion imagery by organizing spatiotemporal tiles in very large format video frames using a temporal cache of tiled pyramid cached data structures. The current version supports WAMI animation, fast intelligent inspection, trajectory visualization and target tracking (digital tagging); the latter by interfacing with external automatic tracking software. One of the critical needs for working with WAMI is a supervised tracking and visualization tool that allows analysts to digitally tag multiple targets, quickly review and correct tracking results and apply geospatial visual analytic tools on the generated trajectories. One-click manual tracking combined with multiple automated tracking algorithms are available to assist the analyst and increase human effectiveness.

Crop classification using temporal stacks of multispectral satellite imagery

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Brumby, Steven P.; Chartrand, Rick; Keisler, Ryan; Longbotham, Nathan; Mertes, Carly; Skillman, Samuel W.; Warren, Michael S.

2017-05-01

The increase in performance, availability, and coverage of multispectral satellite sensor constellations has led to a drastic increase in data volume and data rate. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. The data analysis capability, however, has lagged behind storage and compute developments, and has traditionally focused on individual scene processing. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and can scale with the high-rate and dimensionality of imagery being collected. We investigate and compare the performance of pixel-level crop identification using tree-based classifiers and its dependence on both temporal and spectral features. Classification performance is assessed using as ground-truth Cropland Data Layer (CDL) crop masks generated by the US Department of Agriculture (USDA). The CDL maps contain 30m spatial resolution, pixel-level labels for around 200 categories of land cover, but are however only available post-growing season. The analysis focuses on McCook county in South Dakota and shows crop classification using a temporal stack of Landsat 8 (L8) imagery over the growing season, from April through October. Specifically, we consider the temporal L8 stack depth, as well as different normalized band difference indices, and evaluate their contribution to crop identification. We also show an extension of our algorithm to map corn and soy crops in the state of Mato Grosso, Brazil.

Comparison of C-band and Ku-band scatterometry for medium-resolution tropical forest inventory

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1993-08-01

Since 1978, AVHRR imagery from NOAA polar orbiters has provided coverage of tropical regions at this desirable resolution, but much of the imagery is plagued with heavy cloud cover typical of equatorial regions. Clearly a medium resolution radar sensor would be a useful addition to AVHRR, but none are planned to fly in the future. In contrast, scatterometers are an important radar component of many future earth remote sensing systems, but the inherent resolution of these instruments is too low (approximately equals 50 km) for monitoring earth's land surfaces. However, a recently developed image reconstruction technique can increase the spatial resolution of scatterometer data to levels (approximately equals 4 to 14 km) approaching AVHRR global area coverage (approximately equals 4 km). When reconstructed, scatterometer data may prove to be an important asset in evaluating equatorial land cover. In this paper, the authors compare the utility of reconstructed Seasat scatterometer (SASS), Ku-band microwave data to reconstructed ERS-1 C-band scatterometer imagery for discrimination and monitoring of tropical vegetation formations. In comparative classification experiments conducted on reconstructed images of Brasil, the ERS-1 C-band imagery was slightly superior to its reconstructed SASS Ku-band counterpart for discriminating between several equatorial land cover classes. A classification accuracy approaching .90 was achieved when the two scatterometer images were combined with an AVHRR normalized difference vegetation index (NDVI) image. The success of these experiments indicates that further research into reconstructed image applications to tropical forest monitoring is warranted.

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

NASA Astrophysics Data System (ADS)

Chybicki, Andrzej; Łubniewski, Zbigniew

2017-09-01

Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth's environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

Spatial structure, sampling design and scale in remotely-sensed imagery of a California savanna woodland

NASA Technical Reports Server (NTRS)

Mcgwire, K.; Friedl, M.; Estes, J. E.

1993-01-01

This article describes research related to sampling techniques for establishing linear relations between land surface parameters and remotely-sensed data. Predictive relations are estimated between percentage tree cover in a savanna environment and a normalized difference vegetation index (NDVI) derived from the Thematic Mapper sensor. Spatial autocorrelation in original measurements and regression residuals is examined using semi-variogram analysis at several spatial resolutions. Sampling schemes are then tested to examine the effects of autocorrelation on predictive linear models in cases of small sample sizes. Regression models between image and ground data are affected by the spatial resolution of analysis. Reducing the influence of spatial autocorrelation by enforcing minimum distances between samples may also improve empirical models which relate ground parameters to satellite data.

Historical Analysis of Melt Pond Fraction on Arctic Sea Ice Through the Synthesis of High- and Medium- Resolution Optical Satellite Remote Sensing.

NASA Astrophysics Data System (ADS)

Wright, N.; Polashenski, C. M.

2017-12-01

Snow, ice, and melt ponds cover the surface of the Arctic Ocean in fractions that change throughout the seasons. These surfaces exert tremendous influence over the energy balance of the Arctic Ocean by controlling the absorption of solar radiation. Here we demonstrate the use of a newly released, open source, image classification algorithm designed to identify surface features in high resolution optical satellite imagery of sea ice. Through explicitly resolving individual features on the surface, the algorithm can determine the percentage of ice that is covered by melt ponds with a high degree of certainty. We then compare observations of melt pond fraction extracted from these images with an established method of estimating melt pond fraction from medium resolution satellite images (e.g. MODIS). Because high resolution satellite imagery does not provide the spatial footprint needed to examine the entire Arctic basin, we propose a method of synthesizing both high and medium resolution satellite imagery for an improved determination of melt pond fraction across whole Arctic. We assess the historical trends of melt pond fraction in the Arctic ocean, and address the question: Is pond coverage changing in response to changing ice conditions? Furthermore, we explore the image area that must be observed in order to get a locally representative sample (i.e. the aggregate scale), and show that it is possible to determine accurate estimates of melt pond fraction by observing sample areas significantly smaller than the typical footprint of high-resolution satellite imagery.

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...

Large Scale Crop Mapping in Ukraine Using Google Earth Engine

NASA Astrophysics Data System (ADS)

Shelestov, A.; Lavreniuk, M. S.; Kussul, N.

2016-12-01

There are no globally available high resolution satellite-derived crop specific maps at present. Only coarse-resolution imagery (> 250 m spatial resolution) has been utilized to derive global cropland extent. In 2016 we are going to carry out a country level demonstration of Sentinel-2 use for crop classification in Ukraine within the ESA Sen2-Agri project. But optical imagery can be contaminated by cloud cover that makes it difficult to acquire imagery in an optimal time range to discriminate certain crops. Due to the Copernicus program since 2015, a lot of Sentinel-1 SAR data at high spatial resolution is available for free for Ukraine. It allows us to use the time series of SAR data for crop classification. Our experiment for one administrative region in 2015 showed much higher crop classification accuracy with SAR data than with optical only time series [1, 2]. Therefore, in 2016 within the Google Earth Engine Research Award we use SAR data together with optical ones for large area crop mapping (entire territory of Ukraine) using cloud computing capabilities available at Google Earth Engine (GEE). This study compares different classification methods for crop mapping for the whole territory of Ukraine using data and algorithms from GEE. Classification performance assessed using overall classification accuracy, Kappa coefficients, and user's and producer's accuracies. Also, crop areas from derived classification maps compared to the official statistics [3]. S. Skakun et al., "Efficiency assessment of multitemporal C-band Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine," IEEE Journal of Selected Topics in Applied Earth Observ. and Rem. Sens., 2015, DOI: 10.1109/JSTARS.2015.2454297. N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "The use of satellite SAR imagery to crop classification in Ukraine within JECAM project," IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1497-1500, 13-18 July 2014, Quebec City, Canada. F.J. Gallego, N. Kussul, S. Skakun, O. Kravchenko, A. Shelestov, O. Kussul, "Efficiency assessment of using satellite data for crop area estimation in Ukraine," International Journal of Applied Earth Observation and Geoinformation vol. 29, pp. 22-30, 2014.

Processing of SeaMARC swath sonar imagery

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

Pratson, L.; Malinverno, A.; Edwards, M.

1990-05-01

Side-scan swath sonar systems have become an increasingly important means of mapping the sea floor. Two such systems are the deep-towed, high-resolution SeaMARC I sonar, which has a variable swath width of up to 5 km, and the shallow-towed, lower-resolution SeaMARC II sonar, which has a swath width of 10 km. The sea-floor imagery of acoustic backscatter output by the SeaMARC sonars is analogous to aerial photographs and airborne side-looking radar images of continental topography. Geologic interpretation of the sea-floor imagery is greatly facilitated by image processing. Image processing of the digital backscatter data involves removal of noise by medianmore » filtering, spatial filtering to remove sonar scans of anomalous intensity, across-track corrections to remove beam patterns caused by nonuniform response of the sonar transducers to changes in incident angle, and contrast enhancement by histogram equalization to maximize the available dynamic range. Correct geologic interpretation requires submarine structural fabrics to be displayed in their proper locations and orientations. Geographic projection of sea-floor imagery is achieved by merging the enhanced imagery with the sonar vehicle navigation and correcting for vehicle attitude. Co-registration of bathymetry with sonar imagery introduces sea-floor relief and permits the imagery to be displayed in three-dimensional perspectives, furthering the ability of the marine geologist to infer the processes shaping formerly hidden subsea terrains.« less

Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using quickbird satellite imagery

USGS Publications Warehouse

Laba, M.; Downs, R.; Smith, S.; Welsh, S.; Neider, C.; White, S.; Richmond, M.; Philpot, W.; Baveye, P.

2008-01-01

The National Estuarine Research Reserve (NERR) program is a nationally coordinated research and monitoring program that identifies and tracks changes in ecological resources of representative estuarine ecosystems and coastal watersheds. In recent years, attention has focused on using high spatial and spectral resolution satellite imagery to map and monitor wetland plant communities in the NERRs, particularly invasive plant species. The utility of this technology for that purpose has yet to be assessed in detail. To that end, a specific high spatial resolution satellite imagery, QuickBird, was used to map plant communities and monitor invasive plants within the Hudson River NERR (HRNERR). The HRNERR contains four diverse tidal wetlands (Stockport Flats, Tivoli Bays, Iona Island, and Piermont), each with unique water chemistry (i.e., brackish, oligotrophic and fresh) and, consequently, unique assemblages of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). A maximum-likelihood classification was used to produce 20-class land cover maps for each of the four marshes within the HRNERR. Conventional contingency tables and a fuzzy set analysis served as a basis for an accuracy assessment of these maps. The overall accuracies, as assessed by the contingency tables, were 73.6%, 68.4%, 67.9%, and 64.9% for Tivoli Bays, Stockport Flats, Piermont, and Iona Island, respectively. Fuzzy assessment tables lead to higher estimates of map accuracies of 83%, 75%, 76%, and 76%, respectively. In general, the open water/tidal channel class was the most accurately mapped class and Scirpus sp. was the least accurately mapped. These encouraging accuracies suggest that high-resolution satellite imagery offers significant potential for the mapping of invasive plant species in estuarine environments. ?? 2007 Elsevier Inc. All rights reserved.

Assessment of synthetic image fidelity

NASA Astrophysics Data System (ADS)

Mitchell, Kevin D.; Moorhead, Ian R.; Gilmore, Marilyn A.; Watson, Graham H.; Thomson, Mitch; Yates, T.; Troscianko, Tomasz; Tolhurst, David J.

2000-07-01

Computer generated imagery is increasingly used for a wide variety of purposes ranging from computer games to flight simulators to camouflage and sensor assessment. The fidelity required for this imagery is dependent on the anticipated use - for example when used for camouflage design it must be physically correct spectrally and spatially. The rendering techniques used will also depend upon the waveband being simulated, spatial resolution of the sensor and the required frame rate. Rendering of natural outdoor scenes is particularly demanding, because of the statistical variation in materials and illumination, atmospheric effects and the complex geometric structures of objects such as trees. The accuracy of the simulated imagery has tended to be assessed subjectively in the past. First and second order statistics do not capture many of the essential characteristics of natural scenes. Direct pixel comparison would impose an unachievable demand on the synthetic imagery. For many applications, such as camouflage design, it is important that nay metrics used will work in both visible and infrared wavebands. We are investigating a variety of different methods of comparing real and synthetic imagery and comparing synthetic imagery rendered to different levels of fidelity. These techniques will include neural networks (ICA), higher order statistics and models of human contrast perception. This paper will present an overview of the analyses we have carried out and some initial results along with some preliminary conclusions regarding the fidelity of synthetic imagery.

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.

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.

Assessment and Mission Planning Capability For Quantitative Aerothermodynamic Flight Measurements Using Remote Imaging

NASA Technical Reports Server (NTRS)

Horvath, Thomas; Splinter, Scott; Daryabeigi, Kamran; Wood, William; Schwartz, Richard; Ross, Martin

2008-01-01

High resolution calibrated infrared imagery of vehicles during hypervelocity atmospheric entry or sustained hypersonic cruise has the potential to provide flight data on the distribution of surface temperature and the state of the airflow over the vehicle. In the early 1980 s NASA sought to obtain high spatial resolution infrared imagery of the Shuttle during entry. Despite mission execution with a technically rigorous pre-planning capability, the single airborne optical system for this attempt was considered developmental and the scientific return was marginal. In 2005 the Space Shuttle Program again sponsored an effort to obtain imagery of the Orbiter. Imaging requirements were targeted towards Shuttle ascent; companion requirements for entry did not exist. The engineering community was allowed to define observation goals and incrementally demonstrate key elements of a quantitative spatially resolved measurement capability over a series of flights. These imaging opportunities were extremely beneficial and clearly demonstrated capability to capture infrared imagery with mature and operational assets of the US Navy and the Missile Defense Agency. While successful, the usefulness of the imagery was, from an engineering perspective, limited. These limitations were mainly associated with uncertainties regarding operational aspects of data acquisition. These uncertainties, in turn, came about because of limited pre-flight mission planning capability, a poor understanding of several factors including the infrared signature of the Shuttle, optical hardware limitations, atmospheric effects and detector response characteristics. Operational details of sensor configuration such as detector integration time and tracking system algorithms were carried out ad hoc (best practices) which led to low probability of target acquisition and detector saturation. Leveraging from the qualified success during Return-to-Flight, the NASA Engineering and Safety Center sponsored an assessment study focused on increasing the probability of returning spatially resolved scientific/engineering thermal imagery. This paper provides an overview of the assessment task and the systematic approach designed to establish confidence in the ability of existing assets to reliably acquire, track and return global quantitative surface temperatures of the Shuttle during entry. A discussion of capability demonstration in support of a potential Shuttle boundary layer transition flight test is presented. Successful demonstration of a quantitative, spatially resolved, global temperature measurement on the proposed Shuttle boundary layer transition flight test could lead to potential future applications with hypersonic flight test programs within the USAF and DARPA along with flight test opportunities supporting NASA s project Constellation.

On the Role of Urban and Vegetative Land Cover in the Identification of Tornado Damage Using Dual-Resolution Multispectral Satellite Imagery

NASA Astrophysics Data System (ADS)

Kingfield, D.; de Beurs, K.

2014-12-01

It has been demonstrated through various case studies that multispectral satellite imagery can be utilized in the identification of damage caused by a tornado through the change detection process. This process involves the difference in returned surface reflectance between two images and is often summarized through a variety of ratio-based vegetation indices (VIs). Land cover type plays a large contributing role in the change detection process as the reflectance properties of vegetation can vary based on several factors (e.g. species, greenness, density). Consequently, this provides the possibility for a variable magnitude of loss, making certain land cover regimes less reliable in the damage identification process. Furthermore, the tradeoff between sensor resolution and orbital return period may also play a role in the ability to detect catastrophic loss. Moderate resolution imagery (e.g. Moderate Resolution Imaging Spectroradiometer (MODIS)) provides relatively coarse surface detail with a higher update rate which could hinder the identification of small regions that underwent a dynamic change. Alternatively, imagery with higher spatial resolution (e.g. Landsat) have a longer temporal return period between successive images which could result in natural recovery underestimating the absolute magnitude of damage incurred. This study evaluates the role of land cover type and sensor resolution on four high-end (EF3+) tornado events occurring in four different land cover groups (agriculture, forest, grassland, urban) in the spring season. The closest successive clear images from both Landsat 5 and MODIS are quality controlled for each case. Transacts of surface reflectance across a homogenous land cover type both inside and outside the damage swath are extracted. These metrics are synthesized through the calculation of six different VIs to rank the calculated change metrics by land cover type, sensor resolution and VI.

Towards a real-time wide area motion imagery system

NASA Astrophysics Data System (ADS)

Young, R. I.; Foulkes, S. B.

2015-10-01

It is becoming increasingly important in both the defence and security domains to conduct persistent wide area surveillance (PWAS) of large populations of targets. Wide Area Motion Imagery (WAMI) is a key technique for achieving this wide area surveillance. The recent development of multi-million pixel sensors has provided sensors with wide field of view replete with sufficient resolution for detection and tracking of objects of interest to be achieved across these extended areas of interest. WAMI sensors simultaneously provide high spatial and temporal resolutions, giving extreme pixel counts over large geographical areas. The high temporal resolution is required to enable effective tracking of targets. The provision of wide area coverage with high frame rates generates data deluge issues; these are especially profound if the sensor is mounted on an airborne platform, with finite data-link bandwidth and processing power that is constrained by size, weight and power (SWAP) limitations. These issues manifest themselves either as bottlenecks in the transmission of the imagery off-board or as latency in the time taken to analyse the data due to limited computational processing power.

Spatial statistical analysis of tree deaths using airborne digital imagery

NASA Astrophysics Data System (ADS)

Chang, Ya-Mei; Baddeley, Adrian; Wallace, Jeremy; Canci, Michael

2013-04-01

High resolution digital airborne imagery offers unprecedented opportunities for observation and monitoring of vegetation, providing the potential to identify, locate and track individual vegetation objects over time. Analytical tools are required to quantify relevant information. In this paper, locations of trees over a large area of native woodland vegetation were identified using morphological image analysis techniques. Methods of spatial point process statistics were then applied to estimate the spatially-varying tree death risk, and to show that it is significantly non-uniform. [Tree deaths over the area were detected in our previous work (Wallace et al., 2008).] The study area is a major source of ground water for the city of Perth, and the work was motivated by the need to understand and quantify vegetation changes in the context of water extraction and drying climate. The influence of hydrological variables on tree death risk was investigated using spatial statistics (graphical exploratory methods, spatial point pattern modelling and diagnostics).

Evaluation of ERTS-1 image sensor spatial resolution in photographic form

NASA Technical Reports Server (NTRS)

Slater, P. N. (Principal Investigator); Schowengerdt, R. A.

1975-01-01

The author has identified the following significant results. The digital Optical Transfer Function (OTF) measurements showed the following: (1) there are no significant differences in optical performance, in terms of OTF, among all four bands of the multispectral scanner, (2) no substantial changes in the OTF's of bands 4, 5, and 6 during the period November 1972 to May 1973, and (3) comparison between the photographic and digital (CCT) two-dimensional OTF's indicated a strong asymmetry in the photographic product OTF between the MSS scan direction and across scan direction. The coherent light Fourier analysis program showed the following: (1) for agricultural areas, bands 5 and 7 of the MSS are superior in terms of image definition, and therefore mapping and acreage estimation, (2) amplitude modulation in imagery from MSS bands 4 and 5 is between 65 to 90 percent of that in corresponding bands of Apollo 9 imagery (SO65), and (3) MSS band 5 imagery has a ground resolution between 55 to 75 percent of that exhibited in the corresponding band of Apollo 9 imagery (SO65).

Towards a New Assessment of Urban Areas from Local to Global Scales

NASA Astrophysics Data System (ADS)

Bhaduri, B. L.; Roy Chowdhury, P. K.; McKee, J.; Weaver, J.; Bright, E.; Weber, E.

2015-12-01

Since early 2000s, starting with NASA MODIS, satellite based remote sensing has facilitated collection of imagery with medium spatial resolution but high temporal resolution (daily). This trend continues with an increasing number of sensors and data products. Increasing spatial and temporal resolutions of remotely sensed data archives, from both public and commercial sources, have significantly enhanced the quality of mapping and change data products. However, even with automation of such analysis on evolving computing platforms, rates of data processing have been suboptimal largely because of the ever-increasing pixel to processor ratio coupled with limitations of the computing architectures. Novel approaches utilizing spatiotemporal data mining techniques and computational architectures have emerged that demonstrates the potential for sustained and geographically scalable landscape monitoring to be operational. We exemplify this challenge with two broad research initiatives on High Performance Geocomputation at Oak Ridge National Laboratory: (a) mapping global settlement distribution; (b) developing national critical infrastructure databases. Our present effort, on large GPU based architectures, to exploit high resolution (1m or less) satellite and airborne imagery for extracting settlements at global scale is yielding understanding of human settlement patterns and urban areas at unprecedented resolution. Comparison of such urban land cover database, with existing national and global land cover products, at various geographic scales in selected parts of the world is revealing intriguing patterns and insights for urban assessment. Early results, from the USA, Taiwan, and Egypt, indicate closer agreements (5-10%) in urban area assessments among databases at larger, aggregated geographic extents. However, spatial variability at local scales could be significantly different (over 50% disagreement).

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.

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

PubMed

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Duong, Nguyen Dinh; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment

PubMed Central

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Dinh Duong, Nguyen; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. PMID:24465714

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.

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.

Comparing the information content of coral reef geomorphological and biological habitat maps, Amirantes Archipelago (Seychelles), Western Indian Ocean

NASA Astrophysics Data System (ADS)

Hamylton, S.; Andréfouët, S.; Spencer, T.

2012-10-01

Increasing the use of geomorphological map products in marine spatial planning has the potential to greatly enhance return on mapping investment as they are commonly two orders of magnitude cheaper to produce than biologically-focussed maps of benthic communities and shallow substrates. The efficacy of geomorphological maps derived from remotely sensed imagery as surrogates for habitat diversity is explored by comparing two map sets of the platform reefs and atolls of the Amirantes Archipelago (Seychelles), Western Indian Ocean. One mapping campaign utilised Compact Airborne Spectrographic Imagery (19 wavebands, 1 m spatial resolution) to classify 11 islands and associated reefs into 25 biological habitat classes while the other campaign used Landsat 7 + ETM imagery (7 bands, 30 m spatial resolution) to generate maps of 14 geomorphic classes. The maps were compared across a range of characteristics, including habitat richness (number of classes mapped), diversity (Shannon-Weiner statistic) and thematic content (Cramer's V statistic). Between maps, a strong relationship was revealed for habitat richness (R2 = 0.76), a moderate relationship for class diversity and evenness (R2 = 0.63) and a variable relationship for thematic content, dependent on site complexity (V range 0.43-0.93). Geomorphic maps emerged as robust predictors of the habitat richness in the Amirantes. Such maps therefore demonstrate high potential value for informing coastal management activities and conservation planning by drawing on information beyond their own thematic content and thus maximizing the return on mapping investment.

On validating remote sensing simulations using coincident real data

NASA Astrophysics Data System (ADS)

Wang, Mingming; Yao, Wei; Brown, Scott; Goodenough, Adam; van Aardt, Jan

2016-05-01

The remote sensing community often requires data simulation, either via spectral/spatial downsampling or through virtual, physics-based models, to assess systems and algorithms. The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is one such first-principles, physics-based model for simulating imagery for a range of modalities. Complex simulation of vegetation environments subsequently has become possible, as scene rendering technology and software advanced. This in turn has created questions related to the validity of such complex models, with potential multiple scattering, bidirectional distribution function (BRDF), etc. phenomena that could impact results in the case of complex vegetation scenes. We selected three sites, located in the Pacific Southwest domain (Fresno, CA) of the National Ecological Observatory Network (NEON). These sites represent oak savanna, hardwood forests, and conifer-manzanita-mixed forests. We constructed corresponding virtual scenes, using airborne LiDAR and imaging spectroscopy data from NEON, ground-based LiDAR data, and field-collected spectra to characterize the scenes. Imaging spectroscopy data for these virtual sites then were generated using the DIRSIG simulation environment. This simulated imagery was compared to real AVIRIS imagery (15m spatial resolution; 12 pixels/scene) and NEON Airborne Observation Platform (AOP) data (1m spatial resolution; 180 pixels/scene). These tests were performed using a distribution-comparison approach for select spectral statistics, e.g., established the spectra's shape, for each simulated versus real distribution pair. The initial comparison results of the spectral distributions indicated that the shapes of spectra between the virtual and real sites were closely matched.

Snow Coverage Analysis Using ASTER over the Sierra Nevada Mountain Range

NASA Astrophysics Data System (ADS)

Ross, B.

2017-12-01

Snow has strong impacts on human behavior, state and local activities, and the economy. The Sierra Nevada snowpack is California's most important natural reservoir of water. Such snow is melting sooner and faster. A recent California drought study showed that there was a deficit of 1.5 million acre-feet of water in 2014 due to the fast melting rates. Scientists have been using the Moderate Resolution Imaging Spectrometer (MODIS) which is available at the spatial resolution of 500-meter, to analyze the changes in snow coverage. While such analysis provides us with the valuable information, it would be more beneficial to employ the imageries at a higher spatial resolution for snow studies. Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER), which acquires the high-resolution imageries ranging from 15-meter to 90-meter, has recently become freely available to the public. Our study utilized two scenes obtained from ASTER to investigate the changes in snow extent over the Sierra Nevada's mountain area for an 8-year period. These two scenes were collected on April 11, 2007 and April 16, 2015 covering the same geographic region. Normalized Difference Snow Index (NDSI) was adopted to delineate the snow coverage in each scene. Our study shows a substantial decrease of snow coverage in the studied geographic region by pixel count.

High-angular-resolution stellar imaging with occultations from the Cassini spacecraft - III. Mira

NASA Astrophysics Data System (ADS)

Stewart, Paul N.; Tuthill, Peter G.; Nicholson, Philip D.; Hedman, Matthew M.

2016-04-01

We present an analysis of spectral and spatial data of Mira obtained by the Cassini spacecraft, which not only observed the star's spectra over a broad range of near-infrared wavelengths, but was also able to obtain high-resolution spatial information by watching the star pass behind Saturn's rings. The observed spectral range of 1-5 microns reveals the stellar atmosphere in the crucial water-bands which are unavailable to terrestrial observers, and the simultaneous spatial sampling allows the origin of spectral features to be located in the stellar environment. Models are fitted to the data, revealing the spectral and spatial structure of molecular layers surrounding the star. High-resolution imagery is recovered revealing the layered and asymmetric nature of the stellar atmosphere. The observational data set is also used to confront the state-of-the-art cool opacity-sampling dynamic extended atmosphere models of Mira variables through a detailed spectral and spatial comparison, revealing in general a good agreement with some specific departures corresponding to particular spectral features.

High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier

NASA Astrophysics Data System (ADS)

Mirik, Mustafa; Ansley, R. James; Steddom, Karl; Rush, Charles M.; Michels, Gerald J.; Workneh, Fekede; Cui, Song; Elliott, Norman C.

2014-01-01

The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA, Diuraphis noxia) is an insect pest that causes damage to wheat (Triticum aestivum L.). We proposed that concentrated RWA feeding areas, referred to as "hot spots," could be identified and isolated from uninfested areas within a field for site specific aphid management using remotely sensed data. Our objectives were to (1) investigate the reflectance characteristics of infested and uninfested wheat by RWA and (2) evaluate utility of airborne hyperspectral imagery with 1-m spatial resolution for detecting, quantifying, and mapping RWA infested areas in commercial winter wheat fields using the constrained energy minimization classifier. Percent surface reflectance from uninfested wheat was lower in the visible and higher in the near infrared portions of the spectrum when compared with RWA-infested wheat. The overall classification accuracies of >89% for damage detection were achieved. These results indicate that hyperspectral imagery can be effectively used for accurate detection and quantification of RWA infestation in wheat for site-specific aphid management.

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery

PubMed Central

Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

2017-01-01

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features. PMID:28914787

A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

PubMed

Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

2017-09-15

Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

High Spatial Resolution Bidirectional Reflectance Retrieval Using Satellite Data

DTIC Science & Technology

2010-12-01

of a region of interest (ROI), also known as its revisit time. It is useful for change detection in imagery. For example, deforestation studies do...hyperspectral sensors are disadvantageous as they increase processing and increase the complexity and cost of the satellite’s operation; however

EFFECTS OF GREEN MACROALGAE ON CLASSIFICATION OF SEAGRASS IN SIDE SCAN SONAR IMAGERY

EPA Science Inventory

High resolution maps of seagrass beds are useful for monitoring estuarine condition, managing fish habitats, and modeling estuarine processes. Side scan sonar (SSS) is one method for producing spatially accurate seagrass maps, although it has not been used widely. Our team rece...

The potential of satellite spectro-imagery for monitoring CO2 emissions from large cities

NASA Astrophysics Data System (ADS)

Broquet, Grégoire; Bréon, François-Marie; Renault, Emmanuel; Buchwitz, Michael; Reuter, Maximilian; Bovensmann, Heinrich; Chevallier, Frédéric; Wu, Lin; Ciais, Philippe

2018-02-01

This study assesses the potential of 2 to 10 km resolution imagery of CO2 concentrations retrieved from the shortwave infrared measurements of a space-borne passive spectrometer for monitoring the spatially integrated emissions from the Paris area. Such imagery could be provided by missions similar to CarbonSat, which was studied as a candidate Earth Explorer 8 mission by the European Space Agency (ESA). This assessment is based on observing system simulation experiments (OSSEs) with an atmospheric inversion approach at city scale. The inversion system solves for hourly city CO2 emissions and natural fluxes, or for these fluxes per main anthropogenic sector or ecosystem, during the 6 h before a given satellite overpass. These 6 h correspond to the period during which emissions produce CO2 plumes that can be identified on the image from this overpass. The statistical framework of the inversion accounts for the existence of some prior knowledge with 50 % uncertainty on the hourly or sectorial emissions, and with ˜ 25 % uncertainty on the 6 h mean emissions, from an inventory based on energy use and carbon fuel consumption statistics. The link between the hourly or sectorial emissions and the vertically integrated column of CO2 observed by the satellite is simulated using a coupled flux and atmospheric transport model. This coupled model is built with the information on the spatial and temporal distribution of emissions from the emission inventory produced by the local air-quality agency (Airparif) and a 2 km horizontal resolution atmospheric transport model. Tests are conducted for different realistic simulations of the spatial coverage, resolution, precision and accuracy of the imagery from sun-synchronous polar-orbiting missions, corresponding to the specifications of CarbonSat and Sentinel-5 or extrapolated from these specifications. First, OSSEs are conducted with a rather optimistic configuration in which the inversion system is perfectly informed about the statistics of the limited number of error sources. These OSSEs indicate that the image resolution has to be finer than 4 km to decrease the uncertainty in the 6 h mean emissions by more than 50 %. More complex experiments assess the impact of more realistic error estimates that current inversion methods do not properly account for, in particular, the systematic measurement errors with spatially correlated patterns. These experiments highlight the difficulty to improve current knowledge on CO2 emissions for urban areas like Paris with CO2 observations from satellites, and call for more technological innovations in the remote sensing of vertically integrated columns of CO2 and in the inversion systems that exploit it.

Coastal and Inland Water Applications of High Resolution Optical Satellite Data from Landsat-8 and Sentinel-2

NASA Astrophysics Data System (ADS)

Vanhellemont, Q.

2016-02-01

Since the launch of Landsat-8 (L8) in 2013, a joint NASA/USGS programme, new applications of high resolution imagery for coastal and inland waters have become apparent. The optical imaging instrument on L8, the Operational Land Imager (OLI), is much improved compared to its predecessors on L5 and L7, especially with regards to SNR and digitization, and is therefore well suited for retrieving water reflectances and derived parameters such as turbidity and suspended sediment concentration. In June 2015, the European Space Agency (ESA) successfully launched a similar instrument, the MultiSpectral Imager (MSI), on board of Sentinel-2A (S2A). Imagery from both L8 and S2A are free of charge and publicly available (S2A starting at the end of 2015). Atmospheric correction schemes and processing software is under development in the EC-FP7 HIGHROC project. The spatial resolution of these instruments (10-60 m) is a great improvement over typical moderate resolution ocean colour sensors such as MODIS and MERIS (0.25 - 1 km). At higher resolution, many more lakes, rivers, ports and estuaries are spatially resolved, and can thus now be studied using satellite data, unlocking potential for mandatory monitoring e.g. under European Directives such as the Marine Strategy Framework Directive and the Water Framework Directive. We present new applications of these high resolution data, such as monitoring of offshore constructions, wind farms, sediment transport, dredging and dumping, shipping and fishing activities. The spatial variability at sub moderate resolution (0.25 - 1 km) scales can be assessed, as well as the impact of sub grid scale variability (including ships and platforms used for validation) on the moderate pixel retrieval. While the daily revisit time of the moderate resolution sensors is vastly superior to those of the high resolution satellites, at the equator respectively 16 and 10 days for L8 and S2A, the low revisit times can be partially mitigated by combining data streams. Time-series of L8 and S2A imagery are presented to show the power of combining the two satellite missions. With the launch of Sentinel-2B (expected mid-2016), the time-series will be extended with another high resolution sensor. S2B will be on the same orbit as S2A, spaced 180 degrees apart, bringing the S2A+B combined revisit time down to 5 days.

Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy)

PubMed Central

Cavalli, Rosa Maria; Fusilli, Lorenzo; Pascucci, Simone; Pignatti, Stefano; Santini, Federico

2008-01-01

This study aims at comparing the capability of different sensors to detect land cover materials within an historical urban center. The main objective is to evaluate the added value of hyperspectral sensors in mapping a complex urban context. In this study we used: (a) the ALI and Hyperion satellite data, (b) the LANDSAT ETM+ satellite data, (c) MIVIS airborne data and (d) the high spatial resolution IKONOS imagery as reference. The Venice city center shows a complex urban land cover and therefore was chosen for testing the spectral and spatial characteristics of different sensors in mapping the urban tissue. For this purpose, an object-oriented approach and different common classification methods were used. Moreover, spectra of the main anthropogenic surfaces (i.e. roofing and paving materials) were collected during the field campaigns conducted on the study area. They were exploited for applying band-depth and sub-pixel analyses to subsets of Hyperion and MIVIS hyperspectral imagery. The results show that satellite data with a 30m spatial resolution (ALI, LANDSAT ETM+ and HYPERION) are able to identify only the main urban land cover materials. PMID:27879879

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

An automated tool for a daily harmful algal bloom monitoring using MODIS imagery downscaled to 250 meters spatial resolution

NASA Astrophysics Data System (ADS)

El Alem, A.

2016-12-01

Harmful algal bloom (HAB) causes negative impacts to other organisms by producing natural toxins, mechanical damage to other micro-organisms, or simply by degrading waters quality. Contaminated waters could expose several billions of population to serious intoxications problems. Traditionally, HAB monitoring is made with standard methods limited to a restricted network of sampling points. However, rapid evolution of HABs makes it difficult to monitor their variation in time and space, threating then public safety. Daily monitoring is then the best way to control and to mitigate their harmful effect upon population, particularly for sources feeding cities. Recently, an approach for estimating chlorophyll-a (Chl-a) concentration, as a proxy of HAB presence, in inland waters based MODIS imagery downscaled to 250 meters spatial resolution was developed. Statistical evaluation of the developed approach highlighted the accuracy of Chl-a estimate with a R2 = 0.98, a relative RMSE of 15%, a relative BIAS of -2%, and a relative NASH of 0.95. Temporal resolution of MODIS sensor allows then a daily monitoring of HAB spatial distribution for inland waters of more than 2.25 Km2 of surface. Groupe-Hemisphere, a company specialized in environmental and sustainable planning in Quebec, has shown a great interest to the developed approach. Given the complexity of the preprocessing (geometric and atmospheric corrections as well as downscaling spatial resolution) and processing (Chl-a estimate) of images, a standalone application under the MATLAB's GUI environment was developed. The application allows an automated process for all preprocessing and processing steps. Outputs produced by the application for end users, many of whom may be decision makers or policy makers in the public and private sectors, allows a near-real time monitoring of water quality for a more efficient management.

Portable Imagery Quality Assessment Test Field for Uav Sensors

NASA Astrophysics Data System (ADS)

Dąbrowski, R.; Jenerowicz, A.

2015-08-01

Nowadays the imagery data acquired from UAV sensors are the main source of all data used in various remote sensing applications, photogrammetry projects and in imagery intelligence (IMINT) as well as in other tasks as decision support. Therefore quality assessment of such imagery is an important task. The research team from Military University of Technology, Faculty of Civil Engineering and Geodesy, Geodesy Institute, Department of Remote Sensing and Photogrammetry has designed and prepared special test field- The Portable Imagery Quality Assessment Test Field (PIQuAT) that provides quality assessment in field conditions of images obtained with sensors mounted on UAVs. The PIQuAT consists of 6 individual segments, when combined allow for determine radiometric, spectral and spatial resolution of images acquired from UAVs. All segments of the PIQuAT can be used together in various configurations or independently. All elements of The Portable Imagery Quality Assessment Test Field were tested in laboratory conditions in terms of their radiometry and spectral reflectance characteristics.

Capability of Hyperspectral data in Spatial Variability Distribution of Chlorophyll and Water Stress in Rice Agriculture System

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2016-12-01

Abstract : The mapping and analysis of spatial variability within the field is a challenging task. However, field variability of a single vegetation cover does not give satisfactory results mainly due to low spectral resolution and non-availability of remote sensing data. From the NASA Earth Observing-1 (EO-1) satellite data, spatial distribution of biophysical parameters like chlorophyll and relative water content in a rice agriculture system is carried out in the present study. Hyperion L1R product composed of 242 spectral bands with 30m spatial resolution was acquired for Assam, India. This high dimensional data is allowed for pre-processing to get an atmospherically corrected imagery. Moreover, ground based hyperspectral measurements are collected from experimental rice fields from the study site using hand held ASD spectroradiometer (350-1050 nm). Published indices specifically designed for chlorophyll (OASVI, mSR, and MTCI indices) and water content (WI and WBI indices) are selected based on stastical performance of the in-situ hyperspectral data. Index models are established for the respective biophysical parameters and observed that the aforementioned indices followed different linear and nonlinear relationships which are completely different from the published indices. By employing the presently developed relationships, spatial variation of total chlorophyll and water stress are mapped for a rice agriculture system from Hyperion imagery. The findings showed that, the variation of chlorophyll and water content ranged from 1.77-10.61mg/g and 40-90% respectively for the studied rice agriculture system. The spatial distribution of these parameters resulted from presently developed index models are well captured from Hyperion imagery and they have good agreement with observed field based chlorophyll (1.14-7.26 mg/g) and water content (60-95%) of paddy crop. This study can be useful in providing essential information to assess the paddy field heterogeneity in an agriculture system. Keywords: Paddy crop, vegetation index, hyperspectral data, chlorophyll, water content

The investigation of classification methods of high-resolution imagery

Treesearch

Tracey S. Frescino; Gretchen G. Moisen; Larry DeBlander; Michel Guerin

2007-01-01

As remote-sensing technology advances, high-resolution imagery, such as Quickbird and photography from the National Agriculture Imagery Program (NAIP), is becoming more readily available for use in forestry applications. Quickbird imagery is currently the highest resolution imagery commercially available. It consists of 2.44-m (8-ft) resolution multispectral bands...

Reconstructing recent volcanic histories from high-resolution AUV sidescan sonar imagery

NASA Astrophysics Data System (ADS)

Yeo, I. A.

2016-12-01

Detecting high-resolution differences in age between young basaltic lava flows on the seafloor is notoriously difficult. However, using sediment thickness as a proxy for age it is possible to derive information on spatial extents, surface morphologies and lava flow age simultaneously using high-resolution sidescan sonar imagery. Ground truthing of this new method on cruise POS502 (July 2016) using photogrammetry from ROV cameras has provided constraints on the method allowing the detailed morphological changes and sediment cover thicknesses to be calibrated to produce reliable, quantitative ages for individual flow units. Sediment thickness is shown to be the primary controlling factor in backscatter intensity in most cases, although sediment redistribution by different flow morphologies can also affect the recorded reflection amplitudes. Seafloor lava flows were found to be very morphologically complicated on small scales, which may explain their relative unimportance when amplitude values are averaged over several tens of meters.

Preliminary analysis of remote infrared imagery of shuttle during entry: An aerothermodynamic flight experiment

NASA Technical Reports Server (NTRS)

Swenson, B. L.; Edsinger, L. E.

1977-01-01

The preliminary feasibility of remote high-resolution infrared imagery of the space shuttle orbiter lower surface during entry to obtain accurate measurements of aerodynamic heat transfer to that vehicle was examined. In general, it was determined that such such images can be taken from an existing aircraft/telescope system (the C-141 AIRO) with a minimum modification or addition of systems using available technology. These images will have a spatial resolution of about 0.3 m and a temperature resolution much better than 2.5 percent. The data from these images will be at conditions and at a scale not reproducible in ground based facilities and should aid in the reduction of the prudent factors of safety required to account for phenomenological uncertainties on the thermal protection system design. Principal phenomena to be observed include laminar heating, boundary-layer transition, turbulent heating, surface catalysis, and flow separation and reattachment.

Retrieval of total suspended matter concentrations from high resolution WorldView-2 imagery: a case study of inland rivers

NASA Astrophysics Data System (ADS)

Shi, Liangliang; Mao, Zhihua; Wang, Zheng

2018-02-01

Satellite imagery has played an important role in monitoring water quality of lakes or coastal waters presently, but scarcely been applied in inland rivers. This paper presents an attempt of feasibility to apply regression model to quantify and map the concentrations of total suspended matter (CTSM) in inland rivers which have a large scale of spatial and a high CTSM dynamic range by using high resolution satellite remote sensing data, WorldView-2. An empirical approach to quantify CTSM by integrated use of high resolution WorldView-2 multispectral data and 21 in situ CTSM measurements. Radiometric correction, geometric and atmospheric correction involved in image processing procedure is carried out for deriving the surface reflectance to correlate the CTSM and satellite data by using single-variable and multivariable regression technique. Results of regression model show that the single near-infrared (NIR) band 8 of WorldView-2 have a relative strong relationship (R2=0.93) with CTSM. Different prediction models were developed on various combinations of WorldView-2 bands, the Akaike Information Criteria approach was used to choose the best model. The model involving band 1, 3, 5, and 8 of WorldView-2 had a best performance, whose R2 reach to 0.92, with SEE of 53.30 g/m3. The spatial distribution maps were produced by using the best multiple regression model. The results of this paper indicated that it is feasible to apply the empirical model by using high resolution satellite imagery to retrieve CTSM of inland rivers in routine monitoring of water quality.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

PubMed Central

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

Effects of image spatial and radiometric resolutions on the detection of cotton plants

USDA-ARS?s Scientific Manuscript database

Accurate and timely detection of volunteer and regrowth cotton plants is important for the eradication of boll weevils in south Texas. Airborne remote sensing imagery has the potential to identify volunteer and regrowth cotton plants over large geographic regions. The objective of this study was to ...

