PROGRAM ASPECT - FOR REMOTE SENSING OF AIRBORNE PLUMES

EPA Science Inventory

The SAFEGUARD program is a multi-sensor program for the detection and imaging of chemical plumes and vapors. The system is composed of an airborne sensor suite including an infrared line scanner and a high-speed fourier transform infrared spectrometer. Both systems are integrat...

A multi-sensor remote sensing approach for measuring primary production from space

NASA Technical Reports Server (NTRS)

Gautier, Catherine

1989-01-01

It is proposed to develop a multi-sensor remote sensing method for computing marine primary productivity from space, based on the capability to measure the primary ocean variables which regulate photosynthesis. The three variables and the sensors which measure them are: (1) downwelling photosynthetically available irradiance, measured by the VISSR sensor on the GOES satellite, (2) sea-surface temperature from AVHRR on NOAA series satellites, and (3) chlorophyll-like pigment concentration from the Nimbus-7/CZCS sensor. These and other measured variables would be combined within empirical or analytical models to compute primary productivity. With this proposed capability of mapping primary productivity on a regional scale, we could begin realizing a more precise and accurate global assessment of its magnitude and variability. Applications would include supplementation and expansion on the horizontal scale of ship-acquired biological data, which is more accurate and which supplies the vertical components of the field, monitoring oceanic response to increased atmospheric carbon dioxide levels, correlation with observed sedimentation patterns and processes, and fisheries management.

The use of multisensor images for Earth Science applications

NASA Technical Reports Server (NTRS)

Evans, D.; Stromberg, B.

1983-01-01

The use of more than one remote sensing technique is particularly important for Earth Science applications because of the compositional and textural information derivable from the images. The ability to simultaneously analyze images acquired by different sensors requires coregistration of the multisensor image data sets. In order to insure pixel to pixel registration in areas of high relief, images must be rectified to eliminate topographic distortions. Coregistered images can be analyzed using a variety of multidimensional techniques and the acquired knowledge of topographic effects in the images can be used in photogeologic interpretations.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

Remote sensing of ecosystem health: opportunities, challenges, and future perspectives.

PubMed

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-11-07

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Exploring the use of multi-sensor data fusion for daily evapotranspiration mapping at field scale

USDA-ARS?s Scientific Manuscript database

Modern practices of water management in agriculture can significantly benefit from accurate mapping of crop water consumption at field scale. Assuming that actual evapotranspiration (ET) is the main water loss in land hydrological balance, remote sensing data represent an invaluable tool for water u...

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

PubMed Central

Li, Zhaoqin; Xu, Dandan; Guo, Xulin

2014-01-01

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges. PMID:25386759

An integrated approach for high spatial resolution mapping of water and carbon fluxes using multi-sensor data

USDA-ARS?s Scientific Manuscript database

In the last few years, modeling of surface processes, such as water and carbon balances, vegetation growth and energy budgets, has focused on integrated approaches that combine aspects of hydrology, biology and meteorology into unified analyses. In this context, remotely sensed data often have a cor...

Numerically stable algorithm for combining census and sample estimates with the multivariate composite estimator

Treesearch

R. L. Czaplewski

2009-01-01

The minimum variance multivariate composite estimator is a relatively simple sequential estimator for complex sampling designs (Czaplewski 2009). Such designs combine a probability sample of expensive field data with multiple censuses and/or samples of relatively inexpensive multi-sensor, multi-resolution remotely sensed data. Unfortunately, the multivariate composite...

Multi-sensor Cloud Retrieval Simulator and Remote Sensing from Model Parameters . Pt. 1; Synthetic Sensor Radiance Formulation; [Synthetic Sensor Radiance Formulation

NASA Technical Reports Server (NTRS)

Wind, G.; DaSilva, A. M.; Norris, P. M.; Platnick, S.

2013-01-01

In this paper we describe a general procedure for calculating synthetic sensor radiances from variable output from a global atmospheric forecast model. In order to take proper account of the discrepancies between model resolution and sensor footprint, the algorithm takes explicit account of the model subgrid variability, in particular its description of the probability density function of total water (vapor and cloud condensate.) The simulated sensor radiances are then substituted into an operational remote sensing algorithm processing chain to produce a variety of remote sensing products that would normally be produced from actual sensor output. This output can then be used for a wide variety of purposes such as model parameter verification, remote sensing algorithm validation, testing of new retrieval methods and future sensor studies.We show a specific implementation using the GEOS-5 model, the MODIS instrument and the MODIS Adaptive Processing System (MODAPS) Data Collection 5.1 operational remote sensing cloud algorithm processing chain (including the cloud mask, cloud top properties and cloud optical and microphysical properties products). We focus on clouds because they are very important to model development and improvement.

Mapping Palm Swamp Wetland Ecosystems in the Peruvian Amazon: a Multi-Sensor Remote Sensing Approach

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schroeder, R.; Pinto, N.; Zimmerman, R.; Horna, V.

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. Spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Other remote sensing systems: Retrospect and outlook

NASA Technical Reports Server (NTRS)

1982-01-01

The history of remote sensing is reviewed and the scope and versatility of the several remote sensing systems already in orbit are discussed, especially those with sensors operating in other EM spectral modes. The multisensor approach is examined by interrelating LANDSAT observations with data from other satellite systems. The basic principles and practices underlying the use of thermal infrared and radar sensors are explored and the types of observations and interpretations emanating from the Nimbus, Heat Capacity Mapping Mission, and SEASAT programs are examined. Approved or proposed Earth resources oriented missions for the 1980's previewed include LANDSAT D, Stereosat, Gravsat, the French satellite SPOT-1, and multimission modular spacecraft launched from space shuttle. The pushbroom imager, the linear array pushbroom radiometer, the multispectral linear array, and the operational LANDSAT observing system, to be designed the LANDSAT-E series are also envisioned for this decade.

New Approaches to the Use and Integration of Multi-Sensor Remote Sensing for Historic Resource Identification and Evaluation

DTIC Science & Technology

2006-11-10

features based on shape are easy to come by. The Great Pyramids at Giza are instantly identified from space, even at the very coarse spatial... Pyramids at Giza , Egypt, are recognized by their triangular faces in this 1 m resolution Ikonos image, as are nearby rectangular tombs (credit: Space

Multi-sensor data processing method for improved satellite retrievals

NASA Astrophysics Data System (ADS)

Fan, Xingwang

2017-04-01

Satellite remote sensing has provided massive data that improve the overall accuracy and extend the time series of environmental studies. In reflective solar bands, satellite data are related to land surface properties via radiative transfer (RT) equations. These equations generally include sensor-related (calibration coefficients), atmosphere-related (aerosol optical thickness) and surface-related (surface reflectance) parameters. It is an ill-posed problem to solve three parameters with only one RT equation. Even if there are two RT equations (dual-sensor data), the problem is still unsolvable. However, a robust solution can be obtained when any two parameters are known. If surface and atmosphere are known, sensor intercalibration can be performed. For example, the Advanced Very High Resolution Radiometer (AVHRR) was calibrated to the MODerate-resolution Imaging Spectroradiometer (MODIS) in Fan and Liu (2014) [Fan, X., and Liu, Y. (2014). Quantifying the relationship between intersensor images in solar reflective bands: Implications for intercalibration. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7727-7737.]. If sensor and surface are known, atmospheric data can be retrieved. For example, aerosol data were retrieved using tandem TERRA and AQUA MODIS images in Fan and Liu (2016a) [Fan, X., and Liu, Y. (2016a). Exploiting TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. Remote Sensing, 8(12), 996.]. If sensor and atmosphere are known, data consistency can be obtained. For example, Normalized Difference Vegetation Index (NDVI) data were intercalibrated among coarse-resolution sensors in Fan and Liu (2016b) [Fan, X., and Liu, Y. (2016b). A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 177-191.], and among fine-resolution sensors in Fan and Liu (2017) [Fan, X., and Liu, Y. (2017). A generalized model for intersensor NDVI calibration and its comparison with regression approaches. IEEE Transactions on Geoscience and Remote Sensing, 55(3), doi: 10.1109/TGRS.2016.2635802.]. These studies demonstrate the success of multi-sensor data and novel methods in the research domain of geoscience. These data will benefit remote sensing of terrestrial parameters in decadal timescales, such as soil salinity content in Fan et al. (2016) [Fan, X., Weng, Y., and Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 52, 32-41.].

Tools and Data Services from the NASA Earth Satellite Observations for Remote Sensing Commercial Applications

NASA Technical Reports Server (NTRS)

Vicente, Gilberto

2005-01-01

Several commercial applications of remote sensing data, such as water resources management, environmental monitoring, climate prediction, agriculture, forestry, preparation for and migration of extreme weather events, require access to vast amounts of archived high quality data, software tools and services for data manipulation and information extraction. These on the other hand require gaining detailed understanding of the data's internal structure and physical implementation of data reduction, combination and data product production. The time-consuming task must be undertaken before the core investigation can begin and is an especially difficult challenge when science objectives require users to deal with large multi-sensor data sets of different formats, structures, and resolutions.

A Vision for an International Multi-Sensor Snow Observing Mission

NASA Technical Reports Server (NTRS)

Kim, Edward

2015-01-01

Discussions within the international snow remote sensing community over the past two years have led to encouraging consensus regarding the broad outlines of a dedicated snow observing mission. The primary consensus - that since no single sensor type is satisfactory across all snow types and across all confounding factors, a multi-sensor approach is required - naturally leads to questions about the exact mix of sensors, required accuracies, and so on. In short, the natural next step is to collect such multi-sensor snow observations (with detailed ground truth) to enable trade studies of various possible mission concepts. Such trade studies must assess the strengths and limitations of heritage as well as newer measurement techniques with an eye toward natural sensitivity to desired parameters such as snow depth and/or snow water equivalent (SWE) in spite of confounding factors like clouds, lack of solar illumination, forest cover, and topography, measurement accuracy, temporal and spatial coverage, technological maturity, and cost.

Multi-Sensor Registration of Earth Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Cole-Rhodes, Arlene; Eastman, Roger; Johnson, Kisha; Morisette, Jeffrey; Netanyahu, Nathan S.; Stone, Harold S.; Zavorin, Ilya; Zukor, Dorothy (Technical Monitor)

2001-01-01

Assuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

Automatic registration of optical imagery with 3d lidar data using local combined mutual information

NASA Astrophysics Data System (ADS)

Parmehr, E. G.; Fraser, C. S.; Zhang, C.; Leach, J.

2013-10-01

Automatic registration of multi-sensor data is a basic step in data fusion for photogrammetric and remote sensing applications. The effectiveness of intensity-based methods such as Mutual Information (MI) for automated registration of multi-sensor image has been previously reported for medical and remote sensing applications. In this paper, a new multivariable MI approach that exploits complementary information of inherently registered LiDAR DSM and intensity data to improve the robustness of registering optical imagery and LiDAR point cloud, is presented. LiDAR DSM and intensity information has been utilised in measuring the similarity of LiDAR and optical imagery via the Combined MI. An effective histogramming technique is adopted to facilitate estimation of a 3D probability density function (pdf). In addition, a local similarity measure is introduced to decrease the complexity of optimisation at higher dimensions and computation cost. Therefore, the reliability of registration is improved due to the use of redundant observations of similarity. The performance of the proposed method for registration of satellite and aerial images with LiDAR data in urban and rural areas is experimentally evaluated and the results obtained are discussed.

Quantification of urban structure on building block level utilizing multisensoral remote sensing data

NASA Astrophysics Data System (ADS)

Wurm, Michael; Taubenböck, Hannes; Dech, Stefan

2010-10-01

Dynamics of urban environments are a challenge to a sustainable development. Urban areas promise wealth, realization of individual dreams and power. Hence, many cities are characterized by a population growth as well as physical development. Traditional, visual mapping and updating of urban structure information of cities is a very laborious and cost-intensive task, especially for large urban areas. For this purpose, we developed a workflow for the extraction of the relevant information by means of object-based image classification. In this manner, multisensoral remote sensing data has been analyzed in terms of very high resolution optical satellite imagery together with height information by a digital surface model to retrieve a detailed 3D city model with the relevant land-use / land-cover information. This information has been aggregated on the level of the building block to describe the urban structure by physical indicators. A comparison between the indicators derived by the classification and a reference classification has been accomplished to show the correlation between the individual indicators and a reference classification of urban structure types. The indicators have been used to apply a cluster analysis to group the individual blocks into similar clusters.

Analysis of remote sensing data collected for detection and mapping of oil spills: Reduction and analysis of multi-sensor airborne data of the NASA Wallops oil spill exercise of November 1978

NASA Technical Reports Server (NTRS)

1982-01-01

Airborne, remotely sensed data of the NASA Wallops controlled oil spill were corrected, reduced and analysed. Sensor performance comparisons were made by registering data sets from different sensors, which were near-coincident in time and location. Multispectral scanner images were, in turn, overlayed with profiles of correlation between airborne and laboratory-acquired fluorosensor spectra of oil; oil-thickness contours derived (by NASA) from a scanning fluorosensor and also from a two-channel scanning microwave radiometer; and synthetic aperture radar X-HH images. Microwave scatterometer data were correlated with dual-channel (UV and TIR) line scanner images of the oil slick.

Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

Characterization of precipitation features over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2013-12-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, surface observations, and models to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets of TMPA 3B42, CMORPH, and PERSIANN. The satellite based QPEs are compared over the concurrent period with the NCEP Stage IV product, which is a near real time product providing precipitation data at the hourly temporal scale gridded at a nominal 4-km spatial resolution. In addition, remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model), which provides gridded precipitation estimates that are used as a baseline for multi-sensor QPE products comparison. The comparisons are performed at the annual, seasonal, monthly, and daily scales with focus on selected river basins (Southeastern US, Pacific Northwest, Great Plains). While, unconditional annual rain rates present a satisfying agreement between all products, results suggest that satellite QPE datasets exhibit important biases in particular at higher rain rates (≥4 mm/day). Conversely, on seasonal scales differences between remotely sensed data and ground surface observations can be greater than 50% and up to 90% for low daily accumulation (≤1 mm/day) such as in the Western US (summer) and Central US (winter). The conditional analysis performed using different daily rainfall accumulation thresholds (from low rainfall intensity to intense precipitation) shows that while intense events measured at the ground are infrequent (around 2% for daily accumulation above 2 inches/day), remotely sensed products displayed differences from 20-50% and up to 90-100%. A discussion on the impact of differing spatial and temporal resolutions with respect to the datasets ability to capture extreme precipitation events is also provided. Furthermore, this work is part of a broader effort to evaluate long-term multi-sensor QPEs in the perspective of developing Climate Data Records (CDRs) for precipitation.

Sea surface and remotely sensed temperatures off Cape Mendocino, California

NASA Technical Reports Server (NTRS)

Breaker, L. C.; Arvesen, J. C.; Frydenlund, D.; Myers, J. S.; Short, K.

1985-01-01

During September 3 to 5, 1979, a multisensor oceanographic experiment was conducted off Cape Mendocino, California. The purpose of this experiment was to validate the use of remote sensing techniques over an area along the U.S. west coast where coasted upwelling is known to be intense. Remotely sensed mutlispectral data, including thermal infrared imagery, were collected above an upwelling feature off Cape Mendocino. Data were acquired from the TIRNOS-N and NOAA-6 polar orbiting satellites, the NASA Ames Research Center's high altitude U-2 aircraft, and a U.S. Coast Guard C-130 aircraft. Supporting surface truth data over the same feature were collected aboard the National Oceanic and Atmospheric Administration (NOAA) ship, OCEANOGRAPHER. Atmospheric soundings were also taken aboard the ship. The results indicate that shipboard measurements of sea surface temperatures can be reproduction within 1 C or better through remote observation of absolute infrared radiance values (whether measured aboard the NOAA polar orbiting satellite, the U-2 aircraft, or the Coast Guard aircraft) by using appropriate atmospheric corrections. Also, the patterns of sea surface temperature which were derived independently from the various remote platforms provide a consistent interpretation of the surface temperature field.

Hyperspectral landcover classification for the Yakima Training Center, Yakima, Washington

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

Steinmaus, K.L.; Perry, E.M.; Petrie, G.M.

1998-04-01

The US Department of Energy`s (DOE`s) Pacific Northwest National Laboratory (PNNL) was tasked in FY97-98 to conduct a multisensor feature extraction project for the Terrain Modeling Project Office (TMPO) of the National Imagery and Mapping Agency (NIMA). The goal of this research is the development of near-autonomous methods to remotely classify and characterize regions of military interest, in support of the TMPO of NIMA. These methods exploit remotely sensed datasets including hyperspectral (HYDICE) imagery, near-infrared and thermal infrared (Daedalus 3600), radar, and terrain datasets. The study site for this project is the US Army`s Yakima Training Center (YTC), a 326,741-acremore » training area located near Yakima, Washington. Two study areas at the YTC were selected to conduct and demonstrate multisensor feature extraction, the 2-km x 2-km Cantonment Area and the 3-km x 3-km Choke Point area. Classification of the Cantonment area afforded a comparison of classification results at different scales.« less

Remote sensing advances in agricultural inventories

NASA Technical Reports Server (NTRS)

Dragg, J. L.; Bizzell, R. M.; Trichel, M. C.; Hatch, R. E.; Phinney, D. E.; Baker, T. C.

1984-01-01

As the complexity of the world's agricultural industry increases, more timely and more accurate world-wide agricultural information is required to support production and marketing decisions, policy formulation, and technology development. The Inventory Technology Development Project of the AgRISTARS Program has developed new automated technology that uses data sets acquired by spaceborne remote sensors. Research has emphasized the development of multistage, multisensor sampling and estimation techniques for use in global environments where reliable ground observations are not available. This paper presents research results obtained from data sets acquired by four different sensors: Landsat MSS, Landsat TM, Shuttle-Imaging Radar and environmental satellite (AVHRR).

DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco P.; Pasquariello, Guido

2018-03-01

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

Goddard Atmospheric Composition Data Center: Aura Data and Services in One Place

NASA Technical Reports Server (NTRS)

Leptoukh, G.; Kempler, S.; Gerasimov, I.; Ahmad, S.; Johnson, J.

2005-01-01

The Goddard Atmospheric Composition Data and Information Services Center (AC-DISC) is a portal to the Atmospheric Composition specific, user driven, multi-sensor, on-line, easy access archive and distribution system employing data analysis and visualization, data mining, and other user requested techniques for the better science data usage. It provides convenient access to Atmospheric Composition data and information from various remote-sensing missions, from TOMS, UARS, MODIS, and AIRS, to the most recent data from Aura OMI, MLS, HIRDLS (once these datasets are released to the public), as well as Atmospheric Composition datasets residing at other remote archive site.

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

Remote sensing science for the Nineties; Proceedings of IGARSS '90 - 10th Annual International Geoscience and Remote Sensing Symposium, University of Maryland, College Park, May 20-24, 1990. Vols. 1, 2, & 3

NASA Technical Reports Server (NTRS)

1990-01-01

Various papers on remote sensing (RS) for the nineties are presented. The general topics addressed include: subsurface methods, radar scattering, oceanography, microwave models, atmospheric correction, passive microwave systems, RS in tropical forests, moderate resolution land analysis, SAR geometry and SNR improvement, image analysis, inversion and signal processing for geoscience, surface scattering, rain measurements, sensor calibration, wind measurements, terrestrial ecology, agriculture, geometric registration, subsurface sediment geology, radar modulation mechanisms, radar ocean scattering, SAR calibration, airborne radar systems, water vapor retrieval, forest ecosystem dynamics, land analysis, multisensor data fusion. Also considered are: geologic RS, RS sensor optical measurements, RS of snow, temperature retrieval, vegetation structure, global change, artificial intelligence, SAR processing techniques, geologic RS field experiment, stochastic modeling, topography and Digital Elevation model, SAR ocean waves, spaceborne lidar and optical, sea ice field measurements, millimeter waves, advanced spectroscopy, spatial analysis and data compression, SAR polarimetry techniques. Also discussed are: plant canopy modeling, optical RS techniques, optical and IR oceanography, soil moisture, sea ice back scattering, lightning cloud measurements, spatial textural analysis, SAR systems and techniques, active microwave sensing, lidar and optical, radar scatterometry, RS of estuaries, vegetation modeling, RS systems, EOS/SAR Alaska, applications for developing countries, SAR speckle and texture.

Linking goniometer measurements to hyperspectral and multisensor imagery for retrieval of beach properties and coastal characterization

NASA Astrophysics Data System (ADS)

Bachmann, Charles M.; Gray, Deric; Abelev, Andrei; Philpot, William; Montes, Marcos J.; Fusina, Robert; Musser, Joseph; Li, Rong-Rong; Vermillion, Michael; Smith, Geoffrey; Korwan, Daniel; Snow, Charlotte; Miller, W. David; Gardner, Joan; Sletten, Mark; Georgiev, Georgi; Truitt, Barry; Killmon, Marcus; Sellars, Jon; Woolard, Jason; Parrish, Christopher; Schwarzscild, Art

2012-06-01

In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements. Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500), topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR'11). Focus areas for VCR'11 were properties of beaches and tidal flats and barrier island vegetation and, in the water column, shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density, moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007 related to beach properties and shallow water bathymetry. A priority for VCR'11 was to collect and model relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery, as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR'11.

Multi-Sensor Systems and Data Fusion for Telecommunications, Remote Sensing and Radar (les Systemes multi-senseurs et le fusionnement des donnees pour les telecommunications, la teledetection et les radars)

DTIC Science & Technology

1998-04-01

The result of the project is a demonstration of the fusion process, the sensors management and the real-time capabilities using simulated sensors...demonstrator (TAD) is a system that demonstrates the core ele- ment of a battlefield ground surveillance system by simulation in near real-time. The core...Management and Sensor/Platform simulation . The surveillance system observes the real world through a non-collocated heterogene- ous multisensory system

Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

NASA Astrophysics Data System (ADS)

Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the project ESA's ENVISAT satellite, which will be launched in 2002, will serve as remote sensing data source. Until EN- VISAT data is available, algorithm retrieval, software development and product gener- ation is performed using existing sensors with ENVISAT-like specifications. Based on these data sets test cases and demonstration runs are conducted and will be presented to prove the advantages of the approach.

Self-Referenced Fiber Optic System For Remote Methane Detection

NASA Astrophysics Data System (ADS)

Zientkiewicz, Jacek K.

1989-10-01

The paper discusses a fiber optic multisensor methane detection system matched to topology and environment of the underground mine. The system involves time domain multiplexed (TDM) methane sensors based on selective absorption of source radiation by atomic/molecular species in the gas sensing heads. A two-wavelength ratiometric approach allows simple self-referencing, cancels out errors arising from other contaminants, and improves the measurement contrast. The laboratory system consists of a high radiance LED source, multimode fiber, optical sensing head, optical bandpass filters, and involves synchronous detection with low noise photodiodes and a lock-in amplifier. Detection sensitivity versus spectral resolution of the optical filters has also been investigated and described. The system performance was evaluated and the results are presented.

Flood risks in urbanized areas - multi-sensoral approaches using remotely sensed data for risk assessment

NASA Astrophysics Data System (ADS)

Taubenböck, H.; Wurm, M.; Netzband, M.; Zwenzner, H.; Roth, A.; Rahman, A.; Dech, S.

2011-02-01

Estimating flood risks and managing disasters combines knowledge in climatology, meteorology, hydrology, hydraulic engineering, statistics, planning and geography - thus a complex multi-faceted problem. This study focuses on the capabilities of multi-source remote sensing data to support decision-making before, during and after a flood event. With our focus on urbanized areas, sample methods and applications show multi-scale products from the hazard and vulnerability perspective of the risk framework. From the hazard side, we present capabilities with which to assess flood-prone areas before an expected disaster. Then we map the spatial impact during or after a flood and finally, we analyze damage grades after a flood disaster. From the vulnerability side, we monitor urbanization over time on an urban footprint level, classify urban structures on an individual building level, assess building stability and quantify probably affected people. The results show a large database for sustainable development and for developing mitigation strategies, ad-hoc coordination of relief measures and organizing rehabilitation.

Characterizing land processes in the biosphere

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Tuyahov, A. J.

1984-01-01

NASA long-term planning for the satellite remote sensing of land areas is discussed from the perspective of a holistic interdisciplinary approach to the study of the biosphere. The earth is characterized as a biogeochemical system; the impact of human activity on this system is considered; and the primary scientific goals for their study are defined. Remote-sensing programs are seen as essential in gaining an improved understanding of energy budgets, the hydrological cycle, other biogeological cycles, and the coupling between these cycles, with the construction of a global data base and eventually the development of predictive simulation models which can be used to assess the impact of planned human activities. Current sensor development at NASA includes a multilinear array for the visible and IR and the L-band Shuttle Imaging Radar B, both to be flown on Shuttle missions in the near future; for the 1990s, a large essentially permanent man-tended interdisciplinary multisensor platform connected to an advanced data network is being planned.

The challenges of remote monitoring of wetlands

USGS Publications Warehouse

Gallant, Alisa L.