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...

What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?

USDA-ARS?s Scientific Manuscript database

Civilian applications of unmanned aircraft systems (UAS, also called drones) are rapidly expanding into crop production. UAS acquire high spatial resolution remote sensing imagery that can be used three different ways in agriculture. One is to assist crop scouts looking for problems in crop fields....

Assessing Field-Specific Risk of Soybean Sudden Death Syndrome Using Satellite Imagery in Iowa.

PubMed

Yang, S; Li, X; Chen, C; Kyveryga, P; Yang, X B

2016-08-01

Moderate resolution imaging spectroradiometer (MODIS) satellite imagery from 2004 to 2013 were used to assess the field-specific risks of soybean sudden death syndrome (SDS) caused by Fusarium virguliforme in Iowa. Fields with a high frequency of significant decrease (>10%) of the normalized difference vegetation index (NDVI) observed in late July to middle August on historical imagery were hypothetically considered as high SDS risk. These high-risk fields had higher slopes and shorter distances to flowlines, e.g., creeks and drainages, particularly in the Des Moines lobe. Field data in 2014 showed a significantly higher SDS level in the high-risk fields than fields selected without considering NDVI information. On average, low-risk fields had 10 times lower F. virguliforme soil density, determined by quantitative polymerase chain reaction, compared with other surveyed fields. Ordinal logistic regression identified positive correlations between SDS and slope, June NDVI, and May maximum temperature, but high June maximum temperature hindered SDS. A modeled SDS risk map showed a clear trend of potential disease occurrences across Iowa. Landsat imagery was analyzed similarly, to discuss the ability to utilize higher spatial resolution data. The results demonstrated the great potential of both MODIS and Landsat imagery for SDS field-specific risk assessment.

Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery

USGS Publications Warehouse

Garrity, Steven R.; Allen, Craig D.; Brumby, Steven P.; Gangodagamage, Chandana; McDowell, Nate G.; Cai, D. Michael

2013-01-01

Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. Here we assess the capability of high spatial resolution satellite imagery for tree mortality detection in a southwestern U.S. mixed species woodland using archived satellite images acquired prior to mortality and well after dead trees had dropped their leaves. We developed a multistep classification approach that uses: supervised masking of non-tree image elements; bi-temporal (pre- and post-mortality) differencing of normalized difference vegetation index (NDVI) and red:green ratio (RGI); and unsupervised multivariate clustering of pixels into live and dead tree classes using a Gaussian mixture model. Classification accuracies were improved in a final step by tuning the rules of pixel classification using the posterior probabilities of class membership obtained from the Gaussian mixture model. Classifications were produced for two images acquired post-mortality with overall accuracies of 97.9% and 98.5%, respectively. Classified images were combined with land cover data to characterize the spatiotemporal characteristics of tree mortality across areas with differences in tree species composition. We found that 38% of tree crown area was lost during the drought period between 2002 and 2006. The majority of tree mortality during this period was concentrated in piñon-juniper (Pinus edulis-Juniperus monosperma) woodlands. An additional 20% of the tree canopy died or was removed between 2006 and 2011, primarily in areas experiencing wildfire and management activity. -Our results demonstrate that unsupervised clustering of bi-temporal NDVI and RGI differences can be used to detect tree mortality resulting from numerous causes and in several forest cover types.

Benefits and shortcomings of non-destructive benthic imagery for monitoring hard-bottom habitats.

PubMed

Beisiegel, Kolja; Darr, Alexander; Gogina, Mayya; Zettler, Michael L

2017-08-15

Hard-bottom habitats with complex topography and fragile epibenthic communities are still not adequately considered in benthic monitoring programs, despite their potential ecological importance. While indicators of ecosystem health are defined by major EU directives, methods commonly used to measure them are deficient in quantification of biota on hard surfaces. We address the suitability of seafloor imaging for monitoring activities. We compared the ability of high-resolution imagery and physical sampling methods (grab, dredge, SCUBA-diving) to detect taxonomic and functional components of epibenthos. Results reveal that (1) with minimal habitat disturbance on large spatial scales, imagery provides valuable, cost-effective assessment of rocky reef habitat features and community structure, (2) despite poor taxonomic resolution, image-derived data for habitat-forming taxa might be sufficient to infer richness of small sessile and mobile fauna, (3) physical collections are necessary to develop a robust record of species richness, including species-level taxonomic identifications, and to establish a baseline. Copyright © 2017. Published by Elsevier Ltd.

California coastal processes study: Skylab. [San Pablo and San Francisco Bays

NASA Technical Reports Server (NTRS)

Pirie, D. M.; Steller, D. D. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In San Pablo Bay, the patterns of dredged sediment discharges were plotted over a three month period. It was found that lithogenous particles, kept in suspension by the fresh water from the Sacramento-San Joaquin, were transported downstream to the estuarine area at varying rates depending on the river discharge level. Skylab collected California coastal imagery at limited times and not at constant intervals. Resolution, however, helped compensate for lack of coverage. Increased spatial and spectral resolution provided details not possible utilizing Landsat imagery. The S-192 data was reformatted; band by band image density stretching was utilized to enhance sediment discharge patterns entrainment, boundaries, and eddys. The 26 January 1974 Skylab 4 imagery of San Francisco Bay was taken during an exceptionally high fresh water and suspended sediment discharge period. A three pronged surface sediment pattern was visible where the Sacramento-San Joaquin Rivers entered San Pablo Bay through Carquinez Strait.

Generating High-Temporal and Spatial Resolution TIR Image Data

NASA Astrophysics Data System (ADS)

Herrero-Huerta, M.; Lagüela, S.; Alfieri, S. M.; Menenti, M.

2017-09-01

Remote sensing imagery to monitor global biophysical dynamics requires the availability of thermal infrared data at high temporal and spatial resolution because of the rapid development of crops during the growing season and the fragmentation of most agricultural landscapes. Conversely, no single sensor meets these combined requirements. Data fusion approaches offer an alternative to exploit observations from multiple sensors, providing data sets with better properties. A novel spatio-temporal data fusion model based on constrained algorithms denoted as multisensor multiresolution technique (MMT) was developed and applied to generate TIR synthetic image data at both temporal and spatial high resolution. Firstly, an adaptive radiance model is applied based on spectral unmixing analysis of . TIR radiance data at TOA (top of atmosphere) collected by MODIS daily 1-km and Landsat - TIRS 16-day sampled at 30-m resolution are used to generate synthetic daily radiance images at TOA at 30-m spatial resolution. The next step consists of unmixing the 30 m (now lower resolution) images using the information about their pixel land-cover composition from co-registered images at higher spatial resolution. In our case study, TIR synthesized data were unmixed to the Sentinel 2 MSI with 10 m resolution. The constrained unmixing preserves all the available radiometric information of the 30 m images and involves the optimization of the number of land-cover classes and the size of the moving window for spatial unmixing. Results are still being evaluated, with particular attention for the quality of the data streams required to apply our approach.

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.

Fully Convolutional Network Based Shadow Extraction from GF-2 Imagery

NASA Astrophysics Data System (ADS)

Li, Z.; Cai, G.; Ren, H.

2018-04-01

There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review

PubMed Central

Mosleh, Mostafa K.; Hassan, Quazi K.; Chowdhury, Ehsan H.

2015-01-01

Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World's arable land area during 2012. Rice provided ∼19% of the global dietary energy in recent times and its annual average consumption per capita was ∼65 kg during 2010–2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations. PMID:25569753

Application of remote sensors in mapping rice area and forecasting its production: a review.

PubMed

Mosleh, Mostafa K; Hassan, Quazi K; Chowdhury, Ehsan H

2015-01-05

Rice is one of the staple foods for more than three billion people worldwide. Rice paddies accounted for approximately 11.5% of the World's arable land area during 2012. Rice provided ~19% of the global dietary energy in recent times and its annual average consumption per capita was ~65 kg during 2010-2011. Therefore, rice area mapping and forecasting its production is important for food security, where demands often exceed production due to an ever increasing population. Timely and accurate estimation of rice areas and forecasting its production can provide invaluable information for governments, planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. The aim of this paper was to review the applicability of the remote sensing-based imagery for rice area mapping and forecasting its production. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery have allowed us timely collection of information on the growth and development stages of the rice crop. For elaborative understanding of the application of remote sensing sensors, following issues were described: the rice area mapping and forecasting its production using optical and microwave imagery, synergy between remote sensing-based methods and other developments, and their implications as an operational one. The overview of the studies to date indicated that remote sensing-based methods using optical and microwave imagery found to be encouraging. However, there were having some limitations, such as: (i) optical remote sensing imagery had relatively low spatial resolution led to inaccurate estimation of rice areas; and (ii) radar imagery would suffer from speckles, which potentially would degrade the quality of the images; and also the brightness of the backscatters were sensitive to the interacting surface. In addition, most of the methods used in forecasting rice yield were empirical in nature, so thus it would require further calibration and validation prior to implement over other geographical locations.

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.

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.

Sea Ice Pressure Ridge Height Distributions for the Arctic Ocean in Winter, Just Prior to Melt

NASA Astrophysics Data System (ADS)

Duncan, K.; Farrell, S. L.; Richter-Menge, J.; Hutchings, J.; Dominguez, R.; Connor, L. N.

2016-12-01

Pressure ridges are one of the most dominant morphological features of the Arctic sea ice pack. An impediment to navigation, pressure ridges are also of climatological interest since they impact the mass, energy and momentum transfer budgets for the Arctic Ocean. Understanding the regional and seasonal distributions of ridge sail heights, and their variability, is important for quantifying total sea ice mass, and for improved treatment of sea ice dynamics in high-resolution numerical models. Observations of sail heights from airborne and ship-based platforms have been documented in previous studies, however studies with both high spatial and temporal resolution, across multiple regions of the Arctic, are only recently possible with the advent of dedicated airborne surveys of the Arctic Ocean. In this study we present results from the high-resolution Digital Mapping System (DMS), flown as part of NASA's Operation IceBridge missions. We use DMS imagery to calculate ridge sail heights, derived from the shadows they cast combined with the solar elevation angle and the known pixel size of each image. Our analyses describe sea ice conditions at the end of winter, during the months of March and April, over a period spanning seven years, from 2010 to 2016. The high spatial resolution (0.1m) and temporal extent (seven years) of the DMS data set provides, for the first time, the full sail-height distributions of both first-year and multi-year sea ice. We present the inter-annual variability in sail height distributions for both the Central Arctic and the Beaufort and Chukchi Seas. We validate our results via comparison with spatially coincident high-resolution SAR imagery and airborne laser altimeter elevations.

Land use change detection based on multi-date imagery from different satellite sensor systems

NASA Technical Reports Server (NTRS)

Stow, Douglas A.; Collins, Doretta; Mckinsey, David

1990-01-01

An empirical study is conducted to assess the accuracy of land use change detection using satellite image data acquired ten years apart by sensors with differing spatial resolutions. The primary goals of the investigation were to (1) compare standard change detection methods applied to image data of varying spatial resolution, (2) assess whether to transform the raster grid of the higher resolution image data to that of the lower resolution raster grid or vice versa in the registration process, (3) determine if Landsat/Thermatic Mapper or SPOT/High Resolution Visible multispectral data provide more accurate detection of land use changes when registered to historical Landsat/MSS data. It is concluded that image ratioing of multisensor, multidate satellite data produced higher change detection accuracies than did principal components analysis, and that it is useful as a land use change enhancement method.

Unsupervised individual tree crown detection in high-resolution satellite imagery

DOE PAGES

Skurikhin, Alexei N.; McDowell, Nate G.; Middleton, Richard S.

2016-01-26

Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. Asmore » a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. Furthermore, the method also demonstrates significant potential for further improvement.« less

Unsupervised individual tree crown detection in high-resolution satellite imagery

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

Skurikhin, Alexei N.; McDowell, Nate G.; Middleton, Richard S.

Rapidly and accurately detecting individual tree crowns in satellite imagery is a critical need for monitoring and characterizing forest resources. We present a two-stage semiautomated approach for detecting individual tree crowns using high spatial resolution (0.6 m) satellite imagery. First, active contours are used to recognize tree canopy areas in a normalized difference vegetation index image. Given the image areas corresponding to tree canopies, we then identify individual tree crowns as local extrema points in the Laplacian of Gaussian scale-space pyramid. The approach simultaneously detects tree crown centers and estimates tree crown sizes, parameters critical to multiple ecosystem models. Asmore » a demonstration, we used a ground validated, 0.6 m resolution QuickBird image of a sparse forest site. The two-stage approach produced a tree count estimate with an accuracy of 78% for a naturally regenerating forest with irregularly spaced trees, a success rate equivalent to or better than existing approaches. In addition, our approach detects tree canopy areas and individual tree crowns in an unsupervised manner and helps identify overlapping crowns. Furthermore, the method also demonstrates significant potential for further improvement.« less

Tracking fine-scale seasonal evolution of surface water extent in Central Alaska and the Canadian Shield

NASA Astrophysics Data System (ADS)

Cooley, S. W.; Smith, L. C.; Pitcher, L. H.; Pavelsky, T.; Topp, S.

2017-12-01

Quantifying spatial and temporal variability in surface water storage at high latitudes is critical for assessing environmental sensitivity to climate change. Traditionally the tradeoff between high spatial and high temporal resolution space-borne optical imagery has limited the ability to track fine-scale changes in surface water extent. However, the recent launch of hundreds of earth-imaging CubeSats by commercial satellite companies such as Planet opens up new possibilities for monitoring surface water from space. In this study we present a comparison of seasonal evolution of surface water extent in two study areas with differing geologic, hydrologic and permafrost regimes, namely, the Yukon Flats in Central Alaska and the Canadian Shield north of Yellowknife, N.W.T. Using near-daily 3m Planet CubeSat imagery, we track individual lake surface area from break-up to freeze-up during summer 2017 and quantify the spatial and temporal variability in inundation extent. We validate our water delineation method and inundation extent time series using WorldView imagery, coincident in situ lake shoreline mapping and pressure transducer data for 19 lakes in the Northwest Territories and Alaska collected during the NASA Arctic Boreal Vulnerability Experiment (ABoVE) 2017 field campaign. The results of this analysis demonstrate the value of CubeSat imagery for dynamic surface water research particularly at high latitudes and illuminate fine-scale drivers of cold regions surface water extent.

Science with High Spatial Resolution Far-Infrared Data

NASA Technical Reports Server (NTRS)

Terebey, Susan (Editor); Mazzarella, Joseph M. (Editor)

1994-01-01

The goal of this workshop was to discuss new science and techniques relevant to high spatial resolution processing of far-infrared data, with particular focus on high resolution processing of IRAS data. Users of the maximum correlation method, maximum entropy, and other resolution enhancement algorithms applicable to far-infrared data gathered at the Infrared Processing and Analysis Center (IPAC) for two days in June 1993 to compare techniques and discuss new results. During a special session on the third day, interested astronomers were introduced to IRAS HIRES processing, which is IPAC's implementation of the maximum correlation method to the IRAS data. Topics discussed during the workshop included: (1) image reconstruction; (2) random noise; (3) imagery; (4) interacting galaxies; (5) spiral galaxies; (6) galactic dust and elliptical galaxies; (7) star formation in Seyfert galaxies; (8) wavelet analysis; and (9) supernova remnants.

High-resolution land cover classification using low resolution global data

NASA Astrophysics Data System (ADS)

Carlotto, Mark J.

2013-05-01

A fusion approach is described that combines texture features from high-resolution panchromatic imagery with land cover statistics derived from co-registered low-resolution global databases to obtain high-resolution land cover maps. The method does not require training data or any human intervention. We use an MxN Gabor filter bank consisting of M=16 oriented bandpass filters (0-180°) at N resolutions (3-24 meters/pixel). The size range of these spatial filters is consistent with the typical scale of manmade objects and patterns of cultural activity in imagery. Clustering reduces the complexity of the data by combining pixels that have similar texture into clusters (regions). Texture classification assigns a vector of class likelihoods to each cluster based on its textural properties. Classification is unsupervised and accomplished using a bank of texture anomaly detectors. Class likelihoods are modulated by land cover statistics derived from lower resolution global data over the scene. Preliminary results from a number of Quickbird scenes show our approach is able to classify general land cover features such as roads, built up area, forests, open areas, and bodies of water over a wide range of scenes.

Monitoring of oil pollution in the Arabian Gulf based on medium resolution satellite imagery

NASA Astrophysics Data System (ADS)

Zhao, J.; Ghedira, H.

2013-12-01

A large number of inland and offshore oil fields are located in the Arabian Gulf where about 25% of the world's oil is produced by the countries surrounding the Arabian Gulf region. Almost all of this oil production is shipped by sea worldwide through the Strait of Hormuz making the region vulnerable to environmental and ecological threats that might arise from accidental or intentional oil spills. Remote sensing technologies have the unique capability to detect and monitor oil pollutions over large temporal and spatial scales. Synoptic satellite imaging can date back to 1972 when Landsat-1 was launched. Landsat satellite missions provide long time series of imagery with a spatial resolution of 30 m. MODIS sensors onboard NASA's Terra and Aqua satellites provide a wide and frequent coverage at medium spatial resolution, i.e. 250 m and 500, twice a day. In this study, the capability of medium resolution MODIS and Landsat data in detecting and monitoring oil pollutions in the Arabian Gulf was tested. Oil spills and slicks show negative or positive contrasts in satellite derived RGB images compared with surrounding clean waters depending on the solar/viewing geometry, oil thickness and evolution, etc. Oil-contaminated areas show different spectral characteristics compared with surrounding waters. Rayleigh-corrected reflectance at the seven medium resolution bands of MODIS is lower in oil affected areas. This is caused by high light absorption of oil slicks. 30-m Landsat image indicated the occurrence of oil spill on May 26 2000 in the Arabian Gulf. The oil spill showed positive contrast and lower temperature than surrounding areas. Floating algae index (FAI) images are also used to detect oil pollution. Oil-contaminated areas were found to have lower FAI values. To track the movement of oil slicks found on October 21 2007, ocean circulations from a HYCOM model were examined and demonstrated that the oil slicks were advected toward the coastal areas of United Arab Emirates (UAE). This can help to enable an early alarm for oil pollution and minimize the potential adverse effects. Remote sensing provides an effective tool for monitoring oil pollution. Medium resolution MODIS and Landsat data have shown to be effective in detecting oil pollution over small areas. Combined with remote sensing imagery, ocean circulation models demonstrate their unique value for developing a warning and forecasting system for oil pollution management.

Estuarine Landcover Along the Lower Columbia River Estuary Determined from Compact Ariborne Spectrographic Imager (CASI) Imagery, Technical Report 2003.

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

Garono, Ralph; Robinson, Rob

2003-10-01

Developing an understanding of the distribution and changes in estuarine and riparian habitats is critical to the management of biological resources in the lower Columbia River. In a recently completed comprehensive ecosystem protection and enhancement plan for the lower Columbia River Estuary (CRE), Jerrick (1999) identified habitat loss and modification as one of the key threats to the integrity of the CRE ecosystem. This management plan called for an inventory of habitats as key first step in the CRE long-term restoration effort. While previous studies have produced useful data sets depicting habitat cover types along portions of the lower CREmore » (Thomas, 1980; Thomas, 1983; Graves et al., 1995; NOAA, 1997; Allen, 1999), no single study has produced a description of the habitats for the entire CRE. Moreover, the previous studies differed in data sources and methodologies making it difficult to merge data or to make temporal comparisons. Therefore, the Lower Columbia River Estuary Partnership (Estuary Partnership) initiated a habitat cover mapping project in 2000. The goal of this project was to produce a data set depicting the current habitat cover types along the lower Columbia River, from its mouth to the Bonneville Dam, a distance of {approx}230-km (Fig. 1) using both established and emerging remote sensing techniques. For this project, we acquired two types of imagery, Landsat 7 ETM+ and Compact Airborne Spectrographic Imager (CASI). Landsat and CASI imagery differ in spatial and spectral resolution: the Landsat 7 ETM+ sensor collects reflectance data in seven spectral bands with a spatial resolution of 30-m and the CASI sensor collects reflectance data in 19 bands (in our study) with a spatial resolution of 1.5-m. We classified both sets of imagery and produced a spatially linked, hierarchical habitat data set for the entire CRE and its floodplain. Landsat 7 ETM+ classification results are presented in a separate report (Garono et al., 2003). This report presents classification results from analysis of the CASI imagery. Data sets produced for this project from both types of imagery fill a critical information gap by creating a current description of the condition and extent of estuarine habitat cover types along the lower Columbia River. Results from this study will be used by the Estuary Partnership and its cooperators to: (1) develop indicators of 'habitat health' and biological integrity; (2) develop definitions of 'critical salmonid habitat'; (3) identify and evaluate potential wetland conservation and restoration sites; (4) track exotic and invasive species; and (5) develop an understanding of how estuarine and riverine habitats have changed over the past 200 years. This study focuses on estuarine and riparian habitat cover types important to native species, particularly juvenile salmonids. This study is meant to provide support to the multiple efforts currently underway to recover 12 species of Columbia River salmonids identified as endangered or threatened under the Endangered Species Act.« less

Forest abovegroundbiomass mapping using spaceborne stereo imagery acquired by Chinese ZY-3

NASA Astrophysics Data System (ADS)

Sun, G.; Ni, W.; Zhang, Z.; Xiong, C.

2015-12-01

Besides LiDAR data, another valuable type of data which is also directly sensitive to forest vertical structures and more suitable for regional mapping of forest biomass is the stereo imagery or photogrammetry. Photogrammetry is the traditional technique for deriving terrain elevation. The elevation of the top of a tree canopy can be directly measured from stereo imagery but winter images are required to get the elevation of ground surface because stereo images are acquired by optical sensors which cannot penetrate dense forest canopies with leaf-on condition. Several spaceborne stereoscopic systems with higher spatial resolutions have been launched in the past several years. For example the Chinese satellite Zi Yuan 3 (ZY-3) specifically designed for the collection of stereo imagery with a resolution of 3.6 m for forward and backward views and 2.1 m for the nadir view was launched on January 9, 2012. Our previous studies have demonstrated that the spaceborne stereo imagery acquired in summer has good performance on the description of forest structures. The ground surface elevation could be extracted from spaceborne stereo imagery acquired in winter. This study mainly focused on assessing the mapping of forest biomass through the combination of spaceborne stereo imagery acquired in summer and those in winter. The test sites of this study located at Daxing AnlingMountains areas as shown in Fig.1. The Daxing Anling site is on the south border of boreal forest belonging to frigid-temperate zone coniferous forest vegetation The dominant tree species is Dhurian larch (Larix gmelinii). 10 scenes of ZY-3 stereo images are used in this study. 5 scenes were acquired on March 14,2012 while the other 5 scenes were acquired on September 7, 2012. Their spatial coverage is shown in Fig.2-a. Fig.2-b is the mosaic of nadir images acquired on 09/07/2012 while Fig.2-c is thecorresponding digital surface model (DSM) derived from stereo images acquired on 09/07/2012. Fig.2-d is the difference between the DSM derived from stereo imagery acquired on 09/07/2012 and the digital elevation model (DEM) from stereo imagery acquired on 03/14/2012.The detailed analysis will be given in the final report.

Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

NASA Astrophysics Data System (ADS)

Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.

2015-06-01

Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

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

Garono, Ralph; Robinson, Rob

Developing an understanding of the distribution and changes in estuarine and tidal floodplain ecosystems is critical to the management of biological resources in the lower Columbia River. Columbia River plants, fish, and wildlife require specific physicochemical and ecological conditions to sustain their populations. As habitats are degraded or lost, this capability is altered, often irretrievably; those species that cannot adapt are lost from the ecosystem. The Lower Columbia River Estuary Partnership (Estuary Partnership) completed a comprehensive ecosystem protection and enhancement plan for the lower Columbia River and estuary in 1999 (Jerrick, 1999). The plan identified habitat loss and modification asmore » a critical threat to the integrity of the lower Columbia River ecosystem and called for a habitat inventory as a key first step in its long term restoration efforts. In 2000, the Estuary Partnership initiated a multiphase project to produce a spatial data set describing the current location and distribution of estuarine and tidal freshwater habitat cover types along the lower Columbia River from the river mouth to the Bonneville Dam using a consistent methodology and data sources (Fig. 1). The first phase of the project was the development of a broadbrush description of the estuarine and tidal freshwater habitat cover classes for the entire study area ({approx}146 river miles) using Landsat 7 ETM+ satellite imagery. Phase II of the project entailed analysis of the classified satellite imagery from Phase I. Analysis of change in landcover and a summary of the spatial relationships between cover types are part of Phase II. Phase III of the project included the classification of the high resolution hyperspectral imagery collected in 2000 and 2001 for key focal areas within the larger study area. Finally, Phase IV consists of this final report that presents results from refining the Landsat ETM+ classification and provides recommendations for future actions. Previous studies (Thomas, 1980; Thomas, 1983; Graves et al., 1995; NOAA, 1997; Allen, 1999) produced similar landcover data sets; however, most of these studies used multiple and varied data sources and differed from one another in methodologies. Currently, no single data set has been produced using a consistent methodology and uniform scale data, which describes current estuarine and tidal freshwater floodplain cover types from the Columbia's mouth to the Bonneville Dam (Fig. 1). Results from this study will be used by the Estuary Partnership and its cooperators to: (1) develop indicators of 'habitat health' for target species and populations, and biological integrity at the community and ecosystem scales; (2) develop definitions of 'important salmonid habitat'; (3) identify and evaluate potential wetland conservation and restoration sites; (4) track non-indigenous and invasive species; and (5) develop an understanding of how estuarine and floodplain habitats have changed over the past 200 years. This study focused on estuarine and tidal freshwater floodplain habitat cover types, which are important to native species, particularly juvenile salmonids. Results from this study are meant to provide support for the multiple efforts currently underway to recover 12 species of Columbia River salmonids identified as endangered or threatened under the Endangered Species Act. Spatial scale was an important consideration in this study. Our goal was to create a geographic information system (GIS) coverage depicting habitat cover types for the entire 146 river miles of the study area and the associated floodplain, at a spatial resolution sufficient to resolve important estuarine and floodplain features, wherever possible. Thus, in addition to the small scale (30 m pixel size) satellite imagery covering the study area described in this report, we also acquired high spatial resolution imagery ({approx}1.5 m pixel size) for key portions of the study area using a Compact Airborne Spectrographic Imager (CASI). Compared to the rather coarse, low spectral resolution of the satellite imagery, the CASI imagery provides the spectral resolution (19 CASI bands vs. 7 ETM+ Bands) necessary to discriminate between spectrally similar vegetation types and thus provided a greater ability to resolve habitat features important to species such as salmonids. Unfortunately, image acquisition costs and logistics (time and poor weather) prevented complete coverage of the study area at a 1.5 m spatial scale. However, in 2000/01 CASI imagery was collected for several key focal areas including the Chinook watershed, the area around the Astoria Airport mitigation site, Russian Island, Tenasillahe Island, Sauvie Island, Scappose Bay lowlands, Lord and Walker Islands and much of the shoreline. This report presents the classification methodology and initial results from the satellite image classification.« less

The EO-1 hyperion and advanced land imager sensors for use in tundra classification studies within the Upper Kuparuk River Basin, Alaska

NASA Astrophysics Data System (ADS)

Hall-Brown, Mary

The heterogeneity of Arctic vegetation can make land cover classification vey difficult when using medium to small resolution imagery (Schneider et al., 2009; Muller et al., 1999). Using high radiometric and spatial resolution imagery, such as the SPOT 5 and IKONOS satellites, have helped arctic land cover classification accuracies rise into the 80 and 90 percentiles (Allard, 2003; Stine et al., 2010; Muller et al., 1999). However, those increases usually come at a high price. High resolution imagery is very expensive and can often add tens of thousands of dollars onto the cost of the research. The EO-1 satellite launched in 2002 carries two sensors that have high specral and/or high spatial resolutions and can be an acceptable compromise between the resolution versus cost issues. The Hyperion is a hyperspectral sensor with the capability of collecting 242 spectral bands of information. The Advanced Land Imager (ALI) is an advanced multispectral sensor whose spatial resolution can be sharpened to 10 meters. This dissertation compares the accuracies of arctic land cover classifications produced by the Hyperion and ALI sensors to the classification accuracies produced by the Systeme Pour l' Observation de le Terre (SPOT), the Landsat Thematic Mapper (TM) and the Landsat Enhanced Thematic Mapper Plus (ETM+) sensors. Hyperion and ALI images from August 2004 were collected over the Upper Kuparuk River Basin, Alaska. Image processing included the stepwise discriminant analysis of pixels that were positively classified from coinciding ground control points, geometric and radiometric correction, and principle component analysis. Finally, stratified random sampling was used to perform accuracy assessments on satellite derived land cover classifications. Accuracy was estimated from an error matrix (confusion matrix) that provided the overall, producer's and user's accuracies. This research found that while the Hyperion sensor produced classfication accuracies that were equivalent to the TM and ETM+ sensor (approximately 78%), the Hyperion could not obtain the accuracy of the SPOT 5 HRV sensor. However, the land cover classifications derived from the ALI sensor exceeded most classification accuracies derived from the TM and ETM+ senors and were even comparable to most SPOT 5 HRV classifications (87%). With the deactivation of the Landsat series satellites, the monitoring of remote locations such as in the Arctic on an uninterupted basis thoughout the world is in jeopardy. The utilization of the Hyperion and ALI sensors are a way to keep that endeavor operational. By keeping the ALI sensor active at all times, uninterupted observation of the entire Earth can be accomplished. Keeping the Hyperion sensor as a "tasked" sensor can provide scientists with additional imagery and options for their studies without overburdening storage issues.

Integrating Eddy Covariance, Penman-Monteith and METRIC based Evapotranspiration estimates to generate high resolution space-time ET over the Brazos River Basin

NASA Astrophysics Data System (ADS)

Mbabazi, D.; Mohanty, B.; Gaur, N.

2017-12-01

Evapotranspiration (ET) is an important component of the water and energy balance and accounts for 60 -70% of precipitation losses. However, accurate estimates of ET are difficult to quantify at varying spatial and temporal scales. Eddy covariance methods estimate ET at high temporal resolutions but without capturing the spatial variation in ET within its footprint. On the other hand, remote sensing methods using Landsat imagery provide ET with high spatial resolution but low temporal resolution (16 days). In this study, we used both eddy covariance and remote sensing methods to generate high space-time resolution ET. Daily, monthly and seasonal ET estimates were obtained using the eddy covariance (EC) method, Penman-Monteith (PM) and Mapping Evapotranspiration with Internalized Calibration (METRIC) models to determine cotton and native prairie ET dynamics in the Brazos river basin characterized by varying hydro-climatic and geological gradients. Daily estimates of spatially distributed ET (30 m resolution) were generated using spatial autocorrelation and temporal interpolations between the EC flux variable footprints and METRIC ET for the 2016 and 2017 growing seasons. A comparison of the 2016 and 2017 preliminary daily ET estimates showed similar ET dynamics/trends among the EC, PM and METRIC methods, and 5-20% differences in seasonal ET estimates. This study will improve the spatial estimates of EC ET and temporal resolution of satellite derived ET thus providing better ET data for water use management.

Spatial resolution requirements for urban land cover mapping from space

NASA Technical Reports Server (NTRS)

Todd, William J.; Wrigley, Robert C.

1986-01-01

Very low resolution (VLR) satellite data (Advanced Very High Resolution Radiometer, DMSP Operational Linescan System), low resolution (LR) data (Landsat MSS), medium resolution (MR) data (Landsat TM), and high resolution (HR) satellite data (Spot HRV, Large Format Camera) were evaluated and compared for interpretability at differing spatial resolutions. VLR data (500 m - 1.0 km) is useful for Level 1 (urban/rural distinction) mapping at 1:1,000,000 scale. Feature tone/color is utilized to distinguish generalized urban land cover using LR data (80 m) for 1:250,000 scale mapping. Advancing to MR data (30 m) and 1:100,000 scale mapping, confidence in land cover mapping is greatly increased, owing to the element of texture/pattern which is now evident in the imagery. Shape and shadow contribute to detailed Level II/III urban land use mapping possible if the interpreter can use HR (10-15 m) satellite data; mapping scales can be 1:25,000 - 1:50,000.

Conifer health classification for Colorado, 2008

USGS Publications Warehouse

Cole, Christopher J.; Noble, Suzanne M.; Blauer, Steven L.; Friesen, Beverly A.; Curry, Stacy E.; Bauer, Mark A.

2010-01-01

Colorado has undergone substantial changes in forests due to urbanization, wildfires, insect-caused tree mortality, and other human and environmental factors. The U.S. Geological Survey Rocky Mountain Geographic Science Center evaluated and developed a methodology for applying remotely-sensed imagery for assessing conifer health in Colorado. Two classes were identified for the purposes of this study: healthy and unhealthy (for example, an area the size of a 30- x 30-m pixel with 20 percent or greater visibly dead trees was defined as ?unhealthy?). Medium-resolution Landsat 5 Thematic Mapper imagery were collected. The normalized, reflectance-converted, cloud-filled Landsat scenes were merged to form a statewide image mosaic, and a Normalized Difference Vegetation Index (NDVI) and Renormalized Difference Infrared Index (RDII) were derived. A supervised maximum likelihood classification was done using the Landsat multispectral bands, the NDVI, the RDII, and 30-m U.S. Geological Survey National Elevation Dataset (NED). The classification was constrained to pixels identified in the updated landcover dataset as coniferous or mixed coniferous/deciduous vegetation. The statewide results were merged with a separate health assessment of Grand County, Colo., produced in late 2008. Sampling and validation was done by collecting field data and high-resolution imagery. The 86 percent overall classification accuracy attained in this study suggests that the data and methods used successfully characterized conifer conditions within Colorado. Although forest conditions for Lodgepole Pine (Pinus contorta) are easily characterized, classification uncertainty exists between healthy/unhealthy Ponderosa Pine (Pinus ponderosa), Pi?on (Pinus edulis), and Juniper (Juniperus sp.) vegetation. Some underestimation of conifer mortality in Summit County is likely, where recent (2008) cloud-free imagery was unavailable. These classification uncertainties are primarily due to the spatial and temporal resolution of Landsat, and of the NLCD derived from this sensor. It is believed that high- to moderate-resolution multispectral imagery, coupled with field data, could significantly reduce the uncertainty rates. The USGS produced a four-county follow-up conifer health assessment using high-resolution RapidEye remotely sensed imagery and field data collected in 2009.

Improvement in spatial imagery following sight onset late in childhood.

PubMed

Gandhi, Tapan K; Ganesh, Suma; Sinha, Pawan

2014-03-01

The factors contributing to the development of spatial imagery skills are not well understood. Here, we consider whether visual experience shapes these skills. Although differences in spatial imagery between sighted and blind individuals have been reported, it is unclear whether these differences are truly due to visual deprivation or instead are due to extraneous factors, such as reduced opportunities for the blind to interact with their environment. A direct way of assessing vision's contribution to the development of spatial imagery is to determine whether spatial imagery skills change soon after the onset of sight in congenitally blind individuals. We tested 10 children who gained sight after several years of congenital blindness and found significant improvements in their spatial imagery skills following sight-restoring surgeries. These results provide evidence of vision's contribution to spatial imagery and also have implications for the nature of internal spatial representations.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery

USDA-ARS?s Scientific Manuscript database

The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature election methods have been used in conjunction with OBIA, a robust comparison of th...

Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information

USDA-ARS?s Scientific Manuscript database

A significant challenge in ecological studies has been defining scales of observation that correspond to the relevant ecological scales for organisms or processes of interest. Remote sensing has become commonplace in ecological studies and management, but the default resolution of imagery often used...

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...

In the L1B2 products, why are the block dimensions different for some cameras and bands?

Atmospheric Science Data Center

2014-12-08

Most of the time that MISR is acquiring Earth imagery it operates in a configuration called Global Mode, which allows the spatial resolution to be set for each individual channel (there are 36 channels on MISR: 4 bands at each of 9...

Smart Phone Application Development and Demonstration in Support of EPA HICO Imagery for Coastal and Ocean Protection

EPA Science Inventory

High resolution spectral data from the ISS Hyperspectral Imager of the Coastal Ocean (HICO) system has been used to map the spatial distribution of selected water quality indicators for four Florida Gulf Coast estuaries from 2010-2012. HICO is the first hyperspectral imager speci...

Evaluation of SLAR and thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1981-01-01

Training and test data sets for CAM1S from NS-001 MSS data for two dates (geometrically adjusted to 30 meter resolution) were used to evaluate wavelength band. Two sets of tapes containing digitized HH and HV polarization data were obtained. Because the SAR data on the 9 track tapes contained no meaningful data, the 7 track tapes were copied onto 9 track tapes at LARS. The LARSYS programs were modified and a program was written to reformat the digitized SAR data into a LARSYS format. The radar imagery is being qualitatively interpreted. Results are to be used to identify possible cover types, to produce a classification map to aid in the numerical evaluation classification of radar data, and to develop an interpretation key for radar imagery. The four spatial resolution data sets were analyzed. A program was developed to reduce the spatial distortions resulting from variable viewing distance, and geometrically adjusted data sets were generated. A flowchart of steps taken to geometrically adjust a data set from the NS-001 scanner is presented.

Remote Sensing Product Verification and Validation at the NASA Stennis Space Center

NASA Technical Reports Server (NTRS)

Stanley, Thomas M.

2005-01-01

Remote sensing data product verification and validation (V&V) is critical to successful science research and applications development. People who use remote sensing products to make policy, economic, or scientific decisions require confidence in and an understanding of the products' characteristics to make informed decisions about the products' use. NASA data products of coarse to moderate spatial resolution are validated by NASA science teams. NASA's Stennis Space Center (SSC) serves as the science validation team lead for validating commercial data products of moderate to high spatial resolution. At SSC, the Applications Research Toolbox simulates sensors and targets, and the Instrument Validation Laboratory validates critical sensors. The SSC V&V Site consists of radiometric tarps, a network of ground control points, a water surface temperature sensor, an atmospheric measurement system, painted concrete radial target and edge targets, and other instrumentation. NASA's Applied Sciences Directorate participates in the Joint Agency Commercial Imagery Evaluation (JACIE) team formed by NASA, the U.S. Geological Survey, and the National Geospatial-Intelligence Agency to characterize commercial systems and imagery.

The users, uses, and value of Landsat and other moderate-resolution satellite imagery in the United States-Executive report

USGS Publications Warehouse

Miller, Holly M.; Sexton, Natalie R.; Koontz, Lynne; Loomis, John; Koontz, Stephen R.; Hermans, Caroline

2011-01-01

Moderate-resolution imagery (MRI), such as that provided by the Landsat satellites, provides unique spatial information for use by many people both within and outside of the United States (U.S.). However, exactly who these users are, how they use the imagery, and the value and benefits derived from the information are, to a large extent, unknown. To explore these issues, social scientists at the USGS Fort Collins Science Center conducted a study of U.S.-based MRI users from 2008 through 2010 in two parts: 1) a user identification and 2) a user survey. The objectives for this study were to: 1) identify and classify U.S.-based users of this imagery; 2) better understand how and why MRI, and specifically Landsat, is being used; and 3) qualitatively and quantitatively measure the value and societal benefits of MRI (focusing on Landsat specifically). The results of the survey revealed that respondents from multiple sectors use Landsat imagery in many different ways, as demonstrated by the breadth of project locations and scales, as well as application areas. The value of Landsat imagery to these users was demonstrated by the high importance placed on the imagery, the numerous benefits received from projects using Landsat imagery, the negative impacts if Landsat imagery was no longer available, and the substantial willingness to pay for replacement imagery in the event of a data gap. The survey collected information from users who are both part of and apart from the known user community. The diversity of the sample delivered results that provide a baseline of knowledge about the users, uses, and value of Landsat imagery. While the results supply a wealth of information on their own, they can also be built upon through further research to generate a more complete picture of the population of Landsat users as a whole.

Current and Future Applications of Multispectral (RGB) Satellite Imagery for Weather Analysis and Forecasting Applications

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Fuell, Kevin K.; LaFontaine, Frank; McGrath, Kevin; Smith, Matt

2013-01-01

Current and future satellite sensors provide remotely sensed quantities from a variety of wavelengths ranging from the visible to the passive microwave, from both geostationary and low ]Earth orbits. The NASA Short ]term Prediction Research and Transition (SPoRT) Center has a long history of providing multispectral imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard NASA fs Terra and Aqua satellites in support of NWS forecast office activities. Products from MODIS have recently been extended to include a broader suite of multispectral imagery similar to those developed by EUMETSAT, based upon the spectral channels available from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard METEOSAT ]9. This broader suite includes products that discriminate between air mass types associated with synoptic ]scale features, assists in the identification of dust, and improves upon paired channel difference detection of fog and low cloud events. Future instruments will continue the availability of these products and also expand upon current capabilities. The Advanced Baseline Imager (ABI) on GOES ]R will improve the spectral, spatial, and temporal resolution of our current geostationary capabilities, and the recent launch of the Suomi National Polar ]Orbiting Partnership (S ]NPP) carries instruments such as the Visible Infrared Imager Radiometer Suite (VIIRS), the Cross ]track Infrared Sounder (CrIS), and the Advanced Technology Microwave Sounder (ATMS), which have unrivaled spectral and spatial resolution, as precursors to the JPSS era (i.e., the next generation of polar orbiting satellites. New applications from VIIRS extend multispectral composites available from MODIS and SEVIRI while adding new capabilities through incorporation of additional CrIS channels or information from the Near Constant Contrast or gDay ]Night Band h, which provides moonlit reflectance from clouds and detection of fires or city lights. This presentation will present a review of SPoRT, CIRA, and NRL collaborations regarding multispectral satellite imagery and recent applications within the operational forecasting environment

Satellite Imagery Analysis for Automated Global Food Security Forecasting

NASA Astrophysics Data System (ADS)

Moody, D.; Brumby, S. P.; Chartrand, R.; Keisler, R.; Mathis, M.; Beneke, C. M.; Nicholaeff, D.; Skillman, S.; Warren, M. S.; Poehnelt, J.

2017-12-01

The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate temporal, spatial, and spectral information and that can scale with the high-rate and dimensionality of imagery being collected. We focus on the problem of monitoring food crop productivity across the Middle East and North Africa, and show how an analysis-ready, multi-sensor data platform enables quick prototyping of satellite imagery analysis algorithms, from land use/land cover classification and natural resource mapping, to yearly and monthly vegetative health change trends at the structural field level.

Chromotomosynthesis for high speed hyperspectral imagery

NASA Astrophysics Data System (ADS)

Bostick, Randall L.; Perram, Glen P.

2012-09-01

A rotating direct vision prism, chromotomosynthetic imaging (CTI) system operating in the visible creates hyperspectral imagery by collecting a set of 2D images with each spectrally projected at a different rotation angle of the prism. Mathematical reconstruction techniques that have been well tested in the field of medical physics are used to reconstruct the data to produce the 3D hyperspectral image. The instrument operates with a 100 mm focusing lens in the spectral range of 400-900 nm with a field of view of 71.6 mrad and angular resolution of 0.8-1.6 μrad. The spectral resolution is 0.6 nm at the shortest wavelengths, degrading to over 10 nm at the longest wavelengths. Measurements using a pointlike target show that performance is limited by chromatic aberration. The accuracy and utility of the instrument is assessed by comparing the CTI results to spatial data collected by a wideband image and hyperspectral data collected using a liquid crystal tunable filter (LCTF). The wide-band spatial content of the scene reconstructed from the CTI data is of same or better quality as a single frame collected by the undispersed imaging system with projections taken at every 1°. Performance is dependent on the number of projections used, with projections at 5° producing adequate results in terms of target characterization. The data collected by the CTI system can provide spatial information of equal quality as a comparable imaging system, provide high-frame rate slitless 1-D spectra, and generate 3-D hyperspectral imagery which can be exploited to provide the same results as a traditional multi-band spectral imaging system. While this prototype does not operate at high speeds, components exist which will allow for CTI systems to generate hyperspectral video imagery at rates greater than 100 Hz. The instrument has considerable potential for characterizing bomb detonations, muzzle flashes, and other battlefield combustion events.

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

NASA Astrophysics Data System (ADS)

Bindhu, V. M.; Narasimhan, B.

2015-03-01

Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60 m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30 km × 25 km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240 m compared well with concurrent MODIS NDVI at 240 m (R2 > 0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.

Use of Visible Satellite Imagery to Determine Velocity in Tidal Rivers

NASA Astrophysics Data System (ADS)

Mied, R. P.; Donato, T. F.; Chen, W.

2006-05-01

In the open ocean and on the continental shelf, current velocities have traditionally been calculated remotely using the Maximum Correlation Coefficient (MCC) technique to track features between sequential sea surface temperature image scenes. These images are obtained from NOAA polar orbiters having an effective ground pixel size of 1.47 km. In contrast to this relatively large distance, spatial scales over which current velocities can vary in rivers and estuaries are hundreds of meters; associated temporal scales vary from tens of minutes to hours. Traditional in-situ measurements can be instructive in determining some aspects of the flow, but truly synoptic overviews are possible only with remote sensing, provided high-resolution imagery is available. With the advent of a constellation of moderate- to high-resolution imaging systems (e.g., Landsat, ASTER, SPOT, Quickbird, Ikonos, and Orbview-3) it is now available to extend current estimations to these areas. For instance, Landsat-7 and ASTER produce imagery with spatial resolutions on the order of 30 m or less and within 30 min of each other. This is sufficient to spatially resolve a wide variety of surface features, and to maintain feature integrity over time for tracking purposes. We apply this approach to a portion of the tidal Potomac River by using pairs of co-registered, sequential, multi-spectral Landsat-7 and ASTER images. The final data used in the analysis set contain three spectral bands (green, red, and near-infrared), and have a ground pixel spacing (GSD) of 30m. The time step between each Landsat-7 and ASTER pair is approximately 29 minutes. Two image sets are used in the present study, one occurring on 5 October 2001 and the other on 2 April 2003. We show current maps derived from both image pairs an discuss the results in the light of model and

Image Fusion Applied to Satellite Imagery for the Improved Mapping and Monitoring of Coral Reefs: a Proposal

NASA Astrophysics Data System (ADS)

Gholoum, M.; Bruce, D.; Hazeam, S. Al

2012-07-01

A coral reef ecosystem, one of the most complex marine environmental systems on the planet, is defined as biologically diverse and immense. It plays an important role in maintaining a vast biological diversity for future generations and functions as an essential spawning, nursery, breeding and feeding ground for many kinds of marine species. In addition, coral reef ecosystems provide valuable benefits such as fisheries, ecological goods and services and recreational activities to many communities. However, this valuable resource is highly threatened by a number of environmental changes and anthropogenic impacts that can lead to reduced coral growth and production, mass coral mortality and loss of coral diversity. With the growth of these threats on coral reef ecosystems, there is a strong management need for mapping and monitoring of coral reef ecosystems. Remote sensing technology can be a valuable tool for mapping and monitoring of these ecosystems. However, the diversity and complexity of coral reef ecosystems, the resolution capabilities of satellite sensors and the low reflectivity of shallow water increases the difficulties to identify and classify its features. This paper reviews the methods used in mapping and monitoring coral reef ecosystems. In addition, this paper proposes improved methods for mapping and monitoring coral reef ecosystems based on image fusion techniques. This image fusion techniques will be applied to satellite images exhibiting high spatial and low to medium spectral resolution with images exhibiting low spatial and high spectral resolution. Furthermore, a new method will be developed to fuse hyperspectral imagery with multispectral imagery. The fused image will have a large number of spectral bands and it will have all pairs of corresponding spatial objects. This will potentially help to accurately classify the image data. Accuracy assessment use ground truth will be performed for the selected methods to determine the quality of the information derived from image classification. The research will be applied to the Kuwait's southern coral reefs: Kubbar and Um Al-Maradim.

Optical vs. electronic enhancement of remote sensing imagery

NASA Technical Reports Server (NTRS)

Colwell, R. N.; Katibah, E. F.

1976-01-01

Basic aspects of remote sensing are considered and a description is provided of the methods which are employed in connection with the optical or electronic enhancement of remote sensing imagery. The advantages and limitations of various image enhancement methods and techniques are evaluated. It is pointed out that optical enhancement methods and techniques are currently superior to electronic ones with respect to spatial resolution and equipment cost considerations. Advantages of electronic procedures, on the other hand, are related to a greater flexibility regarding the presentation of the information as an aid for the interpretation by the image analyst.

Different atmospheric effects in remote sensing of uniform and nonuniform surfaces

NASA Technical Reports Server (NTRS)

Kaufman, Y. J.; Fraser, R. S.

1982-01-01

The atmospheric effect on the radiance of sunlight scattered from the earth-atmosphere system is greatly dependent on the surface reflectance pattern, the contrast between adjacent fields, and the optical properties of the atmosphere. In addition, the atmospheric effect is described by the range and magnitude of the adjacency effects, the atmospheric modulation transfer function, and the apparent spatial resolution of remotely sensed imagery. This paper discusses the atmospheric effect on classification of surface features and shows that surface nonuniformity can be used for developing procedures to remove the atmospheric effect from the satellite imagery.

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.

Object versus spatial visual mental imagery in patients with schizophrenia

PubMed Central

Aleman, André; de Haan, Edward H.F.; Kahn, René S.

2005-01-01

Objective Recent research has revealed a larger impairment of object perceptual discrimination than of spatial perceptual discrimination in patients with schizophrenia. It has been suggested that mental imagery may share processing systems with perception. We investigated whether patients with schizophrenia would show greater impairment regarding object imagery than spatial imagery. Methods Forty-four patients with schizophrenia and 20 healthy control subjects were tested on a task of object visual mental imagery and on a task of spatial visual mental imagery. Both tasks included a condition in which no imagery was needed for adequate performance, but which was in other respects identical to the imagery condition. This allowed us to adjust for nonspecific differences in individual performance. Results The results revealed a significant difference between patients and controls on the object imagery task (F1,63 = 11.8, p = 0.001) but not on the spatial imagery task (F1,63 = 0.14, p = 0.71). To test for a differential effect, we conducted a 2 (patients v. controls) х 2 (object task v. spatial task) analysis of variance. The interaction term was statistically significant (F1,62 = 5.2, p = 0.026). Conclusions Our findings suggest a differential dysfunction of systems mediating object and spatial visual mental imagery in schizophrenia. PMID:15644999

Global lunar-surface mapping experiment using the Lunar Imager/Spectrometer on SELENE

NASA Astrophysics Data System (ADS)

Haruyama, Junichi; Matsunaga, Tsuneo; Ohtake, Makiko; Morota, Tomokatsu; Honda, Chikatoshi; Yokota, Yasuhiro; Torii, Masaya; Ogawa, Yoshiko

2008-04-01

The Moon is the nearest celestial body to the Earth. Understanding the Moon is the most important issue confronting geosciences and planetary sciences. Japan will launch the lunar polar orbiter SELENE (Kaguya) (Kato et al., 2007) in 2007 as the first mission of the Japanese long-term lunar exploration program and acquire data for scientific knowledge and possible utilization of the Moon. An optical sensing instrument called the Lunar Imager/Spectrometer (LISM) is loaded on SELENE. The LISM requirements for the SELENE project are intended to provide high-resolution digital imagery and spectroscopic data for the entire lunar surface, acquiring these data for scientific knowledge and possible utilization of the Moon. Actually, LISM was designed to include three specialized sub-instruments: a terrain camera (TC), a multi-band imager (MI), and a spectral profiler (SP). The TC is a high-resolution stereo camera with 10-m spatial resolution from a SELENE nominal altitude of 100 km and a stereo angle of 30° to provide stereo pairs from which digital terrain models (DTMs) with a height resolution of 20 m or better will be produced. The MI is a multi-spectral imager with four and five color bands with 20 m and 60 m spatial resolution in visible and near-infrared ranges, which will provide data to be used to distinguish the geological units in detail. The SP is a line spectral profiler with a 400-m-wide footprint and 300 spectral bands with 6-8 nm spectral resolution in the visible to near-infrared ranges. The SP data will be sufficiently powerful to identify the lunar surface's mineral composition. Moreover, LISM will provide data with a spatial resolution, signal-to-noise ratio, and covered spectral range superior to that of past Earth-based and spacecraft-based observations. In addition to the hardware instrumentation, we have studied operation plans for global data acquisition within the limited total data volume allotment per day. Results show that the TC and MI can achieve global observations within the restrictions by sharing the TC and MI observation periods, adopting appropriate data compression, and executing necessary SELENE orbital plane change operations to ensure global coverage by MI. Pre-launch operation planning has resulted in possible global TC high-contrast imagery, TC stereoscopic imagery, and MI 9-band imagery in one nominal mission period. The SP will also acquire spectral line profiling data for nearly the entire lunar surface. The east-west interval of the SP strip data will be 3-4 km at the equator by the end of the mission and shorter at higher latitudes. We have proposed execution of SELENE roll cant operations three times during the nominal mission period to execute calibration site observations, and have reached agreement on this matter with the SELENE project. We present LISM global surface mapping experiments for instrumentation and operation plans. The ground processing systems and the data release plan for LISM data are discussed briefly.

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.

An operational methodology for riparian land cover fine scale regional mapping for the study of landscape influence on river ecological status

NASA Astrophysics Data System (ADS)

Tormos, T.; Kosuth, P.; Souchon, Y.; Villeneuve, B.; Durrieu, S.; Chandesris, A.

2010-12-01

Preservation and restoration of river ecosystems require an improved understanding of the mechanisms through which they are influenced by landscape at multiple spatial scales and particularly at river corridor scale considering the role of riparian vegetation for regulating and protecting river ecological status and the relevance of this specific area for implementing efficient and realistic strategies. Assessing correctly this influence over large river networks involves accurate broad scale (i.e. at least regional) information on Land Cover within Riparian Areas (LCRA). As the structure of land cover along rivers is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose we developed a generic multi-scale Object Based Image Analysis (OBIA) scheme able to produce LCRA maps in different geographic context by exploiting information available from very high spatial resolution imagery (satellite or airborne) and/or metric to decametric spatial thematic data on a given study zone thanks to fuzzy expert knowledge classification rules. A first experimentation was carried out on the Herault river watershed (southern of France), a 2650 square kilometers basin that presents a contrasted landscape (different ecoregions) and a total stream length of 1150 Km, using high and very high multispectral remotely-sensed images (10m Spot5 multispectral images and 0.5m aerial photography) and existing spatial thematic data. Application of the OBIA scheme produced a detailed (22 classes) LCRA map with an overall accuracy of 89% and a Kappa index of 83% according to a land cover pressures typology (six categories). A second experimentation (using the same data sources) was carried out on a larger test zone, a part of the Normandy river network (25 000 square kilometers basin; 6000 km long river network; 155 ecological stations). This second work aimed at elaborating a robust statistical eco-regional model to study links between land cover spatial indicators calculated at local and watershed scales, and river ecological status assessed with macroinvertebrate indicators. Application of the OBIA scheme produced a detailed (62 classes) LCRA map which allowed the model to highlight influence of specific land use patterns: (i) the significant beneficial effect of 20-m riparian tree vegetation strip near a station and 20-m riparian grassland strip along the upstream network of a station and (ii) the negative impact on river ecological status of urban areas and roads on the upstream flood plain of a station. Results of these two experimentations highlight that (i) the application of an OBIA scheme using multi-source spatial data provides an efficient approach for mapping and monitoring LCRA that can be implemented operationally at regional or national scale and (ii) and the interest of using LCRA-maps derived from very high spatial resolution imagery (satellite or airborne) and/or metric spatial thematic data to study landscape influence on river ecological status and support managers in the definition of optimized riparian preservation and restoration strategies.

Factor structure of the Spanish version of the Object-Spatial Imagery and Verbal Questionnaire.

PubMed

Campos, Alfredo; Pérez-Fabello, María José

2011-04-01

The reliability and factor structure of the Spanish version of the Object-Spatial Imagery and Verbal Questionnaire (OSIVQ) were assessed in a sample of 213 Spanish university graduates. The questionnaire measures three types of processing preferences (verbal, object imagery, and spatial imagery). Principal components analysis with varimax rotation identified three factors, corresponding to the three scales proposed in the original version, explaining 33.1% of the overall variance. Cronbach's alphas were .72, .77, and .81 for the verbal, object imagery, and spatial imagery scales, respectively.

MTF analysis of LANDSAT-4 Thematic Mapper

NASA Technical Reports Server (NTRS)

Schowengerdt, R.

1983-01-01

The spatial radiance distribution of a ground target must be known to a resolution at least four to five times greater than that of the system under test when measuring a satellite sensor's modulation transfer function. Calibration of the target requires either the use of man-made special purpose targets with known properties, e.g., a small reflective mirror or a dark-light linear pattern such as line or edge, or use of relatively high resolution underflight imagery to calibrate an arbitrary ground scene. Both approaches are to be used in addition a technique that utilizes an analytical model for the scene spatial frequency power spectrum is being investigated as an alternative to calibration of the scene.

MTF Analysis of LANDSAT-4 Thematic Mapper

NASA Technical Reports Server (NTRS)

Schowengerdt, R.

1985-01-01

The spatial radiance distribution of a ground target must be known to a resolution at least four to five times greater than that of the system under test when measuring a satellite sensor's modulation transfer function. Calibration of the target requires either the use of man-made special purpose targets with known properties, e.g., a small reflective mirror or a dark-light linear pattern such as line or edge, or use of relatively high resolution underflight imagery to calibrate an arbitrary ground scene. Both approaches are to be used, in addition a technique that utilizes an analytical model of the scene spatial frequency power spectrum is being investigated as an alternative to calibration of the scene.

Estimating the effective spatial resolution of an AVHRR time series

USGS Publications Warehouse

Meyer, D.J.

1996-01-01

A method is proposed to estimate the spatial degradation of geometrically rectified AVHRR data resulting from misregistration and off-nadir viewing, and to infer the cumulative effect of these degradations over time. Misregistrations are measured using high resolution imagery as a geometric reference, and pixel sizes are computed directly from satellite zenith angles. The influence or neighbouring features on a nominal 1 km by 1 km pixel over a given site is estimated from the above information, and expressed as a spatial distribution whose spatial frequency response is used to define an effective field-of-view (EFOV) for a time series. In a demonstration of the technique applied to images from the Conterminous U.S. AVHRR data set, an EFOV of 3·1km in the east-west dimension and 19 km in the north-south dimension was estimated for a time series accumulated over a grasslands test site.

Design of a multi-spectral imager built using the compressive sensing single-pixel camera architecture

NASA Astrophysics Data System (ADS)

McMackin, Lenore; Herman, Matthew A.; Weston, Tyler

2016-02-01

We present the design of a multi-spectral imager built using the architecture of the single-pixel camera. The architecture is enabled by the novel sampling theory of compressive sensing implemented optically using the Texas Instruments DLP™ micro-mirror array. The array not only implements spatial modulation necessary for compressive imaging but also provides unique diffractive spectral features that result in a multi-spectral, high-spatial resolution imager design. The new camera design provides multi-spectral imagery in a wavelength range that extends from the visible to the shortwave infrared without reduction in spatial resolution. In addition to the compressive imaging spectrometer design, we present a diffractive model of the architecture that allows us to predict a variety of detailed functional spatial and spectral design features. We present modeling results, architectural design and experimental results that prove the concept.

Pathfinder in flight over Hawaii

NASA Image and Video Library

1997-08-28

Pathfinder, NASA's solar-powered, remotely-piloted aircraft is shown while it was conducting a series of science flights to highlight the aircraft's science capabilities while collecting imagery of forest and coastal zone ecosystems on Kauai, Hawaii. The flights also tested two new scientific instruments, a high spectral resolution Digital Array Scanned Interferometer (DASI) and a high spatial resolution Airborne Real-Time Imaging System (ARTIS). The remote sensor payloads were designed by NASA's Ames Research Center, Moffett Field, California, to support NASA's Mission to Planet Earth science programs.

Pathfinder over runway in Hawaii

NASA Image and Video Library

1997-08-28

Pathfinder, NASA's solar-powered, remotely-piloted aircraft is shown while it was conducting a series of science flights to highlight the aircraft's science capabilities while collecting imagery of forest and coastal zone ecosystems on Kauai, Hawaii. The flights also tested two new scientific instruments, a high-spectral-resolution Digital Array Scanned Interferometer (DASI) and a high-spatial-resolution Airborne Real-Time Imaging System (ARTIS). The remote sensor payloads were designed by NASA's Ames Research Center, Moffett Field, California, to support NASA's Mission to Planet Earth science programs.

Downscaling of Seasonal Landsat-8 and MODIS Land Surface Temperature (LST) in Kolkata, India

NASA Astrophysics Data System (ADS)

Garg, R. D.; Guha, S.; Mondal, A.; Lakshmi, V.; Kundu, S.

2017-12-01

The quality of life of urban people is affected by urban heat environment. The urban heat studies can be carried out using remotely sensed thermal infrared imagery for retrieving Land Surface Temperature (LST). Currently, high spatial resolution (<200 m) thermal images are limited and their temporal resolution is low (e.g., 17 days of Landsat-8). Coarse spatial resolution (1000 m) and high temporal resolution (daily) thermal images of MODIS (Moderate Resolution Imaging Spectroradiometer) are frequently available. The present study is to downscale spatially coarser resolution of the thermal image to fine resolution thermal image using regression based downscaling technique. This method is based on the relationship between (LST) and vegetation indices (e.g., Normalized Difference Vegetation Index or NDVI) over a heterogeneous landscape. The Kolkata metropolitan city, which experiences a tropical wet-and-dry type of climate has been selected for the study. This study applied different seasonal open source satellite images viz., Landsat-8 and Terra MODIS. The Landsat-8 images are aggregated at 960 m resolution and downscaled into 480, 240 120 and 60 m. Optical and thermal resolution of Landsat-8 and MODIS are 30 m and 60 m; 250 m and 1000 m respectively. The homogeneous land cover areas have shown better accuracy than heterogeneous land cover areas. The downscaling method plays a crucial role while the spatial resolution of thermal band renders it unable for advanced study. Key words: Land Surface Temperature (LST), Downscale, MODIS, Landsat, Kolkata

Mapping Land Cover and Land Use Changes in the Congo Basin Forests with Optical Satellite Remote Sensing: a Pilot Project Exploring Methodologies that Improve Spatial Resolution and Map Accuracy

NASA Astrophysics Data System (ADS)

Molinario, G.; Baraldi, A.; Altstatt, A. L.; Nackoney, J.

2011-12-01

The University of Maryland has been a USAID Central Africa Rregional Program for the Environment (CARPE) cross-cutting partner for many years, providing remote sensing derived information on forest cover and forest cover changes in support of CARPE's objectives of diminishing forest degradation, loss and biodiversity loss as a result of poor or inexistent land use planning strategies. Together with South Dakota State University, Congo Basin-wide maps have been provided that map forest cover loss at a maximum of 60m resolution, using Landsat imagery and higher resolution imagery for algorithm training and validation. However, to better meet the needs within the CARPE Landscapes, which call for higher resolution, more accurate land cover change maps, UMD has been exploring the use of the SIAM automatic spectral -rule classifier together with pan-sharpened Landsat data (15m resolution) and Very High Resolution imagery from various sources. The pilot project is being developed in collaboration with the African Wildlife Foundation in the Maringa Lopori Wamba CARPE Landscape. If successful in the future this methodology will make the creation of high resolution change maps faster and easier, making it accessible to other entities in the Congo Basin that need accurate land cover and land use change maps in order, for example, to create sustainable land use plans, conserve biodiversity and resources and prepare Reducing Emissions from forest Degradation and Deforestation (REDD) Measurement, Reporting and Verification (MRV) projects. The paper describes the need for higher resolution land cover change maps that focus on forest change dynamics such as the cycling between primary forests, secondary forest, agriculture and other expanding and intensifying land uses in the Maringa Lopori Wamba CARPE Landscape in the Equateur Province of the Democratic Republic of Congo. The Methodology uses the SIAM remote sensing imagery automatic spectral rule classifier, together with pan-sharpened Landsat imagery with 15m resolution and Very High Resolution imagery from different sensors, obtained from the Department of Defense database that was recently opened to NASA and its Earth Observation partners. Particular emphasis is placed on the detection of agricultural fields and their expansion in primary forests or intensification in secondary forests and fallow fields, as this is the primary driver of deforestation in this area. Fields in this area area also of very small size and irregular shapes, often partly obscured by neighboring forest canopy, hence the technical challenge of correctly detecting them and tracking them through time. Finally, the potential for use of this methodology in other regions where information on land cover changes is needed for land use sustainability planning, is also addressed.

Mapping regional distribution of a single tree species: Whitebark pine in the Greater Yellowstone Ecosystem

USGS Publications Warehouse

Landenburger, L.; Lawrence, R.L.; Podruzny, S.; Schwartz, C.C.

2008-01-01

Moderate resolution satellite imagery traditionally has been thought to be inadequate for mapping vegetation at the species level. This has made comprehensive mapping of regional distributions of sensitive species, such as whitebark pine, either impractical or extremely time consuming. We sought to determine whether using a combination of moderate resolution satellite imagery (Landsat Enhanced Thematic Mapper Plus), extensive stand data collected by land management agencies for other purposes, and modern statistical classification techniques (boosted classification trees) could result in successful mapping of whitebark pine. Overall classification accuracies exceeded 90%, with similar individual class accuracies. Accuracies on a localized basis varied based on elevation. Accuracies also varied among administrative units, although we were not able to determine whether these differences related to inherent spatial variations or differences in the quality of available reference data.

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.

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.

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

High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier

USDA-ARS?s Scientific Manuscript database

The effects of insect infestation in agricultural crops are of major ecological and economic interest because of reduced yield, increased cost of pest control, and increased risk of environmental contamination from insecticide application. The Russian wheat aphid (RWA, Diuraphis noxia) is an insect...

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-...

Mapping Crop Patterns in Central US Agricultural Systems from 2000 to 2014 Based on Landsat Data: To What Degree Does Fusing MODIS Data Improve Classification Accuracies?

NASA Astrophysics Data System (ADS)

Zhu, L.; Radeloff, V.; Ives, A. R.; Barton, B.

2015-12-01

Deriving crop pattern with high accuracy is of great importance for characterizing landscape diversity, which affects the resilience of food webs in agricultural systems in the face of climatic and land cover changes. Landsat sensors were originally designed to monitor agricultural areas, and both radiometric and spatial resolution are optimized for monitoring large agricultural fields. Unfortunately, few clear Landsat images per year are available, which has limited the use of Landsat for making crop classification, and this situation is worse in cloudy areas of the Earth. Meanwhile, the MODerate Resolution Imaging Spectroradiometer (MODIS) data has better temporal resolution but cannot capture fine spatial heterogeneity of agricultural systems. Our question was to what extent fusing imagery from both sensors could improve crop classifications. We utilized the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to simulate Landsat-like images at MODIS temporal resolution. Based on Random Forests (RF) classifier, we tested whether and by what degree crop maps from 2000 to 2014 of the Arlington Agricultural Research Station (Wisconsin, USA) were improved by integrating available clear Landsat images each year with synthetic images. We predicted that the degree to which classification accuracy can be improved by incorporating synthetic imagery depends on the number and acquisition time of clear Landsat images. Moreover, multi-season data are essential for mapping crop types by capturing their phenological dynamics, and STARFM-simulated images can be used to compensate for missing Landsat observations. Our study is helpful for eliminating the limits of the use of Landsat data in mapping crop patterns, and can provide a benchmark of accuracy when choosing STARFM-simulated images to make crop classification at broader scales.

Drone based estimation of actual evapotranspiration over different forest types

NASA Astrophysics Data System (ADS)

Marzahn, Philip; Gampe, David; Castro, Saulo; Vega-Araya, Mauricio; Sanchez-Azofeifa, Arturo; Ludwig, Ralf

2017-04-01

Actual evapotranspiration (Eta) plays an important role in surface-atmosphere interactions. Traditionally, Eta is measured by means of lysimeters, eddy-covariance systems or fiber optics, providing estimates which are spatially restricted to a footprint from a few square meters up to several hectares . In the past, several methods have been developed to derive Eta by means of multi-spectral remote sensing data using thermal and VIS/NIR satellite imagery of the land surface. As such approaches do have their justification on coarser scales, they do not provide Eta information on the fine resolution plant level over large areas which is mandatory for the detection of water stress or tree mortality. In this study, we present a comparison of a drone based assessment of Eta with eddy-covariance measurements over two different forest types - a deciduous forest in Alberta, Canada and a tropical dry forest in Costa Rica. Drone based estimates of Eta were calculated applying the Triangle-Method proposed by Jiang and Islam (1999). The Triangle-Method estimates actual evapotranspiration (Eta) by means of the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) provided by two camera systems (MicaSense RedEdge, FLIR TAU2 640) flown simultaneously on an octocopter. . Results indicate a high transferability of the original approach from Jiang and Islam (1999) developed for coarse to medium resolution satellite imagery tothe high resolution drone data, leading to a deviation in Eta estimates of 10% compared to the eddy-covariance measurements. In addition, the spatial footprint of the eddy-covariance measurement can be detected with this approach, by showing the spatial heterogeneities of Eta due to the spatial distribution of different trees and understory vegetation.

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.

Essential climatic variables estimation with satellite imagery

NASA Astrophysics Data System (ADS)

Kolotii, A.; Kussul, N.; Shelestov, A.; Lavreniuk, M. S.

2016-12-01

According to Sendai Framework for Disaster Risk Reduction 2015 - 2030 Leaf Area Index (LAI) is considered as one of essential climatic variables. This variable represents the amount of leaf material in ecosystems and controls the links between biosphere and atmosphere through various processes and enables monitoring and quantitative assessment of vegetation state. LAI has added value for such important global resources monitoring tasks as drought mapping and crop yield forecasting with use of data from different sources [1-2]. Remote sensing data from space can be used to estimate such biophysical parameter at regional and national scale. High temporal satellite imagery is usually required to capture main parameters of crop growth [3]. Sentinel-2 mission launched in 2015 be ESA is a source of high spatial and temporal resolution satellite imagery for mapping biophysical parameters. Products created with use of automated Sen2-Agri system deployed during Sen2-Agri country level demonstration project for Ukraine will be compared with our independent results of biophysical parameters mapping. References Shelestov, A., Kolotii, A., Camacho, F., Skakun, S., Kussul, O., Lavreniuk, M., & Kostetsky, O. (2015, July). Mapping of biophysical parameters based on high resolution EO imagery for JECAM test site in Ukraine. In 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1733-1736 Kolotii, A., Kussul, N., Shelestov, A., Skakun, S., Yailymov, B., Basarab, R., ... & Ostapenko, V. (2015). Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 39-44. Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. Parcel-Based Crop Classification in Ukraine Using Landsat-8 Data and Sentinel-1A Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 9 (6), 2500-2508.

Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery

NASA Astrophysics Data System (ADS)

Sun, Chao; Liu, Yongxue; Zhao, Saishuai; Zhou, Minxi; Yang, Yuhao; Li, Feixue

2016-03-01

Salt marshes are seen as the most dynamic and valuable ecosystems in coastal zones, and in these areas, it is crucial to obtain accurate remote sensing information on the spatial distributions of species over time. However, discriminating various types of salt marsh is rather difficult because of their strong spectral similarities. Previous salt marsh mapping studies have focused mainly on high spatial and spectral (i.e., hyperspectral) resolution images combined with auxiliary information; however, the results are often limited to small regions. With a high temporal and moderate spatial resolution, the Chinese HuanJing-1 (HJ-1) satellite optical imagery can be used not only to monitor phenological changes of salt marsh vegetation over short-time intervals, but also to obtain coverage of large areas. Here, we apply HJ-1 satellite imagery to the middle coast of Jiangsu in east China to monitor changes in saltmarsh vegetation cover. First, we constructed a monthly NDVI time-series to classify various types of salt marsh and then we tested the possibility of using compressed time-series continuously, to broaden the applicability of this particular approach. Our principal findings are as follows: (1) the overall accuracy of salt marsh mapping based on the monthly NDVI time-series was 90.3%, which was ∼16.0% higher than the single-phase classification strategy; (2) a compressed time-series, including NDVI from six key months (April, June-September, and November), demonstrated very little reduction (2.3%) in overall accuracy but led to obvious improvements in unstable regions; and (3) a simple rule for Spartina alterniflora identification was established using a scene solely from November, which may provide an effective way for regularly monitoring its distribution.

Applications of Satellite Remote Sensing for Response to and Recovery from Meteorological Disasters

NASA Technical Reports Server (NTRS)

Molthan, Andrew I.; Burks, Jason E.; McGrath, Kevin M.; Bell, Jordan R.

2014-01-01

Numerous on-orbit satellites provide a wide range of spatial, spectral, and temporal resolutions supporting the use of their resulting imagery in assessments of disasters that are meteorological in nature. This presentation will provide an overview of recent use of Earth remote sensing by NASA's Short-term Prediction Research and Transition (SPoRT) Center in response to disaster activities in 2012 and 2013, along with case studies supporting ongoing research and development. The SPoRT Center, with support from NASA's Applied Sciences Program, has explored a variety of new applications of Earth-observing sensors to support disaster response. In May 2013, the SPoRT Center developed unique power outage composites representing the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF-5 tornado that occurred on May 20. Subsequent ASTER, MODIS, Landsat-7 and Landsat-8 imagery help to identify the damaged areas. Higher resolution imagery of Moore, Oklahoma were provided by commercial satellites and the recently available International Space Station (ISS) SERVIR Environmental Research and Visualization System (ISERV) instrument. New techniques are being explored by the SPoRT team in order to better identify damage visible in high resolution imagery, and to monitor ongoing recovery for Moore, Oklahoma. This presentation will provide an overview of near real-time data products developed for dissemination to SPoRT's partners in NOAA's National Weather Service, through collaboration with the USGS and other federal agencies. Specifically, it will focus on integration of various data sets within the NOAA National Weather Service Damage Assessment Toolkit, which allows meteorologists in the field to consult available satellite imagery while performing their damage assessment.

Quantifying sub-pixel urban impervious surface through fusion of optical and inSAR imagery

USGS Publications Warehouse

Yang, L.; Jiang, L.; Lin, H.; Liao, M.

2009-01-01

In this study, we explored the potential to improve urban impervious surface modeling and mapping with the synergistic use of optical and Interferometric Synthetic Aperture Radar (InSAR) imagery. We used a Classification and Regression Tree (CART)-based approach to test the feasibility and accuracy of quantifying Impervious Surface Percentage (ISP) using four spectral bands of SPOT 5 high-resolution geometric (HRG) imagery and three parameters derived from the European Remote Sensing (ERS)-2 Single Look Complex (SLC) SAR image pair. Validated by an independent ISP reference dataset derived from the 33 cm-resolution digital aerial photographs, results show that the addition of InSAR data reduced the ISP modeling error rate from 15.5% to 12.9% and increased the correlation coefficient from 0.71 to 0.77. Spatially, the improvement is especially noted in areas of vacant land and bare ground, which were incorrectly mapped as urban impervious surfaces when using the optical remote sensing data. In addition, the accuracy of ISP prediction using InSAR images alone is only marginally less than that obtained by using SPOT imagery. The finding indicates the potential of using InSAR data for frequent monitoring of urban settings located in cloud-prone areas.

Fine-resolution repeat topographic surveying of dryland landscapes using UAS-based structure-from-motion photogrammetry: Assessing accuracy and precision against traditional ground-based erosion measurements

USGS Publications Warehouse

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

2017-01-01

Structure-from-motion (SfM) photogrammetry from unmanned aerial system (UAS) imagery is an emerging tool for repeat topographic surveying of dryland erosion. These methods are particularly appealing due to the ability to cover large landscapes compared to field methods and at reduced costs and finer spatial resolution compared to airborne laser scanning. Accuracy and precision of high-resolution digital terrain models (DTMs) derived from UAS imagery have been explored in many studies, typically by comparing image coordinates to surveyed check points or LiDAR datasets. In addition to traditional check points, this study compared 5 cm resolution DTMs derived from fixed-wing UAS imagery with a traditional ground-based method of measuring soil surface change called erosion bridges. We assessed accuracy by comparing the elevation values between DTMs and erosion bridges along thirty topographic transects each 6.1 m long. Comparisons occurred at two points in time (June 2014, February 2015) which enabled us to assess vertical accuracy with 3314 data points and vertical precision (i.e., repeatability) with 1657 data points. We found strong vertical agreement (accuracy) between the methods (RMSE 2.9 and 3.2 cm in June 2014 and February 2015, respectively) and high vertical precision for the DTMs (RMSE 2.8 cm). Our results from comparing SfM-generated DTMs to check points, and strong agreement with erosion bridge measurements suggests repeat UAS imagery and SfM processing could replace erosion bridges for a more synoptic landscape assessment of shifting soil surfaces for some studies. However, while collecting the UAS imagery and generating the SfM DTMs for this study was faster than collecting erosion bridge measurements, technical challenges related to the need for ground control networks and image processing requirements must be addressed before this technique could be applied effectively to large landscapes.

Using high resolution satellite multi-temporal interferometry for landslide hazard detection in tropical environments: the case of Haiti

NASA Astrophysics Data System (ADS)

Wasowski, Janusz; Nutricato, Raffaele; Nitti, Davide Oscar; Bovenga, Fabio; Chiaradia, Maria Teresa; Piard, Boby Emmanuel; Mondesir, Philemon

2015-04-01

Synthetic aperture radar (SAR) multi-temporal interferometry (MTI) is one of the most promising satellite-based remote sensing techniques for fostering new opportunities in landslide hazard detection and assessment. MTI is attractive because it can provide very precise quantitative information on slow slope displacements of the ground surface over huge areas with limited vegetation cover. Although MTI is a mature technique, we are only beginning to realize the benefits of the high-resolution imagery that is currently acquired by the new generation radar satellites (e.g., COSMO-SkyMed, TerraSAR-X). In this work we demonstrate the potential of high resolution X-band MTI for wide-area detection of slope instability hazards even in tropical environments that are typically very harsh (eg. coherence loss) for differential interferometry applications. This is done by presenting an example from the island of Haiti, a tropical region characterized by dense and rapidly growing vegetation, as well as by significant climatic variability (two rainy seasons) with intense precipitation events. Despite the unfavorable setting, MTI processing of nearly 100 COSMO-SkyMed (CSK) mages (2011-2013) resulted in the identification of numerous radar targets even in some rural (inhabited) areas thanks to the high resolution (3 m) of CSK radar imagery, the adoption of a patch wise processing SPINUA approach and the presence of many man-made structures dispersed in heavily vegetated terrain. In particular, the density of the targets resulted suitable for the detection of some deep-seated and shallower landslides, as well as localized, very slow slope deformations. The interpretation and widespread exploitation of high resolution MTI data was facilitated by Google EarthTM tools with the associated high resolution optical imagery. Furthermore, our reconnaissance in situ checks confirmed that MTI results provided useful information on landslides and marginally stable slopes that can represent a considerable hazard to the local population and infrastructure. The case of Haiti suggests that in the future MTI applications can become increasingly more important in cases where little or no conventional monitoring is feasible because of limited funds. Acknowledgements The Italian Spatial Agency (ASI) provided CSK imagery of Haiti in the framework of a scientific collaboration between the Centre National de l'Information Géo-Spatiale (CNIGS), Haiti and the Department of Physics of the Politecnico di Bari, Italy. We also thank Aldo Giovacchini (Consorzio ITA) and Luciano Guerriero for their help with the project.

Collaborative classification of hyperspectral and visible images with convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Mengmeng; Li, Wei; Du, Qian

2017-10-01

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Image variance and spatial structure in remotely sensed scenes. [South Dakota, California, Missouri, Kentucky, Louisiana, Tennessee, District of Columbia, and Oregon

NASA Technical Reports Server (NTRS)

Woodcock, C. E.; Strahler, A. H.

1984-01-01

Digital images derived by scanning air photos and through acquiring aircraft and spcecraft scanner data were studied. Results show that spatial structure in scenes can be measured and logically related to texture and image variance. Imagery data were used of a South Dakota forest; a housing development in Canoga Park, California; an agricltural area in Mississppi, Louisiana, Kentucky, and Tennessee; the city of Washington, D.C.; and the Klamath National Forest. Local variance, measured as the average standard deviation of brightness values within a three-by-three moving window, reaches a peak at a resolution cell size about two-thirds to three-fourths the size of the objects within the scene. If objects are smaller than the resolution cell size of the image, this peak does not occur and local variance simply decreases with increasing resolution as spatial averaging occurs. Variograms can also reveal the size, shape, and density of objects in the scene.

The edge-preservation multi-classifier relearning framework for the classification of high-resolution remotely sensed imagery

NASA Astrophysics Data System (ADS)

Han, Xiaopeng; Huang, Xin; Li, Jiayi; Li, Yansheng; Yang, Michael Ying; Gong, Jianya

2018-04-01

In recent years, the availability of high-resolution imagery has enabled more detailed observation of the Earth. However, it is imperative to simultaneously achieve accurate interpretation and preserve the spatial details for the classification of such high-resolution data. To this aim, we propose the edge-preservation multi-classifier relearning framework (EMRF). This multi-classifier framework is made up of support vector machine (SVM), random forest (RF), and sparse multinomial logistic regression via variable splitting and augmented Lagrangian (LORSAL) classifiers, considering their complementary characteristics. To better characterize complex scenes of remote sensing images, relearning based on landscape metrics is proposed, which iteratively quantizes both the landscape composition and spatial configuration by the use of the initial classification results. In addition, a novel tri-training strategy is proposed to solve the over-smoothing effect of relearning by means of automatic selection of training samples with low classification certainties, which always distribute in or near the edge areas. Finally, EMRF flexibly combines the strengths of relearning and tri-training via the classification certainties calculated by the probabilistic output of the respective classifiers. It should be noted that, in order to achieve an unbiased evaluation, we assessed the classification accuracy of the proposed framework using both edge and non-edge test samples. The experimental results obtained with four multispectral high-resolution images confirm the efficacy of the proposed framework, in terms of both edge and non-edge accuracy.

On the soil moisture estimate at basin scale in Mediterranean basins with the ASAR sensor: the Mulargia basin case study

NASA Astrophysics Data System (ADS)

Fois, Laura; Montaldo, Nicola

2017-04-01

Soil moisture plays a key role in water and energy exchanges between soil, vegetation and atmosphere. For water resources planning and managementthesoil moistureneeds to be accurately and spatially monitored, specially where the risk of desertification is high, such as Mediterranean basins. In this sense active remote sensors are very attractive for soil moisture monitoring. But Mediterranean basinsaretypicallycharacterized by strong topography and high spatial variability of physiographic properties, and only high spatial resolution sensorsare potentially able to monitor the strong soil moisture spatial variability.In this regard the Envisat ASAR (Advanced Synthetic Aperture Radar) sensor offers the attractive opportunity ofsoil moisture mapping at fine spatial and temporal resolutions(up to 30 m, every 30 days). We test the ASAR sensor for soil moisture estimate in an interesting Sardinian case study, the Mulargia basin withan area of about 70 sq.km. The position of the Sardinia island in the center of the western Mediterranean Sea basin, its low urbanization and human activity make Sardinia a perfect reference laboratory for Mediterranean hydrologic studies. The Mulargia basin is a typical Mediterranean basinin water-limited conditions, and is an experimental basin from 2003. For soil moisture mapping23 satellite ASAR imagery at single and dual polarization were acquired for the 2003-2004period.Satellite observationsmay bevalidated through spatially distributed soil moisture ground-truth data, collected over the whole basin using the TDR technique and the gravimetric method, in days with available radar images. The results show that ASAR sensor observations can be successfully used for soil moisture mapping at different seasons, both wet and dry, but an accurate calibration with field data is necessary. We detect a strong relationship between the soil moisture spatial variability and the physiographic properties of the basin, such as soil water storage capacity, deep and texture of soils, type and density of vegetation, and topographic parameters. Finally we demonstrate that the high resolution ASAR imagery are an attractive tool for estimating surface soil moisture at basin scale, offering a unique opportunity for monitoring the soil moisture spatial variability in typical Mediterranean basins.