2015-01-01

Wetlands are highly productive and support a wide variety of ecosystem goods and services. Various forms of global change impose compelling needs for timely and reliable information on the status of wetlands worldwide, but several characteristics of wetlands make them challenging to monitor remotely: they lack a single, unifying land-cover feature; they tend to be highly dynamic and their energy signatures are constantly changing; and steep environmental gradients in and around wetlands produce narrow ecotones that often are below the resolving capacity of remote sensors. These challenges and needs set the context for a special issue focused on wetland remote sensing. Contributed papers responded to one of three overarching questions aimed at improving remote, large-area monitoring of wetlands: (1) What approaches and data products are being developed specifically to support regional to global long-term monitoring of wetland landscapes? (2) What are the promising new technologies and sensor/multisensor approaches for more accurate and consistent detection of wetlands? (3) Are there studies that demonstrate how remote long-term monitoring of wetland landscapes can reveal changes that correspond with changes in land cover and land use and/or changes in climate?

Multisensor and Multispectral Approach in Documenting and Analyzing Liquefaction Hazard using Remote Sensing

NASA Astrophysics Data System (ADS)

Oommen, T.; Baise, L. G.; Gens, R.; Prakash, A.; Gupta, R. P.

2008-12-01

Seismic liquefaction is the loss of strength of soil due to shaking that leads to various ground failures such as lateral spreading, settlements, tilting, and sand boils. It is important to document these failures after earthquakes to advance our study of when and where liquefaction occurs. The current approach of mapping these failures by field investigation teams suffers due to the inaccessibility to some of the sites immediately after the event, short life of some of these failures, difficulties in mapping the aerial extent of the failure, incomplete coverage etc. After the 2001 Bhuj earthquake (India), researchers, using the Indian remote sensing satellite, illustrated that satellite remote sensing can provide a synoptic view of the terrain and offer unbiased estimates of liquefaction failures. However, a multisensor (data from different sensors onboard of the same or different satellites) and multispectral (data collected in different spectral regions) approach is needed to efficiently document liquefaction incidences and/or its potential of occurrence due to the possibility of a particular satellite being located inappropriately to image an area shortly after an earthquake. The use of SAR satellite imagery ensures the acquisition of data in all weather conditions at day and night as well as information complimentary to the optical data sets. In this study, we analyze the applicability of the various satellites (Landsat, RADARSAT, Terra-MISR, IRS-1C, IRS-1D) in mapping liquefaction failures after the 2001 Bhuj earthquake using Support Vector Data Description (SVDD). The SVDD is a kernel based nonparametric outlier detection algorithm inspired by the Support Vector Machines (SVMs), which is a new generation learning algorithm based on the statistical learning theory. We present the applicability of SVDD for unsupervised change-detection studies (i.e. to identify post-earthquake liquefaction failures). The liquefaction occurrences identified from the different satellites using SVDD have been compared to the ground truth in terms of documented liquefaction failures by other researchers. We present the applicability and appropriateness of the various satellites and spectral regions for documenting liquefaction related failures. Results illustrate that the SVDD is a promising unsupervised change-detection algorithm, which can help in automating the documentation of earthquake induced liquefaction failures.

SenseCube--A Novel Inexpensive Wireless Multisensor for Physics Lab Experimentations

ERIC Educational Resources Information Center

Mehta, Vedant; Lane, Charles D.

2018-01-01

SenseCube is a multisensor capable of measuring many different real-time events and changes in environment. Most conventional sensors used in introductory-physics labs use their own software and have wires that must be attached to a computer or an alternate device to analyze the data. This makes the standard sensors time consuming, tedious, and…

Detection and Monitoring of Small-Scale Mining Operations in the Eastern Democratic Republic of the Congo (DRC) Using Multi-Temporal, Multi-Sensor Remote Sensing Data

NASA Astrophysics Data System (ADS)

Walther, Christian; Frei, Michaela

2017-04-01

Mining of so-called "conflict minerals" is often related with small-scale mining activities. The here discussed activities are located in forested areas in the eastern DRC, which are often remote, difficult to access and insecure for traditional geological field inspection. In order to accelerate their CTC (Certified Trading Chain)-certification process, remote sensing data are used for detection and monitoring of these small-scale mining operations. This requires a high image acquisition frequency due to mining site relocations and for compensation of year-round high cloud coverage, especially for optical data evaluation. Freely available medium resolution optical data of Sentinel-2 and Landsat-8 as well as SAR data of Sentinel-1 are used for detecting small mining targets with a minimum size of approximately 0.5 km2. The developed method enables a robust multi-temporal detection of mining sites, monitoring of mining site spatio-temporal relocations and environmental changes. Since qualitative and quantitative comparable results are generated, the followed change detection approach is objective and transparent and may push the certification process forward.

Integration of Multi-sensor Data for Desertification Monitoring

NASA Astrophysics Data System (ADS)

Lin, S.; Kim, J.

2010-12-01

The desert area has been rapidly expanding globally due to reasons such as climate change, uninhibited human activities, etc. The continuous desertification has seriously affected in (and near) desert area all over the world. As sand dune activity has been recognised as an essential indicator of desertification (it is the signature and the consequence of desertification), an accurate monitoring of desert dune movement hence becomes crucial for understanding and modelling the progress of desertification. In order to determine dune’s moving speed and tendency, also to understand the propagation occurring in transition region between desert and soil rich area, a monitoring system applying multi-temporal and multi-sensor remote sensed data are proposed and implemented. Remote sensed data involved in the monitoring scheme include space-borne optical image, Synthetic Aperture Radar (SAR) data, multi- and hyper-spectral image, and terrestrial close range image. In order to determine the movement of dunes, a reference terrain surface is required. To this end, a digital terrain model (DTM) covering the test site is firstly produced using high resolution optical stereo satellite images. Subsequently, ERS-1/2 SAR imagery are employed as another resource for dune field observation. Through the interferometric SAR (InSAR) technique combining with image-based stereo DTM, the surface displacements are obtained. From which the movement and speed of the dunes can be determined. To understand the effect of desertification combating activities, the correlation between dune activities and the landcover change is also an important issue to be covered in the monitoring scheme. The task is accomplished by tracing soil and vegetation canopy variation with the multi and hyper spectral image analysis using Hyperion and Ali imagery derived from Earth Observation Mission 1 (EO-1). As a result, the correlation between the soil restorations, expanding of vegetation canopy and the ceasing of dune activities can be clearly revealed. For the very detailed measurement, a terrestrial system applying close range photogrammetry will be set up in the test sites to acquire sequential images and used to generate 4D model of the dunes in future. Finally, all the outputs from the multi-sensor data will be crossly verified and compiled to model the desertification process and the consequences. A desertification combating activity which is performed by Korea-China NGO alliance has been conducted in Qubuqi desert in Nei Mongol, China. The method and system proposed above will be established and applied to monitor the dune mobility occurring in this area. The results are expected to be of great value to demonstrate the first case of remote sensing monitoring over the combat desertification activities.

Sea ice-atmosphere interaction: Application of multispectral satellite data in polar surface energy flux estimates

NASA Technical Reports Server (NTRS)

Steffen, K.; Schweiger, A.; Maslanik, J.; Key, J.; Weaver, R.; Barry, R.

1990-01-01

The application of multi-spectral satellite data to estimate polar surface energy fluxes is addressed. To what accuracy and over which geographic areas large scale energy budgets can be estimated are investigated based upon a combination of available remote sensing and climatological data sets. The general approach was to: (1) formulate parameterization schemes for the appropriate sea ice energy budget terms based upon the remotely sensed and/or in-situ data sets; (2) conduct sensitivity analyses using as input both natural variability (observed data in regional case studies) and theoretical variability based upon energy flux model concepts; (3) assess the applicability of these parameterization schemes to both regional and basin wide energy balance estimates using remote sensing data sets; and (4) assemble multi-spectral, multi-sensor data sets for at least two regions of the Arctic Basin and possibly one region of the Antarctic. The type of data needed for a basin-wide assessment is described and the temporal coverage of these data sets are determined by data availability and need as defined by parameterization scheme. The titles of the subjects are as follows: (1) Heat flux calculations from SSM/I and LANDSAT data in the Bering Sea; (2) Energy flux estimation using passive microwave data; (3) Fetch and stability sensitivity estimates of turbulent heat flux; and (4) Surface temperature algorithm.

Downscaling of Remotely Sensed Land Surface Temperature with multi-sensor based products

NASA Astrophysics Data System (ADS)

Jeong, J.; Baik, J.; Choi, M.

2016-12-01

Remotely sensed satellite data provides a bird's eye view, which allows us to understand spatiotemporal behavior of hydrologic variables at global scale. Especially, geostationary satellite continuously observing specific regions is useful to monitor the fluctuations of hydrologic variables as well as meteorological factors. However, there are still problems regarding spatial resolution whether the fine scale land cover can be represented with the spatial resolution of the satellite sensor, especially in the area of complex topography. To solve these problems, many researchers have been trying to establish the relationship among various hydrological factors and combine images from multi-sensor to downscale land surface products. One of geostationary satellite, Communication, Ocean and Meteorological Satellite (COMS), has Meteorological Imager (MI) and Geostationary Ocean Color Imager (GOCI). MI performing the meteorological mission produce Rainfall Intensity (RI), Land Surface Temperature (LST), and many others every 15 minutes. Even though it has high temporal resolution, low spatial resolution of MI data is treated as major research problem in many studies. This study suggests a methodology to downscale 4 km LST datasets derived from MI in finer resolution (500m) by using GOCI datasets in Northeast Asia. Normalized Difference Vegetation Index (NDVI) recognized as variable which has significant relationship with LST are chosen to estimate LST in finer resolution. Each pixels of NDVI and LST are separated according to land cover provided from MODerate resolution Imaging Spectroradiometer (MODIS) to achieve more accurate relationship. Downscaled LST are compared with LST observed from Automated Synoptic Observing System (ASOS) for assessing its accuracy. The downscaled LST results of this study, coupled with advantage of geostationary satellite, can be applied to observe hydrologic process efficiently.

A new multi-sensor integrated index for drought monitoring

NASA Astrophysics Data System (ADS)

Jiao, W.; Wang, L.; Tian, C.

2017-12-01

Drought is perceived as one of the most expensive and least understood natural disasters. The remote-sensing-based integrated drought indices, which integrate multiple variables, could reflect the drought conditions more comprehensively than single drought indices. However, most of current remote-sensing-based integrated drought indices focus on agricultural drought (i.e., deficit in soil moisture), their application in monitoring meteorological drought (i.e., deficit in precipitation) was limited. More importantly, most of the remote-sensing-based integrated drought indices did not take into consideration of the spatially non-stationary nature of the related variables, so such indices may lose essential local details when integrating multiple variables. In this regard, we proposed a new mathematical framework for generating integrated drought index for meteorological drought monitoring. The geographically weighted regression (GWR) model and principal component analysis were used to composite Moderate-resolution Imaging Spectroradiometer (MODIS) based temperature condition index (TCI), the Vegetation Index based on the Universal Pattern Decomposition method (VIUPD) based vegetation condition index (VCI), tropical rainfall measuring mission (TRMM) based Precipitation Condition Index (PCI) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) based soil moisture condition index (SMCI). We called the new remote-sensing-based integrated drought index geographical-location-based integrated drought index (GLIDI). We examined the utility of the GLIDI for drought monitoring in various climate divisions across the continental United States (CONUS). GLIDI showed high correlations with in-situ drought indices and outperformed most other existing drought indices. The results also indicate that the performance of GLIDI is not affected by environmental factors such as land cover, precipitation, temperature and soil conditions. As such, the GLIDI has considerable potential for drought monitoring across various environmental conditions.