Near Real-Time Applications of Earth Remote Sensing for Response to Meteorological Disasters

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Bell, Jordan R.

2013-01-01

Numerous on-orbit satellites provide a wide range of spatial, spectral, and temporal resolutions supporting the use of their resulting imagery in assessments of disasters that are meteorological in nature. This presentation will provide an overview of recent use of Earth remote sensing by NASA's Short-term Prediction Research and Transition (SPoRT) Center in response to disaster activities in 2012 and 2013, along with case studies supporting ongoing research and development. The SPoRT Center, with support from NASA's Applied Sciences Program, has explored a variety of new applications of Earth-observing sensors to support disaster response. In May 2013, the SPoRT Center developed unique power outage composites representing the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF-5 tornado that occurred on May 20. Subsequent ASTER, MODIS, Landsat-7 and Landsat-8 imagery help to identify the damaged area. Higher resolution imagery of Moore, Oklahoma were provided by commercial satellites and the recently available International Space Station (ISS) SERVIR Environmental Research and Visualization System (ISERV) instrument. New techniques are being explored by the SPoRT team in order to better identify damage visible in high resolution imagery, and to monitor ongoing recovery for Moore, Oklahoma. Other applications are being developed to refine light source detections with the VIIRS day-night band and to map hail during the growing season through combination of available satellite and radar imagery. The aforementioned products and support are not useful unless they are distributed in a timely manner and within an appropriate decision support system. This presentation will provide an update on ongoing activities to support inclusion of these data sets within the NOAA National Weather Service Damage Assessment Toolkit, which allows meteorologists in the field to consult available satellite imagery while performing their damage assessment.

Near Real-­Time Applications of Earth Remote Sensing for Response to Meteorological Disasters

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Bell, Jordan R.

2013-01-01

Numerous on-orbit satellites provide a wide range of spatial, spectral, and temporal resolutions supporting the use of their resulting imagery in assessments of disasters that are meteorological in nature. This presentation will provide an overview of recent use of Earth remote sensing by NASA's Short-term Prediction Research and Transition (SPoRT) Center in response to disaster activities in 2012 and 2013, along with case studies supporting ongoing research and development. The SPoRT Center, with support from NASA's Applied Sciences Program, has explored a variety of new applications of Earth-observing sensors to support disaster response. In May 2013, the SPoRT Center developed unique power outage composites representing the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF-5 tornado that occurred on May 20. Subsequent ASTER, MODIS, Landsat-7 and Landsat-8 imagery help to identify the damaged area. Higher resolution imagery of Moore, Oklahoma were provided by commercial satellites and the recently available International Space Station (ISS) SERVIR Environmental Research and Visualization System (ISERV) instrument. New techniques are being explored by the SPoRT team in order to better identify damage visible in high resolution imagery, and to monitor ongoing recovery for Moore, Oklahoma. Other applications are being developed to refine light source detections with the VIIRS day-night band and to map hail during the growing season through combination of available satellite and radar imagery. The aforementioned products and support are not useful unless they are distributed in a timely manner and within an appropriate decision support system. This presentation will provide an update on ongoing activities to support inclusion of these data sets within the NOAA National Weather Service Damage Assessment Toolkit, which allows meteorologists in the field to consult available satellite imagery while performing their damage assessment.

Evaluation of the U.S. Geological Survey Landsat burned area essential climate variable across the conterminous U.S. using commercial high-resolution imagery

USGS Publications Warehouse

Vanderhoof, Melanie; Brunner, Nicole M.; Beal, Yen-Ju G.; Hawbaker, Todd J.

2017-01-01

The U.S. Geological Survey has produced the Landsat Burned Area Essential Climate Variable (BAECV) product for the conterminous United States (CONUS), which provides wall-to-wall annual maps of burned area at 30 m resolution (1984–2015). Validation is a critical component in the generation of such remotely sensed products. Previous efforts to validate the BAECV relied on a reference dataset derived from Landsat, which was effective in evaluating the product across its timespan but did not allow for consideration of inaccuracies imposed by the Landsat sensor itself. In this effort, the BAECV was validated using 286 high-resolution images, collected from GeoEye-1, QuickBird-2, Worldview-2 and RapidEye satellites. A disproportionate sampling strategy was utilized to ensure enough burned area pixels were collected. Errors of omission and commission for burned area averaged 22 ± 4% and 48 ± 3%, respectively, across CONUS. Errors were lowest across the western U.S. The elevated error of commission relative to omission was largely driven by patterns in the Great Plains which saw low errors of omission (13 ± 13%) but high errors of commission (70 ± 5%) and potentially a region-growing function included in the BAECV algorithm. While the BAECV reliably detected agricultural fires in the Great Plains, it frequently mapped tilled areas or areas with low vegetation as burned. Landscape metrics were calculated for individual fire events to assess the influence of image resolution (2 m, 30 m and 500 m) on mapping fire heterogeneity. As the spatial detail of imagery increased, fire events were mapped in a patchier manner with greater patch and edge densities, and shape complexity, which can influence estimates of total greenhouse gas emissions and rates of vegetation recovery. The increasing number of satellites collecting high-resolution imagery and rapid improvements in the frequency with which imagery is being collected means greater opportunities to utilize these sources of imagery for Landsat product validation. 

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

Evaluation of methods for delineating riparian zones in a semi-arid montane watershed

Treesearch

Jessica A. Salo; David M. Theobald; Thomas C. Brown

2016-01-01

Riparian zones in semi-arid, mountainous regions provide a disproportionate amount of the available wildlife habitat and ecosystem services. Despite their importance, there is little guidance on the best way to map riparian zones for broad spatial extents (e.g., large watersheds) when detailed maps from field data or high-resolution imagery and terrain data...

Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data.

Treesearch

D.J. Hayes; W.B. Cohen

2006-01-01

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse-resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal Moderate...

Evaluating the ASTER sensor for mapping and characterizing forest fire fuels in northern Idaho

Treesearch

Michael J. Falkowski; Paul Gessler; Penelope Morgan; Alistair M. S. Smith; Andrew T. Hudak

2004-01-01

Land managers need cost-effective methods for mapping and characterizing fire fuels quickly and accurately. The advent of sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the Advanced Spaceborne Thermal Emission and Reflection...

Object-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery

Treesearch

G. Chen; M.R. Metz; D.M. Rizzo; W.W. Dillon; R.K. Meentemeyer

2015-01-01

Forest ecosystems are subject to a variety of disturbances with increasing intensities and frequencies, which may permanently change the trajectories of forest recovery and disrupt the ecosystem services provided by trees. Fire and invasive species, especially exotic disease-causing pathogens and insects, are examples of disturbances that together could pose major...

Object-oriented classification of forest structure from light detection and ranging data for stand mapping

Treesearch

Alicia A. Sullivan; Robert J. McGaughey; Hans-Erik Andersen; Peter Schiess

2009-01-01

Stand delineation is an important step in the process of establishing a forest inventory and provides the spatial framework for many forest management decisions. Many methods for extracting forest structure characteristics for stand delineation and other purposes have been researched in the past, primarily focusing on high-resolution imagery and satellite data. High-...

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....

Surface terrain characteristics and monsoon season mass balance of debris-covered glaciers in the Khumbu Himal, Nepal, obtained from high resolution Pléiades imagery.

NASA Astrophysics Data System (ADS)

Klug, Christoph; Nicholson, Lindsey; Rieg, Lorenzo; Sailer, Rudolf; Wirbel, Anna

2016-04-01

Debris-covered glaciers in the eastern Himalaya have pronounced surface relief consisting of hummocks and hollows, ice cliffs, lakes and former lake beds. This relief and spatially variable surface properties are expected to influence the spatially distributed surface energy balance and related ice mass loss and atmospheric interactions, but only a few studies have so far explicitly examined the nature of the surface terrain and its textures . In this work we present a new high-resolution digital terrain model (DTM) of a portion of the Khumbu Himal in the eastern Nepalese Himalaya, derived from Pléiades satellite imagery sampled in spring 2015. We use this DTM to study the terrain characteristics of five sample glaciers and analyse the inter- and intra- glacier variability of terrain characteristics in the context of glacier flow velocities and surface changes presented in previous studies in the area. In parallel to this analysis we also present the seasonal geodetic mass balance between spring and fall 2015, and relate it to the terrain properties, surface velocity and limited knowledge of the local lapse rates in meteorological conditions during this monsoon season.

System Characterization Results for the QuickBird Sensor

NASA Technical Reports Server (NTRS)

Holekamp, Kara; Ross, Kenton; Blonski, Slawomir

2007-01-01

An overall system characterization was performed on several DigitalGlobe' QuickBird image products by the NASA Applied Research & Technology Project Office (formerly the Applied Sciences Directorate) at the John C. Stennis Space Center. This system characterization incorporated geopositional accuracy assessments, a spatial resolution assessment, and a radiometric calibration assessment. Geopositional assessments of standard georeferenced multispectral products were obtained using an array of accurately surveyed geodetic targets evenly spaced throughout a scene. Geopositional accuracy was calculated in terms of circular error. Spatial resolution of QuickBird panchromatic imagery was characterized based on edge response measurements using edge targets and the tilted-edge technique. Relative edge response was estimated as a geometric mean of normalized edge response differences measured in two directions of image pixels at points distanced from the edge by -0.5 and 0.5 of ground sample distance. A reflectance-based vicarious calibration approach, based on ground-based measurements and radiative transfer calculations, was used to estimate at-sensor radiance. These values were compared to those measured by the sensor to determine the sensor's radiometric accuracy. All imagery analyzed was acquired between fall 2005 and spring 2006. These characterization results were compared to previous years' results to identify any temporal drifts or trends.

AVIRIS calibration and application in coastal oceanic environments

NASA Technical Reports Server (NTRS)

Carder, Kendall L.

1992-01-01

The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) is a test-bed for future spacecraft sensors such as the High-Resolution Imaging Spectrometer and the Moderate-Resolution Imaging Spectrometers planned for the Earth Observing System. To use this sensor for ocean applications, S/N was increased by spatial averaging of images. Post-flight recalibration was accomplished using in situ the water-leaving radiance measured at flight time, modeling radiance transmission to the aircraft, and adding modeled atmospheric radiance to that value. The preflight calibration curve was then adjusted until aircraft and modeled total radiance values matched. Water-leaving radiance values from the recalibrated AVIRIS imagery were consistent with in situ data supporting the validity of the approach. Imagery of the absorption coefficient at 415 nm and backscattering coefficient at 671 nm were used to depict the dissolved and particulate constituents of an ebb-tidal esturance plume on the East coast of Florida.

Evaluation of a technique for satellite-derived area estimation of forest fires

NASA Technical Reports Server (NTRS)

Cahoon, Donald R., Jr.; Stocks, Brian J.; Levine, Joel S.; Cofer, Wesley R., III; Chung, Charles C.

1992-01-01

The advanced very high resolution radiometer (AVHRR), has been found useful for the location and monitoring of both smoke and fires because of the daily observations, the large geographical coverage of the imagery, the spectral characteristics of the instrument, and the spatial resolution of the instrument. This paper will discuss the application of AVHRR data to assess the geographical extent of burning. Methods have been developed to estimate the surface area of burning by analyzing the surface area effected by fire with AVHRR imagery. Characteristics of the AVHRR instrument, its orbit, field of view, and archived data sets are discussed relative to the unique surface area of each pixel. The errors associated with this surface area estimation technique are determined using AVHRR-derived area estimates of target regions with known sizes. This technique is used to evaluate the area burned during the Yellowstone fires of 1988.

Identifying Stratospheric Air Intrusions and Associated Hurricane-Force Wind Events over the North Pacific Ocean

NASA Technical Reports Server (NTRS)

Malloy, Kelsey; Folmer, Michael J.; Phillips, Joseph; Sienkiewicz, Joseph M.; Berndt, Emily

2017-01-01

Motivation: Ocean data is sparse: reliance on satellite imagery for marine forecasting; Ocean Prediction Center (OPC) –“mariner’s weather lifeline”. Responsible for: Pacific, Atlantic, Pacific Alaska surface analyses –24, 48, 96 hrs.; Wind & wave analyses –24, 48, 96 hrs.; Issue warnings, make decisions, Geostationary Operational Environmental Satellite –R Series (now GOES-16), Compared to the old GOES: 3 times spectral resolution, 4 times spatial resolution, 5 times faster coverage; Comparable to Japanese Meteorological Agency’s Himawari-8, used a lot throughout this research. Research Question: How can integrating satellite data imagery and derived products help forecasters improve prognosis of rapid cyclogenesis and hurricane-force wind events? Phase I –Identifying stratospheric air intrusions: Water Vapor –6.2, 6.9, 7.3 micron channels; Airmass RGB Product; AIRS, IASI, NUCAPS total column ozone and ozone anomaly; ASCAT (A/B) and AMSR-2 wind data.

MTF Analysis of LANDSAT-4 Thematic Mapper

NASA Technical Reports Server (NTRS)

Schowengerdt, R.

1984-01-01

A research program to measure the LANDSAT 4 Thematic Mapper (TM) modulation transfer function (MTF) is described. Measurement of a satellite sensor's MTF requires the use of a calibrated ground target, i.e., the spatial radiance distribution of the target must be known to a resolution at least four to five times greater than that of the system under test. A small reflective mirror or a dark light linear pattern such as line or edge, and relatively high resolution underflight imagery are used to calibrate the target. A technique that utilizes an analytical model for the scene spatial frequency power spectrum will be investigated as an alternative to calibration of the scene. The test sites and analysis techniques are also described.

Imagery for Disaster Response and Recovery

NASA Astrophysics Data System (ADS)

Bethel, G. R.

2011-12-01

Exposing the remotely sensed imagery for disaster response and recovery can provide the basis for an unbiased understanding of current conditions. Having created consolidated remotely sensed and geospatial data sources documents for US and Foreign disasters over the past six years, availability and usability are continuing to evolve. By documenting all existing sources of imagery and value added products, the disaster response and recovery community can develop actionable information. The past two years have provided unique situations to use imagery including a major humanitarian disaster and response effort in Haiti, a major environmental disaster in the Gulf of Mexico, a killer tornado in Joplin Missouri and long-term flooding in the Midwest. Each disaster presents different challenges and requires different spatial resolutions, spectral properties and/or multi-temporal collections. The community of data providers continues to expand with organized actives such as the International Charter for Space and Major Disasters and acquisitions by the private sector for the public good rather than for profit. However, data licensing, the lack of cross-calibration and inconsistent georeferencing hinder optimal use. Recent pre-event imagery is a critial component to any disaster response.

The Joint Agency Commercial Imagery Evaluation (JACIE) Team: Overview and IKONOS Joint Characterization Approach

NASA Technical Reports Server (NTRS)

Zanoni, Vicki; Ryan, Robert; Pagnutti, Mary; Baldridge, Braxton; Roylance, Spencer; Snyder, Greg; Lee, George; Stanley, Tom

2002-01-01

An overview of the Joint Agency Commercial Imagery Evalation (JACIE) team is presented. JACIE, composed of the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA), and the U.S. Geological Survey (USGS), was formed to leverage government agencies' capabilities for the characterization of commercial remote sensing data. Each JACIE agency purchases, or plans to purchase, commercial imagery to support its research and applications. It is critical that the data be assessed for its accuracy and utility. Through JACIE, NASA, NIMA, and USGS jointly characterized image products from Space Imaging's IKONOS satellite. Each JACIE agency performed an aspect of the characterization based on its expertise. NASA and its university partners performed a system characterization focusing on radiometric calibration, geopositional accuracy, and spatial resolution assessment; NIMA performed image interpretability and feature extraction evaluations; and USGS assessed geopositional accuracy of several IKONOS products. The JACIE team purchased IKONOS imagery of several study sites to perform the assessments and presented results at an industry-government workshop. Future plans for JACIE include the characterization of DigitalGlobe's QuickBird-2 image products.

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.

VIIRS Data and Data Access at the NASA National Snow and Ice Data Center Distributed Active Archive Center

NASA Astrophysics Data System (ADS)

Moth, P.; Johnston, T.; Fowler, D. K.

2017-12-01

Working collaboratively, NASA and NOAA are producing data from the Visible Infrared Imaging Radiometer Suite (VIIRS). The National Snow and Ice Data Center (NSIDC), a NASA Distributed Active Archive Center (DAAC), is distributing VIIRS snow cover, ice surface temperature, and sea ice cover products. Data is available in .nc and HDF5 formats with a temporal coverage of 1 January 2012 and onward. VIIRS, NOAA's latest radiometer, was launched aboard the Suomi National Polar-orbiting Partnership (SNPP) satellite on October 28, 2011. The instrument comprises 22 bands; five for high-resolution imagery, 16 at moderate resolution, and one panchromatic day/night band. VIIRS is a whiskbroom scanning radiometer that covers the spectrum between 0.412 μm and 12.01 μm and acquires spatial resolutions at nadir of 750 m, 375 m, and 750 m, respectively. One distinct advantage of VIIRS is to ensure continuity that will lead to the development of snow and sea ice climate data records with data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the NASA Earth Observing System (EOS) Aqua and Terra satellites. Combined with the Advanced Very-High-resolution Radiometer (AVHRR), the AVHRR-MODIS-VIIRS timeline will start in the early 1980s and span at least four decades-and perhaps beyond-enabling researchers to produce and gain valuable insight from long, high-quality Earth System Data Records (ESDRs). Several options are available to view and download VIIRS data: Direct download from NSIDC via HTTPS. Using NASA Earthdata Search, users can explore and download VIIRS data with temporal and/or spatial filters, re-format, re-project, and subset by spatial extent and parameter. API access is also available for all these options; Using NASA Worldview, users can view Global Imagery Browse Services (GIBS) from VIIRS data; Users can join a VIIRS subscription list to have new VIIRS data automatically ftp'd or staged on a local server as it is archived at NSIDC.

Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling

NASA Astrophysics Data System (ADS)

Turner, Darren; Lucieer, Arko; Malenovský, Zbyněk; King, Diana; Robinson, Sharon A.

2018-06-01

Moss beds are one of very few terrestrial vegetation types that can be found on the Antarctic continent and as such mapping their extent and monitoring their health is important to environmental managers. Across Antarctica, moss beds are experiencing changes in health as their environment changes. As Antarctic moss beds are spatially fragmented with relatively small extent they require very high resolution remotely sensed imagery to monitor their distribution and dynamics. This study demonstrates that multi-sensor imagery collected by an Unmanned Aircraft System (UAS) provides a novel data source for assessment of moss health. In this study, we train a Random Forest Regression Model (RFM) with long-term field quadrats at a study site in the Windmill Islands, East Antarctica and apply it to UAS RGB and 6-band multispectral imagery, derived vegetation indices, 3D topographic data, and thermal imagery to predict moss health. Our results suggest that moss health, expressed as a percentage between 0 and 100% healthy, can be estimated with a root mean squared error (RMSE) between 7 and 12%. The RFM also quantifies the importance of input variables for moss health estimation showing the multispectral sensor data was important for accurate health prediction, such information being essential for planning future field investigations. The RFM was applied to the entire moss bed, providing an extrapolation of the health assessment across a larger spatial area. With further validation the resulting maps could be used for change detection of moss health across multiple sites and seasons.

Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques.

PubMed

Eugenio, Francisco; Marcello, Javier; Martin, Javier; Rodríguez-Esparragón, Dionisio

2017-11-16

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones.

Benthic Habitat Mapping Using Multispectral High-Resolution Imagery: Evaluation of Shallow Water Atmospheric Correction Techniques

PubMed Central

Eugenio, Francisco; Marcello, Javier; Martin, Javier

2017-01-01

Remote multispectral data can provide valuable information for monitoring coastal water ecosystems. Specifically, high-resolution satellite-based imaging systems, as WorldView-2 (WV-2), can generate information at spatial scales needed to implement conservation actions for protected littoral zones. However, coastal water-leaving radiance arriving at the space-based sensor is often small as compared to reflected radiance. In this work, complex approaches, which usually use an accurate radiative transfer code to correct the atmospheric effects, such as FLAASH, ATCOR and 6S, have been implemented for high-resolution imagery. They have been assessed in real scenarios using field spectroradiometer data. In this context, the three approaches have achieved excellent results and a slightly superior performance of 6S model-based algorithm has been observed. Finally, for the mapping of benthic habitats in shallow-waters marine protected environments, a relevant application of the proposed atmospheric correction combined with an automatic deglinting procedure is presented. This approach is based on the integration of a linear mixing model of benthic classes within the radiative transfer model of the water. The complete methodology has been applied to selected ecosystems in the Canary Islands (Spain) but the obtained results allow the robust mapping of the spatial distribution and density of seagrass in coastal waters and the analysis of multitemporal variations related to the human activity and climate change in littoral zones. PMID:29144444

The extent of burning in African savanna

NASA Technical Reports Server (NTRS)

Cahoon, D. R. JR.; Levine, J. S.; Cofer, W. R. Iii; Stocks, B. J.

1994-01-01

The temporal and spatial distribution of African savanna grassland fires has been examined, and the areal extent of these fires has been estimated for the subequatorial African continent. African savanna fires have been investigated using remote sensing techniques and imagery collected by low-light sensors on Defense Meteorological Satellite Program (DMSP) satellites and by the Advanced Very High Resolution Radiometer (AVHRR) which is aboard polar orbiting National Oceanic and Atmospheric Administration (NOAA) satellites. DMSP imagery has been used to map the evolution of savanna burning over all of the African continent and the analysis of AVHRR imagery has been used to estimate the areal extent of the burning in the southern hemispheric African savannas. The work presented primarily reflects the analysiscompleted for the year 1987. However, comparisons have been made with other years and the representativeness of the 1987 analysis is discussed.

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.

Identification of understory invasive exotic plants with remote sensing in urban forests

NASA Astrophysics Data System (ADS)

Shouse, Michael; Liang, Liang; Fei, Songlin

2013-04-01

Invasive exotic plants (IEP) pose a significant threat to many ecosystems. To effectively manage IEP, it is important to efficiently detect their presences and determine their distribution patterns. Remote sensing has been a useful tool to map IEP but its application is limited in urban forests, which are often the sources and sinks for IEP. In this study, we examined the feasibility and tradeoffs of species level IEP mapping using multiple remote sensing techniques in a highly complex urban forest setting. Bush honeysuckle (Lonicera maackii), a pervasive IEP in eastern North America, was used as our modeling species. Both medium spatial resolution (MSR) and high spatial resolution (HSR) imagery were employed in bush honeysuckle mapping. The importance of spatial scale was also examined using an up-scaling simulation from the HSR object based classification. Analysis using both MSR and HSR imagery provided viable results for IEP distribution mapping in urban forests. Overall mapping accuracy ranged from 89.8% to 94.9% for HSR techniques and from 74.6% to 79.7% for MSR techniques. As anticipated, classification accuracy reduces as pixel size increases. HSR based techniques produced the most desirable results, therefore is preferred for precise management of IEP in heterogeneous environment. However, the use of MSR techniques should not be ruled out given their wide availability and moderate accuracy.

The Early Detection of the Emerald Ash Borer (eab) Using Multi-Source Remotely Sensed Data

NASA Astrophysics Data System (ADS)

Hu, B.; Naveed, F.; Tasneem, F.; Xing, C.

2018-04-01

The objectives of this study were to exploit the synergy of hyperspectral imagery, Light Detection And Ranging (LiDAR) and high spatial resolution data and their synergy in the early detection of the EAB (Emerald Ash Borer) presence in trees within urban areas and to develop a framework to combine information extracted from multiple data sources. To achieve these, an object-oriented framework was developed to combine information derived from available data sets to characterize ash trees. Within this framework, an advanced individual tree delineation method was developed to delineate individual trees using the combined high-spatial resolution worldview-3 imagery was used together with LiDAR data. Individual trees were then classified to ash and non-ash trees using spectral and spatial information. In order to characterize the health state of individual ash trees, leaves from ash trees with various health states were sampled and measured using a field spectrometer. Based on the field measurements, the best indices that sensitive to leaf chlorophyll content were selected. The developed framework and methods were tested using worldview-3, airborne LiDAR data over the Keele campus of York University Toronto Canada. Satisfactory results in terms of individual tree crown delineation, ash tree identification and characterization of the health state of individual ash trees. Quantitative evaluations is being carried out.

Restoration of multichannel microwave radiometric images

NASA Technical Reports Server (NTRS)

Chin, R. T.; Yeh, C. L.; Olson, W. S.

1983-01-01

A constrained iterative image restoration method is applied to multichannel diffraction-limited imagery. This method is based on the Gerchberg-Papoulis algorithm utilizing incomplete information and partial constraints. The procedure is described using the orthogonal projection operators which project onto two prescribed subspaces iteratively. Some of its properties and limitations are also presented. The selection of appropriate constraints was emphasized in a practical application. Multichannel microwave images, each having different spatial resolution, were restored to a common highest resolution to demonstrate the effectiveness of the method. Both noise-free and noisy images were used in this investigation.

Effects of instrument characteristics on cloud properties retrieved from satellite imagery data

NASA Technical Reports Server (NTRS)

Baldwin, D. G.; Coakley, J. A., Jr.; Zhang, M. S.

1986-01-01

The relationships between sensor resolution and derived cloud properties in satellite remote sensing were studied by comparisons of cloud characteristics determined by spatial coherence analysis of AVHRR and GOES data. The latter data were simulated from 11 microns AVHRR data and were assigned a resolution (8 sq km) half that of the AVHRR. Day and nighttime passes were considered for single-layer maritime cloud systems. Sample radiance vs local standard deviation plots of 1024 points are provided for the same area from AVHRR and GOES-East sensors, demonstrating a qualitative agreement.

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 forms and mental imagery.

PubMed

Price, Mark C

2009-01-01

Four studies investigated how general mental imagery might be involved in mediating the phenomenon of 'synaesthetic' spatial forms - i.e., the experience that sequences such as months or numbers have spatial locations. In Study 1, people with spatial forms scored higher than controls on visual imagery self-report scales. This is consistent with the suggestion that strong general imagery is at least a necessary condition to experience spatial forms. However self-reported spatial imagery did not differ between groups, suggesting either that the spatial nature of forms is mediated by special synaesthetic mechanisms, or that forms are depictive visual images rather than explicit spatial models. A methodological implication of Study 1 was that a general tendency for people with spatial forms to use imagery strategies might account for some of their previously-reported behavioural differences with control groups. This concern was supported by Studies 2-4. Normal participants were encouraged to visually image the months in various spatial layouts, and spatial associations for months were tested using left/right key presses to classify month names as belonging to the first or second half of the year (Studies 2-3) or as odd/even (Study 4). Reaction times showed month-SNARC (Spatial Numerical Association of Response Codes) effects of similar magnitude to previously-reported data from spatial form participants (Price and Mentzoni, 2008). Additionally, reversing the spatial associations within instructed images was sufficient to reverse the direction of observed month-SNARC effects (i.e., positive vs negative slope), just as different spatial forms were previously shown to modulate the direction of effects (ibid.). Results challenge whether previously observed behavioural differences between spatial form and control groups need to be explained in terms of special synaesthetic mechanisms rather than intentional imagery strategies. It is argued that usually strong general imagery processes should complement synaesthetic mechanisms as possible explanations of spatial forms.

Association between Social Anxiety and Visual Mental Imagery of Neutral Scenes: The Moderating Role of Effortful Control.

PubMed

Moriya, Jun

2017-01-01

According to cognitive theories, verbal processing attenuates emotional processing, whereas visual imagery enhances emotional processing and contributes to the maintenance of social anxiety. Individuals with social anxiety report negative mental images in social situations. However, the general ability of visual mental imagery of neutral scenes in individuals with social anxiety is still unclear. The present study investigated the general ability of non-emotional mental imagery (vividness, preferences for imagery vs. verbal processing, and object or spatial imagery) and the moderating role of effortful control in attenuating social anxiety. The participants ( N = 231) completed five questionnaires. The results showed that social anxiety was not necessarily associated with all aspects of mental imagery. As suggested by theories, social anxiety was not associated with a preference for verbal processing. However, social anxiety was positively correlated with the visual imagery scale, especially the object imagery scale, which concerns the ability to construct pictorial images of individual objects. Further, it was negatively correlated with the spatial imagery scale, which concerns the ability to process information about spatial relations between objects. Although object imagery and spatial imagery positively and negatively predicted the degree of social anxiety, respectively, these effects were attenuated when socially anxious individuals had high effortful control. Specifically, in individuals with high effortful control, both object and spatial imagery were not associated with social anxiety. Socially anxious individuals might prefer to construct pictorial images of individual objects in natural scenes through object imagery. However, even in individuals who exhibit these features of mental imagery, effortful control could inhibit the increase in social anxiety.

Generation of High Resolution Land Surface Parameters in the Community Land Model

NASA Astrophysics Data System (ADS)

Ke, Y.; Coleman, A. M.; Wigmosta, M. S.; Leung, L.; Huang, M.; Li, H.

2010-12-01

The Community Land Model (CLM) is the land surface model used for the Community Atmosphere Model (CAM) and the Community Climate System Model (CCSM). It examines the physical, chemical, and biological processes across a variety of spatial and temporal scales. Currently, efforts are being made to improve the spatial resolution of the CLM, in part, to represent finer scale hydrologic characteristics. Current land surface parameters of CLM4.0, in particular plant functional types (PFT) and leaf area index (LAI), are generated from MODIS and calculated at a 0.05 degree resolution. These MODIS-derived land surface parameters have also been aggregated to coarser resolutions (e.g., 0.5, 1.0 degrees). To evaluate the response of CLM across various spatial scales, higher spatial resolution land surface parameters need to be generated. In this study we examine the use of Landsat TM/ETM+ imagery and data fusion techniques for generating land surface parameters at a 1km resolution within the Pacific Northwest United States. . Land cover types and PFTs are classified based on Landsat multi-season spectral information, DEM, National Land Cover Database (NLCD) and the USDA-NASS Crop Data Layer (CDL). For each PFT, relationships between MOD15A2 high quality LAI values, Landsat-based vegetation indices, climate variables, terrain, and laser-altimeter derived vegetation height are used to generate monthly LAI values at a 30m resolution. The high-resolution PFT and LAI data are aggregated to create a 1km model grid resolution. An evaluation and comparison of CLM land surface response at both fine and moderate scale is presented.

Globally scalable generation of high-resolution land cover from multispectral imagery

NASA Astrophysics Data System (ADS)

Stutts, S. Craig; Raskob, Benjamin L.; Wenger, Eric J.

2017-05-01

We present an automated method of generating high resolution ( 2 meter) land cover using a pattern recognition neural network trained on spatial and spectral features obtained from over 9000 WorldView multispectral images (MSI) in six distinct world regions. At this resolution, the network can classify small-scale objects such as individual buildings, roads, and irrigation ponds. This paper focuses on three key areas. First, we describe our land cover generation process, which involves the co-registration and aggregation of multiple spatially overlapping MSI, post-aggregation processing, and the registration of land cover to OpenStreetMap (OSM) road vectors using feature correspondence. Second, we discuss the generation of land cover derivative products and their impact in the areas of region reduction and object detection. Finally, we discuss the process of globally scaling land cover generation using cloud computing via Amazon Web Services (AWS).

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery

NASA Astrophysics Data System (ADS)

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhou, Yuting; Zhu, Zhe; Zhang, Geli; Du, Guoming; Jin, Cui; Kou, Weili; Wang, Jie; Li, Xiangping

2015-07-01

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms (R2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery.

PubMed

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhou, Yuting; Zhu, Zhe; Zhang, Geli; Du, Guoming; Jin, Cui; Kou, Weili; Wang, Jie; Li, Xiangping

2015-07-01

Accurate and timely rice paddy field maps with a fine spatial resolution would greatly improve our understanding of the effects of paddy rice agriculture on greenhouse gases emissions, food and water security, and human health. Rice paddy field maps were developed using optical images with high temporal resolution and coarse spatial resolution (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) or low temporal resolution and high spatial resolution (e.g., Landsat TM/ETM+). In the past, the accuracy and efficiency for rice paddy field mapping at fine spatial resolutions were limited by the poor data availability and image-based algorithms. In this paper, time series MODIS and Landsat ETM+/OLI images, and the pixel- and phenology-based algorithm are used to map paddy rice planting area. The unique physical features of rice paddy fields during the flooding/open-canopy period are captured with the dynamics of vegetation indices, which are then used to identify rice paddy fields. The algorithm is tested in the Sanjiang Plain (path/row 114/27) in China in 2013. The overall accuracy of the resulted map of paddy rice planting area generated by both Landsat ETM+ and OLI is 97.3%, when evaluated with areas of interest (AOIs) derived from geo-referenced field photos. The paddy rice planting area map also agrees reasonably well with the official statistics at the level of state farms ( R 2 = 0.94). These results demonstrate that the combination of fine spatial resolution images and the phenology-based algorithm can provide a simple, robust, and automated approach to map the distribution of paddy rice agriculture in a year.

A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space.

PubMed

Lagomasino, David; Fatoyinbo, Temilola; Lee, SeungKuk; Feliciano, Emanuelle; Trettin, Carl; Simard, Marc

2016-04-01

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.

The Joint Role of Spatial Ability and Imagery Strategy in Sustaining the Learning of Spatial Descriptions under Spatial Interference

ERIC Educational Resources Information Center

Meneghetti, Chiara; De Beni, Rossana; Gyselinck, Valerie; Pazzaglia, Francesca

2013-01-01

The present study investigates the joint role of spatial ability, imagery strategy and visuospatial working memory (VSWM) in spatial text processing. A set of 180 participants, half of them trained on the use of imagery strategy (training vs no-training groups), was further divided according to participants' high or low mental rotation ability…

Influence of resolution in irrigated area mapping and area estimation

USGS Publications Warehouse

Velpuri, N.M.; Thenkabail, P.S.; Gumma, M.K.; Biradar, C.; Dheeravath, V.; Noojipady, P.; Yuanjie, L.

2009-01-01

The overarching goal of this paper was to determine how irrigated areas change with resolution (or scale) of imagery. Specific objectives investigated were to (a) map irrigated areas using four distinct spatial resolutions (or scales), (b) determine how irrigated areas change with resolutions, and (c) establish the causes of differences in resolution-based irrigated areas. The study was conducted in the very large Krishna River basin (India), which has a high degree of formal contiguous, and informal fragmented irrigated areas. The irrigated areas were mapped using satellite sensor data at four distinct resolutions: (a) NOAA AVHRR Pathfinder 10,000 m, (b) Terra MODIS 500 m, (c) Terra MODIS 250 m, and (d) Landsat ETM+ 30 m. The proportion of irrigated areas relative to Landsat 30 m derived irrigated areas (9.36 million hectares for the Krishna basin) were (a) 95 percent using MODIS 250 m, (b) 93 percent using MODIS 500 m, and (c) 86 percent using AVHRR 10,000 m. In this study, it was found that the precise location of the irrigated areas were better established using finer spatial resolution data. A strong relationship (R2 = 0.74 to 0.95) was observed between irrigated areas determined using various resolutions. This study proved the hypotheses that "the finer the spatial resolution of the sensor used, greater was the irrigated area derived," since at finer spatial resolutions, fragmented areas are detected better. Accuracies and errors were established consistently for three classes (surface water irrigated, ground water/conjunctive use irrigated, and nonirrigated) across the four resolutions mentioned above. The results showed that the Landsat data provided significantly higher overall accuracies (84 percent) when compared to MODIS 500 m (77 percent), MODIS 250 m (79 percent), and AVHRR 10,000 m (63 percent). ?? 2009 American Society for Photogrammetry and Remote Sensing.

Cloud classification in polar regions using AVHRR textural and spectral signatures

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Weger, R. C.; Christopher, S. A.; Kuo, K. S.; Carsey, F. D.

1990-01-01

Arctic clouds and ice-covered surfaces are classified on the basis of textural and spectral features obtained with AVHRR 1.1-km spatial resolution imagery over the Beaufort Sea during May-October, 1989. Scenes were acquired about every 5 days, for a total of 38 cases. A list comprising 20 arctic-surface and cloud classes is compiled using spectral measures defined by Garand (1988).

A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data

Treesearch

B. Tyler Wilson; Andrew J. Lister; Rachel I. Riemann

2012-01-01

The paper describes an efficient approach for mapping multiple individual tree species over large spatial domains. The method integrates vegetation phenology derived from MODIS imagery and raster data describing relevant environmental parameters with extensive field plot data of tree species basal area to create maps of tree species abundance and distribution at a 250-...

The extent of forest in dryland biomes

Treesearch

Jean-Francois Bastin; Nora Berrahmouni; Alan Grainger; Danae Maniatis; Danilo Mollicone; Rebecca Moore; Chiara Patriarca; Nicolas Picard; Ben Sparrow; Elena Maria Abraham; Kamel Aloui; Ayhan Atesoglu; Fabio Attore; Caglar Bassullu; Adia Bey; Monica Garzuglia; Luis G. GarcÌa-Montero; Nikee Groot; Greg Guerin; Lars Laestadius; Andrew J. Lowe; Bako Mamane; Giulio Marchi; Paul Patterson; Marcelo Rezende; Stefano Ricci; Ignacio Salcedo; Alfonso Sanchez-Paus Diaz; Fred Stolle; Venera Surappaeva; Rene Castro

2017-01-01

Dryland biomes cover two-fifths of Earth’s land surface, but their forest area is poorly known. Here, we report an estimate of global forest extent in dryland biomes, based on analyzing more than 210,000 0.5-hectare sample plots through a photo-interpretation approach using large databases of satellite imagery at (i) very high spatial resolution and (ii) very high...

Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty

Treesearch

Katharine White; Jennifer Pontius; Paul Schaberg

2014-01-01

Current remote sensing studies of phenology have been limited to coarse spatial or temporal resolution and often lack a direct link to field measurements. To address this gap, we compared remote sensing methodologies using Landsat Thematic Mapper (TM) imagery to extensive field measurements in a mixed northern hardwood forest. Five vegetation indices, five mathematical...

Above Ground Carbon Stock Estimates of Mangrove Forest Using Worldview-2 Imagery in Teluk Benoa, Bali

NASA Astrophysics Data System (ADS)

Candra, E. D.; Hartono; Wicaksono, P.

2016-11-01

Mangrove forests have a role as an absorbent and a carbon sink to a reduction CO2 in the atmosphere. Based on the previous studies found that mangrove forests have the ability to sequestering carbon through photosynthesis and carbon burial of sediment effectively. The value and distribution of carbon stock are important to understand through remote sensing technology. In this study, will estimate the carbon stock using WorldView-2 imagery with and without distinction mangrove species. Worldview-2 is a high resolution image with 2 meters spatial resolution and eight spectral bands. Worldview-2 potential to estimate carbon stock in detail. Vegetation indices such as DVI (Difference Vegetation Index), EVI (Enhanced Vegetation Index), and MRE-SR (Modified Red Edge-Simple Ratio) and field data were modeled to determine the best vegetation indices to estimate carbon stocks. Carbon stock estimated by allometric equation approach specific to each species of mangrove. Worldview-2 imagery to map mangrove species with an accuracy of 80.95%. Total carbon stock estimation results in the study area of 35.349,87 tons of dominant species Rhizophora apiculata, Rhizophora mucronata and Sonneratia alba.

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.

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.

Sequential Imaging of Earth by Astronauts: 50 Years of Global Change

NASA Technical Reports Server (NTRS)

Evans, Cynthia A.

2009-01-01

For nearly 50 years, astronauts have collected sequential imagery of the Earth. In fact, the collection of astronaut photography comprises one of the earliest sets of data (1961 to present) available to scientists to study the regional context of the Earth s surface and how it changes. While today s availability of global high resolution satellite imagery enables anyone with an internet connection to examine specific features on the Earth s surface with a regional context, historical satellite imagery adds another dimension (time) that provides researchers and students insight about the features and processes of a region. For example, one of the geographic areas with the longest length of record contained within the astronaut photography database is the lower Nile River. The database contains images that document the flooding of Lake Nasser (an analog to today s flooding behind China s Three Gorges Dam), the changing levels of Lake Nasser s water with multiyear cycles of flood and drought, the recent flooding and drying of the Toshka Lakes, as well as urban growth, changes in agriculture and coastal subsidence. The imagery database allows investigations using different time scales (hours to decades) and spatial scales (resolutions and fields of view) as variables. To continue the imagery collection, the astronauts on the International Space Station are trained to understand basic the Earth Sciences and look for and photograph major events such as tropical storms, landslides, and volcanic eruptions, and document landscapes undergoing change (e.g., coastal systems, cities, changing forest cover). We present examples of selected sequences of astronaut imagery that illustrate the interdependence of geological processes, climate cycles, human geography and development, and prompt additional questions about the underlying elements of change.

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.

Canopies to Continents: What spatial scales are needed to represent landcover distributions in earth system models?

NASA Astrophysics Data System (ADS)

Guenther, A. B.; Duhl, T.

2011-12-01

Increasing computational resources have enabled a steady improvement in the spatial resolution used for earth system models. Land surface models and landcover distributions have kept ahead by providing higher spatial resolution than typically used in these models. Satellite observations have played a major role in providing high resolution landcover distributions over large regions or the entire earth surface but ground observations are needed to calibrate these data and provide accurate inputs for models. As our ability to resolve individual landscape components improves, it is important to consider what scale is sufficient for providing inputs to earth system models. The required spatial scale is dependent on the processes being represented and the scientific questions being addressed. This presentation will describe the development a contiguous U.S. landcover database using high resolution imagery (1 to 1000 meters) and surface observations of species composition and other landcover characteristics. The database includes plant functional types and species composition and is suitable for driving land surface models (CLM and MEGAN) that predict land surface exchange of carbon, water, energy and biogenic reactive gases (e.g., isoprene, sesquiterpenes, and NO). We investigate the sensitivity of model results to landcover distributions with spatial scales ranging over six orders of magnitude (1 meter to 1000000 meters). The implications for predictions of regional climate and air quality will be discussed along with recommendations for regional and global earth system modeling.