Miniaturized Airborne Imaging Central Server System

NASA Technical Reports Server (NTRS)

Sun, Xiuhong

2011-01-01

In recent years, some remote-sensing applications require advanced airborne multi-sensor systems to provide high performance reflective and emissive spectral imaging measurement rapidly over large areas. The key or unique problem of characteristics is associated with a black box back-end system that operates a suite of cutting-edge imaging sensors to collect simultaneously the high throughput reflective and emissive spectral imaging data with precision georeference. This back-end system needs to be portable, easy-to-use, and reliable with advanced onboard processing. The innovation of the black box backend is a miniaturized airborne imaging central server system (MAICSS). MAICSS integrates a complex embedded system of systems with dedicated power and signal electronic circuits inside to serve a suite of configurable cutting-edge electro- optical (EO), long-wave infrared (LWIR), and medium-wave infrared (MWIR) cameras, a hyperspectral imaging scanner, and a GPS and inertial measurement unit (IMU) for atmospheric and surface remote sensing. Its compatible sensor packages include NASA s 1,024 1,024 pixel LWIR quantum well infrared photodetector (QWIP) imager; a 60.5 megapixel BuckEye EO camera; and a fast (e.g. 200+ scanlines/s) and wide swath-width (e.g., 1,920+ pixels) CCD/InGaAs imager-based visible/near infrared reflectance (VNIR) and shortwave infrared (SWIR) imaging spectrometer. MAICSS records continuous precision georeferenced and time-tagged multisensor throughputs to mass storage devices at a high aggregate rate, typically 60 MB/s for its LWIR/EO payload. MAICSS is a complete stand-alone imaging server instrument with an easy-to-use software package for either autonomous data collection or interactive airborne operation. Advanced multisensor data acquisition and onboard processing software features have been implemented for MAICSS. With the onboard processing for real time image development, correction, histogram-equalization, compression, georeference, and data organization, fast aerial imaging applications, including the real time LWIR image mosaic for Google Earth, have been realized for NASA fs LWIR QWIP instrument. MAICSS is a significant improvement and miniaturization of current multisensor technologies. Structurally, it has a complete modular and solid-state design. Without rotating hard drives and other moving parts, it is operational at high altitudes and survivable in high-vibration environments. It is assembled from a suite of miniaturized, precision-machined, standardized, and stackable interchangeable embedded instrument modules. These stackable modules can be bolted together with the interconnection wires inside for the maximal simplicity and portability. Multiple modules are electronically interconnected as stacked. Alternatively, these dedicated modules can be flexibly distributed to fit the space constraints of a flying vehicle. As a flexibly configurable system, MAICSS can be tailored to interface a variety of multisensor packages. For example, with a 1,024x1,024 pixel LWIR and a 8,984x6,732 pixel EO payload, the complete MAICSS volume is approximately 7x9x11 in. (=18x23x28 cm), with a weight of 25 lb (=11.4 kg).

Land cover/use classification of Cairns, Queensland, Australia: A remote sensing study involving the conjunctive use of the airborne imaging spectrometer, the large format camera and the thematic mapper simulator

NASA Technical Reports Server (NTRS)

Heric, Matthew; Cox, William; Gordon, Daniel K.

1987-01-01

In an attempt to improve the land cover/use classification accuracy obtainable from remotely sensed multispectral imagery, Airborne Imaging Spectrometer-1 (AIS-1) images were analyzed in conjunction with Thematic Mapper Simulator (NS001) Large Format Camera color infrared photography and black and white aerial photography. Specific portions of the combined data set were registered and used for classification. Following this procedure, the resulting derived data was tested using an overall accuracy assessment method. Precise photogrammetric 2D-3D-2D geometric modeling techniques is not the basis for this study. Instead, the discussion exposes resultant spectral findings from the image-to-image registrations. Problems associated with the AIS-1 TMS integration are considered, and useful applications of the imagery combination are presented. More advanced methodologies for imagery integration are needed if multisystem data sets are to be utilized fully. Nevertheless, research, described herein, provides a formulation for future Earth Observation Station related multisensor studies.

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.

Multi-Sensor Approach for Assessing the Taiga-Tundra Boundary

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Kharuk, V. I.; Kovacs, K.

2003-01-01

Monitoring the dynamics of the tundra-taiga boundary is critical for our understanding of the causes and consequences of the changes in this area. Because of its inaccessibility, remote sensing data will play an important role. In this study we examined the use of several remote sensing techniques for identifying the existing tundra-taiga ecotone. These include Landsat, MISR and RADARSAT data. High-resolution IKONOS images were used for local ground truth. It was found that on Landsat ETM+ summer images, reflectance from tundra and taiga at band 4 (NIR) is similar, but different at other bands such as red, and MIR bands. When the incidence angle is small, C-band HH-pol backscattering coefficients from both tundra and taiga are relatively high. The backscattering from tundra targets decreases faster than taiga targets when the incidence angle increases, because the tundra targets look smoother than taiga. Because of the shading effect of the vegetation, the MISR data, both multi-spectral data at nadir looking and multi-angle data at red and NIR bands, clearly show the transition zone.

Hybrid Image Fusion for Sharpness Enhancement of Multi-Spectral Lunar Images

NASA Astrophysics Data System (ADS)

Awumah, Anna; Mahanti, Prasun; Robinson, Mark

2016-10-01

Image fusion enhances the sharpness of a multi-spectral (MS) image by incorporating spatial details from a higher-resolution panchromatic (Pan) image [1,2]. Known applications of image fusion for planetary images are rare, although image fusion is well-known for its applications to Earth-based remote sensing. In a recent work [3], six different image fusion algorithms were implemented and their performances were verified with images from the Lunar Reconnaissance Orbiter (LRO) Camera. The image fusion procedure obtained a high-resolution multi-spectral (HRMS) product from the LRO Narrow Angle Camera (used as Pan) and LRO Wide Angle Camera (used as MS) images. The results showed that the Intensity-Hue-Saturation (IHS) algorithm results in a high-spatial quality product while the Wavelet-based image fusion algorithm best preserves spectral quality among all the algorithms. In this work we show the results of a hybrid IHS-Wavelet image fusion algorithm when applied to LROC MS images. The hybrid method provides the best HRMS product - both in terms of spatial resolution and preservation of spectral details. Results from hybrid image fusion can enable new science and increase the science return from existing LROC images.[1] Pohl, Cle, and John L. Van Genderen. "Review article multisensor image fusion in remote sensing: concepts, methods and applications." International journal of remote sensing 19.5 (1998): 823-854.[2] Zhang, Yun. "Understanding image fusion." Photogramm. Eng. Remote Sens 70.6 (2004): 657-661.[3] Mahanti, Prasun et al. "Enhancement of spatial resolution of the LROC Wide Angle Camera images." Archives, XXIII ISPRS Congress Archives (2016).

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

Multisensor multiresolution data fusion for improvement in classification

NASA Astrophysics Data System (ADS)

Rubeena, V.; Tiwari, K. C.

2016-04-01

The rapid advancements in technology have facilitated easy availability of multisensor and multiresolution remote sensing data. Multisensor, multiresolution data contain complementary information and fusion of such data may result in application dependent significant information which may otherwise remain trapped within. The present work aims at improving classification by fusing features of coarse resolution hyperspectral (1 m) LWIR and fine resolution (20 cm) RGB data. The classification map comprises of eight classes. The class names are Road, Trees, Red Roof, Grey Roof, Concrete Roof, Vegetation, bare Soil and Unclassified. The processing methodology for hyperspectral LWIR data comprises of dimensionality reduction, resampling of data by interpolation technique for registering the two images at same spatial resolution, extraction of the spatial features to improve classification accuracy. In the case of fine resolution RGB data, the vegetation index is computed for classifying the vegetation class and the morphological building index is calculated for buildings. In order to extract the textural features, occurrence and co-occurence statistics is considered and the features will be extracted from all the three bands of RGB data. After extracting the features, Support Vector Machine (SVMs) has been used for training and classification. To increase the classification accuracy, post processing steps like removal of any spurious noise such as salt and pepper noise is done which is followed by filtering process by majority voting within the objects for better object classification.

Automatic parameter selection for feature-based multi-sensor image registration

NASA Astrophysics Data System (ADS)

DelMarco, Stephen; Tom, Victor; Webb, Helen; Chao, Alan

2006-05-01

Accurate image registration is critical for applications such as precision targeting, geo-location, change-detection, surveillance, and remote sensing. However, the increasing volume of image data is exceeding the current capacity of human analysts to perform manual registration. This image data glut necessitates the development of automated approaches to image registration, including algorithm parameter value selection. Proper parameter value selection is crucial to the success of registration techniques. The appropriate algorithm parameters can be highly scene and sensor dependent. Therefore, robust algorithm parameter value selection approaches are a critical component of an end-to-end image registration algorithm. In previous work, we developed a general framework for multisensor image registration which includes feature-based registration approaches. In this work we examine the problem of automated parameter selection. We apply the automated parameter selection approach of Yitzhaky and Peli to select parameters for feature-based registration of multisensor image data. The approach consists of generating multiple feature-detected images by sweeping over parameter combinations and using these images to generate estimated ground truth. The feature-detected images are compared to the estimated ground truth images to generate ROC points associated with each parameter combination. We develop a strategy for selecting the optimal parameter set by choosing the parameter combination corresponding to the optimal ROC point. We present numerical results showing the effectiveness of the approach using registration of collected SAR data to reference EO data.

Towards a High-Resolution Global Inundation Delineation Dataset

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.

2011-12-01

Although their importance for biodiversity, flow regulation and ecosystem service provision is widely recognized, wetlands and temporarily inundated landscapes remain poorly mapped globally because of their inherent elusive nature. Inventorying of wetland resources has been identified in international agreements as an essential component of appropriate conservation efforts and management initiatives of these threatened ecosystems. However, despite recent advances in remote sensing surface water monitoring, current inventories of surface water variations remain incomplete at the regional-to-global scale due to methodological limitations restricting truly global application. Remote sensing wetland applications such as SAR L-band are particularly constrained by image availability and heterogeneity of acquisition dates, while coarse resolution passive microwave and multi-sensor methods cannot discriminate distinct surface water bodies. As a result, the most popular global wetland dataset remains to this day the Global Lake & Wetland Database (Lehner and Doll, 2004) a spatially inconsistent database assembled from various existing data sources. The approach taken in this project circumvents the limitations of current global wetland monitoring methods by combining globally available topographic and hydrographic data to downscale coarse resolution global inundation data (Prigent et al., 2007) and thus create a superior inundation delineation map product. The developed procedure downscales inundation data from the coarse resolution (~27km) of current passive microwave sensors to the finer spatial resolution (~500m) of the topographic and hydrographic layers of HydroSHEDS' data suite (Lehner et al., 2006), while retaining the high temporal resolution of the multi-sensor inundation dataset. From the downscaling process emerges new information on the specific location of inundation, but also on its frequency and duration. The downscaling algorithm employs a decision tree classifier trained on regional remote sensing wetland maps, to derive inundation probability followed by a seeded region growing segmentation process to redistribute the inundated area at the finer resolution. Assessment of the algorithm's performance is accomplished by evaluating the level of agreement between its outputted downscaled inundation maps and existing regional remote sensing inundation delineation. Upon completion, this project's will offer a dynamic globally seamless inundation map at an unprecedented spatial and temporal scale, which will provide the baseline inventory long requested by the research community, and will open the door to a wide array of possible conservation and hydrological modeling applications which were until now data-restricted. Literature Lehner, B., K. Verdin, and A. Jarvis. 2008. New global hydrography derived from spaceborne elevation data. Eos 89, no. 10. Lehner, B, and P Doll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, no. 1-4: 1-22. Prigent, C., F. Papa, F. Aires, W. B. Rossow, and E. Matthews. 2007. Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research 112, no. D12: 1-13.

Spatial Aspects of Multi-Sensor Data Fusion: Aerosol Optical Thickness

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory; Zubko, V.; Gopalan, A.

2007-01-01

The Goddard Earth Sciences Data and Information Services Center (GES DISC) investigated the applicability and limitations of combining multi-sensor data through data fusion, to increase the usefulness of the multitude of NASA remote sensing data sets, and as part of a larger effort to integrate this capability in the GES-DISC Interactive Online Visualization and Analysis Infrastructure (Giovanni). This initial study focused on merging daily mean Aerosol Optical Thickness (AOT), as measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites, to increase spatial coverage and produce complete fields to facilitate comparison with models and station data. The fusion algorithm used the maximum likelihood technique to merge the pixel values where available. The algorithm was applied to two regional AOT subsets (with mostly regular and irregular gaps, respectively) and a set of AOT fields that differed only in the size and location of artificially created gaps. The Cumulative Semivariogram (CSV) was found to be sensitive to the spatial distribution of gap areas and, thus, useful for assessing the sensitivity of the fused data to spatial gaps.