Bamboo mapping of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Zhao, Yuanyuan; Feng, Duole; Jayaraman, Durai; Belay, Daniel; Sebrala, Heiru; Ngugi, John; Maina, Eunice; Akombo, Rose; Otuoma, John; Mutyaba, Joseph; Kissa, Sam; Qi, Shuhua; Assefa, Fiker; Oduor, Nellie Mugure; Ndawula, Andrew Kalema; Li, Yanxia; Gong, Peng

2018-04-01

Mapping the spatial distribution of bamboo in East Africa is necessary for biodiversity conservation, resource management and policy making for rural poverty reduction. In this study, we produced a contemporary bamboo cover map of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery series at 30 m spatial resolution. This is the first bamboo map generated using remotely sensed data for these three East African countries that possess most of the African bamboo resource. The producer's and user's accuracies of bamboos are 79.2% and 84.0%, respectively. The hotspots with large amounts of bamboo were identified and the area of bamboo coverage for each region was estimated according to the map. The seasonal growth status of two typical bamboo zones (one highland bamboo and one lowland bamboo) were analyzed and the multi-temporal imagery proved to be useful in differentiating bamboo from other vegetation classes. The images acquired in September to February are less contaminated by clouds and shadows, and the image series cover the dying back process of lowland bamboo, which were helpful for bamboo identification in East Africa.

Evaluating the Impact of Spatial Resolution of Landsat Predictors on the Accuracy of Biomass Models for Large-area Estimation Across the Eastern USA

NASA Astrophysics Data System (ADS)

Deo, R. K.; Domke, G. M.; Russell, M.; Woodall, C. W.

2017-12-01

Landsat data have been widely used to support strategic forest inventory and management decisions despite the limited success of passive optical remote sensing for accurate estimation of aboveground biomass (AGB). The archive of publicly available Landsat data, available at 30-m spatial resolutions since 1984, has been a valuable resource for cost-effective large-area estimation of AGB to inform national requirements such as for the US national greenhouse gas inventory (NGHGI). In addition, other optical satellite data such as MODIS imagery of wider spatial coverage and higher temporal resolution are enriching the domain of spatial predictors for regional scale mapping of AGB. Because NGHGIs require national scale AGB information and there are tradeoffs in the prediction accuracy versus operational efficiency of Landsat, this study evaluated the impact of various resolutions of Landsat predictors on the accuracy of regional AGB models across three different sites in the eastern USA: Maine, Pennsylvania-New Jersey, and South Carolina. We used recent national forest inventory (NFI) data with numerous Landsat-derived predictors at ten different spatial resolutions ranging from 30 to 1000 m to understand the optimal spatial resolution of the optical data for enhanced spatial inventory of AGB for NGHGI reporting. Ten generic spatial models at different spatial resolutions were developed for all sites and large-area estimates were evaluated (i) at the county-level against the independent designed-based estimates via the US NFI Evalidator tool and (ii) within a large number of strips ( 1 km wide) predicted via LiDAR metrics at a high spatial resolution. The county-level estimates by the Evalidator and Landsat models were statistically equivalent and produced coefficients of determination (R2) above 0.85 that varied with sites and resolution of predictors. The mean and standard deviation of county-level estimates followed increasing and decreasing trends, respectively, with models of decreasing resolutions. The Landsat-based total AGB estimates within the strips against the total AGB obtained using LiDAR metrics did not differ significantly and were within ±15 Mg/ha for each of the sites. We conclude that the optical satellite data at resolutions up to 1000 m provide acceptable accuracy for the US' NGHGI.

Comparison of Object-Based Image Analysis Approaches to Mapping New Buildings in Accra, Ghana Using Multi-Temporal QuickBird Satellite Imagery

PubMed Central

Tsai, Yu Hsin; Stow, Douglas; Weeks, John

2013-01-01

The goal of this study was to map and quantify the number of newly constructed buildings in Accra, Ghana between 2002 and 2010 based on high spatial resolution satellite image data. Two semi-automated feature detection approaches for detecting and mapping newly constructed buildings based on QuickBird very high spatial resolution satellite imagery were analyzed: (1) post-classification comparison; and (2) bi-temporal layerstack classification. Feature Analyst software based on a spatial contextual classifier and ENVI Feature Extraction that uses a true object-based image analysis approach of image segmentation and segment classification were evaluated. Final map products representing new building objects were compared and assessed for accuracy using two object-based accuracy measures, completeness and correctness. The bi-temporal layerstack method generated more accurate results compared to the post-classification comparison method due to less confusion with background objects. The spectral/spatial contextual approach (Feature Analyst) outperformed the true object-based feature delineation approach (ENVI Feature Extraction) due to its ability to more reliably delineate individual buildings of various sizes. Semi-automated, object-based detection followed by manual editing appears to be a reliable and efficient approach for detecting and enumerating new building objects. A bivariate regression analysis was performed using neighborhood-level estimates of new building density regressed on a census-derived measure of socio-economic status, yielding an inverse relationship with R2 = 0.31 (n = 27; p = 0.00). The primary utility of the new building delineation results is to support spatial analyses of land cover and land use and demographic change. PMID:24415810

New Possibilities for High-Resolution, Large-Scale Ecosystem Assessment of the World's Semi-Arid Regions

NASA Astrophysics Data System (ADS)

Burney, J. A.; Goldblatt, R.

2016-12-01

Understanding drivers of land use change - and in particular, levels of ecosystem degradation - in semi-arid regions is of critical importance because these agroecosystems (1) are home to the world's poorest populations, almost all of whom depend on agriculture for their livelihoods, (2) play a critical role in the global carbon and climate cycles, and (3) have in many cases seen dramatic changes in temperature and precipitation, relative to global averages, over the past several decades. However, assessing ecosystem health (or, conversely, degradation) presents a difficult measurement problem. Established methods are very labor intensive and rest on detailed questionnaires and field assessments. High-resolution satellite imagery has a unique role semi-arid ecosystem assessment in that it can be used for rapid (or repeated) and very simple measurements of tree and shrub density, an excellent overall indicator for dryland ecosystem health. Because trees and large shrubs are more sparse in semi-arid regions, sub-meter resolution imagery in conjunction with automated image analysis can be used to assess density differences at high spatial resolution without expensive and time-consuming ground-truthing. This could be used down to the farm level, for example, to better assess the larger-scale ecosystem impacts of different management practices, to assess compliance with REDD+ carbon offset protocols, or to evaluate implementation of conservation goals. Here we present results comparing spatial and spectral remote sensing methods for semi-arid ecosystem assessment across new data sources, using the Brazilian Sertão as an example, and the implications for large-scale use in semi-arid ecosystem science.

Recognition and characterization of networks of water bodies in the Arctic ice-wedge polygonal tundra using high-resolution satellite imagery

NASA Astrophysics Data System (ADS)

Skurikhin, A. N.; Gangodagamage, C.; Rowland, J. C.; Wilson, C. J.

2013-12-01

Arctic lowland landscapes underlain by permafrost are often characterized by polygon-like patterns such as ice-wedge polygons outlined by networks of ice wedges and complemented with polygon rims, troughs, shallow ponds and thermokarst lakes. Polygonal patterns and corresponding features are relatively easy to recognize in high spatial resolution satellite imagery by a human, but their automated recognition is challenging due to the variability in their spectral appearance, the irregularity of individual trough spacing and orientation within the patterns, and a lack of unique spectral response attributable to troughs with widths commonly between 1 m and 2 m. Accurate identification of fine scale elements of ice-wedge polygonal tundra is important as their imprecise recognition may bias estimates of water, heat and carbon fluxes in large-scale climate models. Our focus is on the problem of identification of Arctic polygonal tundra fine-scale landscape elements (as small as 1 m - 2 m width). The challenge of the considered problem is that while large water bodies (e.g. lakes and rivers) can be recognized based on spectral response, reliable recognition of troughs is more difficult. Troughs do not have unique spectral signature, their appearance is noisy (edges are not strong), their width is small, and they often form connected networks with ponds and lakes, and thus they have overlapping spectral response with other water bodies and surrounding non-water bodies. We present a semi-automated approach to identify and classify Arctic polygonal tundra landscape components across the range of spatial scales, such as troughs, ponds, river- and lake-like objects, using high spatial resolution satellite imagery. The novelty of the approach lies in: (1) the combined use of segmentation and shape-based classification to identify a broad range of water bodies, including troughs, and (2) the use of high-resolution WorldView-2 satellite imagery (with resolution of 0.6 m) for this identification. The approach starts by segmenting water bodies from an image, which are then categorized using shape-based classification. Segmentation uses combination of pan sharpened multispectral bands and is based on the active contours without edges technique. The segmentation is robust to noise and can detect objects with weak boundaries that is important for extraction of troughs. We then categorize the segmented regions via shape based classification. Because segmentation accuracy is the main factor impacting the quality of the shape-based classification, for segmentation accuracy assessment we created reference image using WorldView-2 satellite image of ice-wedge polygonal tundra. Reference image contained manually labelled image regions which cover components of drainage networks, such as troughs, ponds, rivers and lakes. The evaluation has shown that the approach provides a good accuracy of segmentation and reasonable classification results. The overall accuracy of the segmentation is approximately 95%, the segmentation user's and producer's accuracies are approximately 92% and 97% respectively.

Monitoring the dynamics of surface water fraction from MODIS time series in a Mediterranean environment

NASA Astrophysics Data System (ADS)

Li, Linlin; Vrieling, Anton; Skidmore, Andrew; Wang, Tiejun; Turak, Eren

2018-04-01

Detailed spatial information of changes in surface water extent is needed for water management and biodiversity conservation, particularly in drier parts of the globe where small, temporally-variant wetlands prevail. Although global surface water histories are now generated from 30 m Landsat data, for many locations they contain large temporal gaps particularly for longer periods (>10 years) due to revisit intervals and cloud cover. Daily Moderate Resolution Imaging Spectrometer (MODIS) imagery has potential to fill such gaps, but its relatively coarse spatial resolution may not detect small water bodies, which can be of great ecological importance. To address this problem, this study proposes and tests options for estimating the surface water fraction from MODIS 16-day 500 m Bidirectional Reflectance Distribution Function (BRDF) corrected surface reflectance image composites. The spatial extent of two Landsat tiles over Spain were selected as test areas. We obtained a 500 m reference dataset on surface water fraction by spatially aggregating 30 m binary water masks obtained from the Landsat-derived C-version of Function of Mask (CFmask), which themselves were evaluated against high-resolution Google Earth imagery. Twelve regression tree models were developed with two approaches, Random Forest and Cubist, using spectral metrics derived from MODIS data and topographic parameters generated from a 30 m spatial resolution digital elevation model. Results showed that accuracies were higher when we included annual summary statistics of the spectral metrics as predictor variables. Models trained on a single Landsat tile were ineffective in mapping surface water in the other tile, but global models trained with environmental conditions from both tiles can provide accurate results for both study areas. We achieved the highest accuracy with Cubist global model (R2 = 0.91, RMSE = 11.05%, MAE = 7.67%). Our method was not only effective for mapping permanent water fraction, but also in accurately capturing temporal fluctuations of surface water. Based on this good performance, we produced surface water fraction maps at 16-day interval for the 2000-2015 MODIS archive. Our approach is promising for monitoring surface water fraction at high frequency time intervals over much larger regions provided that training data are collected across the spatial domain for which the model will be applied.

Object-based vegetation classification with high resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Yu, Qian

Vegetation species are valuable indicators to understand the earth system. Information from mapping of vegetation species and community distribution at large scales provides important insight for studying the phenological (growth) cycles of vegetation and plant physiology. Such information plays an important role in land process modeling including climate, ecosystem and hydrological models. The rapidly growing remote sensing technology has increased its potential in vegetation species mapping. However, extracting information at a species level is still a challenging research topic. I proposed an effective method for extracting vegetation species distribution from remotely sensed data and investigated some ways for accuracy improvement. The study consists of three phases. Firstly, a statistical analysis was conducted to explore the spatial variation and class separability of vegetation as a function of image scale. This analysis aimed to confirm that high resolution imagery contains the information on spatial vegetation variation and these species classes can be potentially separable. The second phase was a major effort in advancing classification by proposing a method for extracting vegetation species from high spatial resolution remote sensing data. The proposed classification employs an object-based approach that integrates GIS and remote sensing data and explores the usefulness of ancillary information. The whole process includes image segmentation, feature generation and selection, and nearest neighbor classification. The third phase introduces a spatial regression model for evaluating the mapping quality from the above vegetation classification results. The effects of six categories of sample characteristics on the classification uncertainty are examined: topography, sample membership, sample density, spatial composition characteristics, training reliability and sample object features. This evaluation analysis answered several interesting scientific questions such as (1) whether the sample characteristics affect the classification accuracy and how significant if it does; (2) how much variance of classification uncertainty can be explained by above factors. This research is carried out on a hilly peninsular area in Mediterranean climate, Point Reyes National Seashore (PRNS) in Northern California. The area mainly consists of a heterogeneous, semi-natural broadleaf and conifer woodland, shrub land, and annual grassland. A detailed list of vegetation alliances is used in this study. Research results from the first phase indicates that vegetation spatial variation as reflected by the average local variance (ALV) keeps a high level of magnitude between 1 m and 4 m resolution. (Abstract shortened by UMI.)

Extending a prototype knowledge- and object-based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Extending a prototype knowledge and object based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

A Subpixel Classification of Multispectral Satellite Imagery for Interpetation of Tundra-Taiga Ecotone Vegetation (Case Study on Tuliok River Valley, Khibiny, Russia)

NASA Astrophysics Data System (ADS)

Mikheeva, A. I.; Tutubalina, O. V.; Zimin, M. V.; Golubeva, E. I.

2017-12-01

The tundra-taiga ecotone plays significant role in northern ecosystems. Due to global climatic changes, the vegetation of the ecotone is the key object of many remote-sensing studies. The interpretation of vegetation and nonvegetation objects of the tundra-taiga ecotone on satellite imageries of a moderate resolution is complicated by the difficulty of extracting these objects from the spectral and spatial mixtures within a pixel. This article describes a method for the subpixel classification of Terra ASTER satellite image for vegetation mapping of the tundra-taiga ecotone in the Tuliok River, Khibiny Mountains, Russia. It was demonstrated that this method allows to determine the position of the boundaries of ecotone objects and their abundance on the basis of quantitative criteria, which provides a more accurate characteristic of ecotone vegetation when compared to the per-pixel approach of automatic imagery interpretation.

An Evaluation of Data Fusion Products for the Analysis of Dryland Forest Phenology

NASA Astrophysics Data System (ADS)

Walker, J. J.; de Beurs, K.; Wynne, R. H.; Gao, F.

2010-12-01

Semi-arid forest areas cover a significant proportion of the world’s land surface; in the interior western U.S. alone, dryland forests extend across more than 56 million hectares. The scarcity of water in these systems makes them acutely sensitive to sustained weather fluctuations, such as the higher temperatures and altered water regimes predicted under most climate change scenarios. To understand, monitor, and predict the anticipated spatial and temporal changes in these areas, it is vital to characterize current phenological patterns. Phenological analysis of western U.S. drylands is complicated by patchy land cover and mosaics of plant phenology states at a variety of spatial scales. Our aim is to use complementary satellite sensors to mitigate these difficulties and gain greater insight into phenological patterns in dryland forests. In this study we applied the spatial and temporal adaptive reflectance model (STARFM; Gao et al. 2006) to fuse Landsat and MODIS imagery to create synthetic images at Landsat spatial resolution and MODIS temporal resolution. To determine which MODIS dataset is most appropriate for the creation of synthetic images intended for the analysis of dryland forest phenology, we examined the effect of temporal compositing and BRDF function adjustment on the accuracy of STARFM imagery. We assembled seven Landsat 5 scenes (path/row 37/36) and temporally-coincident 500m MODIS datasets (seven daily (MOD09GA), seven 8-day composite (MOD09A1), and fourteen 16-day nadir BRDF-adjusted composite (MCD43A4) images) spanning the 2006 April - October growing season in northern Arizona, which is characterized by large tracts of dryland forest. The STARFM algorithm was applied to each MODIS data series to produce four synthetic images (one daily; one 8-day composite; and two 16-day composites) corresponding to each Landsat image. Validation of the accuracy of the synthetic images was achieved by comparing the reflectance values of a random sample of the identified dryland forest pixels in both images. Preliminary data analysis of the effect of the temporal resolution and dataset parameters indicates that the MODIS 8-day composite image may be a suitable and sufficient dataset for phenological analysis in this dryland forest ecosystem. Overall, this work demonstrates the feasibility of using data fusion products to assemble an imagery dataset at sufficiently high temporal and spatial scales to permit a more detailed examination of the underlying phenological processes and trends in dryland forest areas.

Evaluation of Crops Moisture Provision by Space Remote Sensing Data

NASA Astrophysics Data System (ADS)

Ilienko, Tetiana

2016-08-01

The article is focused on theoretical and experimental rationale for the use of space data to determine the moisture provision of agricultural landscapes and agricultural plants. The improvement of space remote sensing methods to evaluate plant moisture availability is the aim of this research.It was proved the possibility of replacement of satellite imagery of high spatial resolution on medium spatial resolution which are freely available to determine crop moisture content at the local level. The mathematical models to determine the moisture content of winter wheat plants by spectral indices were developed based on the results of experimental field research and satellite (Landsat, MODIS/Terra, RapidEye, SICH-2) data. The maps of the moisture content in winter wheat plants in test sites by obtained models were constructed using modern GIS technology.

Feasibility of approaches combining sensor and source features in brain-computer interface.

PubMed

Ahn, Minkyu; Hong, Jun Hee; Jun, Sung Chan

2012-02-15

Brain-computer interface (BCI) provides a new channel for communication between brain and computers through brain signals. Cost-effective EEG provides good temporal resolution, but its spatial resolution is poor and sensor information is blurred by inherent noise. To overcome these issues, spatial filtering and feature extraction techniques have been developed. Source imaging, transformation of sensor signals into the source space through source localizer, has gained attention as a new approach for BCI. It has been reported that the source imaging yields some improvement of BCI performance. However, there exists no thorough investigation on how source imaging information overlaps with, and is complementary to, sensor information. Information (visible information) from the source space may overlap as well as be exclusive to information from the sensor space is hypothesized. Therefore, we can extract more information from the sensor and source spaces if our hypothesis is true, thereby contributing to more accurate BCI systems. In this work, features from each space (sensor or source), and two strategies combining sensor and source features are assessed. The information distribution among the sensor, source, and combined spaces is discussed through a Venn diagram for 18 motor imagery datasets. Additional 5 motor imagery datasets from the BCI Competition III site were examined. The results showed that the addition of source information yielded about 3.8% classification improvement for 18 motor imagery datasets and showed an average accuracy of 75.56% for BCI Competition data. Our proposed approach is promising, and improved performance may be possible with better head model. Copyright © 2011 Elsevier B.V. All rights reserved.

Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery

NASA Astrophysics Data System (ADS)

Yang, Jian; He, Yuhong

2017-02-01

Quantifying impervious surfaces in urban and suburban areas is a key step toward a sustainable urban planning and management strategy. With the availability of fine-scale remote sensing imagery, automated mapping of impervious surfaces has attracted growing attention. However, the vast majority of existing studies have selected pixel-based and object-based methods for impervious surface mapping, with few adopting sub-pixel analysis of high spatial resolution imagery. This research makes use of a vegetation-bright impervious-dark impervious linear spectral mixture model to characterize urban and suburban surface components. A WorldView-3 image acquired on May 9th, 2015 is analyzed for its potential in automated unmixing of meaningful surface materials for two urban subsets and one suburban subset in Toronto, ON, Canada. Given the wide distribution of shadows in urban areas, the linear spectral unmixing is implemented in non-shadowed and shadowed areas separately for the two urban subsets. The results indicate that the accuracy of impervious surface mapping in suburban areas reaches up to 86.99%, much higher than the accuracies in urban areas (80.03% and 79.67%). Despite its merits in mapping accuracy and automation, the application of our proposed vegetation-bright impervious-dark impervious model to map impervious surfaces is limited due to the absence of soil component. To further extend the operational transferability of our proposed method, especially for the areas where plenty of bare soils exist during urbanization or reclamation, it is still of great necessity to mask out bare soils by automated classification prior to the implementation of linear spectral unmixing.

Data and Geocomputation: Time Critical Mission Support for the 2017 Hurricane Season

NASA Astrophysics Data System (ADS)

Bhaduri, B. L.; Tuttle, M.; Rose, A.; Sanyal, J.; Thakur, G.; White, D.; Yang, H. H.; Laverdiere, M.; Whitehead, M.; Taylor, H.; Jacob, M.

2017-12-01

A strong spatial data infrastructure and geospatial analysis capabilities are nucleus to the decision-making process during emergency preparedness, response, and recovery operations. For over a decade, the U.S. Department of Energy's Oak Ridge National Laboratory has been developing critical data and analytical capabilities that provide the Federal Emergency Management Agency (FEMA) and the rest of the federal response community assess and evaluate impacts of natural hazards on population and critical infrastructures including the status of the national electricity and oil and natural gas networks. These capabilities range from identifying structures or buildings from very high-resolution satellite imagery, utilizing machine learning and high-performance computing, to daily assessment of electricity restoration highlighting changes in nighttime lights for the impacted region based on the analysis of NOAA JPSS VIIRS Day/Night Band (DNB) imagery. This presentation will highlight our time critical mission support efforts for the 2017 hurricane season that witnessed unprecedented devastation from hurricanes Harvey, Irma, and Maria. ORNL provided 90m resolution LandScan USA population distribution data for identifying vulnerable population as well as structure (buildings) data extracted from 1m imagery for damage assessment. Spatially accurate data for solid waste facilities were developed and delivered to the response community. Human activity signatures were assessed from large scale collection of open source social media data around points of interests (POI) to ascertain level of destruction. The electricity transmission system was monitored in real time from data integration from hundreds of utilities and electricity outage information were provided back to the response community via standardized web-services.

Informed Source Separation of Atmospheric and Surface Signal Contributions in Shortwave Hyperspectral Imagery using Non-negative Matrix Factorization

NASA Astrophysics Data System (ADS)

Wright, L.; Coddington, O.; Pilewskie, P.

2015-12-01

Current challenges in Earth remote sensing require improved instrument spectral resolution, spectral coverage, and radiometric accuracy. Hyperspectral instruments, deployed on both aircraft and spacecraft, are a growing class of Earth observing sensors designed to meet these challenges. They collect large amounts of spectral data, allowing thorough characterization of both atmospheric and surface properties. The higher accuracy and increased spectral and spatial resolutions of new imagers require new numerical approaches for processing imagery and separating surface and atmospheric signals. One potential approach is source separation, which allows us to determine the underlying physical causes of observed changes. Improved signal separation will allow hyperspectral instruments to better address key science questions relevant to climate change, including land-use changes, trends in clouds and atmospheric water vapor, and aerosol characteristics. In this work, we investigate a Non-negative Matrix Factorization (NMF) method for the separation of atmospheric and land surface signal sources. NMF offers marked benefits over other commonly employed techniques, including non-negativity, which avoids physically impossible results, and adaptability, which allows the method to be tailored to hyperspectral source separation. We adapt our NMF algorithm to distinguish between contributions from different physically distinct sources by introducing constraints on spectral and spatial variability and by using library spectra to inform separation. We evaluate our NMF algorithm with simulated hyperspectral images as well as hyperspectral imagery from several instruments including, the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), NASA Hyperspectral Imager for the Coastal Ocean (HICO) and National Ecological Observatory Network (NEON) Imaging Spectrometer.

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

Incorporation of satellite remote sensing pan-sharpened imagery into digital soil prediction and mapping models to characterize soil property variability in small agricultural fields

NASA Astrophysics Data System (ADS)

Xu, Yiming; Smith, Scot E.; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P.

2017-01-01

Soil prediction models based on spectral indices from some multispectral images are too coarse to characterize spatial pattern of soil properties in small and heterogeneous agricultural lands. Image pan-sharpening has seldom been utilized in Digital Soil Mapping research before. This research aimed to analyze the effects of pan-sharpened (PAN) remote sensing spectral indices on soil prediction models in smallholder farm settings. This research fused the panchromatic band and multispectral (MS) bands of WorldView-2, GeoEye-1, and Landsat 8 images in a village in Southern India by Brovey, Gram-Schmidt and Intensity-Hue-Saturation methods. Random Forest was utilized to develop soil total nitrogen (TN) and soil exchangeable potassium (Kex) prediction models by incorporating multiple spectral indices from the PAN and MS images. Overall, our results showed that PAN remote sensing spectral indices have similar spectral characteristics with soil TN and Kex as MS remote sensing spectral indices. There is no soil prediction model incorporating the specific type of pan-sharpened spectral indices always had the strongest prediction capability of soil TN and Kex. The incorporation of pan-sharpened remote sensing spectral data not only increased the spatial resolution of the soil prediction maps, but also enhanced the prediction accuracy of soil prediction models. Small farms with limited footprint, fragmented ownership and diverse crop cycle should benefit greatly from the pan-sharpened high spatial resolution imagery for soil property mapping. Our results show that multiple high and medium resolution images can be used to map soil properties suggesting the possibility of an improvement in the maps' update frequency. Additionally, the results should benefit the large agricultural community through the reduction of routine soil sampling cost and improved prediction accuracy.

Choice of satellite imagery and attribution of changes to disturbance type strongly affects forest carbon balance estimates.

PubMed

Mascorro, Vanessa S; Coops, Nicholas C; Kurz, Werner A; Olguín, Marcela

2015-12-01

Remote sensing products can provide regular and consistent observations of the Earth´s surface to monitor and understand the condition and change of forest ecosystems and to inform estimates of terrestrial carbon dynamics. Yet, challenges remain to select the appropriate satellite data source for ecosystem carbon monitoring. In this study we examine the impacts of three attributes of four remote sensing products derived from Landsat, Landsat-SPOT, and MODIS satellite imagery on estimates of greenhouse gas emissions and removals: (1) the spatial resolution (30 vs. 250 m), (2) the temporal resolution (annual vs. multi-year observations), and (3) the attribution of forest cover changes to disturbance types using supplementary data. With a spatially-explicit version of the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), we produced annual estimates of carbon fluxes from 2002 to 2010 over a 3.2 million ha forested region in the Yucatan Peninsula, Mexico. The cumulative carbon balance for the 9-year period differed by 30.7 million MgC (112.5 million Mg CO 2e ) among the four remote sensing products used. The cumulative difference between scenarios with and without attribution of disturbance types was over 5 million Mg C for a single Landsat scene. Uncertainty arising from activity data (rates of land-cover changes) can be reduced by, in order of priority, increasing spatial resolution from 250 to 30 m, obtaining annual observations of forest disturbances, and by attributing land-cover changes by disturbance type. Even missing a single year in the land-cover observations can lead to substantial errors in ecosystems with rapid forest regrowth, such as the Yucatan Peninsula.

Mapping ephemeral stream networks in desert environments using very-high-spatial-resolution multispectral remote sensing

DOE PAGES

Hamada, Yuki; O'Connor, Ben L.; Orr, Andrew B.; ...

2016-03-26

In this paper, understanding the spatial patterns of ephemeral streams is crucial for understanding how hydrologic processes influence the abundance and distribution of wildlife habitats in desert regions. Available methods for mapping ephemeral streams at the watershed scale typically underestimate the size of channel networks. Although remote sensing is an effective means of collecting data and obtaining information on large, inaccessible areas, conventional techniques for extracting channel features are not sufficient in regions that have small topographic gradients and subtle target-background spectral contrast. By using very high resolution multispectral imagery, we developed a new algorithm that applies landscape information tomore » map ephemeral channels in desert regions of the Southwestern United States where utility-scale solar energy development is occurring. Knowledge about landscape features and structures was integrated into the algorithm using a series of spectral transformation and spatial statistical operations to integrate information about landscape features and structures. The algorithm extracted ephemeral stream channels at a local scale, with the result that approximately 900% more ephemeral streams was identified than what were identified by using the U.S. Geological Survey’s National Hydrography Dataset. The accuracy of the algorithm in detecting channel areas was as high as 92%, and its accuracy in delineating channel center lines was 91% when compared to a subset of channel networks that were digitized by using the very high resolution imagery. Although the algorithm captured stream channels in desert landscapes across various channel sizes and forms, it often underestimated stream headwaters and channels obscured by bright soils and sparse vegetation. While further improvement is warranted, the algorithm provides an effective means of obtaining detailed information about ephemeral streams, and it could make a significant contribution toward improving the hydrological modelling of desert environments.« less

Mapping ephemeral stream networks in desert environments using very-high-spatial-resolution multispectral remote sensing

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

Hamada, Yuki; O'Connor, Ben L.; Orr, Andrew B.

In this paper, understanding the spatial patterns of ephemeral streams is crucial for understanding how hydrologic processes influence the abundance and distribution of wildlife habitats in desert regions. Available methods for mapping ephemeral streams at the watershed scale typically underestimate the size of channel networks. Although remote sensing is an effective means of collecting data and obtaining information on large, inaccessible areas, conventional techniques for extracting channel features are not sufficient in regions that have small topographic gradients and subtle target-background spectral contrast. By using very high resolution multispectral imagery, we developed a new algorithm that applies landscape information tomore » map ephemeral channels in desert regions of the Southwestern United States where utility-scale solar energy development is occurring. Knowledge about landscape features and structures was integrated into the algorithm using a series of spectral transformation and spatial statistical operations to integrate information about landscape features and structures. The algorithm extracted ephemeral stream channels at a local scale, with the result that approximately 900% more ephemeral streams was identified than what were identified by using the U.S. Geological Survey’s National Hydrography Dataset. The accuracy of the algorithm in detecting channel areas was as high as 92%, and its accuracy in delineating channel center lines was 91% when compared to a subset of channel networks that were digitized by using the very high resolution imagery. Although the algorithm captured stream channels in desert landscapes across various channel sizes and forms, it often underestimated stream headwaters and channels obscured by bright soils and sparse vegetation. While further improvement is warranted, the algorithm provides an effective means of obtaining detailed information about ephemeral streams, and it could make a significant contribution toward improving the hydrological modelling of desert environments.« less

Modern thermokarst lake dynamics in the continuous permafrost zone, northern Seward Peninsula, Alaska

USGS Publications Warehouse

Jones, Benjamin M.; Grosse, G.; Arp, C.D.; Jones, M.C.; Walter, Anthony K.M.; Romanovsky, V.E.

2011-01-01

Quantifying changes in thermokarst lake extent is of importance for understanding the permafrost-related carbon budget, including the potential release of carbon via lake expansion or sequestration as peat in drained lake basins. We used high spatial resolution remotely sensed imagery from 1950/51, 1978, and 2006/07 to quantify changes in thermokarst lakes for a 700 km2 area on the northern Seward Peninsula, Alaska. The number of water bodies larger than 0.1 ha increased over the entire observation period (666 to 737 or +10.7%); however, total surface area decreased (5,066 ha to 4,312 ha or -14.9%). This pattern can largely be explained by the formation of remnant ponds following partial drainage of larger water bodies. Thus, analysis of large lakes (>40 ha) shows a decrease of 24% and 26% in number and area, respectively, differing from lake changes reported from other continuous permafrost regions. Thermokarst lake expansion rates did not change substantially between 1950/51 and 1978 (0.35 m/yr) and 1978 and 2006/07 (0.39 m/yr). However, most lakes that drained did expand as a result of surface permafrost degradation before lateral drainage. Drainage rates over the observation period were stable (2.2 to 2.3 per year). Thus, analysis of decadal-scale, high spatial resolution imagery has shown that lake drainage in this region is triggered by lateral breaching and not subterranean infiltration. Future research should be directed toward better understanding thermokarst lake dynamics at high spatial and temporal resolution as these systems have implications for landscape-scale hydrology and carbon budgets in thermokarst lake-rich regions in the circum-Arctic.

Can Satellite Remote Sensing be Applied in Geological Mapping in Tropics?

NASA Astrophysics Data System (ADS)

Magiera, Janusz

2018-03-01

Remote sensing (RS) techniques are based on spectral data registered by RS scanners as energy reflected from the Earth's surface or emitted by it. In "geological" RS the reflectance (or emittence) should come from rock or sediment. The problem in tropical and subtropical areas is a dense vegetation. Spectral response from the rocks and sediments is gathered only from the gaps among the trees and shrubs. Images of high resolution are appreciated here, therefore. New generation of satellites and scanners (Digital Globe WV2, WV3 and WV4) yield imagery of spatial resolution of 2 m and up to 16 spectral bands (WV3). Images acquired by Landsat (TM, ETM+, OLI) and Sentinel 2 have good spectral resolution too (6-12 bands in visible and infrared) and, despite lower spatial resolution (10-60 m of pixel size) are useful in extracting lithological information too. Lithological RS map may reveal good precision (down to a single rock or outcrop of a meter size). Supplemented with the analysis of Digital Elevation Model and high resolution ortophotomaps (Google Maps, Bing etc.) allows for quick and cheap mapping of unsurveyed areas.

Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images

NASA Astrophysics Data System (ADS)

Ardila, Juan P.; Tolpekin, Valentyn A.; Bijker, Wietske; Stein, Alfred

2011-11-01

Identification of tree crowns from remote sensing requires detailed spectral information and submeter spatial resolution imagery. Traditional pixel-based classification techniques do not fully exploit the spatial and spectral characteristics of remote sensing datasets. We propose a contextual and probabilistic method for detection of tree crowns in urban areas using a Markov random field based super resolution mapping (SRM) approach in very high resolution images. Our method defines an objective energy function in terms of the conditional probabilities of panchromatic and multispectral images and it locally optimizes the labeling of tree crown pixels. Energy and model parameter values are estimated from multiple implementations of SRM in tuning areas and the method is applied in QuickBird images to produce a 0.6 m tree crown map in a city of The Netherlands. The SRM output shows an identification rate of 66% and commission and omission errors in small trees and shrub areas. The method outperforms tree crown identification results obtained with maximum likelihood, support vector machines and SRM at nominal resolution (2.4 m) approaches.

Estimating Carbon Storage and Sequestration by Urban Trees at Multiple Spatial Resolutions

NASA Astrophysics Data System (ADS)

Wu, J.; Tran, A.; Liao, A.

2010-12-01

Urban forests are an important component of urban-suburban environments. Urban trees provide not only a full range of social and psychological benefits to city dwellers, but also valuable ecosystem services to communities, such as removing atmospheric carbon dioxide, improving air quality, and reducing storm water runoff. There is an urgent need for developing strategic conservation plans for environmentally sustainable urban-suburban development based on the scientific understanding of the extent and function of urban forests. However, several challenges remain to accurately quantify various environmental benefits provided by urban trees, among which is to deal with the effect of changing spatial resolution and/or scale. In this study, we intended to examine the uncertainties of carbon storage and sequestration associated with the tree canopy coverage of different spatial resolutions. Multi-source satellite imagery data were acquired for the City of Fullerton, located in Orange County of California. The tree canopy coverage of the study area was classified at three spatial resolutions, ranging from 30 m (Landsat-5 Thematic Mapper), 15 m (Advanced Spaceborne Thermal Emission and Reflection Radiometer), to 2.5 m (QuickBird). We calculated the amount of carbon stored in the trees represented on the individual tree coverage maps and the annual carbon taken up by the trees with a model (i.e., CITYgreen) developed by the U.S. Forest Service. The results indicate that urban trees account for significant proportions of land cover in the study area even with the low spatial resolution data. The estimated carbon fixation benefits vary greatly depending on the details of land use and land cover classification. The extrapolation of estimation from the fine-resolution stand-level to the low-resolution landscape-scale will likely not preserve reasonable accuracy.

Spatial and radiometric characterization of multi-spectrum satellite images through multi-fractal analysis

NASA Astrophysics Data System (ADS)

Alonso, Carmelo; Tarquis, Ana M.; Zúñiga, Ignacio; Benito, Rosa M.

2017-03-01

Several studies have shown that vegetation indexes can be used to estimate root zone soil moisture. Earth surface images, obtained by high-resolution satellites, presently give a lot of information on these indexes, based on the data of several wavelengths. Because of the potential capacity for systematic observations at various scales, remote sensing technology extends the possible data archives from the present time to several decades back. Because of this advantage, enormous efforts have been made by researchers and application specialists to delineate vegetation indexes from local scale to global scale by applying remote sensing imagery. In this work, four band images have been considered, which are involved in these vegetation indexes, and were taken by satellites Ikonos-2 and Landsat-7 of the same geographic location, to study the effect of both spatial (pixel size) and radiometric (number of bits coding the image) resolution on these wavelength bands as well as two vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). In order to do so, a multi-fractal analysis of these multi-spectral images was applied in each of these bands and the two indexes derived. The results showed that spatial resolution has a similar scaling effect in the four bands, but radiometric resolution has a larger influence in blue and green bands than in red and near-infrared bands. The NDVI showed a higher sensitivity to the radiometric resolution than EVI. Both were equally affected by the spatial resolution. From both factors, the spatial resolution has a major impact in the multi-fractal spectrum for all the bands and the vegetation indexes. This information should be taken in to account when vegetation indexes based on different satellite sensors are obtained.

Reserch on Spatial and Temporal Distribution of Color Steel Building Based on Multi-Source High-Resolution Satellite Imagery

NASA Astrophysics Data System (ADS)

Yang, S. W.; Ma, J. J.; Wang, J. M.

2018-04-01

As representative vulnerable regions of the city, dense distribution areas of temporary color steel building are a major target for control of fire risks, illegal buildings, environmental supervision, urbanization quality and enhancement for city's image. In the domestic and foreign literature, the related research mainly focuses on fire risks and violation monitoring. However, due to temporary color steel building's special characteristics, the corresponding research about temporal and spatial distribution, and influence on urban spatial form etc. has not been reported. Therefore, firstly, the paper research aim plans to extract information of large-scale color steel building from high-resolution images. Secondly, the color steel plate buildings were classified, and the spatial and temporal distribution and aggregation characteristics of small (temporary buildings) and large (factory building, warehouse, etc.) buildings were studied respectively. Thirdly, the coupling relationship between the spatial distribution of color steel plate and the spatial pattern of urban space was analysed. The results show that there is a good coupling relationship between the color steel plate building and the urban spatial form. Different types of color steel plate building represent the pattern of regional differentiation of urban space and the phased pattern of urban development.

Introduction and Testing of a Monitoring and Colony-Mapping Method for Waterbird Populations That Uses High-Speed and Ultra-Detailed Aerial Remote Sensing

PubMed Central

Bakó, Gábor; Tolnai, Márton; Takács, Ádám

2014-01-01

Remote sensing is a method that collects data of the Earth's surface without causing disturbances. Thus, it is worthwhile to use remote sensing methods to survey endangered ecosystems, as the studied species will behave naturally while undisturbed. The latest passive optical remote sensing solutions permit surveys from long distances. State-of-the-art highly sensitive sensor systems allow high spatial resolution image acquisition at high altitudes and at high flying speeds, even in low-visibility conditions. As the aerial imagery captured by an airplane covers the entire study area, all the animals present in that area can be recorded. A population assessment is conducted by visual interpretations of an ortho image map. The basic objective of this study is to determine whether small- and medium-sized bird species are recognizable in the ortho images by using high spatial resolution aerial cameras. The spatial resolution needed for identifying the bird species in the ortho image map was studied. The survey was adjusted to determine the number of birds in a colony at a given time. PMID:25046012

Geospatial Information from Satellite Imagery for Geovisualisation of Smart Cities in India

NASA Astrophysics Data System (ADS)

Mohan, M.

2016-06-01

In the recent past, there have been large emphasis on extraction of geospatial information from satellite imagery. The Geospatial information are being processed through geospatial technologies which are playing important roles in developing of smart cities, particularly in developing countries of the world like India. The study is based on the latest geospatial satellite imagery available for the multi-date, multi-stage, multi-sensor, and multi-resolution. In addition to this, the latest geospatial technologies have been used for digital image processing of remote sensing satellite imagery and the latest geographic information systems as 3-D GeoVisualisation, geospatial digital mapping and geospatial analysis for developing of smart cities in India. The Geospatial information obtained from RS and GPS systems have complex structure involving space, time and presentation. Such information helps in 3-Dimensional digital modelling for smart cities which involves of spatial and non-spatial information integration for geographic visualisation of smart cites in context to the real world. In other words, the geospatial database provides platform for the information visualisation which is also known as geovisualisation. So, as a result there have been an increasing research interest which are being directed to geospatial analysis, digital mapping, geovisualisation, monitoring and developing of smart cities using geospatial technologies. However, the present research has made an attempt for development of cities in real world scenario particulary to help local, regional and state level planners and policy makers to better understand and address issues attributed to cities using the geospatial information from satellite imagery for geovisualisation of Smart Cities in emerging and developing country, India.

Assessment and Prediction of Natural Hazards from Satellite Imagery

PubMed Central

Gillespie, Thomas W.; Chu, Jasmine; Frankenberg, Elizabeth; Thomas, Duncan

2013-01-01

Since 2000, there have been a number of spaceborne satellites that have changed the way we assess and predict natural hazards. These satellites are able to quantify physical geographic phenomena associated with the movements of the earth’s surface (earthquakes, mass movements), water (floods, tsunamis, storms), and fire (wildfires). Most of these satellites contain active or passive sensors that can be utilized by the scientific community for the remote sensing of natural hazards over a number of spatial and temporal scales. The most useful satellite imagery for the assessment of earthquake damage comes from high-resolution (0.6 m to 1 m pixel size) passive sensors and moderate resolution active sensors that can quantify the vertical and horizontal movement of the earth’s surface. High-resolution passive sensors have been used to successfully assess flood damage while predictive maps of flood vulnerability areas are possible based on physical variables collected from passive and active sensors. Recent moderate resolution sensors are able to provide near real time data on fires and provide quantitative data used in fire behavior models. Limitations currently exist due to atmospheric interference, pixel resolution, and revisit times. However, a number of new microsatellites and constellations of satellites will be launched in the next five years that contain increased resolution (0.5 m to 1 m pixel resolution for active sensors) and revisit times (daily ≤ 2.5 m resolution images from passive sensors) that will significantly improve our ability to assess and predict natural hazards from space. PMID:25170186

NOAA's Use of High-Resolution Imagery

NASA Technical Reports Server (NTRS)

Hund, Erik

2007-01-01

NOAA's use of high-resolution imagery consists of: a) Shoreline mapping and nautical chart revision; b) Coastal land cover mapping; c) Benthic habitat mapping; d) Disaster response; and e) Imagery collection and support for coastal programs.

Fluid Lensing based Machine Learning for Augmenting Earth Science Coral Datasets

NASA Astrophysics Data System (ADS)

Li, A.; Instrella, R.; Chirayath, V.

2016-12-01

Recently, there has been increased interest in monitoring the effects of climate change upon the world's marine ecosystems, particularly coral reefs. These delicate ecosystems are especially threatened due to their sensitivity to ocean warming and acidification, leading to unprecedented levels of coral bleaching and die-off in recent years. However, current global aquatic remote sensing datasets are unable to quantify changes in marine ecosystems at spatial and temporal scales relevant to their growth. In this project, we employ various supervised and unsupervised machine learning algorithms to augment existing datasets from NASA's Earth Observing System (EOS), using high resolution airborne imagery. This method utilizes NASA's ongoing airborne campaigns as well as its spaceborne assets to collect remote sensing data over these afflicted regions, and employs Fluid Lensing algorithms to resolve optical distortions caused by the fluid surface, producing cm-scale resolution imagery of these diverse ecosystems from airborne platforms. Support Vector Machines (SVMs) and K-mean clustering methods were applied to satellite imagery at 0.5m resolution, producing segmented maps classifying coral based on percent cover and morphology. Compared to a previous study using multidimensional maximum a posteriori (MAP) estimation to separate these features in high resolution airborne datasets, SVMs are able to achieve above 75% accuracy when augmented with existing MAP estimates, while unsupervised methods such as K-means achieve roughly 68% accuracy, verified by manually segmented reference data provided by a marine biologist. This effort thus has broad applications for coastal remote sensing, by helping marine biologists quantify behavioral trends spanning large areas and over longer timescales, and to assess the health of coral reefs worldwide.

Calibrating a numerical model's morphology using high-resolution spatial and temporal datasets from multithread channel flume experiments.

NASA Astrophysics Data System (ADS)

Javernick, L.; Bertoldi, W.; Redolfi, M.

2017-12-01

Accessing or acquiring high quality, low-cost topographic data has never been easier due to recent developments of the photogrammetric techniques of Structure-from-Motion (SfM). Researchers can acquire the necessary SfM imagery with various platforms, with the ability to capture millimetre resolution and accuracy, or large-scale areas with the help of unmanned platforms. Such datasets in combination with numerical modelling have opened up new opportunities to study river environments physical and ecological relationships. While numerical models overall predictive accuracy is most influenced by topography, proper model calibration requires hydraulic data and morphological data; however, rich hydraulic and morphological datasets remain scarce. This lack in field and laboratory data has limited model advancement through the inability to properly calibrate, assess sensitivity, and validate the models performance. However, new time-lapse imagery techniques have shown success in identifying instantaneous sediment transport in flume experiments and their ability to improve hydraulic model calibration. With new capabilities to capture high resolution spatial and temporal datasets of flume experiments, there is a need to further assess model performance. To address this demand, this research used braided river flume experiments and captured time-lapse observed sediment transport and repeat SfM elevation surveys to provide unprecedented spatial and temporal datasets. Through newly created metrics that quantified observed and modeled activation, deactivation, and bank erosion rates, the numerical model Delft3d was calibrated. This increased temporal data of both high-resolution time series and long-term temporal coverage provided significantly improved calibration routines that refined calibration parameterization. Model results show that there is a trade-off between achieving quantitative statistical and qualitative morphological representations. Specifically, statistical agreement simulations suffered to represent braiding planforms (evolving toward meandering), and parameterization that ensured braided produced exaggerated activation and bank erosion rates. Marie Sklodowska-Curie Individual Fellowship: River-HMV, 656917

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.

Toward global crop type mapping using a hybrid machine learning approach and multi-sensor imagery

NASA Astrophysics Data System (ADS)

Wang, S.; Le Bras, S.; Azzari, G.; Lobell, D. B.

2017-12-01

Current global scale datasets on agricultural land use do not have sufficient spatial or temporal resolution to meet the needs of many applications. The recent rapid increase in public availability of fine- to moderate-resolution satellite imagery from Landsat OLI and Copernicus Sentinel-2 provides a unique opportunity to improve agricultural land use datasets. This project leverages these new satellite data streams, existing census data, and a novel training approach to develop global, annual maps that indicate the presence of (i) cropland and (ii) specific crops at a 20m resolution. Our machine learning methodology consists of two steps. The first is a supervised classifier trained with explicitly labelled data to distinguish between crop and non-crop pixels, creating a binary mask. For ground truth, we use labels collected by previous mapping efforts (e.g. IIASA's crowdsourced data (Fritz et al. 2015) and AFSIS's geosurvey data) in combination with new data collected manually. The crop pixels output by the binary mask are input to the second step: a semi-supervised clustering algorithm to resolve different crop types and generate a crop type map. We do not use field-level information on crop type to train the algorithm, making this approach scalable spatially and temporally. We instead incorporate size constraints on clusters based on aggregated agricultural land use statistics and other, more generalizable domain knowledge. We employ field-level data from the U.S., Southern Europe, and Eastern Africa to validate crop-to-cluster assignments.

Imaging Fourier transform spectroscopy of the boundary layer plume from laser irradiated polymers and carbon materials

NASA Astrophysics Data System (ADS)

Acosta, Roberto I.

The high-energy laser (HEL) lethality community needs for enhanced laser weapons systems requires a better understanding of a wide variety of emerging threats. In order to reduce the dimensionality of laser-materials interaction it is necessary to develop novel predictive capabilities of these events. The objective is to better understand the fundamentals of laser lethality testing by developing empirical models from hyperspectral imagery, enabling a robust library of experiments for vulnerability assessments. Emissive plumes from laser irradiated fiberglass reinforced polymers (FRP), poly(methyl methacrylate) (PMMA) and porous graphite targets were investigated primarily using a mid-wave infrared (MWIR) imaging Fourier transform spectrometer (FTS). Polymer and graphite targets were irradiated with a continuous wave (cw) fiber lasers. Data was acquired with a spectral resolution of 2 cm-1 and spatial resolution as high as 0.52 mm2 per pixel. Strong emission from H2O, CO, CO2 and hydrocarbons were observed in the MWIR between 1900-4000 cm-1. A single-layer radiative transfer model was developed to estimate spatial maps of temperature and column densities of CO and CO2 from the hyperspectral imagery of the boundary layer plume. The spectral model was used to compute the absorption cross sections of CO and CO2, using spectral line parameters from the high temperature extension of the HITRAN. Also, spatial maps of gas-phase temperature and methyl methacrylate (MMA) concentration were developed from laser irradiated carbon black-pigmented PMMA at irradiances of 4-22 W/cm2. Global kinetics interplay between heterogeneous and homogeneous combustion kinetics are shown from experimental observations at high spatial resolutions. Overall the boundary layer profile at steady-state is consistent with CO being mainly produced at the surface by heterogeneous reactions followed by a rapid homogeneous combustion in the boundary layer towards buoyancy.

Applications of Earth Remote Sensing for Identifying Tornado and Severe Weather Damage

NASA Astrophysics Data System (ADS)

Burks, J. E.; Molthan, A.; Schultz, L. A.; McGrath, K.; Bell, J. R.; Cole, T.; Angle, K.

2014-12-01

In 2014, collaborations between the Short-term Prediction Research and Transition (SPoRT) Center at NASA Marshall Space Flight Center, the National Weather Service (NWS), and the USGS led to the incorporation of Earth remote sensing imagery within the NOAA/NWS Damage Assessment Toolkit (DAT). The DAT is a smartphone, tablet, and web-based application that allows NWS meteorologists to acquire, quality control, and manage various storm damage indicators following a severe weather event, such as a tornado, occurrence of widespread damaging winds, or significant hail. Earth remote sensing supports the damage assessment process by providing a broad overview of how various acquired damage indicators relate to scarring visible from space, ranging from high spatial resolution commercial imagery (~1-4m) acquired via USGS and in collaboration with other federal and private sector partners, to moderate resolution imaging from NASA sensors (~15-30m) such as those aboard Landsat 7 and 8 and Terra's ASTER, to lower resolution but routine imaging from NASA's Terra and Aqua MODIS, or the Suomi-NPP VIIRS instrument. In several cases, the acquisition and delivery of imagery in the days after a severe weather event has proven helpful in confirming or in some cases adjusting the preliminary damage track acquired during a ground survey. For example, limited road networks and access to private property may make it difficult to observe the entire length of a tornado track, while satellite imagery can fill in observation gaps to complete a more detailed damage track assessment. This presentation will highlight successful applications of Earth remote sensing for the improvement of damage surveys, discuss remaining challenges, and provide direction on future efforts that will improve the delivery of remote sensing data and use through new automation processes and training opportunities.

Applications of Earth Remote Sensing for Identifying Tornado and Severe Weather Damage

NASA Astrophysics Data System (ADS)

Burks, J. E.; Molthan, A.; Schultz, L. A.; McGrath, K.; Bell, J. R.; Cole, T.; Angle, K.

2015-12-01

In 2014, collaborations between the Short-term Prediction Research and Transition (SPoRT) Center at NASA Marshall Space Flight Center, the National Weather Service (NWS), and the USGS led to the incorporation of Earth remote sensing imagery within the NOAA/NWS Damage Assessment Toolkit (DAT). The DAT is a smartphone, tablet, and web-based application that allows NWS meteorologists to acquire, quality control, and manage various storm damage indicators following a severe weather event, such as a tornado, occurrence of widespread damaging winds, or significant hail. Earth remote sensing supports the damage assessment process by providing a broad overview of how various acquired damage indicators relate to scarring visible from space, ranging from high spatial resolution commercial imagery (~1-4m) acquired via USGS and in collaboration with other federal and private sector partners, to moderate resolution imaging from NASA sensors (~15-30m) such as those aboard Landsat 7 and 8 and Terra's ASTER, to lower resolution but routine imaging from NASA's Terra and Aqua MODIS, or the Suomi-NPP VIIRS instrument. In several cases, the acquisition and delivery of imagery in the days after a severe weather event has proven helpful in confirming or in some cases adjusting the preliminary damage track acquired during a ground survey. For example, limited road networks and access to private property may make it difficult to observe the entire length of a tornado track, while satellite imagery can fill in observation gaps to complete a more detailed damage track assessment. This presentation will highlight successful applications of Earth remote sensing for the improvement of damage surveys, discuss remaining challenges, and provide direction on future efforts that will improve the delivery of remote sensing data and use through new automation processes and training opportunities.

Forest Biomass Mapping from Stereo Imagery and Radar Data

NASA Astrophysics Data System (ADS)

Sun, G.; Ni, W.; Zhang, Z.

2013-12-01

Both InSAR and lidar data provide critical information on forest vertical structure, which are critical for regional mapping of biomass. However, the regional application of these data is limited by the availability and acquisition costs. Some researchers have demonstrated potentials of stereo imagery in the estimation of forest height. Most of these researches were conducted on aerial images or spaceborne images with very high resolutions (~0.5m). Space-born stereo imagers with global coverage such as ALOS/PRISM have coarser spatial resolutions (2-3m) to achieve wider swath. The features of stereo images are directly affected by resolutions and the approaches use by most of researchers need to be adjusted for stereo imagery with lower resolutions. This study concentrated on analyzing the features of point clouds synthesized from multi-view stereo imagery over forested areas. The small footprint lidar and lidar waveform data were used as references. The triplets of ALOS/PRISM data form three pairs (forward/nadir, backward/nadir and forward/backward) of stereo images. Each pair of the stereo images can be used to generate points (pixels) with 3D coordinates. By carefully co-register these points from three pairs of stereo images, a point cloud data was generated. The height of each point above ground surface was then calculated using DEM from National Elevation Dataset, USGS as the ground surface elevation. The height data were gridded into pixel of different sizes and the histograms of the points within a pixel were analyzed. The average height of the points within a pixel was used as the height of the pixel to generate a canopy height map. The results showed that the synergy of point clouds from different views were necessary, which increased the point density so the point cloud could detect the vertical structure of sparse and unclosed forests. The top layer of multi-layered forest could be captured but the dense forest prevented the stereo imagery to see through. The canopy height map exhibited spatial patterns of roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlation with RH50 of LVIS data at 30m pixel size with a gain of 1.04, bias of 4.3m and R2 of 0.74 (Fig. 1). The canopy height map from PRISM and dual-pol PALSAR data were used together to map biomass in our study area near Howland, Maine, and the results were evaluated using biomass map generated from LVIS waveform data independently. The results showed that adding CHM from PRISM significantly improved biomass accuracy and raised the biomass saturation level of L-band SAR data in forest biomass mapping.

Climate Change Mitigation: Can the U.S. Intelligence Community Help?

DTIC Science & Technology

2013-06-01

satellite sensors to establish the concentration of atmospheric CO2 parts per million (ppm mole fraction) in samples collected at multiple...measurements. Spatial sampling density, the number of sensors or—in the case of satellite imagery the number and resolution of the images—likewise influences...Somewhat paradoxically, sensor accuracy from either remote ( satellites ) or in situ sensors is an important consideration, but it must also be evaluated

Accuracy assessment of biomass and forested area classification from modis, landstat-tm satellite imagery and forest inventory plot data

Treesearch

Dumitru Salajanu; Dennis M. Jacobs

2007-01-01

The objective of this study was to determine how well forestfnon-forest and biomass classifications obtained from Landsat-TM and MODIS satellite data modeled with FIA plots, compare to each other and with forested area and biomass estimates from the national inventory data, as well as whether there is an increase in overall accuracy when pixel size (spatial resolution...

Statistical strategy for inventorying and monitoring the ecosystem resources of the Mexican States of Jalisco and Colima at multiple scales and resolution levels

Treesearch

H. T. Schreuder; M. S. Williams; C. Aguirre-Bravo; P. L. Patterson

2003-01-01

The sampling strategy is presented for the initial phase of the natural resources pilot project in the Mexican States of Jalisco and Colima. The sampling design used is ground-based cluster sampling with poststratification based on Landsat Thematic Mapper imagery. The data collected will serve as a basis for additional data collection, mapping, and spatial modeling...

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.

A morphology independent methodology for quantifying planview river change and characteristics from remotely sensed imagery

DOE PAGES

Rowland, Joel C.; Shelef, Eitan; Pope, Paul A.; ...

2016-07-15

Remotely sensed imagery of rivers has long served as a means for characterizing channel properties and detection of planview change. In the last decade the dramatic increase in the availability of satellite imagery and processing tools has created the potential to greatly expand the spatial and temporal scale of our understanding of river morphology and dynamics. To date, the majority of GIS and automated analyses of planview changes in rivers from remotely sensed data has been developed for single-threaded meandering river systems. These methods have limited applicability to many of the earth's rivers with complex multi-channel planforms. Here we presentmore » the methodologies of a set of analysis algorithms collectively called Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM). SCREAM analyzes planview river metrics regardless of river morphology. These algorithms quantify both the erosion and accretion rates of riverbanks from binary masks of channels generated from imagery acquired at two time periods. Additionally, the program quantifies the area of change between river channels and the surrounding floodplain and area of islands lost or formed between these two time periods. To examine variations in erosion rates in relation to local channel attributes and make rate comparisons between river systems of varying sizes, the program determines channel widths and bank curvature at every bank pixel. SCREAM was developed and tested on rivers with diverse and complex planform morphologies in imagery acquired from a range of observational platforms with varying spatial resolutions. Here, validation and verification of SCREAM-generated metrics against manual measurements show no significant measurement errors in determination of channel width, erosion, and bank aspects. SCREAM has the potential to provide data for both the quantitative examination of the controls on erosion rates and for the comparison of these rates across river systems ranging broadly in size and planform morphology.« less

Analysis of brook trout spatial behavior during passage attempts in corrugated culverts using near-infrared illumination video imagery

USGS Publications Warehouse

Bergeron, Normand E.; Constantin, Pierre-Marc; Goerig, Elsa; Castro-Santos, Theodore R.

2016-01-01

We used video recording and near-infrared illumination to document the spatial behavior of brook trout of various sizes attempting to pass corrugated culverts under different hydraulic conditions. Semi-automated image analysis was used to digitize fish position at high temporal resolution inside the culvert, which allowed calculation of various spatial behavior metrics, including instantaneous ground and swimming speed, path complexity, distance from side walls, velocity preference ratio (mean velocity at fish lateral position/mean crosssectional velocity) as well as number and duration of stops in forward progression. The presentation summarizes the main results and discusses how they could be used to improve fish passage performance in culverts.

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

Monitoring Oilfield Operations and GHG Emissions Sources Using Object-based Image Analysis of High Resolution Spatial Imagery

NASA Astrophysics Data System (ADS)

Englander, J. G.; Brodrick, P. G.; Brandt, A. R.

2015-12-01

Fugitive emissions from oil and gas extraction have become a greater concern with the recent increases in development of shale hydrocarbon resources. There are significant gaps in the tools and research used to estimate fugitive emissions from oil and gas extraction. Two approaches exist for quantifying these emissions: atmospheric (or 'top down') studies, which measure methane fluxes remotely, or inventory-based ('bottom up') studies, which aggregate leakage rates on an equipment-specific basis. Bottom-up studies require counting or estimating how many devices might be leaking (called an 'activity count'), as well as how much each device might leak on average (an 'emissions factor'). In a real-world inventory, there is uncertainty in both activity counts and emissions factors. Even at the well level there are significant disagreements in data reporting. For example, some prior studies noted a ~5x difference in the number of reported well completions in the United States between EPA and private data sources. The purpose of this work is to address activity count uncertainty by using machine learning algorithms to classify oilfield surface facilities using high-resolution spatial imagery. This method can help estimate venting and fugitive emissions sources from regions where reporting of oilfield equipment is incomplete or non-existent. This work will utilize high resolution satellite imagery to count well pads in the Bakken oil field of North Dakota. This initial study examines an area of ~2,000 km2 with ~1000 well pads. We compare different machine learning classification techniques, and explore the impact of training set size, input variables, and image segmentation settings to develop efficient and robust techniques identifying well pads. We discuss the tradeoffs inherent to different classification algorithms, and determine the optimal algorithms for oilfield feature detection. In the future, the results of this work will be leveraged to be provide activity counts of oilfield surface equipment including tanks, pumpjacks, and holding ponds.

Features of Point Clouds Synthesized from Multi-View ALOS/PRISM Data and Comparisons with LiDAR Data in Forested Areas

NASA Technical Reports Server (NTRS)

Ni, Wenjian; Ranson, Kenneth Jon; Zhang, Zhiyu; Sun, Guoqing

2014-01-01

LiDAR waveform data from airborne LiDAR scanners (ALS) e.g. the Land Vegetation and Ice Sensor (LVIS) havebeen successfully used for estimation of forest height and biomass at local scales and have become the preferredremote sensing dataset. However, regional and global applications are limited by the cost of the airborne LiDARdata acquisition and there are no available spaceborne LiDAR systems. Some researchers have demonstrated thepotential for mapping forest height using aerial or spaceborne stereo imagery with very high spatial resolutions.For stereo imageswith global coverage but coarse resolution newanalysis methods need to be used. Unlike mostresearch based on digital surface models, this study concentrated on analyzing the features of point cloud datagenerated from stereo imagery. The synthesizing of point cloud data from multi-view stereo imagery increasedthe point density of the data. The point cloud data over forested areas were analyzed and compared to small footprintLiDAR data and large-footprint LiDAR waveform data. The results showed that the synthesized point clouddata from ALOSPRISM triplets produce vertical distributions similar to LiDAR data and detected the verticalstructure of sparse and non-closed forests at 30mresolution. For dense forest canopies, the canopy could be capturedbut the ground surface could not be seen, so surface elevations from other sourceswould be needed to calculatethe height of the canopy. A canopy height map with 30 m pixels was produced by subtracting nationalelevation dataset (NED) fromthe averaged elevation of synthesized point clouds,which exhibited spatial featuresof roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlationwith RH50 of LVIS data with a slope of 1.04 and R2 of 0.74 indicating that the canopy height derived fromPRISM triplets can be used to estimate forest biomass at 30 m resolution.

Digital Elevation Models of Greenland based on combined radar and laser altimetry as well as high-resolution stereoscopic imagery

NASA Astrophysics Data System (ADS)

Levinsen, J. F.; Smith, B. E.; Sandberg Sorensen, L.; Khvorostovsky, K.; Simonsen, S. B.; Forsberg, R.

2015-12-01

A number of Digital Elevation Models (DEMs) of Greenland exist, each of which are applicable for different purposes. This study presents two such DEMs: One developed by merging contemporary radar and laser altimeter data, and one derived from high-resolution stereoscopic imagery. All products are made freely available. The former DEM covers the entire Greenland. It is specific to the year 2010, providing it with an advantage over previous models suffering from either a reduced spatial/ temporal data coverage or errors from surface elevation changes (SEC) occurring during data acquisition. Radar data are acquired with Envisat and CryoSat-2, and laser data with the Ice, Cloud, and land Elevation Satellite, the Land, Vegetation, and Ice Sensor, and the Airborne Topographic Mapper. Correcting radar data for errors from slope effects and surface penetration of the echoes, and merging these with laser data, yields a DEM capable of resolving both surface depressions as well as topographic features at higher altitudes. The spatial resolution is 2 x 2 km, making the DEM ideal for application in surface mass balance studies, SEC detection from radar altimetry, or for correcting such data for slope-induced errors. The other DEM is developed in a pilot study building the expertise to map all ice-free parts of Greenland. The work combines WorldView-2 and -3 as well as GeoEye1 imagery from 2014 and 2015 over the Disko, Narsaq, Tassilaq, and Zackenberg regions. The novelty of the work is the determination of the product specifications after elaborate discussions with interested parties from government institutions, the tourist industry, etc. Thus, a 10 m DEM, 1.5 m orthophotos, and vector maps are produced. This opens to the possibility of using orthophotos with up-to-date contour lines or for deriving updated coastlines to aid, e.g., emergency management. This allows for a product development directly in line with the needs of parties with specific interests in Greenland.

Evaluating rapid ground sampling and scaling estimated plant cover using UAV imagery up to Landsat for mapping arctic vegetation

NASA Astrophysics Data System (ADS)

Nelson, P.; Paradis, D. P.

2017-12-01

The small stature and spectral diversity of arctic plant taxa presents challenges in mapping arctic vegetation. Mapping vegetation at the appropriate scale is needed to visualize effects of disturbance, directional vegetation change or mapping of specific plant groups for other applications (eg. habitat mapping). Fine spatial grain of remotely sensed data (ca. 10 cm pixels) is often necessary to resolve patches of many arctic plant groups, such as bryophytes and lichens. These groups are also spectrally different from mineral, litter and vascular plants. We sought to explore method to generate high-resolution spatial and spectral data to explore better mapping methods for arctic vegetation. We sampled ground vegetation at seven sites north or west of tree-line in Alaska, four north of Fairbanks and three northwest of Bethel, respectively. At each site, we estimated cover of plant functional types in 1m2 quadrats spaced approximately every 10 m along a 100 m long transect. Each quadrat was also scanned using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm range) and photographed from multiple perspectives. We then flew our small UAV with a RGB camera over the transect and at least 50 m on either side collecting on imagery of the plot, which were used to generate a image mosaic and digital surface model of the plot. We compare plant functional group cover ocular estimated in situ to post-hoc estimation, either automated or using a human observer, using the quadrat photos. We also compare interpolated lichen cover from UAV scenes to estimated lichen cover using a statistical models using Landsat data, with focus on lichens. Light and yellow lichens are discernable in the UAV imagery but certain lichens, especially dark colored lichens or those with spectral signatures similar to graminoid litter, present challenges. Future efforts will focus on integrating UAV-upscaled ground cover estimates to hyperspectral sensors (eg. AVIRIS ng) for better combined spectral and spatial resolution.

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.

Tracking surface and subsurface lakes on the Greenland Ice Sheet using Sentinel-1 SAR and Landsat-8 OLI imagery

NASA Astrophysics Data System (ADS)

Miles, Katie; Willis, Ian; Benedek, Corinne; Williamson, Andrew; Tedesco, Marco

2017-04-01

Supraglacial lakes (SGLs) on the Greenland Ice Sheet (GrIS) are an important component of the ice sheet's mass balance and hydrology, with their drainage affecting ice dynamics. This study uses imagery from the recently launched Sentinel-1A Synthetic Aperture Radar (SAR) to investigate SGLs in West Greenland. SAR can image through cloud and in darkness, overcoming some of the limitations of commonly used optical sensors. A semi automated algorithm is developed to detect surface lakes from Sentinel images during the 2015 summer. It generally detects water in all locations where a Landsat-8 NDWI classification (with a relatively high threshold value) detects water. A combined set of images from Landsat-8 and Sentinel-1 is used to track lake behaviour at a comparable temporal resolution to that which is possible with MODIS, but at a higher spatial resolution. A fully automated lake drainage detection algorithm is used to investigate both rapid and slow drainages for both small and large lakes through the summer. Our combined Landsat-Sentinel dataset, with a temporal resolution of three days, could track smaller lakes (mean 0.089 km2) than are resolvable in MODIS (minimum 0.125 km2). Small lake drainage events (lakes smaller than can be detected using MODIS) were found to occur at lower elevations ( 200 m) and slightly earlier in the melt season than larger events, as were slow lake drainage events compared to rapid events. The Sentinel imagery allows the analysis to be extended manually into the early winter to calculate the dates and elevations of lake freeze-through more precisely than is possible with optical imagery (mean 30 August, 1270 m mean elevation). Finally, the Sentinel imagery allows subsurface lakes (which are invisible to optical sensors) to be detected, and, for the first time, their dates of appearance and freeze-through to be calculated (mean 9 August and 7 October, respectively). These subsurface lakes occur at higher elevations than the surface lakes detected in this study (1593 m mean elevation). Sentinel imagery therefore provides great potential for tracking melting, water movement and freezing within the firn zone of the GrIS.

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.

Stream Temperature Estimation From Thermal Infrared Images

NASA Astrophysics Data System (ADS)

Handcock, R. N.; Kay, J. E.; Gillespie, A.; Naveh, N.; Cherkauer, K. A.; Burges, S. J.; Booth, D. B.

2001-12-01

Stream temperature is an important water quality indicator in the Pacific Northwest where endangered fish populations are sensitive to elevated water temperature. Cold water refugia are essential for the survival of threatened salmon when events such as the removal of riparian vegetation result in elevated stream temperatures. Regional assessment of stream temperatures is limited by sparse sampling of temperatures in both space and time. If critical watersheds are to be properly managed it is necessary to have spatially extensive temperature measurements of known accuracy. Remotely sensed thermal infrared (TIR) imagery can be used to derive spatially distributed estimates of the skin temperature (top 100 nm) of streams. TIR imagery has long been used to estimate skin temperatures of the ocean, where split-window techniques have been used to compensate for atmospheric affects. Streams are a more complex environment because 1) most are unresolved in typical TIR images, and 2) the near-bank environment of stream corridors may consist of tall trees or hot rocks and soils that irradiate the stream surface. As well as compensating for atmospheric effects, key problems to solve in estimating stream temperatures include both subpixel unmixing and multiple scattering. Additionally, fine resolution characteristics of the stream surface such as evaporative cooling due to wind, and water surface roughness, will effect measurements of radiant skin temperatures with TIR devices. We apply these corrections across the Green River and Yakima River watersheds in Washington State to assess the accuracy of remotely sensed stream surface temperature estimates made using fine resolution TIR imagery from a ground-based sensor (FLIR), medium resolution data from the airborne MASTER sensor, and coarse-resolution data from the Terra-ASTER satellite. We use linear spectral mixture analysis to isolate the fraction of land-leaving radiance originating from unresolved streams. To compensate the data for atmospheric effects we combine radiosonde profiles with a physically based radiative transfer model (MODTRAN) and an in-scene relative correction adapted from the ISAC algorithm. Laboratory values for water emissivities are used as a baseline estimate of stream emissivities. Emitted radiance reflected by trees in the stream near-bank environment is estimated from the height and canopy temperature, using a radiosity model.

A Multi-Temporal Remote Sensing Approach to Freshwater Turtle Conservation

NASA Astrophysics Data System (ADS)

Mui, Amy B.

Freshwater turtles are a globally declining taxa, and estimates of population status are not available for many species. Primary causes of decline stem from widespread habitat loss and degradation, and obtaining spatially-explicit information on remaining habitat across a relevant spatial scale has proven challenging. The discipline of remote sensing science has been employed widely in studies of biodiversity conservation, but it has not been utilized as frequently for cryptic, and less vagile species such as turtles, despite their vulnerable status. The work presented in this thesis investigates how multi-temporal remote sensing imagery can contribute key information for building spatially-explicit and temporally dynamic models of habitat and connectivity for the threatened, Blanding's turtle (Emydoidea blandingii) in southern Ontario, Canada. I began with outlining a methodological approach for delineating freshwater wetlands from high spatial resolution remote sensing imagery, using a geographic object-based image analysis (GEOBIA) approach. This method was applied to three different landscapes in southern Ontario, and across two biologically relevant seasons during the active (non-hibernating) period of Blanding's turtles. Next, relevant environmental variables associated with turtle presence were extracted from remote sensing imagery, and a boosted regression tree model was developed to predict the probability of occurrence of this species. Finally, I analysed the movement potential for Blanding's turtles in a disturbed landscape using a combination of approaches. Results indicate that (1) a parsimonious GEOBIA approach to land cover mapping, incorporating texture, spectral indices, and topographic information can map heterogeneous land cover with high accuracy, (2) remote-sensing derived environmental variables can be used to build habitat models with strong predictive power, and (3) connectivity potential is best estimated using a variety of approaches, though accurate estimates across human-altered landscapes is challenging. Overall, this body of work supports the use of remote sensing imagery in species distribution models to strengthen the precision, and power of predictive models, and also draws attention to the need to consider a multi-temporal examination of species habitat requirements.

Assessment on spatiotemporal relationship between rainfall and cloud top temperature from new generation weather satellite imagery

NASA Astrophysics Data System (ADS)

Wei, Chiang; Yeh, Hui-Chung; Chen, Yen-Chang

2017-04-01

This study addressed the relationship between rainfall and cloud top temperature (CCT) from new generation satellite Himawari-8 imagery at different spatiotemporal scale. This satellite provides higher band, more bits for data format, spatial and temporal resolution compared with previous GMS series. The multi-infrared channels with 10-minute and 1-2 km resolution make it possible for rainfall estimating/forecasting in small/medium watershed. The preliminary result investigated at Chenyulan watershed (443.6 square kilometer) of Central Taiwan in 2016 Typhoon Megi shows the regression coefficient fitted by negative exponential equation of largest rainfall vs. CCT (B8 band) at pixel scale increases as time scales enlarges and reach 0.462 for 120-minute accumulative rainfall; the value (CTT of B15 band) decreases from 0.635 for 10-minute to 0.423 for 120-minute accumulative rainfall at basin-wide scale. More rainfall events for different regime are yet to evaluate to get solid results.

Potential for Small Unmanned Aircraft Systems applications for identifying groundwater-surface water exchange in a meandering river reach

USGS Publications Warehouse

Pai, H.; Malenda, H.; Briggs, Martin A.; Singha, K.; González-Pinzón, R.; Gooseff, M.; Tyler, S.W.; ,

2017-01-01

The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here, we describe the use of a suite of high spatial-resolution remote-sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index (NDVI) mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW “shortcutting” through meander necks, which was corroborated by temperature data at the riverbed interface.

Planetary-scale surface water detection from space

NASA Astrophysics Data System (ADS)

Donchyts, G.; Baart, F.; Winsemius, H.; Gorelick, N.

2017-12-01

Accurate, efficient and high-resolution methods of surface water detection are needed for a better water management. Datasets on surface water extent and dynamics are crucial for a better understanding of natural and human-made processes, and as an input data for hydrological and hydraulic models. In spite of considerable progress in the harmonization of freely available satellite data, producing accurate and efficient higher-level surface water data products remains very challenging. This presentation will provide an overview of existing methods for surface water extent and change detection from multitemporal and multi-sensor satellite imagery. An algorithm to detect surface water changes from multi-temporal satellite imagery will be demonstrated as well as its open-source implementation (http://aqua-monitor.deltares.nl). This algorithm was used to estimate global surface water changes at high spatial resolution. These changes include climate change, land reclamation, reservoir construction/decommissioning, erosion/accretion, and many other. This presentation will demonstrate how open satellite data and open platforms such as Google Earth Engine have helped with this research.

Potential for Small Unmanned Aircraft Systems Applications for Identifying Groundwater-Surface Water Exchange in a Meandering River Reach

NASA Astrophysics Data System (ADS)

Pai, H.; Malenda, H. F.; Briggs, M. A.; Singha, K.; González-Pinzón, R.; Gooseff, M. N.; Tyler, S. W.

2017-12-01

The exchange of groundwater and surface water (GW-SW), including dissolved constituents and energy, represents a critical yet challenging characterization problem for hydrogeologists and stream ecologists. Here we describe the use of a suite of high spatial resolution remote sensing techniques, collected using a small unmanned aircraft system (sUAS), to provide novel and complementary data to analyze GW-SW exchange. sUAS provided centimeter-scale resolution topography and water surface elevations, which are often drivers of exchange along the river corridor. Additionally, sUAS-based vegetation imagery, vegetation-top elevation, and normalized difference vegetation index mapping indicated GW-SW exchange patterns that are difficult to characterize from the land surface and may not be resolved from coarser satellite-based imagery. We combined these data with estimates of sediment hydraulic conductivity to provide a direct estimate of GW "shortcutting" through meander necks, which was corroborated by temperature data at the riverbed interface.

Unmixing AVHRR Imagery to Assess Clearcuts and Forest Regrowth in Oregon

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Spanner, Michael A.

1995-01-01

Advanced Very High Resolution Radiometer imagery provides frequent and low-cost coverage of the earth, but its coarse spatial resolution (approx. 1.1 km by 1.1 km) does not lend itself to standard techniques of automated categorization of land cover classes because the pixels are generally mixed; that is, the extent of the pixel includes several land use/cover classes. Unmixing procedures were developed to extract land use/cover class signatures from mixed pixels, using Landsat Thematic Mapper data as a source for the training set, and to estimate fractions of class coverage within pixels. Application of these unmixing procedures to mapping forest clearcuts and regrowth in Oregon indicated that unmixing is a promising approach for mapping major trends in land cover with AVHRR bands 1 and 2. Including thermal bands by unmixing AVHRR bands 1-4 did not lead to significant improvements in accuracy, but experiments with unmixing these four bands did indicate that use of weighted least squares techniques might lead to improvements in other applications of unmixing.

Global Single and Multiple Cloud Classification with a Fuzzy Logic Expert System

NASA Technical Reports Server (NTRS)

Welch, Ronald M.; Tovinkere, Vasanth; Titlow, James; Baum, Bryan A.

1996-01-01

An unresolved problem in remote sensing concerns the analysis of satellite imagery containing both single and multiple cloud layers. While cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget, most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. Coakley (1983) used a spatial coherence method to determine whether a region contained more than one cloud layer. Baum et al. (1995) developed a scheme for detection and analysis of daytime multiple cloud layers using merged AVHRR (Advanced Very High Resolution Radiometer) and HIRS (High-resolution Infrared Radiometer Sounder) data collected during the First ISCCP Regional Experiment (FIRE) Cirrus 2 field campaign. Baum et al. (1995) explored the use of a cloud classification technique based on AVHRR data. This study examines the feasibility of applying the cloud classifier to global satellite imagery.

Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010

USGS Publications Warehouse

Enwright, Nicholas M.; Hartley, Stephen B.; Couvillion, Brady R.; Michael G. Brasher,; Jenneke M. Visser,; Michael K. Mitchell,; Bart M. Ballard,; Mark W. Parr,; Barry C. Wilson,

2015-07-23

This study incorporates about 9,800 ground reference locations collected via helicopter surveys in coastal wetland areas. Decision-tree analyses were used to classify emergent marsh vegetation types by using ground reference data from helicopter vegetation surveys and independent variables such as multitemporal satellite-based multispectral imagery from 2009 to 2011, bare-earth digital elevation models based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables. Image objects were created from 2010 National Agriculture Imagery Program color-infrared aerial photography. The final classification is a 10-meter raster dataset that was produced by using a majority filter to classify image objects according to the marsh vegetation type covering the majority of each image object. The classification is dated 2010 because the year is both the midpoint of the classified multitemporal satellite-based imagery (2009–11) and the date of the high-resolution airborne imagery that was used to develop image objects. The seamless classification produced through this work can be used to help develop and refine conservation efforts for priority natural resources.

Interdisciplinary research on the application of ERTS-1 data to the regional land use planning process

NASA Technical Reports Server (NTRS)

Clapp, J. L. (Principal Investigator); Kiefer, R. W.; Mccarthy, M. M.; Niemann, B. J., Jr.

1972-01-01

The author has identified the following significant results. Although the degree to which ERTS-1 imagery can satisfy regional land use planning data needs is not yet known, it appears to offer means by which the data acquisition process can be immeasurably improved. The initial experiences of an interdisciplinary group attempting to formulate ways of analyzing the effectiveness of ERTS-1 imagery as a base for environmental monitoring and the resolution of regional land allocation problems are documented. Application of imagery to the regional planning process consists of utilizing representative geographical regions within the state of Wisconsin. Because of the need to describe and depict regional resource complexity in an interrelatable state, certain resources within the geographical regions have been inventoried and stored in a two-dimensional computer-based map form. Computer oriented processes were developed to provide for the economical storage, analysis, and spatial display of natural and cultural data for regional land use planning purposes. The authors are optimistic that the imagery will provide revelant data for land use decision making at regional levels.

An Evolutionary Algorithm for Fast Intensity Based Image Matching Between Optical and SAR Satellite Imagery

NASA Astrophysics Data System (ADS)

Fischer, Peter; Schuegraf, Philipp; Merkle, Nina; Storch, Tobias

2018-04-01

This paper presents a hybrid evolutionary algorithm for fast intensity based matching between satellite imagery from SAR and very high-resolution (VHR) optical sensor systems. The precise and accurate co-registration of image time series and images of different sensors is a key task in multi-sensor image processing scenarios. The necessary preprocessing step of image matching and tie-point detection is divided into a search problem and a similarity measurement. Within this paper we evaluate the use of an evolutionary search strategy for establishing the spatial correspondence between satellite imagery of optical and radar sensors. The aim of the proposed algorithm is to decrease the computational costs during the search process by formulating the search as an optimization problem. Based upon the canonical evolutionary algorithm, the proposed algorithm is adapted for SAR/optical imagery intensity based matching. Extensions are drawn using techniques like hybridization (e.g. local search) and others to lower the number of objective function calls and refine the result. The algorithm significantely decreases the computational costs whilst finding the optimal solution in a reliable way.

Temporal and spatial change in coastal ecosystems using remote sensing: Example with Florida Bay, USA, emphasizing AVHRR imagery

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

Stumpf, R.P.; Frayer, M.L.

1997-06-01

Florida Bay, at the southern tip of Florida, USA, has undergone dramatic changes in recent years. Following seagrass dieoffs starting in the late 1980`s, both algal blooms and high turbidity (the latter from resuspended sediments) have been reported as more common in the Bay. Remotely sensed data, particularly from the AVHRR (advanced very high resolution radiometer), can provide information on conditions prior to the start of monitoring programs as well as provide additional spatial detail on water clarity and particulate loads in this estuary . The AVHRR record currently available to us consists of over 600 usable scenes from December,more » 1989. Comparisons with field data have provided relationships with light attenuation, total suspended solids, and other turbidity measures. The imagery shows the seasonal change in turbidity resulting from high winds associated with winter cold fronts. Over the seven-year record, areas of clear water have decreased in the north-central Bay, while expanding in the southwestern Bay.« less

Urban Spatial Ecological Performance Based on the Data of Remote Sensing of Guyuan

NASA Astrophysics Data System (ADS)

Ren, X.-J.; Chen, X.-J.; Ma, Q.

2018-04-01

The evolution analysis of urban landuse and spatial ecological performance are necessary and useful to recognizing the stage of urban development and revealing the regularity and connotation of urban spatial expansion. Moreover, it lies in the core that should be exmined in the urban sustainable development. In this paper, detailed information has been acquired from the high-resolution satellite imageries of Guyuan, China case study. With the support of GIS, the land-use mapping information and the land cover changes are analyzed, and the process of urban spatial ecological performance evolution by the hierarchical methodology is explored. Results demonstrate that in the past 11 years, the urban spatial ecological performance show an improved process with the dramatic landcover change in Guyuan. Firstly, the landuse structure of Guyuan changes significantly and shows an obvious stage characteristic. Secondly, the urban ecological performance of Guyuan continues to be optimized over the 11 years. Thirdly, the findings suggest that a dynamic monitoring mechanism of urban land use based on high-resolution remote sensing data should be established in urban development, and the rational development of urban land use should be guided by the spatial ecological performance as the basic value orientation.

A Unified Fisher's Ratio Learning Method for Spatial Filter Optimization.

PubMed

Li, Xinyang; Guan, Cuntai; Zhang, Haihong; Ang, Kai Keng

To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.To detect the mental task of interest, spatial filtering has been widely used to enhance the spatial resolution of electroencephalography (EEG). However, the effectiveness of spatial filtering is undermined due to the significant nonstationarity of EEG. Based on regularization, most of the conventional stationary spatial filter design methods address the nonstationarity at the cost of the interclass discrimination. Moreover, spatial filter optimization is inconsistent with feature extraction when EEG covariance matrices could not be jointly diagonalized due to the regularization. In this paper, we propose a novel framework for a spatial filter design. With Fisher's ratio in feature space directly used as the objective function, the spatial filter optimization is unified with feature extraction. Given its ratio form, the selection of the regularization parameter could be avoided. We evaluate the proposed method on a binary motor imagery data set of 16 subjects, who performed the calibration and test sessions on different days. The experimental results show that the proposed method yields improvement in classification performance for both single broadband and filter bank settings compared with conventional nonunified methods. We also provide a systematic attempt to compare different objective functions in modeling data nonstationarity with simulation studies.