Multisensor of Remotely Sensed Data for Characterizing Seismotectonic Activities in Malaysia

NASA Astrophysics Data System (ADS)

Abu Bakar, Rabieahtul; Azahari Razak, Khamarrul; Anuar Jamaludin, Tajul; Tongkul, Felix; Mohamad, Zakaria; Ramli, Zamri; Abd Manap, Mohamad; Rahman, Muhammad Zulkarnain Abdul

2015-04-01

Seismically induced events pose serious hazards yet are difficult to predict. Despite remarkable efforts of mapping, monitoring and modelling of such great events at regional or local scales, the understanding of the processes in the Earth's dynamic system remains elusive. Although Malaysia is in a relatively low seismic hazard zone, the current trend and pattern of seismotectonic activities triggered a series of fundamental study to better understand the relationship between the earthquakes, recent tectonics and seismically active fault zones. Several conventional mapping techniques have been intensively used but shown some limitations. Remote sensing is the preferable mean to quantify the seismic activity accurately in a larger area within a short period. Still, only few of such studies have been carried out in this subduction region. Characterization of seismotectonic activities from space in a tropical environment is very challenging given the complexity of its physiographic, climatic, geologic conditions and anthropogenic activities. There are many factors controlling the success rate of the implementation mainly due to the lack of historical earthquakes, geomorphological evidence, and proper identification of regional tectonic patterns. In this study, we aim at providing better insight to extract and characterize seismotectonic activities by integrating passive and active remotely-sensed data, geodetic data, historical records, GIS-based data analysis and in-situ measurements as well quantify them based on field investigation and expert knowledge. It is crucial to perform spatiotemporal analysis of its activities in the most seismically induced region in North-Western Sabah. A comprehensive geodatabase of seismotectonic events are developed and allowed us to analyse the spatiotemporal activities. A novelty of object-based image method for extracting tropical seismically active faults and related seismotectonic features are introduced and evaluated. We aim to develop the exchangeable and transferable rule-set with optimal parameterization for such aforementioned tasks. A geomorphometric-based remotely sensed approach is used to understand the tectonic geomorphology in processes affecting the environment at different spatial scales. As a result of this study, questions related to cascading natural disasters, e.g. landslides can be quantitatively answered. Development and applications of seismically induced landslide hazard and risk zonation at different scales are conceptually presented and critically discussed. So far, quantification evaluation of uncertainties associated to spatial seismic hazard and risks prediction remains very challenging to understand and it is an interest of on-going research. In the near-future, it is crucial to address the changes of climate and land-use-land-cover in relation to temporal and spatial pattern of seismically induced landslides. It is also important to assess, model and incorporate the changes due to natural disasters into a sustainable risk management. As a conclusion, the characteristics, development and function of tectonic movement, as one of the components for geomorphological process-response system is crucial for a regional seismic study. With newly emerging multi-sensor of remotely sensed data coupled with the satellite positioning system promises a better mapping and monitoring tool for seismotectonic activities in such a way that it can be used to map, monitor, and model related seismically induced processes for a comprehensive hazard and associated risk assessment.

Disease detection in sugar beet fields: a multi-temporal and multi-sensoral approach on different scales

NASA Astrophysics Data System (ADS)

Mahlein, Anne-Katrin; Hillnhütter, Christian; Mewes, Thorsten; Scholz, Christine; Steiner, Ulrike; Dehne, Heinz-Willhelm; Oerke, Erich-Christian

2009-09-01

Depending on environmental factors fungal diseases of crops are often distributed heterogeneously in fields. Precision agriculture in plant protection implies a targeted fungicide application adjusted these field heterogeneities. Therefore an understanding of the spatial and temporal occurrence of pathogens is elementary. As shown in previous studies, remote sensing techniques can be used to detect and observe spectral anomalies in the field. In 2008, a sugar beet field site was observed at different growth stages of the crop using different remote sensing techniques. The experimental field site consisted of two treatments. One plot was sprayed with a fungicide to avoid fungal infections. In order to obtain sugar beet plants infected with foliar diseases the other plot was not sprayed. Remote sensing data were acquired from the high-resolution airborne hyperspectral imaging ROSIS in July 2008 at sugar beet growth stage 39 and from the HyMap sensor systems in August 2008 at sugar beet growth stage 45, respectively. Additionally hyperspectral signatures of diseased and non-diseased sugar beet plants were measured with a non-imaging hand held spectroradiometer at growth stage 49 in September. Ground truth data, in particular disease severity were collected at 50 sampling points in the field. Changes of reflection rates were related to disease severity increasing with time. Erysiphe betae causing powdery mildew was the most frequent leaf pathogen. A classification of healthy and diseased sugar beets in the field was possible by using hyperspectral vegetation indices calculated from canopy reflectance.

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

PubMed

Gangodagamage, Chandana; Rowland, Joel C; Hubbard, Susan S; Brumby, Steven P; Liljedahl, Anna K; Wainwright, Haruko; Wilson, Cathy J; Altmann, Garrett L; Dafflon, Baptiste; Peterson, John; Ulrich, Craig; Tweedie, Craig E; Wullschleger, Stan D

2014-08-01

Landscape attributes that vary with microtopography, such as active layer thickness ( ALT ), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km 2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r 2  = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT , consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

Embry-Riddle Aeronautical University multispectral sensor and data fusion laboratory: a model for distributed research and education

NASA Astrophysics Data System (ADS)

McMullen, Sonya A. H.; Henderson, Troy; Ison, David

2017-05-01

The miniaturization of unmanned systems and spacecraft, as well as computing and sensor technologies, has opened new opportunities in the areas of remote sensing and multi-sensor data fusion for a variety of applications. Remote sensing and data fusion historically have been the purview of large government organizations, such as the Department of Defense (DoD), National Aeronautics and Space Administration (NASA), and National Geospatial-Intelligence Agency (NGA) due to the high cost and complexity of developing, fielding, and operating such systems. However, miniaturized computers with high capacity processing capabilities, small and affordable sensors, and emerging, commercially available platforms such as UAS and CubeSats to carry such sensors, have allowed for a vast range of novel applications. In order to leverage these developments, Embry-Riddle Aeronautical University (ERAU) has developed an advanced sensor and data fusion laboratory to research component capabilities and their employment on a wide-range of autonomous, robotic, and transportation systems. This lab is unique in several ways, for example, it provides a traditional campus laboratory for students and faculty to model and test sensors in a range of scenarios, process multi-sensor data sets (both simulated and experimental), and analyze results. Moreover, such allows for "virtual" modeling, testing, and teaching capability reaching beyond the physical confines of the facility for use among ERAU Worldwide students and faculty located around the globe. Although other institutions such as Georgia Institute of Technology, Lockheed Martin, University of Dayton, and University of Central Florida have optical sensor laboratories, the ERAU virtual concept is the first such lab to expand to multispectral sensors and data fusion, while focusing on the data collection and data products and not on the manufacturing aspect. Further, the initiative is a unique effort among Embry-Riddle faculty to develop multi-disciplinary, cross-campus research to facilitate faculty- and student-driven research. Specifically, the ERAU Worldwide Campus, with locations across the globe and delivering curricula online, will be leveraged to provide novel approaches to remote sensor experimentation and simulation. The purpose of this paper and presentation is to present this new laboratory, research, education, and collaboration process.

Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

NASA Astrophysics Data System (ADS)

Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

2016-12-01

satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

Investigating change detection of archaeological sites by multiscale and multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Lasaponara, R.; Lanorte, A.; Coluzzi, R.; Masini, N.

2009-04-01

The systematic monitoring of cultural and natural heritage is a basic step for its conservation. Monitoring strategies should constitute an integral component of policies relating to land use, development, and planning. To this aim remote sensing technologies can be used profitably. This paper deals with the use of multitemporal, multisensors, and multiscale satellite data for assessing and monitoring changes affecting cultural landscapes and archaeological sites. The discussion is focused on some significant test cases selected in Peru (South America) and Southern Italy . Artifacts, unearthed sites, and marks of buried remains have been investigated by using multitemporal aerial and satellite data, such as Quickbird, ASTER, Landsat MSS and TM.

Top/bottom multisensor remote sensing of Arctic sea ice

NASA Technical Reports Server (NTRS)

Comiso, J. C.; Wadhams, P.; Krabill, W. B.; Swift, R. N.; Crawford, J. P.

1991-01-01

Results are presented on the Aircraft/Submarine Sea Ice Project experiment carried out in May 1987 to investigate concurrently the top and the bottom features of the Arctic sea-ice cover. Data were collected nearly simultaneously by instruments aboard two aircraft and a submarine, which included passive and active (SAR) microwave sensors, upward looking and sidescan sonars, a lidar profilometer, and an IR sensor. The results described fall into two classes of correlations: (1) quantitative correlations between profiles, such as ice draft (sonar), ice elevation (laser), SAR backscatter along the track line, and passive microwave brightness temperatures; and (2) qualitative and semiquantitative correlations between corresponding areas of imagery (i.e., passive microwave, AR, and sidescan sonar).

Multi-Sensor Radiometric Study to Detect Pathologies in Historical Buildings

NASA Astrophysics Data System (ADS)

Del Pozo, S.; Herrero-Pascual, J.; Felipe-García, B.; Hernández-López, D.; Rodríguez-Gonzálvez, P.; González-Aguilera, D.

2015-02-01

This paper presents a comparative study with different remote sensing technologies to recognize pathologies in façades of historical buildings. Building materials deteriorate over the years due to different extrinsic and intrinsic agents, so assessing these diseases in a non-invasive way is crucial to help preserve them. Most of these buildings are extremely valuable and some of them have been declared monuments of cultural interest. In this way through close range remote sensing techniques, it is possible to study material pathologies in a rigorous way and in a short duration field campaign. For the investigation two different acquisition systems were applied, active and passive methods. The terrestrial laser scanner FARO Focus 3D was used as active sensor, working at the wavelength of 905 nm. For the case of passive sensors, a Nikon D-5000 and a 6- bands Mini-MCA multispectral camera (530-801 nm) were applied covering visible and near infrared spectral range. This analysis allows assessing the sensor, or sensors combination, suitability for pathologies detection, addressing the limitations according to the spatial and spectral resolution. Moreover, the pathology detection by unsupervised classification methods is addressed in order to evaluate the automation capability of this process.

Remote sensing of deep hermatypic coral reefs in Puerto Rico and the U.S. Virgin Islands using the Seabed autonomous underwater vehicle

NASA Astrophysics Data System (ADS)

Armstrong, Roy A.; Singh, Hanumant

2006-09-01

Optical imaging of coral reefs and other benthic communities present below one attenuation depth, the limit of effective airborne and satellite remote sensing, requires the use of in situ platforms such as autonomous underwater vehicles (AUVs). The Seabed AUV, which was designed for high-resolution underwater optical and acoustic imaging, was used to characterize several deep insular shelf reefs of Puerto Rico and the US Virgin Islands using digital imagery. The digital photo transects obtained by the Seabed AUV provided quantitative data on living coral, sponge, gorgonian, and macroalgal cover as well as coral species richness and diversity. Rugosity, an index of structural complexity, was derived from the pencil-beam acoustic data. The AUV benthic assessments could provide the required information for selecting unique areas of high coral cover, biodiversity and structural complexity for habitat protection and ecosystem-based management. Data from Seabed sensors and related imaging technologies are being used to conduct multi-beam sonar surveys, 3-D image reconstruction from a single camera, photo mosaicking, image based navigation, and multi-sensor fusion of acoustic and optical data.

Agricultural Land Use mapping by multi-sensor approach for hydrological water quality monitoring

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Kodes, Vit

2010-05-01

The main objective of this study is to demonstrate potential of operational use of the high and medium resolution remote sensing data for hydrological water quality monitoring by mapping agriculture intensity and crop structures. In particular use of remote sensing mapping for optimization of pesticide monitoring. The agricultural mapping task is tackled by means of medium spatial and high temporal resolution ESA Envisat MERIS FR images together with single high spatial resolution IRS AWiFS image covering the whole area of interest (the Czech Republic). High resolution data (e.g. SPOT, ALOS, Landsat) are often used for agricultural land use classification, but usually only at regional or local level due to data availability and financial constraints. AWiFS data (nominal spatial resolution 56 m) due to the wide satellite swath seems to be more suitable for use at national level. Nevertheless, one of the critical issues for such a classification is to have sufficient image acquisitions over the whole vegetation period to describe crop development in appropriate way. ESA MERIS middle-resolution data were used in several studies for crop classification. The high temporal and also spectral resolution of MERIS data has indisputable advantage for crop classification. However, spatial resolution of 300 m results in mixture signal in a single pixel. AWiFS-MERIS data synergy brings new perspectives in agricultural Land Use mapping. Also, the developed methodology procedure is fully compatible with future use of ESA (GMES) Sentinel satellite images. The applied methodology of hybrid multi-sensor approach consists of these main stages: a/ parcel segmentation and spectral pre-classification of high resolution image (AWiFS); b/ ingestion of middle resolution (MERIS) vegetation spectro-temporal features; c/ vegetation signatures unmixing; and d/ semantic object-oriented classification of vegetation classes into final classification scheme. These crop groups were selected to be classified: winter crops, spring crops, oilseed rape, legumes, summer and other crops. This study highlights operational potentials of high temporal full resolution MERIS images in agricultural land use monitoring. Practical application of this methodology is foreseen, among others, in the water quality monitoring. Effective pesticide monitoring relies also on spatial distribution of applied pesticides, which can be derived from crop - plant protection product relationship. Knowledge of areas with predominant occurrence of specific crop based on remote sensing data described above can be used for a forecast of probable plant protection product application, thus cost-effective pesticide monitoring. The remote sensing data used on a continuous basis can be used in other long-term water management issues and provide valuable data for decision makers. Acknowledgement: Authors acknowledge the financial support of the Ministry of Education, Youth and Sports of the Czech Republic (grants No. 2B06095 and No. MSM 6046070901). The study was also supported by ESA CAT-1 (ref. 4358) and SOSI projects (Spatial Observation Services and Infrastructure; ref. GSTP-RTDA-EOPG-SW-08-0004).