Evaluation and sensitivity testing of a coupled Landsat-MODIS downscaling method for land surface temperature and vegetation indices in semi-arid regions

NASA Astrophysics Data System (ADS)

Kim, Jongyoun; Hogue, Terri S.

2012-01-01

The current study investigates a method to provide land surface parameters [i.e., land surface temperature (LST) and normalized difference vegetation index (NDVI)] at a high spatial (˜30 and 60 m) and temporal (daily and 8-day) resolution by combining advantages from Landsat and moderate-resolution imaging spectroradiometer (MODIS) satellites. We adopt a previously developed subtraction method that merges the spatial detail of higher-resolution imagery (Landsat) with the temporal change observed in coarser or moderate-resolution imagery (MODIS). Applying the temporal difference between MODIS images observed at two different dates to a higher-resolution Landsat image allows prediction of a combined or fused image (Landsat+MODIS) at a future date. Evaluation of the resultant merged products is undertaken within the Southeastern Arizona region where data is available from a range of flux tower sites. The Landsat+MODIS fused products capture the raw Landsat values and also reflect the MODIS temporal variation. The predicted Landsat+MODIS LST improves mean absolute error around 5°C at the more heterogeneous sites compared to the original satellite products. The fused Landsat+MODIS NDVI product also shows good correlation to ground-based data and is relatively consistent except during the acute (monsoon) growing season. The sensitivity of the fused product relative to temporal gaps in Landsat data appears to be more affected by uncertainty associated with regional precipitation and green-up, than the length of the gap associated with Landsat viewing, suggesting the potential to use a minimal number of original Landsat images during relatively stable land surface and climate conditions. Our extensive validation yields insight on the ability of the proposed method to integrate multiscale platforms and the potential for reducing costs associated with high-resolution satellite systems (e.g., SPOT, QuickBird, IKONOS).

Estimating Above-Ground Carbon Biomass in a Newly Restored Coastal Plain Wetland Using Remote Sensing

PubMed Central

Riegel, Joseph B.; Bernhardt, Emily; Swenson, Jennifer

2013-01-01

Developing accurate but inexpensive methods for estimating above-ground carbon biomass is an important technical challenge that must be overcome before a carbon offset market can be successfully implemented in the United States. Previous studies have shown that LiDAR (light detection and ranging) is well-suited for modeling above-ground biomass in mature forests; however, there has been little previous research on the ability of LiDAR to model above-ground biomass in areas with young, aggrading vegetation. This study compared the abilities of discrete-return LiDAR and high resolution optical imagery to model above-ground carbon biomass at a young restored forested wetland site in eastern North Carolina. We found that the optical imagery model explained more of the observed variation in carbon biomass than the LiDAR model (adj-R2 values of 0.34 and 0.18 respectively; root mean squared errors of 0.14 Mg C/ha and 0.17 Mg C/ha respectively). Optical imagery was also better able to predict high and low biomass extremes than the LiDAR model. Combining both the optical and LiDAR improved upon the optical model but only marginally (adj-R2 of 0.37). These results suggest that the ability of discrete-return LiDAR to model above-ground biomass may be rather limited in areas with young, small trees and that high spatial resolution optical imagery may be the better tool in such areas. PMID:23840837

Filtering high resolution hyperspectral imagery and analyzing it for quantification of water quality parameters and aquatic vegetation

NASA Astrophysics Data System (ADS)

Pande-Chhetri, Roshan

High resolution hyperspectral imagery (airborne or ground-based) is gaining momentum as a useful analytical tool in various fields including agriculture and aquatic systems. These images are often contaminated with stripes and noise resulting in lower signal-to-noise ratio, especially in aquatic regions where signal is naturally low. This research investigates effective methods for filtering high spatial resolution hyperspectral imagery and use of the imagery in water quality parameter estimation and aquatic vegetation classification. The striping pattern of the hyperspectral imagery is non-parametric and difficult to filter. In this research, a de-striping algorithm based on wavelet analysis and adaptive Fourier domain normalization was examined. The result of this algorithm was found superior to other available algorithms and yielded highest Peak Signal to Noise Ratio improvement. The algorithm was implemented on individual image bands and on selected bands of the Maximum Noise Fraction (MNF) transformed images. The results showed that image filtering in the MNF domain was efficient and produced best results. The study investigated methods of analyzing hyperspectral imagery to estimate water quality parameters and to map aquatic vegetation in case-2 waters. Ground-based hyperspectral imagery was analyzed to determine chlorophyll-a (Chl-a) concentrations in aquaculture ponds. Two-band and three-band indices were implemented and the effect of using submerged reflectance targets was evaluated. Laboratory measured values were found to be in strong correlation with two-band and three-band spectral indices computed from the hyperspectral image. Coefficients of determination (R2) values were found to be 0.833 and 0.862 without submerged targets and stronger values of 0.975 and 0.982 were obtained using submerged targets. Airborne hyperspectral images were used to detect and classify aquatic vegetation in a black river estuarine system. Image normalization for water surface reflectance and water depths was conducted and non-parametric classifiers such as ANN, SVM and SAM were tested and compared. Quality assessment indicated better classification and detection when non-parametric classifiers were applied to normalized or depth invariant transform images. Best classification accuracy of 73% was achieved when ANN is applied on normalized image and best detection accuracy of around 92% was obtained when SVM or SAM was applied on depth invariant images.

Alerts of forest disturbance from MODIS imagery

NASA Astrophysics Data System (ADS)

Hammer, Dan; Kraft, Robin; Wheeler, David

2014-12-01

This paper reports the methodology and computational strategy for a forest cover disturbance alerting system. Analytical techniques from time series econometrics are applied to imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to detect temporal instability in vegetation indices. The characteristics from each MODIS pixel's spectral history are extracted and compared against historical data on forest cover loss to develop a geographically localized classification rule that can be applied across the humid tropical biome. The final output is a probability of forest disturbance for each 500 m pixel that is updated every 16 days. The primary objective is to provide high-confidence alerts of forest disturbance, while minimizing false positives. We find that the alerts serve this purpose exceedingly well in Pará, Brazil, with high probability alerts garnering a user accuracy of 98 percent over the training period and 93 percent after the training period (2000-2005) when compared against the PRODES deforestation data set, which is used to assess spatial accuracy. Implemented in Clojure and Java on the Hadoop distributed data processing platform, the algorithm is a fast, automated, and open source system for detecting forest disturbance. It is intended to be used in conjunction with higher-resolution imagery and data products that cannot be updated as quickly as MODIS-based data products. By highlighting hotspots of change, the algorithm and associated output can focus high-resolution data acquisition and aid in efforts to enforce local forest conservation efforts.

Multispectral image sharpening using a shift-invariant wavelet transform and adaptive processing of multiresolution edges

USGS Publications Warehouse

Lemeshewsky, G.P.; Rahman, Z.-U.; Schowengerdt, R.A.; Reichenbach, S.E.

2002-01-01

Enhanced false color images from mid-IR, near-IR (NIR), and visible bands of the Landsat thematic mapper (TM) are commonly used for visually interpreting land cover type. Described here is a technique for sharpening or fusion of NIR with higher resolution panchromatic (Pan) that uses a shift-invariant implementation of the discrete wavelet transform (SIDWT) and a reported pixel-based selection rule to combine coefficients. There can be contrast reversals (e.g., at soil-vegetation boundaries between NIR and visible band images) and consequently degraded sharpening and edge artifacts. To improve performance for these conditions, I used a local area-based correlation technique originally reported for comparing image-pyramid-derived edges for the adaptive processing of wavelet-derived edge data. Also, using the redundant data of the SIDWT improves edge data generation. There is additional improvement because sharpened subband imagery is used with the edge-correlation process. A reported technique for sharpening three-band spectral imagery used forward and inverse intensity, hue, and saturation transforms and wavelet-based sharpening of intensity. This technique had limitations with opposite contrast data, and in this study sharpening was applied to single-band multispectral-Pan image pairs. Sharpening used simulated 30-m NIR imagery produced by degrading the spatial resolution of a higher resolution reference. Performance, evaluated by comparison between sharpened and reference image, was improved when sharpened subband data were used with the edge correlation.

The pan-sharpening of satellite and UAV imagery for agricultural applications

NASA Astrophysics Data System (ADS)

Jenerowicz, Agnieszka; Woroszkiewicz, Malgorzata

2016-10-01

Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods. This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space

NASA Technical Reports Server (NTRS)

Lagomasino, David; Fatoyinbo, Temilola; Lee, SeungKuk; Feliciano, Emanuelle; Trettin, Carl; Simard, Marc

2016-01-01

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement (DEM), and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 meters were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes.

A Comparison of Mangrove Canopy Height Using Multiple Independent Measurements from Land, Air, and Space

PubMed Central

Lagomasino, David; Fatoyinbo, Temilola; Lee, SeungKuk; Feliciano, Emanuelle; Trettin, Carl; Simard, Marc

2017-01-01

Canopy height is one of the strongest predictors of biomass and carbon in forested ecosystems. Additionally, mangrove ecosystems represent one of the most concentrated carbon reservoirs that are rapidly degrading as a result of deforestation, development, and hydrologic manipulation. Therefore, the accuracy of Canopy Height Models (CHM) over mangrove forest can provide crucial information for monitoring and verification protocols. We compared four CHMs derived from independent remotely sensed imagery and identified potential errors and bias between measurement types. CHMs were derived from three spaceborne datasets; Very-High Resolution (VHR) stereophotogrammetry, TerraSAR-X add-on for Digital Elevation Measurement, and Shuttle Radar Topography Mission (TanDEM-X), and lidar data which was acquired from an airborne platform. Each dataset exhibited different error characteristics that were related to spatial resolution, sensitivities of the sensors, and reference frames. Canopies over 10 m were accurately predicted by all CHMs while the distributions of canopy height were best predicted by the VHR CHM. Depending on the guidelines and strategies needed for monitoring and verification activities, coarse resolution CHMs could be used to track canopy height at regional and global scales with finer resolution imagery used to validate and monitor critical areas undergoing rapid changes. PMID:29629207

Mapping and monitoring small stakholder agriculture in Tigray, Ethiopia using sub-meter Worldview and Landsat imagery and high performance computing.

NASA Astrophysics Data System (ADS)

Carroll, M.; McCarty, J. L.; Neigh, C. S. R.; Wooten, M.

2016-12-01

Very high resolution (VHR) satellite data is experiencing rapid annual growth, producing petabytes of remotely sensed data per year. The WorldView constellation, operated by DigitalGlobe, images over 1.2 billion km2 annually at a > 2 m spatial resolution. Due to computation, data cost, and methodological concerns, VHR satellite data has mainly been used to produce needed geospatial information for site-specific phenomenon. This project produced a VHR spatiotemporally-explicit wall-to-wall cropland area map for the rainfed residential cropland mosaic of Tigray Region, Ethiopia, which is comprised entirely of smallholder farms. Moderate resolution satellite data is not adequate in spatial or temporal resolution to capture total area occupied by smallholder farms, i.e., farms with agricultural fields of ≥ 45 × 45 m in dimension. In order to accurately map smallholder crop area over a large region, hundreds of VHR images spanning two or more years are needed. Sub-meter WorldView-1 and WorldView-2 segmentation results were combined median phenology amplitude from Landsat 8 data. VHR WorldView-1, -2 data were obtained from the U.S. National Geospatial-Intelligence Agency (NGA) Commercial Archive Data at NASA Goddard Space Flight Center (GSFC) (http://cad4nasa.gsfc.nasa.gov/). Over 2700 scenes were processed from raw imagery to completed crop map in 1 week in a semi-automated method using the large computing capacity of the Advanced Data Analytics Platform (ADAPT) at NASA GSFC's NCCS (http://www.nccs.nasa.gov/services/adapt). This methodology is extensible to any land cover type and can easily be expanded to run on much larger regions.

Measurement of water colour using AVIRIS imagery to assess the potential for an operational monitoring capability in the Pamlico Sound Estuary, USA

PubMed Central

Ross, S. Lunetta; Joseph, F. Knight; Hans, W. Paerl; John, J. Streicher; Benjamin, L. Peierls; Tom, Gallo; John, G. Lyon; Thomas, H. Mace; Christopher, P. Buzzelli

2009-01-01

The monitoring of water colour parameters can provide an important diagnostic tool for the assessment of aquatic ecosystem condition. Remote sensing has long been used to effectively monitor chlorophyll concentrations in open ocean systems; however, operational monitoring in coastal and estuarine areas has been limited because of the inherent complexities of coastal systems, and the coarse spectral and spatial resolutions of available satellite systems. Data were collected using the National Aeronautics and Space Administration (NASA) Advanced Visible-Infrared Imaging Spectrometer (AVIRIS) flown at an altitude of approximately 20000 m to provide hyperspectral imagery and simulate both MEdium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectrometer (MODIS) data. AVIRIS data were atmospherically corrected using a radiative transfer modelling approach and analysed using band ratio and linear regression models. Regression analysis was performed with simultaneous field measurements data in the Neuse River Estuary (NRE) and Pamlico Sound on 15 May 2002. Chlorophyll a (Chl a) concentrations were optimally estimated using AVIRIS bands (9.5 nm) centred at 673.6 and 692.7 nm, resulting in a coefficient of determination (R2) of 0.98. Concentrations of Chromophoric Dissolved Organic Matter (CDOM), Total Suspended Solids (TSS) and Fixed Suspended Solids (FSS) were also estimated, resulting in coefficients of determination of R2=0.90, 0.59 and 0.64, respectively. Ratios of AVIRIS bands centred at or near those corresponding to the MERIS and MODIS sensors indicated that relatively good satellite-based estimates could potentially be derived for water colour constituents at a spatial resolution of 300 and 500 m, respectively. PMID:25937680

The differential contributions of visual imagery constructs on autobiographical thinking.

PubMed

Aydin, Cagla

2018-02-01

There is a growing theoretical and empirical consensus on the central role of visual imagery in autobiographical memory. However, findings from studies that explore how individual differences in visual imagery are reflected on autobiographical thinking do not present a coherent story. One reason for the mixed findings was suggested to be the treatment of visual imagery as an undifferentiated construct while evidence shows that there is more than one type of visual imagery. The present study investigates the relative contributions of different imagery constructs; namely, object and spatial imagery, on autobiographical memory processes. Additionally, it explores whether a similar relation extends to imagining the future. The results indicate that while object imagery was significantly correlated with several phenomenological characteristics, such as the level of sensory and perceptual details for past events - but not for future events - spatial imagery predicted the level of episodic specificity for both past and future events. We interpret these findings as object imagery being recruited in tasks of autobiographical memory that employ reflective processes while spatial imagery is engaged during direct retrieval of event details. Implications for the role of visual imagery in autobiographical thinking processes are discussed.

Reconstructed high-resolution scatterometer data: a comparison with AVHRR vegetation index images for regional-scale monitoring of tropical rain forests

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1993-08-01

There is considerable interest in utilizing microwave and visible spectrum imagery for the assessment of tropical rain forests. Because rain forest spans large sub-continental areas, medium resolution (1 - 16 km) imagery will play an important role in providing a global perspective of any forest removal or change. Since 1978, AVHRR imagery from NOAA polar orbiters has provided coverage of tropical regions at this desirable resolution, but much of the imagery is plagued with heavy cloud cover typical of equatorial regions. In contrast, no historical source of active microwave imagery at native 1 - 16 km resolution exists for all the global rain forest regions. In this paper, the authors compare the utility of Seasat scatterometer (SASS) ku-band microwave data to early-date AVHRR vegetation index products for discrimination of tropical vegetation formations. When considered separately, both the AVHRR imagery and the SASS imagery could be used to distinguish between broad categories of equatorial land cover, but the AVHRR imagery was slightly superior. When combined, the two data sets provided discrimination capability superior than could be obtained by using either set alone.

Fire Impacts on Mixed Pine-oak Forests Assessed with High Spatial Resolution Imagery, Imaging Spectroscopy, and LiDAR

NASA Astrophysics Data System (ADS)

Meng, R.; Wu, J.; Zhao, F. R.; Kathy, S. L.; Dennison, P. E.; Cook, B.; Hanavan, R. P.; Serbin, S.

2016-12-01

As a primary disturbance agent, fire significantly influences forest ecosystems, including the modification or resetting of vegetation composition and structure, which can then significantly impact landscape-scale plant function and carbon stocks. Most ecological processes associated with fire effects (e.g. tree damage, mortality, and vegetation recovery) display fine-scale, species specific responses but can also vary spatially within the boundary of the perturbation. For example, both oak and pine species are fire-adapted, but fire can still induce changes in composition, structure, and dominance in a mixed pine-oak forest, mainly because of their varying degrees of fire adaption. Evidence of post-fire shifts in dominance between oak and pine species has been documented in mixed pine-oak forests, but these processes have been poorly investigated in a spatially explicit manner. In addition, traditional field-based means of quantifying the response of partially damaged trees across space and time is logistically challenging. Here we show how combining high resolution satellite imagery (i.e. Worldview-2,WV-2) and airborne imaging spectroscopy and LiDAR (i.e. NASA Goddard's Lidar, Hyperspectral and Thermal airborne imager, G-LiHT) can be effectively used to remotely quantify spatial and temporal patterns of vegetation recovery following a top-killing fire that occurred in 2012 within mixed pine-oak forests in the Long Island Central Pine Barrens Region, New York. We explore the following questions: 1) what are the impacts of fire on species composition, dominance, plant health, and vertical structure; 2) what are the recovery trajectories of forest biomass, structure, and spectral properties for three years following the fire; and 3) to what extent can fire impacts be captured and characterized by multi-sensor remote sensing techniques from active and passive optical remote sensing.

Techniques for automatic large scale change analysis of temporal multispectral imagery

NASA Astrophysics Data System (ADS)

Mercovich, Ryan A.

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst's job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change.

Statistical correction of lidar-derived digital elevation models with multispectral airborne imagery in tidal marshes

USGS Publications Warehouse

Buffington, Kevin J.; Dugger, Bruce D.; Thorne, Karen M.; Takekawa, John Y.

2016-01-01

Airborne light detection and ranging (lidar) is a valuable tool for collecting large amounts of elevation data across large areas; however, the limited ability to penetrate dense vegetation with lidar hinders its usefulness for measuring tidal marsh platforms. Methods to correct lidar elevation data are available, but a reliable method that requires limited field work and maintains spatial resolution is lacking. We present a novel method, the Lidar Elevation Adjustment with NDVI (LEAN), to correct lidar digital elevation models (DEMs) with vegetation indices from readily available multispectral airborne imagery (NAIP) and RTK-GPS surveys. Using 17 study sites along the Pacific coast of the U.S., we achieved an average root mean squared error (RMSE) of 0.072 m, with a 40–75% improvement in accuracy from the lidar bare earth DEM. Results from our method compared favorably with results from three other methods (minimum-bin gridding, mean error correction, and vegetation correction factors), and a power analysis applying our extensive RTK-GPS dataset showed that on average 118 points were necessary to calibrate a site-specific correction model for tidal marshes along the Pacific coast. By using available imagery and with minimal field surveys, we showed that lidar-derived DEMs can be adjusted for greater accuracy while maintaining high (1 m) resolution.

Regional assessment of lake ecological states using Landsat: A classification scheme for alkaline-saline, flamingo lakes in the East African Rift Valley

NASA Astrophysics Data System (ADS)

Tebbs, E. J.; Remedios, J. J.; Avery, S. T.; Rowland, C. S.; Harper, D. M.

2015-08-01

In situ reflectance measurements and Landsat satellite imagery were combined to develop an optical classification scheme for alkaline-saline lakes in the Eastern Rift Valley. The classification allows the ecological state and consequent value, in this case to Lesser Flamingos, to be determined using Landsat satellite imagery. Lesser Flamingos depend on a network of 15 alkaline-saline lakes in East African Rift Valley, where they feed by filtering cyanobacteria and benthic diatoms from the lakes' waters. The classification developed here was based on a decision tree which used the reflectance in Landsat ETM+ bands 2-4 to assign one of six classes: low phytoplankton biomass; suspended sediment-dominated; microphytobenthos; high cyanobacterial biomass; cyanobacterial scum and bleached cyanobacterial scum. The classification accuracy was 77% when verified against in situ measurements. Classified imagery and timeseries were produced for selected lakes, which show the different ecological behaviours of these complex systems. The results have highlighted the importance to flamingos of the food resources offered by the extremely remote Lake Logipi. This study has demonstrated the potential of high spatial resolution, low spectral resolution sensors for providing ecologically valuable information at a regional scale, for alkaline-saline lakes and similar hypereutrophic inland waters.

Calibration of UAS imagery inside and outside of shadows for improved vegetation index computation

NASA Astrophysics Data System (ADS)

Bondi, Elizabeth; Salvaggio, Carl; Montanaro, Matthew; Gerace, Aaron D.

2016-05-01

Vegetation health and vigor can be assessed with data from multi- and hyperspectral airborne and satellite- borne sensors using index products such as the normalized difference vegetation index (NDVI). Recent advances in unmanned aerial systems (UAS) technology have created the opportunity to access these same image data sets in a more cost effective manner with higher temporal and spatial resolution. Another advantage of these systems includes the ability to gather data in almost any weather condition, including complete cloud cover, when data has not been available before from traditional platforms. The ability to collect in these varied conditions, meteorological and temporal, will present researchers and producers with many new challenges. Particularly, cloud shadows and self-shadowing by vegetation must be taken into consideration in imagery collected from UAS platforms to avoid variation in NDVI due to changes in illumination within a single scene, and between collection flights. A workflow is presented to compensate for variations in vegetation indices due to shadows and variation in illumination levels in high resolution imagery collected from UAS platforms. Other calibration methods that producers may currently be utilizing produce NDVI products that still contain shadow boundaries and variations due to illumination, whereas the final NDVI mosaic from this workflow does not.

Extracting forest canopy structure from spatial information of high resolution optical imagery: tree crown size versus leaf area index

Treesearch

C. Song; M.B. Dickinson

2008-01-01

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a...

Evaluation of Sun Glint Correction Algorithms for High-Spatial Resolution Hyperspectral Imagery

DTIC Science & Technology

2012-09-01

ACRONYMS AND ABBREVIATIONS AISA Airborne Imaging Spectrometer for Applications AVIRIS Airborne Visible/Infrared Imaging Spectrometer BIL Band...sensor bracket mount combining Airborne Imaging Spectrometer for Applications ( AISA ) Eagle and Hawk sensors into a single imaging system (SpecTIR 2011...The AISA Eagle is a VNIR sensor with a wavelength range of approximately 400–970 nm and the AISA Hawk sensor is a SWIR sensor with a wavelength

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

Predictions of Tropical Forest Biomass and Biomass Growth Based on Stand Height or Canopy Area Are Improved by Landsat-Scale Phenology across Puerto Rico and the U.S. Virgin Islands

Treesearch

David Gwenzi; Eileen Helmer; Xiaolin Zhu; Michael Lefsky; Humfredo Marcano-Vega

2017-01-01

Remotely-sensed estimates of forest biomass are usually based on various measurements of canopy height, area, volume or texture, as derived from LiDAR, radar or fine spatial resolution imagery. These measurements are then calibrated to estimates of stand biomass that are primarily based on tree stem diameters. Although humid tropical...

Study of hyperspectral characteristics of different types of flares and smoke candles

NASA Astrophysics Data System (ADS)

Farley, Vincent; Chamberland, Martin; Lagueux, Philippe; Kastek, Mariusz; Piatkowski, Tadeusz; Dulski, Rafal

2012-06-01

Modern infrared camouflage and countermeasure technologies used in the context of military operations have evolved rapidly over the last decade. Indeed, some infrared seekers and decoy/flares tend to have spectral sensitivity tailored to closely match the emission signatures of military vehicles (such as aircrafts, tanks) and reject other sources. Similarly, some candles (or smoke bombs) are developed to generate large area screens with very high absorption in the infrared. The Military University of Technology has conducted an intensive field campaign where various types of flares and smoke candles were deployed in different conditions and measured. The high spectral, spatial and temporal resolution acquisition of these thermodynamic events was recorded with the Telops Hyper-Cam. The Hyper-Cam enables simultaneous acquisition of spatial and spectral information at high resolutions in both domains. The ability to study combustion systems with high resolution, co-registered imagery and spectral data is made possible. This paper presents the test campaign concept and definition and the analysis of the recorded measurements.

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

USGS Publications Warehouse

Wallace, C.S.A.; Marsh, S.E.

2005-01-01

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts. ?? 2005 Taylor & Francis Group Ltd.

A comparative verification of high resolution precipitation forecasts using model output statistics

NASA Astrophysics Data System (ADS)

van der Plas, Emiel; Schmeits, Maurice; Hooijman, Nicolien; Kok, Kees

2017-04-01

Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution meso-scale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this study a verification strategy based on model output statistics is applied that aims to address both double penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis and lead time. The ELR equations are derived for predictands based on areal calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the non-hydrostatic model Harmonie (2.5 km resolution), Hirlam (11 km resolution) and the ECMWF model (16 km resolution), overall yielding similar Brier skill scores for the 3 post-processed models, but larger differences for individual lead times. Besides, the Fractions Skill Score is computed using the 3 deterministic forecasts, showing somewhat better skill for the Harmonie model. In other words, despite the realism of Harmonie precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the 2 lower resolution models, at least in the Netherlands.

Regional forest land cover characterisation using medium spatial resolution satellite data

USGS Publications Warehouse

Huang, Chengquan; Homer, Collin G.; Yang, Limin; Wulder, Michael A.; Franklin, Steven E.

2003-01-01

Increasing demands on forest resources require comprehensive, consistent and up-to-date information on those resources at spatial scales appropriate for management decision-making and for scientific analysis. While such information can be derived using coarse spatial resolution satellite data (e.g. Tucker et al. 1984; Zhu and Evans 1994; Cihlar et al. 1996; Cihlar et al., Chapter 12), many regional applications require more spatial and thematic details than can be derived by using coarse resolution imagery. High spatial resolution satellite data such as IKONOS and Quick Bird images (Aplin et al. 1997), though usable for deriving detailed forest information (Culvenor, Chapter 9), are currently not feasible for wall-to-wall regional applications because of extremely high data cost, huge data volume, and lack of contiguous coverage over large areas. Forest studies over large areas have often been accomplished using data acquired by intermediate spatial resolution sensor systems, including the Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and the Enhanced Thematic Mapper Plus (ETM+) of Landsat, the High Resolution Visible (HRV) of the Systeme Pour l'Observation de la Terre (SPOT), and the Linear Image Self-Scanner (LISS) of the Indian Remote Sensing satellite. These sensor systems are more appropriate for regional applications because they can routinely produce spatially contiguous data over large areas at relatively low cost, and can be used to derive a host of forest attributes (e.g. Cohen et al. 1995; Kimes et al. 1999; Cohen et al. 2001; Huang et al. 2001; Sugumaran 2001). Of the above intermediate spatial resolution satellites, Landsat is perhaps the most widely used in various types of land remote sensing applications, in part because it has provided more extensive spatial and temporal coverage of the globe than any other intermediate resolution satellite. Spatially contiguous Landsat data have been developed for many regions of the globe (e.g. Lunetta and Sturdevant 1993; Fuller et al. 1994b; Skole et al. 1997), and a circa 1990 Landsat image data set covering the entire land area of the globe has also been developed recently (Jones and Smith 2001). An acquisition strategy aimed at acquiring at least one cloud free image per year for the entire land area of the globe has been initiated for Landsat-7 (Arvidson et al. 2001). This will probably ensure the continued dominance of Landsat in the near future.

Shallow water bathymetry correction using sea bottom classification with multispectral satellite imagery

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Yamamoto, Tomonori

2017-10-01

Bathymetry at shallow water especially shallower than 15m is an important area for environmental monitoring and national defense. Because the depth of shallow water is changeable by the sediment deposition and the ocean waves, the periodic monitoring at shoe area is needed. Utilization of satellite images are well matched for widely and repeatedly monitoring at sea area. Sea bottom terrain model using by remote sensing data have been developed and these methods based on the radiative transfer model of the sun irradiance which is affected by the atmosphere, water, and sea bottom. We adopted that general method of the sea depth extraction to the satellite imagery, WorldView-2; which has very fine spatial resolution (50cm/pix) and eight bands at visible to near-infrared wavelengths. From high-spatial resolution satellite images, there is possibility to know the coral reefs and the rock area's detail terrain model which offers important information for the amphibious landing. In addition, the WorldView-2 satellite sensor has the band at near the ultraviolet wavelength that is transmitted through the water. On the other hand, the previous study showed that the estimation error by the satellite imagery was related to the sea bottom materials such as sand, coral reef, sea alga, and rocks. Therefore, in this study, we focused on sea bottom materials, and tried to improve the depth estimation accuracy. First, we classified the sea bottom materials by the SVM method, which used the depth data acquired by multi-beam sonar as supervised data. Then correction values in the depth estimation equation were calculated applying the classification results. As a result, the classification accuracy of sea bottom materials was 93%, and the depth estimation error using the correction by the classification result was within 1.2m.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data.

PubMed

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R

2015-09-11

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite's Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds.

Evaluating Sentinel-2 for Lakeshore Habitat Mapping Based on Airborne Hyperspectral Data

PubMed Central

Stratoulias, Dimitris; Balzter, Heiko; Sykioti, Olga; Zlinszky, András; Tóth, Viktor R.

2015-01-01

Monitoring of lakeshore ecosystems requires fine-scale information to account for the high biodiversity typically encountered in the land-water ecotone. Sentinel-2 is a satellite with high spatial and spectral resolution and improved revisiting frequency and is expected to have significant potential for habitat mapping and classification of complex lakeshore ecosystems. In this context, investigations of the capabilities of Sentinel-2 in regard to the spatial and spectral dimensions are needed to assess its potential and the quality of the expected output. This study presents the first simulation of the high spatial resolution (i.e., 10 m and 20 m) bands of Sentinel-2 for lakeshore mapping, based on the satellite’s Spectral Response Function and hyperspectral airborne data collected over Lake Balaton, Hungary in August 2010. A comparison of supervised classifications of the simulated products is presented and the information loss from spectral aggregation and spatial upscaling in the context of lakeshore vegetation classification is discussed. We conclude that Sentinel-2 imagery has a strong potential for monitoring fine-scale habitats, such as reed beds. PMID:26378538

Detection of Storm Damage Tracks with EOS Data

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.; Nair, Udaysankar; Haines, Stephanie L.

2006-01-01

The damage surveys conducted by the NWS in the aftermath of a reported tornadic event are used to document the location of the tornado ground damage track (pathlength and width) and an estimation of the tornado intensity. This study explores the possibility of using near-real-time medium and high spatial resolution satellite imagery from the NASA Earth Observing System satellites to provide additional information for the surveys. Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used to study the damage tracks from three tornadic storms: the La Plata, Maryland, storm of 28 April 2002 and the Ellsinore and Marquand, Missouri, storms of 24 April 2002. These storms varied in intensity and occurred over regions with significantly different land cover. It was found that, depending on the nature of the land cover, tornado damage tracks from intense storms (F1 or greater) and hail storms may be evident in ASTER, Landsat, and MODIS satellite imagery. In areas where the land cover is dominated by forests, the scar patterns can show up very clearly, while in areas of grassland and regions with few trees, scar patterns are not as obvious or cannot be seen at all in the satellite imagery. The detection of previously unidentified segments of a damage track caused by the 24 April 2002 Marquand, Missouri, tornado demonstrates the utility of satellite imagery for damage surveys. However, the capability to detect tornado tracks in satellite imagery depends on the ability to observe the ground without obstruction from space and appears to be as much dependent on the nature of the underlying surface and land cover as on the severity of the tornadic storm.

Towards monitoring surface and subsurface lakes on the Greenland Ice Sheet using Sentinel-1 SAR and Landsat-8 OLI imagery

NASA Astrophysics Data System (ADS)

Miles, Katie E.; Willis, Ian C.; Benedek, Corinne L.; Williamson, Andrew G.; Tedesco, Marco

2017-07-01

Supraglacial lakes are an important component of the Greenland Ice Sheet’s mass balance and hydrology, with their drainage affecting ice dynamics. This study uses imagery from the recently launched Sentinel-1A Synthetic Aperture Radar (SAR) satellite to investigate supraglacial lakes in West Greenland. A semi-automated algorithm is developed to detect surface lakes from Sentinel-1 images during the 2015 summer. A combined Landsat-8 and Sentinel-1 dataset, which has a comparable temporal resolution to MODIS (3 days versus daily) but a higher spatial resolution (25-40 m versus 250-500 m), is then used together with a fully-automated lake drainage detection algorithm. Rapid (< 4 days) and slow (> 4 days) drainages are investigated for both small (< 0.125 km2, the minimum size detectable by MODIS) and large (≥ 0.125 km2) lakes through the summer. Drainage events of small lakes occur at lower elevations (mean 159 m), and slightly earlier (mean 4.5 days) in the melt season than those of large lakes. The analysis is extended manually into the early winter to calculate the dates and elevations of lake freeze-through more precisely than is possible with optical imagery (mean 30 August; 1270 m mean elevation). Finally, the Sentinel-1 imagery is used to detect subsurface lakes and, for the first time, their dates of appearance and freeze-through (mean 9 August and 7 October, respectively). These subsurface lakes occur at higher elevations than the surface lakes detected in this study (mean 1593 m and 1185 m, respectively). Sentinel-1 imagery therefore provides great potential for tracking melting, water movement and freezing within both the firn zone and ablation area of the Greenland Ice Sheet.

Fine-resolution imaging of solar features using Phase-Diverse Speckle

NASA Technical Reports Server (NTRS)

Paxman, Richard G.

1995-01-01

Phase-diverse speckle (PDS) is a novel imaging technique intended to overcome the degrading effects of atmospheric turbulence on fine-resolution imaging. As its name suggests, PDS is a blend of phase-diversity and speckle-imaging concepts. PDS reconstructions on solar data were validated by simulation, by demonstrating internal consistency of PDS estimates, and by comparing PDS reconstructions with those produced from well accepted speckle-imaging processing. Several sources of error in data collected with the Swedish Vacuum Solar Telescope (SVST) were simulated: CCD noise, quantization error, image misalignment, and defocus error, as well as atmospheric turbulence model error. The simulations demonstrate that fine-resolution information can be reliably recovered out to at least 70% of the diffraction limit without significant introduction of image artifacts. Additional confidence in the SVST restoration is obtained by comparing its spatial power spectrum with previously-published power spectra derived from both space-based images and earth-based images corrected with traditional speckle-imaging techniques; the shape of the spectrum is found to match well the previous measurements. In addition, the imagery is found to be consistent with, but slightly sharper than, imagery reconstructed with accepted speckle-imaging techniques.

Detecting personnel around UGVs using stereo vision

NASA Astrophysics Data System (ADS)

Bajracharya, Max; Moghaddam, Baback; Howard, Andrew; Matthies, Larry H.

2008-04-01

Detecting people around unmanned ground vehicles (UGVs) to facilitate safe operation of UGVs is one of the highest priority issues in the development of perception technology for autonomous navigation. Research to date has not achieved the detection ranges or reliability needed in deployed systems to detect upright pedestrians in flat, relatively uncluttered terrain, let alone in more complex environments and with people in postures that are more difficult to detect. Range data is essential to solve this problem. Combining range data with high resolution imagery may enable higher performance than range data alone because image appearance can complement shape information in range data and because cameras may offer higher angular resolution than typical range sensors. This makes stereo vision a promising approach for several reasons: image resolution is high and will continue to increase, the physical size and power dissipation of the cameras and computers will continue to decrease, and stereo cameras provide range data and imagery that are automatically spatially and temporally registered. We describe a stereo vision-based pedestrian detection system, focusing on recent improvements to a shape-based classifier applied to the range data, and present frame-level performance results that show great promise for the overall approach.

Influence of Gridded Standoff Measurement Resolution on Numerical Bathymetric Inversion

NASA Astrophysics Data System (ADS)

Hesser, T.; Farthing, M. W.; Brodie, K.

2016-02-01

The bathymetry from the surfzone to the shoreline incurs frequent, active movement due to wave energy interacting with the seafloor. Methodologies to measure bathymetry range from point-source in-situ instruments, vessel-mounted single-beam or multi-beam sonar surveys, airborne bathymetric lidar, as well as inversion techniques from standoff measurements of wave processes from video or radar imagery. Each type of measurement has unique sources of error and spatial and temporal resolution and availability. Numerical bathymetry estimation frameworks can use these disparate data types in combination with model-based inversion techniques to produce a "best-estimate of bathymetry" at a given time. Understanding how the sources of error and varying spatial or temporal resolution of each data type affect the end result is critical for determining best practices and in turn increase the accuracy of bathymetry estimation techniques. In this work, we consider an initial step in the development of a complete framework for estimating bathymetry in the nearshore by focusing on gridded standoff measurements and in-situ point observations in model-based inversion at the U.S. Army Corps of Engineers Field Research Facility in Duck, NC. The standoff measurement methods return wave parameters computed using linear wave theory from the direct measurements. These gridded datasets can range in temporal and spatial resolution that do not match the desired model parameters and therefore could lead to a reduction in the accuracy of these methods. Specifically, we investigate the affect of numerical resolution on the accuracy of an Ensemble Kalman Filter bathymetric inversion technique in relation to the spatial and temporal resolution of the gridded standoff measurements. The accuracies of the bathymetric estimates are compared with both high-resolution Real Time Kinematic (RTK) single-beam surveys as well as alternative direct in-situ measurements using sonic altimeters.

The absolute calibration of KOMPSAT-3 and 3A high spatial resolution satellites using radiometric tarps and MFRSR measurments

NASA Astrophysics Data System (ADS)

Yeom, J. M.

2017-12-01

Recently developed Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which is a continuation of the KOMPSAT-1, 2 and 3 earth observation satellite (EOS) programs from the Korea Aerospace Research Institute (KARI) was launched on March, 25 2015 on a Dnepr-1 launch vehicle from the Jasny Dombarovsky site in Russia. After launched, KARI performed in-orbit-test (IOT) including radiometric calibration for 6 months from 14 Apr. to 4 Sep. 2015. KOMPSAT-3A is equipped with two distinctive sensors; one is a high resolution multispectral optical sensor, namely the Advances Earth Image Sensor System-A (AEISS-A) and the other is the Scanner Infrared Imaging System (SIIS). In this study, we focused on the radiometric calibration of AEISS-A. The multispectral wavelengths of AEISS-A are covering three visible regions: blue (450 - 520 nm), green (520 - 600 nm), red (630 - 690 nm), one near infrared (760 - 900 nm) with a 2.0 m spatial resolution at nadir, whereas the panchromatic imagery (450 - 900 nm) has a 0.5 m resolution. Those are the same spectral response functions were same with KOMPSAT-3 multispectral and panchromatic bands but the spatial resolutions are improved. The main mission of KOMPSAT-3A is to develop for Geographical Information System (GIS) applications in environmental, agriculture, and oceanographic sciences, as well as natural hazard monitoring.

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.

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.

Investigating Hydrocarbon Seep Environments with High-Resolution, Three-Dimensional Geographic Visualizations.

NASA Astrophysics Data System (ADS)

Doolittle, D. F.; Gharib, J. J.; Mitchell, G. A.

2015-12-01

Detailed photographic imagery and bathymetric maps of the seafloor acquired by deep submergence vehicles such as Autonomous Underwater Vehicles (AUV) and Remotely Operated Vehicles (ROV) are expanding how scientists and the public view and ultimately understand the seafloor and the processes that modify it. Several recently acquired optical and acoustic datasets, collected during ECOGIG (Ecosystem Impacts of Oil and Gas Inputs to the Gulf) and other Gulf of Mexico expeditions using the National Institute for Undersea Science Technology (NIUST) Eagle Ray, and Mola Mola AUVs, have been fused with lower resolution data to create unique three-dimensional geovisualizations. Included in these data are multi-scale and multi-resolution visualizations over hydrocarbon seeps and seep related features. Resolution of the data range from 10s of mm to 10s of m. When multi-resolution data is integrated into a single three-dimensional visual environment, new insights into seafloor and seep processes can be obtained from the intuitive nature of three-dimensional data exploration. We provide examples and demonstrate how integration of multibeam bathymetry, seafloor backscatter data, sub-bottom profiler data, textured photomosaics, and hull-mounted multibeam acoustic midwater imagery are made into a series a three-dimensional geovisualizations of actively seeping sites and associated chemosynthetic communities. From these combined and merged datasets, insights on seep community structure, morphology, ecology, fluid migration dynamics, and process geomorphology can be investigated from new spatial perspectives. Such datasets also promote valuable inter-comparisons of sensor resolution and performance.

Mapping Geology and Vegetation using Hyperspectral Data in Antarctica: Current Challenges, New Solutions and Looking to the Future

NASA Astrophysics Data System (ADS)

Black, M.; Riley, T. R.; Fleming, A. H.; Ferrier, G.; Fretwell, P.; Casanovas, P.

2015-12-01

Antarctica is a unique and geographically remote environment. Traditional field campaigns investigating geology and vegetation in the region encounter numerous challenges including the harsh polar climate, the invasive nature of the work, steep topography and high infrastructure costs. Additionally, such field campaigns are often limited in terms of spatial and temporal resolution, and particularly, the topographical challenges presented in the Antarctic mean that many areas remain inaccessible. Remote Sensing, particularly hyperspectral imaging, may provide a solution to overcome the difficulties associated with field based mapping in the Antarctic. Planned satellite launches, such as EnMAP and HyspIRI, if successful, will yield large-scale, repeated hyperspectral imagery of Antarctica. Hyperspectral imagery has proven mapping capabilities and can yield greater information than can be attained using multispectral data. As a precursor to future satellite imagery, we utilise hyperspectral imagery from the first known airborne hyperspectral survey carried out in the Antarctic by the British Antarctic Survey and partners in 2011. Multiple imaging spectrometers were simultaneously deployed covering the visible, shortwave and thermal infrared regions of the electromagnetic spectrum. Additional data was generated during a field campaign deploying multiple ground spectrometers covering the same wavelengths as the airborne imagers. We utilise this imagery to assess the current challenges and propose some new solutions for mapping vegetation and geology, which may be directly applicable to future satellite hyperspectral imagery in the Antarctic.