Characterization of water bodies for mosquito habitat using a multi-sensor approach

NASA Astrophysics Data System (ADS)

Midekisa, A.; Wimberly, M. C.; Senay, G. B.

2012-12-01

Malaria is a major health problem in Ethiopia. Anopheles arabiensis, which inhabits and breeds in a variety of aquatic habitats, is the major mosquito vector for malaria transmission in the region. In the Amhara region of Ethiopia, mosquito breeding sites are heterogeneously distributed. Therefore, accurate characterization of aquatic habitats and potential breeding sites can be used as a proxy to measure the spatial distribution of malaria risk. Satellite remote sensing provides the ability to map the spatial distribution and monitor the temporal dynamics of surface water. The objective of this study is to map the probability of surface water accumulation to identify potential vector breeding sites for Anopheles arabiensis using remote sensing data from sensors at multiple spatial and temporal resolutions. The normalized difference water index (NDWI), which is based on reflectance in the green and the near infrared (NIR) bands were used to estimate fractional cover of surface water. Temporal changes in surface water were mapped using NDWI indices derived from MODIS surface reflectance product (MOD09A1) for the period 2001-2012. Landsat TM and ETM+ imagery were used to train and calibrate model results from MODIS. Results highlighted interannual variation and seasonal changes in surface water that were observed from the MODIS time series. Static topographic indices that estimate the potential for water accumulation were generated from 30 meter Shuttle Radar Topography Mission (SRTM) elevation data. Integrated fractional surface water cover was developed by combining the static topographic indices and dynamic NDWI indices using Geographic Information System (GIS) overlay methods. Accuracy of the results was evaluated based on ground truth data that was collected on presence and absence of surface water immediately after the rainy season. The study provided a multi-sensor approach for mapping areas with a high potential for surface water accumulation that are potential breeding habitats for anopheline mosquitoes. The resulting products are useful for public health decision making towards effective prevention and control of the malaria burden in the Amhara region of Ethiopia.

Multi-sensor Array for High Altitude Balloon Missions to the Stratosphere

NASA Astrophysics Data System (ADS)

Davis, Tim; McClurg, Bryce; Sohl, John

2008-10-01

We have designed and built a microprocessor controlled and expandable multi-sensor array for data collection on near space missions. Weber State University has started a high altitude research balloon program called HARBOR. This array has been designed to data log a base set of measurements for every flight and has room for six guest instruments. The base measurements are absolute pressure, on-board temperature, 3-axis accelerometer for attitude measurement, and 2-axis compensated magnetic compass. The system also contains a real time clock and circuitry for logging data directly to a USB memory stick. In typical operation the measurements will be cycled through in sequence and saved to the memory stick along with the clock's time stamp. The microprocessor can be reprogrammed to adapt to guest experiments with either analog or digital interfacing. This system will fly with every mission and will provide backup data collection for other instrumentation for which the primary task is measuring atmospheric pressure and temperature. The attitude data will be used to determine the orientation of the onboard camera systems to aid in identifying features in the images. This will make these images easier to use for any future GIS (geographic information system) remote sensing missions.

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

Leary, T.J.; Lamb, A.

The Department of Energy`s Office of Arms Control and Non-Proliferation (NN-20) has developed a suite of airborne remote sensing systems that simultaneously collect coincident data from a US Navy P-3 aircraft. The primary objective of the Airborne Multisensor Pod System (AMPS) Program is {open_quotes}to collect multisensor data that can be used for data research, both to reduce interpretation problems associated with data overload and to develop information products more complete than can be obtained from any single sensor.{close_quotes} The sensors are housed in wing-mounted pods and include: a Ku-Band Synthetic Aperture Radar; a CASI Hyperspectral Imager; a Daedalus 3600 Airbornemore » Multispectral Scanner; a Wild Heerbrugg RC-30 motion compensated large format camera; various high resolution, light intensified and thermal video cameras; and several experimental sensors (e.g. the Portable Hyperspectral Imager of Low-Light Spectroscopy (PHILLS)). Over the past year or so, the Coastal Marine Resource Assessment (CAMRA) group at the Florida Department of Environmental Protection`s Marine Research Institute (FMRI) has been working with the Department of Energy through the Naval Research Laboratory to develop applications and products from existing data. Considerable effort has been spent identifying image formats integration parameters. 2 refs., 3 figs., 2 tabs.« less

Adaptive multisensor fusion for planetary exploration rovers

NASA Technical Reports Server (NTRS)

Collin, Marie-France; Kumar, Krishen; Pampagnin, Luc-Henri

1992-01-01

The purpose of the adaptive multisensor fusion system currently being designed at NASA/Johnson Space Center is to provide a robotic rover with assured vision and safe navigation capabilities during robotic missions on planetary surfaces. Our approach consists of using multispectral sensing devices ranging from visible to microwave wavelengths to fulfill the needs of perception for space robotics. Based on the illumination conditions and the sensors capabilities knowledge, the designed perception system should automatically select the best subset of sensors and their sensing modalities that will allow the perception and interpretation of the environment. Then, based on reflectance and emittance theoretical models, the sensor data are fused to extract the physical and geometrical surface properties of the environment surface slope, dielectric constant, temperature and roughness. The theoretical concepts, the design and first results of the multisensor perception system are presented.

The wildfire experiment (WIFE): observations with airborne remote sensors

Treesearch

L.F. Radke; T.L. Clark; J.L. Coen; C.A. Walther; R.N. Lockwood; P.J. Riggan; J.A. Brass; R.G. Higgins

2000-01-01

Airborne remote sensors have long been a cornerstone of wildland fire research, and recently three-dimensional fire behaviour models fully coupled to the atmosphere have begun to show a convincing level of verisimilitude. The WildFire Experiment (WiFE) attempted the marriage of airborne remote sensors, multi-sensor observations together with fire model development and...

Terra and Aqua MODIS Design, Radiometry, and Geometry in Support of Land Remote Sensing

NASA Technical Reports Server (NTRS)

Xiong, Xiaoxiong; Wolfe, Robert; Barnes, William; Guenther, Bruce; Vermote, Eric; Saleous, Nazmi; Salomonson, Vincent

2011-01-01

The NASA Earth Observing System (EOS) mission includes the construction and launch of two nearly identical Moderate Resolution Imaging Spectroradiometer (MODIS) instruments. The MODIS proto-flight model (PFM) is onboard the EOS Terra satellite (formerly EOS AM-1) launched on December 18, 1999 and hereafter referred to as Terra MODIS. Flight model-1 (FM1) is onboard the EOS Aqua satellite (formerly EOS PM-1) launched on May 04, 2002 and referred to as Aqua MODIS. MODIS was developed based on the science community s desire to collect multiyear continuous datasets for monitoring changes in the Earth s land, oceans and atmosphere, and the human contributions to these changes. It was designed to measure discrete spectral bands, which includes many used by a number of heritage sensors, and thus extends the heritage datasets to better understand both long- and short-term changes in the global environment (Barnes and Salomonson 1993; Salomonson et al. 2002; Barnes et al. 2002). The MODIS development, launch, and operation were managed by NASA/Goddard Space Flight Center (GSFC), Greenbelt, Maryland. The sensors were designed, built, and tested by Raytheon/ Santa Barbara Remote Sensing (SBRS), Goleta, California. Each MODIS instrument offers 36 spectral bands, which span the spectral region from the visible (0.41 m) to long-wave infrared (14.4 m). MODIS collects data at three different nadir spatial resolutions: 0.25, 0.5, and 1 km. Key design specifications, such as spectral bandwidths, typical scene radiances, required signal-to-noise ratios (SNR) or noise equivalent temperature differences (NEDT), and primary applications of each MODIS spectral band are summarized in Table 7.1. These parameters were the basis for the MODIS design. More details on the evolution of the NASA EOS and development of the MODIS instruments are provided in Chap. 1. This chapter focuses on the MODIS sensor design, radiometry, and geometry as they apply to land remote sensing. With near-daily coverage of the Earth's surface, MODIS provides comprehensive measurements that enable scientists and policy makers to better understand and effectively manage the natural resources on both regional and global scales. Terra, the first large multisensor EOS satellite, is operated in a 10:30 am (local equatorial crossing time, descending southwards) polar orbit. Aqua, the second multisensor EOS satellite is operated in a 1:30 pm (local equatorial crossing time, ascending northwards) polar orbit. With complementing morning and afternoon observations, the Terra and Aqua MODIS, together with other sensors housed on both satellites, have greatly improved our understanding of the dynamics of the global environmental system.

Geology

NASA Technical Reports Server (NTRS)

Stewart, R. K.; Sabins, F. F., Jr.; Rowan, L. C.; Short, N. M.

1975-01-01

Papers from private industry reporting applications of remote sensing to oil and gas exploration were presented. Digitally processed LANDSAT images were successfully employed in several geologic interpretations. A growing interest in digital image processing among the geologic user community was shown. The papers covered a wide geographic range and a wide technical and application range. Topics included: (1) oil and gas exploration, by use of radar and multisensor studies as well as by use of LANDSAT imagery or LANDSAT digital data, (2) mineral exploration, by mapping from LANDSAT and Skylab imagery and by LANDSAT digital processing, (3) geothermal energy studies with Skylab imagery, (4) environmental and engineering geology, by use of radar or LANDSAT and Skylab imagery, (5) regional mapping and interpretation, and digital and spectral methods.

Information Measures for Multisensor Systems

DTIC Science & Technology

2013-12-11

permuted to generate spectra that were non- physical but preserved the entropy of the source spectra. Another 1000 spectra were constructed to mimic co...Research Laboratory (NRL) has yielded probabilistic models for spectral data that enable the computation of information measures such as entropy and...22308 Chemical sensing Information theory Spectral data Information entropy Information divergence Mass spectrometry Infrared spectroscopy Multisensor

Observability considerations for multi-sensor and product fusion: Bias, information content, and validation (Invited)

NASA Astrophysics Data System (ADS)

Reid, J. S.; Zhang, J.; Hyer, E. J.; Campbell, J. R.; Christopher, S. A.; Ferrare, R. A.; Leptoukh, G. G.; Stackhouse, P. W.

2009-12-01

With the successful development of many aerosol products from the NASA A-train as well as new operational geostationary and polar orbiting sensors, the scientific community now has a host of new parameters to use in their analyses. The variety and quality of products has reached a point where the community has moved from basic observation-based science to sophisticated multi-component research that addresses the complex atmospheric environment. In order for these satellite data contribute to the science their uncertainty levels must move from semi-quantitative to quantitative. Initial attempts to quantify uncertainties have led to some recent debate in the community as to the efficacy of aerosol products from current and future NASA satellite sensors. In an effort to understand the state of satellite product fidelity, the Naval Research Laboratory and a newly reformed Global Energy and Water Cycle Experiment (GEWEX) aerosol panel have both initiated assessments of the nature of aerosol remote sensing uncertainty and bias. In this talk we go over areas of specific concern based on the authors’ experiences with the data, emphasizing the multi-sensor problem. We first enumerate potential biases, including retrieval, sampling/contextual, and cognitive bias. We show examples of how these biases can subsequently lead to the pitfalls of correlated/compensating errors, tautology, and confounding. The nature of bias is closely related to the information content of the sensor signal and its subsequent application to the derived aerosol quantity of interest (e.g., optical depth, flux, index of refraction, etc.). Consequently, purpose-specific validation methods must be employed, especially when generating multi-sensor products. Indeed, cloud and lower boundary condition biases in particular complicate the more typical methods of regressional bias elimination and histogram matching. We close with a discussion of sequestration of uncertainty in multi-sensor applications of these products in both pair-wise and fused fashions.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

PubMed Central

Midekisa, Alemayehu; Senay, Gabriel B; Wimberly, Michael C

2014-01-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region. Key Points Remote sensing produced an accurate wetland map for the Ethiopian highlands Wetlands were associated with spatial variability in malaria risk Mapping and monitoring wetlands can improve malaria spatial decision support PMID:25653462

The distribution and amount of carbon in the largest peatland complex in Amazonia

NASA Astrophysics Data System (ADS)

Draper, Frederick C.; Roucoux, Katherine H.; Lawson, Ian T.; Mitchard, Edward T. A.; Honorio Coronado, Euridice N.; Lähteenoja, Outi; Torres Montenegro, Luis; Valderrama Sandoval, Elvis; Zaráte, Ricardo; Baker, Timothy R.

2014-12-01

Peatlands in Amazonian Peru are known to store large quantities of carbon, but there is high uncertainty in the spatial extent and total carbon stocks of these ecosystems. Here, we use a multi-sensor (Landsat, ALOS PALSAR and SRTM) remote sensing approach, together with field data including 24 forest census plots and 218 peat thickness measurements, to map the distribution of peatland vegetation types and calculate the combined above- and below-ground carbon stock of peatland ecosystems in the Pastaza-Marañon foreland basin in Peru. We find that peatlands cover 35 600 ± 2133 km2 and contain 3.14 (0.44-8.15) Pg C. Variation in peat thickness and bulk density are the most important sources of uncertainty in these values. One particular ecosystem type, peatland pole forest, is found to be the most carbon-dense ecosystem yet identified in Amazonia (1391 ± 710 Mg C ha-1). The novel approach of combining optical and radar remote sensing with above- and below-ground carbon inventories is recommended for developing regional carbon estimates for tropical peatlands globally. Finally, we suggest that Amazonian peatlands should be a priority for research and conservation before the developing regional infrastructure causes an acceleration in the exploitation and degradation of these ecosystems.

Assessing the Tundra-taiga Boundary with Multi-Sensor Satellite Data

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Kharuk, V. I.; Kovacs, K.

2004-01-01

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world's largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for identifying the existing tundra- taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth. It was found that a tundra-taiga transitional area can be characterized using multi- spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent.

Airborne multispectral remote sensing data to estimate several oenological parameters in vineyard production. A case study of application of remote sensing data to precision viticulture in central Italy.

NASA Astrophysics Data System (ADS)

Tramontana, Gianluca; Girard, Filippo; Belli, Claudio; Comandini, Maria Cristina; Pietromarchi, Paolo; Tiberi, Domenico; Papale, Dario

2010-05-01

It is widely recognized that environmental differences within the vineyard, with respect to soils, microclimate, and topography, can influence grape characteristics and crop yields. Besides, the central Italy landscape is characterized by a high level of fragmentation and heterogeneity It requires stringent Remote sensing technical features in terms of spectral, geometric and temporal resolution to aimed at supporting applications for precision viticulture. In response to the needs of the Italian grape and wine industry for an evaluation of precision viticulture technologies, the DISAFRI (University of Tuscia) and the Agricultural Research Council - Oenological research unit (ENC-CRA) jointly carried out an experimental study during the year 2008. The study was carried out on 2 areas located in the town of Velletri, near Rome; for each area, two varieties (red and white grape) were studied: Nero d'Avola and Sauvignon blanc in first area , Merlot and Sauvignon blanc in second. Remote sensing data were acquired in different periods using a low cost multisensor Airborne remote sensing platform developed by DISAFRI (ASPIS-2 Advanced Spectroscopic Imager System). ASPIS-2, an evolution of the ASPIS sensor (Papale et al 2008, Sensors), is a multispectral sensor based on 4 CCD and 3 interferential filters per CCD. The filters are user selectable during the flight and in this way Aspis is able to acquire data in 12 bands in the visible and near infrared regions with a bandwidth of 10 or 20 nm. To the purposes of this study 7 spectral band were acquired and 15 vegetation indices calculated. During the ripeness period several vegetative and oenochemical parameters were monitored. Anova test shown that several oenochemical variables, such as sugars, total acidity, polyphenols and anthocyanins differ according to the variety taken into consideration. In order to evaluate the time autocorrelation of several oenological parameters value, a simple linear regression between oenological variables monitored during the season and have been carried out. This statistical analysis shown a significant time autocorrelation of series in particular for sugar content and total acidity. In order to estimate the empirical relationships between the oenochemical parameters acquired during the ripeness period and the remote sensing variables, a simple regression analysis has been carried out. The remotely sensed data were significantly correlated with the following oenochemical parameters: Leaf Surface Exposed (SFE) (correlation coefficient R2 ~ 0.8), wood pruning (R2 ~ 0.8), reducing sugars (R2 ~ 0.6 and Root Mean Square Error ~ 5g/l), acidity holder (R2 ~ 0.6 and RMSE ~ 0.5g/l), polyphenols content (R2 ~ 0.9) and Anthocyanins (R2 ~ 0.89). Vegetation index that showed better relationship with oenological variables was MCARI1 (1.2*[2.5*(R800- R670)-1.3*(R800- R550)]. This study demonstrates that the low cost airborne multispectral remote sensing systems like ASPIS can support the precision viticulture. The empirical relationships between oenological parameters and remote sensing data can be applied to obtain thematic and predictive maps. These maps will be simple and effective tools to guide producers in differentiating harvest and winemaking and to improve the quality of wine production.

Observing a Severe Dust Storm Event over China using Multiple Satellite Data

NASA Astrophysics Data System (ADS)

Xu, Hui; Xue, Yong; Guang, Jie; Mei, Linlu

2013-04-01

A severe dust storm (SDS) event occurred from 19 to 21 March 2010 in China, originated in western China and Mongolia and propagated into eastern/southern China, affecting human's life in a large area. As reported by National Meteorological Center of CMA (China Meteorological Administration), 16 provinces (cities) of China were hit by the dust storm (Han et al., 2012). Satellites can provide global measurements of desert dust and have particular importance in remote areas where there is a lack of in situ measurements (Carboni et al., 2012). To observe a dust, it is necessary to estimate the spatial and temporal distributions of dust aerosols. An important metric in the characterisation of aerosol distribution is the aerosol optical depth (AOD) (Adhikary et al., 2008). Satellite aerosol retrievals have improved considerably in the last decade, and numerous satellite sensors and algorithms have been generated. Reliable retrievals of dust aerosol over land were made using POLarization and Directionality of the Earth's Reflectance instrument-POLDER (Deuze et al., 2001), Moderate Resolution Imaging Spectroradiometer-MODIS (Kaufman et al., 1997; Hsu et al., 2004), Multiangle Imaging Spectroradiometer-MISR (Martonchik et al., 1998), and Cloud-aerosol Lidar and infrared pathfinder satellite observations (CALIPSO). However, intercomparison exercises (Myhre et al., 2005) have revealed that discrepancies between satellite measurements are particularly large during events of heavy aerosol loading. The reason is that different AOD retrieval algorithms make use of different instrument characteristics to obtain retrievals over bright surfaces. For MISR, POLDER and MODIS instrument, the multi-angle approaches, the polarization measurements and single-view approaches were used to retrieval AOD respectively. Combining of multi-sensor AOD data can potentially create a more consistent, reliable and complete picture of the space-time evolution of dust storms (Ehlers, 1991). In order to make use of all useful satellite data to observe one severe dust procedure, multi-sensor and multi-algorithm AOD data were combined. In this paper, the satellite instruments considered are MISR, MODIS, POLDER and CALIPSO. In addition, air pollution index (API) data were used to validate the satellite AOD data. We chose the study region with a longitude range from 76°N to 136°N and a latitude range from 15°E to 60°E. Index Terms—aerosol optical depth, dust, satellite REFERENCES Adhikary, B., Kulkarni, S., Dallura A., Tang, Y., Chai, T., Leung, L. R., Qian, Y., Chung, C. E., Ramanathan,V. and Carmichael, G. R., 2008, A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique, Atmospheric Environment, 42(37), 8600-8615. Carboni, E., Thomas, G. E., Sayer, A. M., Siddans, R., Poulsen, C. A., Grainger, R. G., Ahn, C., Antoine, D., Bevan, S., Braak, R., Brindley, H., DeSouza-Machado, S., Deuz'e, J. L., Diner, D., Ducos, F., Grey, W., Hsu, C., Kalashnikova, O. V., Kahn, R., North, P. R. J., Salustro, C., Smith, A., Tanr'e, D., Torres, O., and Veihelmann, B., 2012, Intercomparison of desert dust optical depth from satellite measurements, Atmospheric Measurement Techniques, 5, 1973-2002. Deuze', J. L., Bre'on, F. M., Devaux, C., Goloub, Herman, M., Lafrance, B., Maignan, F., Marchand, A.,Nadal, F., Perry, G., and Tanre', D., 2001, Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements, Journal of Geophysical Research, 106(D5), 4913-4926. Ehlers, M., 1991, Multisensor image fusion techniques in remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 46, 19-30. Han, X., Ge. C., Tao, J. H., Zhang, M. G., Zhang, R. J., 2012, Air Quality Modeling for a Strong Dust Event in East Asia in March 2010, Aerosol and Air Quality Research, 12: 615-628. Hsu, N. C., Tsay, S. C., King, M. D. and Herman, J. R., 2004, Aerosol Properties over Bright-Reflecting Source Regions, IEEE Transactions on Geoscience and Remote Sensing, 42(3), 557-569. Martonchik, J. V., Diner, D. J., Kahn, R., Ackerman, T. P., Verstraete, M. M., Pinty, B., and Gordon, H. R., 1998, Techniques for the retrieval of aerosol properties over land and ocean using multiangle imaging, IEEE Transactions on Geoscience and Remote Sensing, 36(4), 1212-1227. Myhre, G., Stordal, F., Johnsrud, M., Diner, D. J., Geogdzhayev, I. V., Haywood, J. M., Holben, B. N., Holzer-Popp, T., Ignatov, A., Kahn, R. A., Kaufman, Y. J., Loeb, N., Martonchik, J. V., Mishchenko, M. I., Nalli, N. R., Remer, L. A., Schroedter-Homscheidt, M., Tanr'e, D., Torres, O., and Wang, M., 2005, Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000, Atmospheric Chemistry and Physics, 5, 1697-1719. Kaufman, Y.J., Tanre', D., Remer, L.A., Vermote, E.F., Chu, A., and Holben, B.N., 1997, Operationalremote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, Journal of Geophysical Research, 102(D14), 17,051-17,067.

Colorizing SENTINEL-1 SAR Images Using a Variational Autoencoder Conditioned on SENTINEL-2 Imagery

NASA Astrophysics Data System (ADS)

Schmitt, M.; Hughes, L. H.; Körner, M.; Zhu, X. X.

2018-05-01

In this paper, we have shown an approach for the automatic colorization of SAR backscatter images, which are usually provided in the form of single-channel gray-scale imagery. Using a deep generative model proposed for the purpose of photograph colorization and a Lab-space-based SAR-optical image fusion formulation, we are able to predict artificial color SAR images, which disclose much more information to the human interpreter than the original SAR data. Future work will aim at further adaption of the employed procedure to our special case of multi-sensor remote sensing imagery. Furthermore, we will investigate if the low-level representations learned intrinsically by the deep network can be used for SAR image interpretation in an end-to-end manner.

Remote sensing of rainfall for flash flood prediction in the United States

NASA Astrophysics Data System (ADS)

Gourley, J. J.; Flamig, Z.; Vergara, H. J.; Clark, R. A.; Kirstetter, P.; Terti, G.; Hong, Y.; Howard, K.

2015-12-01

This presentation will briefly describe the Multi-Radar Multi-Sensor (MRMS) system that ingests all NEXRAD and Canadian weather radar data and produces accurate rainfall estimates at 1-km resolution every 2 min. This real-time system, which was recently transitioned for operational use in the National Weather Service, provides forcing to a suite of flash flood prediction tools. The Flooded Locations and Simulated Hydrographs (FLASH) project provides 6-hr forecasts of impending flash flooding across the US at the same 1-km grid cell resolution as the MRMS rainfall forcing. This presentation will describe the ensemble hydrologic modeling framework, provide an evaluation at gauged basins over a 10-year period, and show the FLASH tools' performance during the record-setting floods in Oklahoma and Texas in May and June 2015.

Microwave Remote Sensing and the Cold Land Processes Field Experiment

NASA Technical Reports Server (NTRS)

Kim, Edward J.; Cline, Don; Davis, Bert; Hildebrand, Peter H. (Technical Monitor)

2001-01-01

The Cold Land Processes Field Experiment (CLPX) has been designed to advance our understanding of the terrestrial cryosphere. Developing a more complete understanding of fluxes, storage, and transformations of water and energy in cold land areas is a critical focus of the NASA Earth Science Enterprise Research Strategy, the NASA Global Water and Energy Cycle (GWEC) Initiative, the Global Energy and Water Cycle Experiment (GEWEX), and the GEWEX Americas Prediction Project (GAPP). The movement of water and energy through cold regions in turn plays a large role in ecological activity and biogeochemical cycles. Quantitative understanding of cold land processes over large areas will require synergistic advancements in 1) understanding how cold land processes, most comprehensively understood at local or hillslope scales, extend to larger scales, 2) improved representation of cold land processes in coupled and uncoupled land-surface models, and 3) a breakthrough in large-scale observation of hydrologic properties, including snow characteristics, soil moisture, the extent of frozen soils, and the transition between frozen and thawed soil conditions. The CLPX Plan has been developed through the efforts of over 60 interested scientists that have participated in the NASA Cold Land Processes Working Group (CLPWG). This group is charged with the task of assessing, planning and implementing the required background science, technology, and application infrastructure to support successful land surface hydrology remote sensing space missions. A major product of the experiment will be a comprehensive, legacy data set that will energize many aspects of cold land processes research. The CLPX will focus on developing the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. The experiment will particularly emphasize developing a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. The experimental design is a multi-sensor, multi-scale (1-ha to 160,000 km ^ {2}) approach to providing the comprehensive data set necessary to address several experiment objectives. A description focusing on the microwave remote sensing components (ground, airborne, and spaceborne) of the experiment will be presented.