The spatial variability of coastal surface water temperature during upwelling. [in Lake Superior

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Green, T., III

1979-01-01

Thermal scanner imagery acquired during a field experiment designed to study an upwelling event in Lake Superior is investigated. Temperature data were measured by the thermal scanner, with a spatial resolution of 7 m. These data were correlated with temperatures measured from boats. One- and two-dimensional Fourier transforms of the data were calculated and temperature variances as a function of wavenumber were plotted. A k-to-the-minus-three dependence of the temperature variance on wavenumber was found in the wavenumber range of 1-25/km. At wavenumbers greater than 25/km, a k-to-the-minus-five-thirds dependence was found.

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

PubMed Central

Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

2017-01-01

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. PMID:28492515

Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

PubMed

Calera, Alfonso; Campos, Isidro; Osann, Anna; D'Urso, Guido; Menenti, Massimo

2017-05-11

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

An effective approach for gap-filling continental scale remotely sensed time-series

PubMed Central

Weiss, Daniel J.; Atkinson, Peter M.; Bhatt, Samir; Mappin, Bonnie; Hay, Simon I.; Gething, Peter W.

2014-01-01

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R2 values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets. PMID:25642100

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...

Highlights: US Commercial Remote Sensing Industry Analysis

NASA Technical Reports Server (NTRS)

Rabin, Ron

2002-01-01

This viewgraph presentation profiles the US remote sensing industry based on responses to a survey by 1450 industry professionals. The presentation divides the industry into three sectors: academic, commercial, and government; the survey results from each are covered in a section of the presentation. The presentation also divides survey results on user needs into the following sectors: spatial resolution, geolocation accuracy; elevation accuracy, area coverage, imagery types, and timeliness. Data, information, and software characteristics are also covered in the presentation.

Iao: The New Adaptive Optics Visible Imaging and Photometric System for AEOS

DTIC Science & Technology

2008-09-01

observations of binary stars, asteroids and planets such as Mercury and Mars [2,3,4]. The Visible Imager is also used to take time resolved photometry ...role it takes high spatial resolution imagery of resolved targets. These targets are primarily low Earth orbiting satellites acquired for the...albedo pattern: Comparing the AEOS and TES data sets [5] D.T. Hall et al. 2007, Journal of Spacecraft and Rockets, 44, 910-919, Time - Resolved I-Band

Target surface finding using 3D SAR data

NASA Astrophysics Data System (ADS)

Ruiter, Jason R.; Burns, Joseph W.; Subotic, Nikola S.

2005-05-01

Methods of generating more literal, easily interpretable imagery from 3-D SAR data are being studied to provide all weather, near-visual target identification and/or scene interpretation. One method of approaching this problem is to automatically generate shape-based geometric renderings from the SAR data. In this paper we describe the application of the Marching Tetrahedrons surface finding algorithm to 3-D SAR data. The Marching Tetrahedrons algorithm finds a surface through the 3-D data cube, which provides a recognizable representation of the target surface. This algorithm was applied to the public-release X-patch simulations of a backhoe, which provided densely sampled 3-D SAR data sets. The performance of the algorithm to noise and spatial resolution were explored. Surface renderings were readily recognizable over a range of spatial resolution, and maintained their fidelity even under relatively low Signal-to-Noise Ratio (SNR) conditions.

Mapping turbidity in the Charles River, Boston using a high-resolution satellite.

PubMed

Hellweger, Ferdi L; Miller, Will; Oshodi, Kehinde Sarat

2007-09-01

The usability of high-resolution satellite imagery for estimating spatial water quality patterns in urban water bodies is evaluated using turbidity in the lower Charles River, Boston as a case study. Water turbidity was surveyed using a boat-mounted optical sensor (YSI) at 5 m spatial resolution, resulting in about 4,000 data points. The ground data were collected coincidently with a satellite imagery acquisition (IKONOS), which consists of multispectral (R, G, B) reflectance at 1 m resolution. The original correlation between the raw ground and satellite data was poor (R2 = 0.05). Ground data were processed by removing points affected by contamination (e.g., sensor encounters a particle floc), which were identified visually. Also, the ground data were corrected for the memory effect introduced by the sensor's protective casing using an analytical model. Satellite data were processed to remove pixels affected by permanent non-water features (e.g., shoreline). In addition, water pixels within a certain buffer distance from permanent non-water features were removed due to contamination by the adjacency effect. To determine the appropriate buffer distance, a procedure that explicitly considers the distance of pixels to the permanent non-water features was applied. Two automatic methods for removing the effect of temporary non-water features (e.g., boats) were investigated, including (1) creating a water-only mask based on an unsupervised classification and (2) removing (filling) all local maxima in reflectance. After the various processing steps, the correlation between the ground and satellite data was significantly better (R2 = 0.70). The correlation was applied to the satellite image to develop a map of turbidity in the lower Charles River, which reveals large-scale patterns in water clarity. However, the adjacency effect prevented the application of this method to near-shore areas, where high-resolution patterns were expected (e.g., outfall plumes).

Translation of EEG Spatial Filters from Resting to Motor Imagery Using Independent Component Analysis

PubMed Central

Wang, Yijun; Wang, Yu-Te; Jung, Tzyy-Ping

2012-01-01

Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) often use spatial filters to improve signal-to-noise ratio of task-related EEG activities. To obtain robust spatial filters, large amounts of labeled data, which are often expensive and labor-intensive to obtain, need to be collected in a training procedure before online BCI control. Several studies have recently developed zero-training methods using a session-to-session scenario in order to alleviate this problem. To our knowledge, a state-to-state translation, which applies spatial filters derived from one state to another, has never been reported. This study proposes a state-to-state, zero-training method to construct spatial filters for extracting EEG changes induced by motor imagery. Independent component analysis (ICA) was separately applied to the multi-channel EEG in the resting and the motor imagery states to obtain motor-related spatial filters. The resultant spatial filters were then applied to single-trial EEG to differentiate left- and right-hand imagery movements. On a motor imagery dataset collected from nine subjects, comparable classification accuracies were obtained by using ICA-based spatial filters derived from the two states (motor imagery: 87.0%, resting: 85.9%), which were both significantly higher than the accuracy achieved by using monopolar scalp EEG data (80.4%). The proposed method considerably increases the practicality of BCI systems in real-world environments because it is less sensitive to electrode misalignment across different sessions or days and does not require annotated pilot data to derive spatial filters. PMID:22666377

Integrating reconstructed scatterometer and advanced very high resolution radiometer data for tropical forest inventory

NASA Astrophysics Data System (ADS)

Hardin, Perry J.; Long, David G.

1995-11-01

A scientific effort is currently underway to assess tropical forest degradation and its potential impact on Earth's climate. Because of the large continental regions involved, Advanced Very High Resolution Radiometer (AVHRR) imagery and its derivative vegetation index products with resolutions between 1 and 12 km are typically used to inventory the Earth's equatorial vegetation. Archival AVHRR imagery is also used to obtain a temporal baseline of historical forest extent. Recently however, 50-km Seasat-A Scatterometer (SASS) Ku-band imagery (acquired in 1978) has been reconstructed to approximately equals 4-km resolution, making it a supplement to AVHRR imagery for historical vegetation assessment. In order to test the utility of reconstructed Ku-band scatterometer imagery for this purpose, seasonal AVHRR vegetation index and SASS images of identical resolutions were constructed. Using the imagery, discrimination experiments involving 18 vegetation categories were conducted for a central South America study area. The results of these experiments indicate that AVHRR vegetation- index images are slightly superior to reconstructed SASS images for differentiating between equatorial vegetation classes when used alone. However, combining the scatterometer imagery with the vegetation-index images provides discrimination superior to any other combination of the data sets. Using the two data sets together, 90.3% of the test data could be correctly classified into broad classes of equatorial forest, degraded woodland/forest, woodland/savanna, and caatinga.

Developing a semi/automated protocol to post-process large volume, High-resolution airborne thermal infrared (TIR) imagery for urban waste heat mapping

NASA Astrophysics Data System (ADS)

Rahman, Mir Mustafizur

In collaboration with The City of Calgary 2011 Sustainability Direction and as part of the HEAT (Heat Energy Assessment Technologies) project, the focus of this research is to develop a semi/automated 'protocol' to post-process large volumes of high-resolution (H-res) airborne thermal infrared (TIR) imagery to enable accurate urban waste heat mapping. HEAT is a free GeoWeb service, designed to help Calgary residents improve their home energy efficiency by visualizing the amount and location of waste heat leaving their homes and communities, as easily as clicking on their house in Google Maps. HEAT metrics are derived from 43 flight lines of TABI-1800 (Thermal Airborne Broadband Imager) data acquired on May 13--14, 2012 at night (11:00 pm--5:00 am) over The City of Calgary, Alberta (˜825 km 2) at a 50 cm spatial resolution and 0.05°C thermal resolution. At present, the only way to generate a large area, high-spatial resolution TIR scene is to acquire separate airborne flight lines and mosaic them together. However, the ambient sensed temperature within, and between flight lines naturally changes during acquisition (due to varying atmospheric and local micro-climate conditions), resulting in mosaicked images with different temperatures for the same scene components (e.g. roads, buildings), and mosaic join-lines arbitrarily bisect many thousands of homes. In combination these effects result in reduced utility and classification accuracy including, poorly defined HEAT Metrics, inaccurate hotspot detection and raw imagery that are difficult to interpret. In an effort to minimize these effects, three new semi/automated post-processing algorithms (the protocol) are described, which are then used to generate a 43 flight line mosaic of TABI-1800 data from which accurate Calgary waste heat maps and HEAT metrics can be generated. These algorithms (presented as four peer-reviewed papers)---are: (a) Thermal Urban Road Normalization (TURN)---used to mitigate the microclimatic variability within a thermal flight line based on varying road temperatures; (b) Automated Polynomial Relative Radiometric Normalization (RRN)---which mitigates the between flight line radiometric variability; and (c) Object Based Mosaicking (OBM)---which minimizes the geometric distortion along the mosaic edge between each flight line. A modified Emissivity Modulation technique is also described to correct H-res TIR images for emissivity. This combined radiometric and geometric post-processing protocol (i) increases the visual agreement between TABI-1800 flight lines, (ii) improves radiometric agreement within/between flight lines, (iii) produces a visually seamless mosaic, (iv) improves hot-spot detection and landcover classification accuracy, and (v) provides accurate data for thermal-based HEAT energy models. Keywords: Thermal Infrared, Post-Processing, High Spatial Resolution, Airborne, Thermal Urban Road Normalization (TURN), Relative Radiometric Normalization (RRN), Object Based Mosaicking (OBM), TABI-1800, HEAT, and Automation.

Business innovation symposium ‘At what price? IP-related thoughts on new business models for space information’

NASA Astrophysics Data System (ADS)

Smith, Lesley Jane

2011-09-01

Spatial data and imagery generators are set to become tomorrow's key players in the information society. This is why satellite owners and operators are examining new revenue-producing models for developing space-related products and services. The use and availability of broadband internet width and satellite data-based services will continue to increase in the future. With the capacity to deliver real time precision downstream data, space agencies and the satellite industry can respond to the demand for high resolution digital space information which, with the appropriate technology, can be integrated into a variety of web-based applications. At a time when the traditional roles of space agencies are becoming more hybrid, largely as a result of the greater drive towards commercial markets, new value-added markets for space-related information products are continuing to attract attention. This paper discusses whether traditional data policies on space data access and IP licensing schemes stand to remain the feasible prototype for distributing and marketing space data, and how this growth market might benefit from looking at an 'up and running' global IP management system already operating to manage end user digital demand. PrefaceThe terminology describing the various types of spatial data and space-based information is not uniformly used within the various principles, laws and policies that govern space data. For convenience only this paper refers to primary or raw data gathered by the space-based industry as spatial or raw data, and the data as processed and sold on or distributed by ground-based companies as space information products and services. In practise, spatial data range from generic to specific data sets, digital topography, through to pictures and imagery services at various resolutions, with 3-D perspectives underway. The paper addresses general IP considerations relating to spatial data, with some reference to remote sensing itself. Exact IP details will depend at all times on the final product and service in question.

Air Pollution Measurements by Citizen Scientists and NASA Satellites: Data Integration and Analysis

NASA Astrophysics Data System (ADS)

Gupta, P.; Maibach, J.; Levy, R. C.; Doraiswamy, P.; Pikelnaya, O.; Feenstra, B.; Polidori, A.

2017-12-01

PM2.5, or fine particulate matter, is a category of air pollutant consisting of solid particles with effective aerodynamic diameter of less than 2.5 microns. These particles are hazardous to human health, as their small size allows them to penetrate deep into the lungs. Since the late 1990's, the US Environmental Protection Agency has been monitoring PM2.5 using a network of ground-level sensors. Due to cost and space restrictions, the EPA monitoring network remains spatially sparse. That is, while the network spans the extent of the US, the distance between sensors is large enough that significant spatial variation in PM concentration can go undetected. To increase the spatial resolution of monitoring, previous studies have used satellite data to estimate ground-level PM concentrations. From imagery, one can create a measure of haziness due to aerosols, called aerosol optical depth (AOD), which then can be used to estimate PM concentrations using statistical and physical modeling. Additionally, previous research has identified a number of meteorological variables, such as relative humidity and mixing height, which aide in estimating PM concentrations from AOD. Although the high spatial resolution of satellite data is valuable alone for forecasting air quality, higher resolution ground-level data is needed to effectively study the relationship between PM2.5 concentrations and AOD. To this end, we discuss a citizen-science PM monitoring network deployed in California. Using low-cost PM sensors, this network achieves higher spatial resolution. We additionally discuss a software pipeline for integrating resulting PM measurements with satellite data, as well as initial data analysis.

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

PubMed

Yang, Xiaoyan; Chen, Longgao; Li, Yingkui; Xi, Wenjia; Chen, Longqian

2015-07-01

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

Fast Updating National Geo-Spatial Databases with High Resolution Imagery: China's Methodology and Experience

NASA Astrophysics Data System (ADS)

Chen, J.; Wang, D.; Zhao, R. L.; Zhang, H.; Liao, A.; Jiu, J.

2014-04-01

Geospatial databases are irreplaceable national treasure of immense importance. Their up-to-dateness referring to its consistency with respect to the real world plays a critical role in its value and applications. The continuous updating of map databases at 1:50,000 scales is a massive and difficult task for larger countries of the size of more than several million's kilometer squares. This paper presents the research and technological development to support the national map updating at 1:50,000 scales in China, including the development of updating models and methods, production tools and systems for large-scale and rapid updating, as well as the design and implementation of the continuous updating workflow. The use of many data sources and the integration of these data to form a high accuracy, quality checked product were required. It had in turn required up to date techniques of image matching, semantic integration, generalization, data base management and conflict resolution. Design and develop specific software tools and packages to support the large-scale updating production with high resolution imagery and large-scale data generalization, such as map generalization, GIS-supported change interpretation from imagery, DEM interpolation, image matching-based orthophoto generation, data control at different levels. A national 1:50,000 databases updating strategy and its production workflow were designed, including a full coverage updating pattern characterized by all element topographic data modeling, change detection in all related areas, and whole process data quality controlling, a series of technical production specifications, and a network of updating production units in different geographic places in the country.

Radiometric and geometric assessment of data from the RapidEye constellation of satellites

USGS Publications Warehouse

Chander, Gyanesh; Haque, Md. Obaidul; Sampath, Aparajithan; Brunn, A.; Trosset, G.; Hoffmann, D.; Roloff, S.; Thiele, M.; Anderson, C.

2013-01-01

To monitor land surface processes over a wide range of temporal and spatial scales, it is critical to have coordinated observations of the Earth's surface using imagery acquired from multiple spaceborne imaging sensors. The RapidEye (RE) satellite constellation acquires high-resolution satellite images covering the entire globe within a very short period of time by sensors identical in construction and cross-calibrated to each other. To evaluate the RE high-resolution Multi-spectral Imager (MSI) sensor capabilities, a cross-comparison between the RE constellation of sensors was performed first using image statistics based on large common areas observed over pseudo-invariant calibration sites (PICS) by the sensors and, second, by comparing the on-orbit radiometric calibration temporal trending over a large number of calibration sites. For any spectral band, the individual responses measured by the five satellites of the RE constellation were found to differ <2–3% from the average constellation response depending on the method used for evaluation. Geometric assessment was also performed to study the positional accuracy and relative band-to-band (B2B) alignment of the image data sets. The position accuracy was assessed by comparing the RE imagery against high-resolution aerial imagery, while the B2B characterization was performed by registering each band against every other band to ensure that the proper band alignment is provided for an image product. The B2B results indicate that the internal alignments of these five RE bands are in agreement, with bands typically registered to within 0.25 pixels of each other or better.

OnEarth: An Open Source Solution for Efficiently Serving High-Resolution Mapped Image Products

NASA Astrophysics Data System (ADS)

Thompson, C. K.; Plesea, L.; Hall, J. R.; Roberts, J. T.; Cechini, M. F.; Schmaltz, J. E.; Alarcon, C.; Huang, T.; McGann, J. M.; Chang, G.; Boller, R. A.; Ilavajhala, S.; Murphy, K. J.; Bingham, A. W.

2013-12-01

This presentation introduces OnEarth, a server side software package originally developed at the Jet Propulsion Laboratory (JPL), that facilitates network-based, minimum-latency geolocated image access independent of image size or spatial resolution. The key component in this package is the Meta Raster Format (MRF), a specialized raster file extension to the Geospatial Data Abstraction Library (GDAL) consisting of an internal indexed pyramid of image tiles. Imagery to be served is converted to the MRF format and made accessible online via an expandable set of server modules handling requests in several common protocols, including the Open Geospatial Consortium (OGC) compliant Web Map Tile Service (WMTS) as well as Tiled WMS and Keyhole Markup Language (KML). OnEarth has recently transitioned to open source status and is maintained and actively developed as part of GIBS (Global Imagery Browse Services), a collaborative project between JPL and Goddard Space Flight Center (GSFC). The primary function of GIBS is to enhance and streamline the data discovery process and to support near real-time (NRT) applications via the expeditious ingestion and serving of full-resolution imagery representing science products from across the NASA Earth Science spectrum. Open source software solutions are leveraged where possible in order to utilize existing available technologies, reduce development time, and enlist wider community participation. We will discuss some of the factors and decision points in transitioning OnEarth to a suitable open source paradigm, including repository and licensing agreement decision points, institutional hurdles, and perceived benefits. We will also provide examples illustrating how OnEarth is integrated within GIBS and other applications.

Training site statistics from Landsat and Seasat satellite imagery registered to a common map base

NASA Technical Reports Server (NTRS)

Clark, J.

1981-01-01

Landsat and Seasat satellite imagery and training site boundary coordinates were registered to a common Universal Transverse Mercator map base in the Newport Beach area of Orange County, California. The purpose was to establish a spatially-registered, multi-sensor data base which would test the use of Seasat synthetic aperture radar imagery to improve spectral separability of channels used for land use classification of an urban area. Digital image processing techniques originally developed for the digital mosaics of the California Desert and the State of Arizona were adapted to spatially register multispectral and radar data. Techniques included control point selection from imagery and USGS topographic quadrangle maps, control point cataloguing with the Image Based Information System, and spatial and spectral rectifications of the imagery. The radar imagery was pre-processed to reduce its tendency toward uniform data distributions, so that training site statistics for selected Landsat and pre-processed Seasat imagery indicated good spectral separation between channels.

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.

The Metrics of Spatial Distance Traversed During Mental Imagery

ERIC Educational Resources Information Center

Rinck, Mike; Denis, Michel

2004-01-01

The authors conducted 2 experiments to study the metrics of spatial distance in a mental imagery task. In both experiments, participants first memorized the layout of a building containing 10 rooms with 24 objects. Participants then received mental imagery instructions and imagined how they walked through the building from one room to another. The…

It's time for a crisper image of the Face of the Earth: Landsat and climate time series for massive land cover & climate change mapping at detailed resolution.

NASA Astrophysics Data System (ADS)

Pons, Xavier; Miquel, Ninyerola; Oscar, González-Guerrero; Cristina, Cea; Pere, Serra; Alaitz, Zabala; Lluís, Pesquer; Ivette, Serral; Joan, Masó; Cristina, Domingo; Maria, Serra Josep; Jordi, Cristóbal; Chris, Hain; Martha, Anderson; Juanjo, Vidal

2014-05-01

Combining climate dynamics and land cover at a relative coarse resolution allows a very interesting approach to global studies, because in many cases these studies are based on a quite high temporal resolution, but they may be limited in large areas like the Mediterranean. However, the current availability of long time series of Landsat imagery and spatially detailed surface climate models allow thinking on global databases improving the results of mapping in areas with a complex history of landscape dynamics, characterized by fragmentation, or areas where relief creates intricate climate patterns that can be hardly monitored or modeled at coarse spatial resolutions. DinaCliVe (supported by the Spanish Government and ERDF, and by the Catalan Government, under grants CGL2012-33927 and SGR2009-1511) is the name of the project that aims analyzing land cover and land use dynamics as well as vegetation stress, with a particular emphasis on droughts, and the role that climate variation may have had in such phenomena. To meet this objective is proposed to design a massive database from long time series of Landsat land cover products (grouped in quinquennia) and monthly climate records (in situ climate data) for the Iberian Peninsula (582,000 km2). The whole area encompasses 47 Landsat WRS2 scenes (Landsat 4 to 8 missions, from path 197 to 202 and from rows 30 to 34), and 52 Landsat WRS1 scenes (for the previous Landsat missions, 212 to 221 and 30 to 34). Therefore, a mean of 49.5 Landsat scenes, 8 quinquennia per scene and a about 6 dates per quinquennium , from 1975 to present, produces around 2376 sets resulting in 30 m x 30 m spatial resolution maps. Each set is composed by highly coherent geometric and radiometric multispectral and multitemporal (to account for phenology) imagery as well as vegetation and wetness indexes, and several derived topographic information (about 10 Tbyte of data). Furthermore, on the basis on a previous work: the Digital Climatic Atlas of the Iberian Peninsula, spatio-temporal surface climate data has been generated with a monthly resolution (from January 1950 to December 2010) through a multiple regression model and residuals spatial interpolation using geographic variables (altitude, latitude and continentality) and solar radiation (only in the case of temperatures). This database includes precipitation, mean minimum and mean maximum air temperature and mean air temperature, improving the previous one by using the ASTER GDEM at 30 m spatial resolution, by deepening to a monthly resolution and by increasing the number of meteorological stations used, representing a total amount of 0.7 Tbyte of data. An initial validation shows accuracies higher than 85 % for land cover maps and an RMS of 1.2 ºC, 1.6 ºC and 22 mm for mean and extreme temperatures, and for precipitation, respectively. This amount of new detailed data for the Iberian Peninsula framework will be used to study the spatial direction, velocity and acceleration of the tendencies related to climate change, land cover and tree line dynamics. A global analysis using all these datasets will try to discriminate the climatic signal when interpreted together with anthropogenic driving forces. Ultimately, getting ready for massive database computation and analysis will improve predictions for global models that will require of the growing high-resolution information available.

The Joint Agency Commercial Imagery Evaluation Team and Product Characterization Approach

NASA Technical Reports Server (NTRS)

Zanoni, Vicki; Pagnutti, Mary; Ryan, Robert E.; Snyder, Greg; Lehman, William; Roylance, Spencer

2003-01-01

The Joint Agency Commercial Imagery Evaluation (JACIE) team is a collaborative interagency group focused on the characterization of commercial remote sensing data products. The team members - the National Aeronautics and Space Administration (NASA), the National Imagery and Mapping Agency (NIMA), and the U.S. Geological Survey (USGS) - each have a vested interest in the purchase and use of commercial imagery to support government research and operational applications. For both research and applications, commercial products must be well characterized for precision, accuracy, and repeatability. Since commercial systems are built and operated with no government insight or oversight, the JACIE team provides an independent product characterization of delivered image and image-derived end products. End product characterization differs from the systems calibration approach that is typically used with government systems, where detailed system design information is available. The product characterization approach addresses three primary areas of product performance: geopositional accuracy, image quality, and radiometric accuracy. The JACIE team utilizes well-characterized test sites to support characterization activities. To characterize geopositional accuracy, the team utilizes sites containing several "photo-identifiable" targets and compares their precisely known locations with those defined by the commercial image product. In the area of image quality, spatial response is characterized using edge targets and pulse targets to measure edge response and to estimate image modulation transfer function. Additionally, imagery is also characterized using the National Imagery Interpretability Rating Scale, a means of quantifying the ability to identify certain targets (e.g., rail-cars, airplanes) within an image product. Radiometric accuracy is characterized using reflectance-based vicarious calibration methods at several uniform sites. Each JACIE agency performs an aspect of product characterization based on its area of expertise, thus minimizing duplication of effort. The JACIE team collaborated to perform comprehensive characterization of products from Space Imaging Inc.'s IKONOS satellite and from DigitalGlobe's QuickBird satellite and is currently characterizing products from OrbImage s OrbView-3. JACIE assessments have resulted in several improvements to commercial image product quality and have enhanced working relationships between government and industry. Assessment results are presented at an annual JACIE High Spatial Resolution Commercial Imagery Workshop.

Assessing the value of Landsat imagery: Results from a 2012 comprehensive user survey

NASA Astrophysics Data System (ADS)

Miller, H. M.; Richardson, L.; Loomis, J.; Koontz, S.; Koontz, L.

2012-12-01

Landsat satellite imagery has long been recognized as unique among remotely sensed data due to the combination of its extensive archive, global coverage, and relatively high spatial and temporal resolution. Since the imagery became available at no cost in 2008, the number of users registered with the U.S. Geological Survey (USGS) has increased tenfold while the number of scenes downloaded annually has increased a hundredfold. It is clear that the imagery is being used extensively, and understanding the benefits provided by this imagery can help inform decisions involving its provision. However, the value of Landsat imagery is difficult to measure for a variety of reasons, one of which stems from the fact that the imagery has characteristics of a public good and does not have a direct market price to reflect its value to society. Further, there is not a clear understanding of the full range of users of the imagery, as well as how these users are distributed across the many different end uses this data is applied to. To assess the value of Landsat imagery, we conducted a survey of users registered with USGS in early 2012. Over 11,000 current users of Landsat imagery responded to the survey. The value of the imagery was measured both qualitatively and quantitatively. To explore the qualitative value of the imagery, users were asked about the importance of the imagery to their work, their dependence on the imagery, and the impacts on their work if there was no Landsat imagery. The majority of users deemed Landsat imagery important to their work and stated they were dependent on Landsat imagery to do their work. Additionally, if Landsat imagery was no longer available, over half of the users would have to discontinue some of their work. On average, these users would discontinue half of their current work if the imagery was no longer available. The focus of this presentation will be the quantitative results of a double-bounded contingent valuation analysis which reveals how much users would pay to replace Landsat imagery in the event of a data gap. Overall willingness to pay as well as willingness to pay by sector and citizenship will be reported. The results from this survey indicate that the value of Landsat imagery is substantial for the majority of users and that the loss of Landsat would negatively impact many users.

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.

A 5-year analysis of crop phenologies from the United States Heartland (Invited)

NASA Astrophysics Data System (ADS)

Johnson, D. M.

2010-12-01

Time series imagery data from the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) was intersected with annually updated field-level crop data from the United States Department of Agriculture (USDA) Farm Service Agency (FSA). Phenological metrics were derived for major crop types found in the United States (US) Heartland region. The specific MODIS data consisted of the 16-day composited Normalized Difference Vegetation Index (NDVI) 250 meter spatial resolution imagery from the Terra satellite. Crops evaluated included corn, soybeans, wheat, cotton, sorghum, rice, and other small grains. Charts showing the annual average state-level NDVI phenologies by crop were constructed for the five years between 2006 and 2010. The states of interest covered the intensively cultivated regions in the US Great Plains, Corn Belt, and Mississippi River Alluvial Plain. Results demonstrated the recent biophysical growth cycles of prevalent and widespread US crops and how they varied by geography and year. Linkages between the time series data and planting practices, weather impacts, crop progress reports, and yields were also investigated.

Improving the frequency of high spatial resolution leaf area index maps using Landsat OLI and Sentinel-2 MSI

NASA Astrophysics Data System (ADS)

Li, S.; Ganguly, S.; Dungan, J. L.; Zhang, G.; Ju, J.; Claverie, M.

2015-12-01

The European Space Agency's Sentinel-2 mission successfully launched the first of two satellites in June, 2015. Sentinel 2A's MSI instrument is now providing optical data similar to Landsat 8's OLI imagery and, with its global repeat of 10 days, has the potential to increase the availability of 30m resolution high level products such as leaf area index (LAI). Prior to the launch of S-2A, we simulated MSI imagery using EO-1 Hyperion data and estimated green LAI using an algorithm based on canopy spectral invariants theory. Comparison of the resulting LAI maps resulting from the simulated MSI and corresponding maps derived from OLI data showed a RMSE of 0.1875. Uncertainty bounds on actual MSI data promise to be narrower because of the superior signal-to-noise ratio of MSI. A workflow for the production of LAI and other high level products including data ingest, BRDF correction, cloud masking and atmospheric correction is being developed using the NASA Earth Exchange (NEX) and will improve the capability to examine seasonal changes in canopy LAI.

Comparison of lithological mapping results from airborne hyperspectral VNIR-SWIR, LWIR and combined data

NASA Astrophysics Data System (ADS)

Feng, Jilu; Rogge, Derek; Rivard, Benoit

2018-02-01

This study investigates using the Airborne Hyperspectral Imaging Systems (AISA) visible and short-wave infrared (SWIR) and Spatially Enhanced Broadband Array Spectrograph System (SEBASS) longwave infrared (LWIR) (2 and 4 m spatial resolution, respectively) imagery independently and in combination to produce detailed lithologic maps in a subarctic region (Cape Smith Belt, Nunavik, Canada) where regionally metamorphosed lower greenschist mafic, ultramafic and sedimentary rocks are exposed in the presence of lichen coatings. We make use of continuous wavelet analysis (CWA) to improve the radiometric quality of the imagery through the minimization of random noise and the enhancement of spectral features, the minimization of residual errors in the ISAC radiometric correction and target temperature estimation in the case of the LWIR data, the minimization of line to line residual calibration effects that lead to inconsistencies in data mosaics, and the reduction in variability of the spectral continuum introduced by variable illumination and topography. The use of CWA also provides a platform to directly combine the wavelet scale spectral profiles of the SWIR and LWIR after applying a scalar correction factor to the LWIR such that the dynamic range of two data sets have equal weight. This is possible using CWA as the datasets are normalized to a zero mean allowing spectra from different spectral regions to be adjoined. Lithologic maps are generated using an iterative spectral unmixing approach with image spectral endmembers extracted from the SWIR and LWIR imagery based on locations defined from previous work of the study area and field mapping information. Unmixing results of the independent SWIR and LWIR data, and the combined data show clear benefits to using the CWA combined imagery. The analysis showed SWIR and LWIR imagery highlight similar regions and spatial distributions for the three ultramafic units (dunite, peridotite, pyroxenite). However, significant differences are observed for quartz-rich sediments, with the SWIR overestimating the distribution of these rocks whereas the LWIR provided more consistent results compared with existing maps. Both SWIR and LWIR imagery were impacted by the pervasive lichen coatings on the mafic rocks (basalts and gabbros), although the SWIR provided better results than the LWIR. Limitations observed for the independent data sets were removed using the combined spectral data resulting in all geologically meaningful units mapped correctly in comparison with existing geological maps.

Determining Titan surface topography from Cassini SAR data

USGS Publications Warehouse

Stiles, Bryan W.; Hensley, Scott; Gim, Yonggyu; Bates, David M.; Kirk, Randolph L.; Hayes, Alex; Radebaugh, Jani; Lorenz, Ralph D.; Mitchell, Karl L.; Callahan, Philip S.; Zebker, Howard; Johnson, William T.K.; Wall, Stephen D.; Lunine, Jonathan I.; Wood, Charles A.; Janssen, Michael; Pelletier, Frederic; West, Richard D.; Veeramacheneni, Chandini

2009-01-01

A technique, referred to as SARTopo, has been developed for obtaining surface height estimates with 10 km horizontal resolution and 75 m vertical resolution of the surface of Titan along each Cassini Synthetic Aperture Radar (SAR) swath. We describe the technique and present maps of the co-located data sets. A global map and regional maps of Xanadu and the northern hemisphere hydrocarbon lakes district are included in the results. A strength of the technique is that it provides topographic information co-located with SAR imagery. Having a topographic context vastly improves the interpretability of the SAR imagery and is essential for understanding Titan. SARTopo is capable of estimating surface heights for most of the SAR-imaged surface of Titan. Currently nearly 30% of the surface is within 100 km of a SARTopo height profile. Other competing techniques provide orders of magnitude less coverage. We validate the SARTopo technique through comparison with known geomorphological features such as mountain ranges and craters, and by comparison with co-located nadir altimetry, including a 3000 km strip that had been observed by SAR a month earlier. In this area, the SARTopo and nadir altimetry data sets are co-located tightly (within 5-10 km for one 500 km section), have similar resolution, and as expected agree closely in surface height. Furthermore the region contains prominent high spatial resolution topography, so it provides an excellent test of the resolution and precision of both techniques.

Monitoring the spatial and temporal evolution of slope instability with Digital Image Correlation

NASA Astrophysics Data System (ADS)

Manconi, Andrea; Glueer, Franziska; Loew, Simon

2017-04-01

The identification and monitoring of ground deformation is important for an appropriate analysis and interpretation of unstable slopes. Displacements are usually monitored with in-situ techniques (e.g., extensometers, inclinometers, geodetic leveling, tachymeters and D-GPS), and/or active remote sensing methods (e.g., LiDAR and radar interferometry). In particular situations, however, the choice of the appropriate monitoring system is constrained by site-specific conditions. Slope areas can be very remote and/or affected by rapid surface changes, thus hardly accessible, often unsafe, for field installations. In many cases the use of remote sensing approaches might be also hindered because of unsuitable acquisition geometries, poor spatial resolution and revisit times, and/or high costs. The increasing availability of digital imagery acquired from terrestrial photo and video cameras allows us nowadays for an additional source of data. The latter can be exploited to visually identify changes of the scene occurring over time, but also to quantify the evolution of surface displacements. Image processing analyses, such as Digital Image Correlation (known also as pixel-offset or feature-tracking), have demonstrated to provide a suitable alternative to detect and monitor surface deformation at high spatial and temporal resolutions. However, a number of intrinsic limitations have to be considered when dealing with optical imagery acquisition and processing, including the effects of light conditions, shadowing, and/or meteorological variables. Here we propose an algorithm to automatically select and process images acquired from time-lapse cameras. We aim at maximizing the results obtainable from large datasets of digital images acquired with different light and meteorological conditions, and at retrieving accurate information on the evolution of surface deformation. We show a successful example of application of our approach in the Swiss Alps, more specifically in the Great Aletsch area, where slope instability was recently reactivated due to the progressive glacier retreat. At this location, time-lapse cameras have been installed during the last two years, ranging from low-cost and low-resolution webcams to more expensive high-resolution reflex cameras. Our results confirm that time-lapse cameras provide quantitative and accurate measurements of surface deformation evolution over space and time, especially in situations when other monitoring instruments fail.

Prevalence of pure versus mixed snow cover pixels across spatial resolutions in alpine environments: implications for binary and fractional remote sensing approaches

USGS Publications Warehouse

Selkowitz, David J.; Forster, Richard; Caldwell, Megan K.

2014-01-01

Remote sensing of snow-covered area (SCA) can be binary (indicating the presence/absence of snow cover at each pixel) or fractional (indicating the fraction of each pixel covered by snow). Fractional SCA mapping provides more information than binary SCA, but is more difficult to implement and may not be feasible with all types of remote sensing data. The utility of fractional SCA mapping relative to binary SCA mapping varies with the intended application as well as by spatial resolution, temporal resolution and period of interest, and climate. We quantified the frequency of occurrence of partially snow-covered (mixed) pixels at spatial resolutions between 1 m and 500 m over five dates at two study areas in the western U.S., using 0.5 m binary SCA maps derived from high spatial resolution imagery aggregated to fractional SCA at coarser spatial resolutions. In addition, we used in situ monitoring to estimate the frequency of partially snow-covered conditions for the period September 2013–August 2014 at 10 60-m grid cell footprints at two study areas with continental snow climates. Results from the image analysis indicate that at 40 m, slightly above the nominal spatial resolution of Landsat, mixed pixels accounted for 25%–93% of total pixels, while at 500 m, the nominal spatial resolution of MODIS bands used for snow cover mapping, mixed pixels accounted for 67%–100% of total pixels. Mixed pixels occurred more commonly at the continental snow climate site than at the maritime snow climate site. The in situ data indicate that some snow cover was present between 186 and 303 days, and partial snow cover conditions occurred on 10%–98% of days with snow cover. Four sites remained partially snow-free throughout most of the winter and spring, while six sites were entirely snow covered throughout most or all of the winter and spring. Within 60 m grid cells, the late spring/summer transition from snow-covered to snow-free conditions lasted 17–56 days and averaged 37 days. Our results suggest that mixed snow-covered snow-free pixels are common at the spatial resolutions imaged by both the Landsat and MODIS sensors. This highlights the additional information available from fractional SCA products and suggests fractional SCA can provide a major advantage for hydrological and climatological monitoring and modeling, particularly when accurate representation of the spatial distribution of snow cover is critical.

Mapping the distribution of the main host for plague in a complex landscape in Kazakhstan: An object-based approach using SPOT-5 XS, Landsat 7 ETM+, SRTM and multiple Random Forests

NASA Astrophysics Data System (ADS)

Wilschut, L. I.; Addink, E. A.; Heesterbeek, J. A. P.; Dubyanskiy, V. M.; Davis, S. A.; Laudisoit, A.; Begon, M.; Burdelov, L. A.; Atshabar, B. B.; de Jong, S. M.

2013-08-01

Plague is a zoonotic infectious disease present in great gerbil populations in Kazakhstan. Infectious disease dynamics are influenced by the spatial distribution of the carriers (hosts) of the disease. The great gerbil, the main host in our study area, lives in burrows, which can be recognized on high resolution satellite imagery. In this study, using earth observation data at various spatial scales, we map the spatial distribution of burrows in a semi-desert landscape. The study area consists of various landscape types. To evaluate whether identification of burrows by classification is possible in these landscape types, the study area was subdivided into eight landscape units, on the basis of Landsat 7 ETM+ derived Tasselled Cap Greenness and Brightness, and SRTM derived standard deviation in elevation. In the field, 904 burrows were mapped. Using two segmented 2.5 m resolution SPOT-5 XS satellite scenes, reference object sets were created. Random Forests were built for both SPOT scenes and used to classify the images. Additionally, a stratified classification was carried out, by building separate Random Forests per landscape unit. Burrows were successfully classified in all landscape units. In the ‘steppe on floodplain’ areas, classification worked best: producer's and user's accuracy in those areas reached 88% and 100%, respectively. In the ‘floodplain’ areas with a more heterogeneous vegetation cover, classification worked least well; there, accuracies were 86 and 58% respectively. Stratified classification improved the results in all landscape units where comparison was possible (four), increasing kappa coefficients by 13, 10, 9 and 1%, respectively. In this study, an innovative stratification method using high- and medium resolution imagery was applied in order to map host distribution on a large spatial scale. The burrow maps we developed will help to detect changes in the distribution of great gerbil populations and, moreover, serve as a unique empirical data set which can be used as input for epidemiological plague models. This is an important step in understanding the dynamics of plague.

Shadow Detection from Very High Resoluton Satellite Image Using Grabcut Segmentation and Ratio-Band Algorithms

NASA Astrophysics Data System (ADS)

Kadhim, N. M. S. M.; Mourshed, M.; Bray, M. T.

2015-03-01

Very-High-Resolution (VHR) satellite imagery is a powerful source of data for detecting and extracting information about urban constructions. Shadow in the VHR satellite imageries provides vital information on urban construction forms, illumination direction, and the spatial distribution of the objects that can help to further understanding of the built environment. However, to extract shadows, the automated detection of shadows from images must be accurate. This paper reviews current automatic approaches that have been used for shadow detection from VHR satellite images and comprises two main parts. In the first part, shadow concepts are presented in terms of shadow appearance in the VHR satellite imageries, current shadow detection methods, and the usefulness of shadow detection in urban environments. In the second part, we adopted two approaches which are considered current state-of-the-art shadow detection, and segmentation algorithms using WorldView-3 and Quickbird images. In the first approach, the ratios between the NIR and visible bands were computed on a pixel-by-pixel basis, which allows for disambiguation between shadows and dark objects. To obtain an accurate shadow candidate map, we further refine the shadow map after applying the ratio algorithm on the Quickbird image. The second selected approach is the GrabCut segmentation approach for examining its performance in detecting the shadow regions of urban objects using the true colour image from WorldView-3. Further refinement was applied to attain a segmented shadow map. Although the detection of shadow regions is a very difficult task when they are derived from a VHR satellite image that comprises a visible spectrum range (RGB true colour), the results demonstrate that the detection of shadow regions in the WorldView-3 image is a reasonable separation from other objects by applying the GrabCut algorithm. In addition, the derived shadow map from the Quickbird image indicates significant performance of the ratio algorithm. The differences in the characteristics of the two satellite imageries in terms of spatial and spectral resolution can play an important role in the estimation and detection of the shadow of urban objects.

Generation of co-speech gestures based on spatial imagery from the right-hemisphere: evidence from split-brain patients.

PubMed

Kita, Sotaro; Lausberg, Hedda

2008-02-01

It has been claimed that the linguistically dominant (left) hemisphere is obligatorily involved in production of spontaneous speech-accompanying gestures (Kimura, 1973a, 1973b; Lavergne and Kimura, 1987). We examined this claim for the gestures that are based on spatial imagery: iconic gestures with observer viewpoint (McNeill, 1992) and abstract deictic gestures (McNeill, et al. 1993). We observed gesture production in three patients with complete section of the corpus callosum in commissurotomy or callosotomy (two with left-hemisphere language, and one with bilaterally represented language) and nine healthy control participants. All three patients produced spatial-imagery gestures with the left-hand as well as with the right-hand. However, unlike healthy controls and the split-brain patient with bilaterally represented language, the two patients with left-hemispheric language dominance coordinated speech and spatial-imagery gestures more poorly in the left-hand than in the right-hand. It is concluded that the linguistically non-dominant (right) hemisphere alone can generate co-speech gestures based on spatial imagery, just as the left-hemisphere can.