Bottom-up assessment of the Net Ecosystem Carbon Balance of Russian forests in 2010 for comparison to Top-down estimates.

NASA Astrophysics Data System (ADS)

Maksyutov, S. S.; Shvidenko, A.; Shchepashchenko, D.

2014-12-01

The verified full carbon assessment of Russian forests (FCA) is based on an Integrated Land Information System (ILIS) that includes a multi-layer and multi-scale GIS with basic resolution of 1 km and corresponding attributive databases. The ILIS aggregates all available information about ecosystems and landscapes, sets of empirical and semi-empirical data and aggregations, data of different inventories and surveys, and multi-sensor remote sensing data. The ILIS serves as an information base for application of the landscape-ecosystem approach (LEA) of the FCA and as a systems design for comparison and mutual constraints with other methods of study of carbon cycling of forest ecosystems (eddy covariance; process models; inverse modeling; and multi-sensor application of remote sensing). The LEA is based on a complimentary use of the flux-based method with some elements of the pool-based method. Introduction of climatic parameters of individual years in the LEA, as well as some process-based elements, allows providing a substantial decrease of the uncertainties of carbon cycling yearly indicators of forest ecosystems. Major carbon pools (live biomass, coarse woody debris, soil organic carbon) are estimated based on data on areas, distribution and major biometric characteristics of Russian forests presented in form of the ILIS for the country. The major fluxes accounted for include Net Primary Production (NPP), Soil Heterotrophic Respiration (SHR), as well as fluxes caused by decomposition of Coarse Woody Debris (CWD), harvest and use of forest products, fluxes caused by natural disturbances (fire, insect outbreaks, impacts of unfavorable environment) and lateral fluxes to hydrosphere and lithosphere. Use of landscape-ecosystem approach resulted in the NECB at 573±140 Tg C yr-1 (CI 0.9). While the total carbon sink is high, large forest areas, particularly on permafrost, serve as a carbon source. The ratio between net primary production and soil heterotrophic respiration, together with natural and human-induced disturbances are major drivers of the magnitude and spatial distribution of the NECB of forest ecosystems. We also present comparison to the recent top-down estimates of the Siberian carbon sink.

Multi-sensor satellite and in situ monitoring of phytoplankton development in a eutrophic-mesotrophic lake.

PubMed

Dörnhöfer, Katja; Klinger, Philip; Heege, Thomas; Oppelt, Natascha

2018-01-15

Phytoplankton indicated by its photosynthetic pigment chlorophyll-a is an important pointer on lake ecology and a regularly monitored parameter within the European Water Framework Directive. Along with eutrophication and global warming cyanobacteria gain increasing importance concerning human health aspects. Optical remote sensing may support both the monitoring of horizontal distribution of phytoplankton and cyanobacteria at the lake surface and the reduction of spatial uncertainties associated with limited water sample analyses. Temporal and spatial resolution of using only one satellite sensor, however, may constrain its information value. To discuss the advantages of a multi-sensor approach the sensor-independent, physically based model MIP (Modular Inversion and Processing System) was applied at Lake Kummerow, Germany, and lake surface chlorophyll-a was derived from 33 images of five different sensors (MODIS-Terra, MODIS-Aqua, Landsat 8, Landsat 7 and Sentinel-2A). Remotely sensed lake average chlorophyll-a concentration showed a reasonable development and varied between 2.3±0.4 and 35.8±2.0mg·m -3 from July to October 2015. Match-ups between in situ and satellite chlorophyll-a revealed varying performances of Landsat 8 (RMSE: 3.6 and 19.7mg·m -3 ), Landsat 7 (RMSE: 6.2mg·m -3 ), Sentinel-2A (RMSE: 5.1mg·m -3 ) and MODIS (RMSE: 12.8mg·m -3 ), whereas an in situ data uncertainty of 48% needs to be respected. The temporal development of an index on harmful algal blooms corresponded well with the cyanobacteria biomass development during summer months. Satellite chlorophyll-a maps allowed to follow spatial patterns of chlorophyll-a distribution during a phytoplankton bloom event. Wind conditions mainly explained spatial patterns. Integrating satellite chlorophyll-a into trophic state assessment resulted in different trophic classes. Our study endorsed a combined use of satellite and in situ chlorophyll-a data to alleviate weaknesses of both approaches and to better characterise and understand phytoplankton development in lakes. Copyright © 2017 Elsevier B.V. All rights reserved.

Fifty years dynamics of Russian forests: Impacts on the earth system

NASA Astrophysics Data System (ADS)

Shvidenko, Anatoly; Schepaschenko, Dmitry; Kraxner, Florian

2015-04-01

The paper presents a succinct history of Russian forests during the time period of 1960-2010 and reanalysis of their impacts on global carbon and nitrogen cycles. We present dynamics of land cover change (including major categories of forest land) and biometric characteristics of forests (species composition, age structure, growing stock volume etc.) based on reconciling all relevant information (data of forest and land inventories, official forest management statistics, multi-sensor remote sensing products, data of forest pathological monitoring etc.). Completeness and reliability of background information was different during the period of the study. Forest inventory data and official statistics were partially modified based on relevant auxiliary information and used for 1960-2000. The analysis for 2001-2010 was provided with a crucial use of multi-sensor remote sensing data. For this last period a hybrid forest mask was developed at resolution of 230m by integration of 8 remote sensing products and using geographical weighted regression and data of crowdsourcing. During the considered 50 years forested areas of Russia substantially increased by middle of 1990s and slightly declined (at about 5%) after. Indicators needed for assessment of carbon and nitrogen cycles of forest ecosystems were defined for the entire period (aggregated estimates by decades for 1960-2000 and yearly for 2001-2010) based on unified methodology with some peculiarities following from availability of information. Major results were obtained by landscape-ecosystem method that uses as comprehensive as possible empirical and semi-empirical information on ecosystems and landscapes in form of an Integrated Land Information System and complimentary combines pool- and flux-based methods. We discuss and quantify major drivers of forest cover change (socio-economic, environmental and climatic) including forest management (harvest, reforestation and afforestation), impacts of seasonal weather on carbon fluxes (Net Primary Production, Heterotrophic Respiration), disturbances (fire, outbreaks of insects and diseases), and industrial pressure (land change, air pollution, water and soil contamination). During the entire period Russian forests provided the net carbon sink in range from 350-700 Tg C yr-1 with inter-annual variability in limits of 10-15% for the entire country. The overall sink is a result of superposition of trends of major carbon fluxes (caused by removal of harvested wood and use of forest products; land cover change; impact of climatic trends; change of disturbance regimes) and inter-annual variation of seasonal weather. Major indicators of the nitrogen cycle are assessed and discussed in connection with the carbon cycle. We provide comparative analysis of other results published for the considered period taken into account successive improvements of information and methodology used for studying the major biogeochemical cycles.

Towards operational multisensor registration

NASA Technical Reports Server (NTRS)

Rignot, Eric J. M.; Kwok, Ronald; Curlander, John C.

1991-01-01

To use data from a number of different remote sensors in a synergistic manner, a multidimensional analysis of the data is necessary. However, prior to this analysis, processing to correct for the systematic geometric distortion characteristic of each sensor is required. Furthermore, the registration process must be fully automated to handle a large volume of data and high data rates. A conceptual approach towards an operational multisensor registration algorithm is presented. The performance requirements of the algorithm are first formulated given the spatially, temporally, and spectrally varying factors that influence the image characteristics and the science requirements of various applications. Several registration techniques that fit within the structure of this algorithm are also presented. Their performance was evaluated using a multisensor test data set assembled from LANDSAT TM, SEASAT, SIR-B, Thermal Infrared Multispectral Scanner (TIMS), and SPOT sensors.

Sedimentary Environments Mapping in the Yellow Sea Using TanDEM-X and Optic Satellites

NASA Astrophysics Data System (ADS)

Ryu, J. H.; Lee, Y. K.; Kim, S. W.

2017-12-01

Due to land reclamation and dredging, 57% of China's coastal wetlands have disappeared since the 1950s, and the total area of tidal flats in South Korea decreased from approximately 2,800km2 in 1990 to 2392km2 in 2005(Qiu, 2011 and MLTM, 2010). Intertidal DEM and sedimentary facies are useful for understanding intertidal functions and monitoring their response to natural and anthropogenic actions. Highly accurate intertidal DEMs with 5-m resolution were generated based on the TanDEM-X interferometric SAR (InSAR) technique because TanDEM-X allows the acquisition of the coherent InSAR pairs with no time lag or approximately 10-second temporal baseline between master and slave SAR image. We successfully generated intertidal zone DEMs with 5-7-m spatial resolutions and interferometric height accuracies better than 0.15 m for three representative tidal flats on the west coast of the Korean Peninsula and one site of chinese coastal region in the Yellow Sea. Surface sediment classification based on remotely sensed data must circumspectly consider an effective critical grain size, water content, local topography, and intertidal structures. The earlier studies have some limitation that the classification map is not considered to analysis various environmental conditions. Therefore, the purpose of this study was minutely to mapping the surface sedimentary facies by analyzing the tidal channel, topography with multi-sensor remotely sensed data and in-situ data.

Registration and Fusion of Multiple Source Remotely Sensed Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline

2004-01-01

Earth and Space Science often involve the comparison, fusion, and integration of multiple types of remotely sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, global coverage of an area at multiple resolutions, map updating or validation of new instruments, as well as integration of data provided by multiple instruments carried on multiple platforms, e.g. in spacecraft constellations or fleets of planetary rovers. Our focus is on developing methods to perform fast, accurate and automatic image registration and fusion. General methods for automatic image registration are being reviewed and evaluated. Various choices for feature extraction, feature matching and similarity measurements are being compared, including wavelet-based algorithms, mutual information and statistically robust techniques. Our work also involves studies related to image fusion and investigates dimension reduction and co-kriging for application-dependent fusion. All methods are being tested using several multi-sensor datasets, acquired at EOS Core Sites, and including multiple sensors such as IKONOS, Landsat-7/ETM+, EO1/ALI and Hyperion, MODIS, and SeaWIFS instruments. Issues related to the coregistration of data from the same platform (i.e., AIRS and MODIS from Aqua) or from several platforms of the A-train (i.e., MLS, HIRDLS, OMI from Aura with AIRS and MODIS from Terra and Aqua) will also be considered.

A Reconfigurable Readout Integrated Circuit for Heterogeneous Display-Based Multi-Sensor Systems

PubMed Central

Park, Kyeonghwan; Kim, Seung Mok; Eom, Won-Jin; Kim, Jae Joon

2017-01-01

This paper presents a reconfigurable multi-sensor interface and its readout integrated circuit (ROIC) for display-based multi-sensor systems, which builds up multi-sensor functions by utilizing touch screen panels. In addition to inherent touch detection, physiological and environmental sensor interfaces are incorporated. The reconfigurable feature is effectively implemented by proposing two basis readout topologies of amplifier-based and oscillator-based circuits. For noise-immune design against various noises from inherent human-touch operations, an alternate-sampling error-correction scheme is proposed and integrated inside the ROIC, achieving a 12-bit resolution of successive approximation register (SAR) of analog-to-digital conversion without additional calibrations. A ROIC prototype that includes the whole proposed functions and data converters was fabricated in a 0.18 μm complementary metal oxide semiconductor (CMOS) process, and its feasibility was experimentally verified to support multiple heterogeneous sensing functions of touch, electrocardiogram, body impedance, and environmental sensors. PMID:28368355

A Reconfigurable Readout Integrated Circuit for Heterogeneous Display-Based Multi-Sensor Systems.

PubMed

Park, Kyeonghwan; Kim, Seung Mok; Eom, Won-Jin; Kim, Jae Joon

2017-04-03

This paper presents a reconfigurable multi-sensor interface and its readout integrated circuit (ROIC) for display-based multi-sensor systems, which builds up multi-sensor functions by utilizing touch screen panels. In addition to inherent touch detection, physiological and environmental sensor interfaces are incorporated. The reconfigurable feature is effectively implemented by proposing two basis readout topologies of amplifier-based and oscillator-based circuits. For noise-immune design against various noises from inherent human-touch operations, an alternate-sampling error-correction scheme is proposed and integrated inside the ROIC, achieving a 12-bit resolution of successive approximation register (SAR) of analog-to-digital conversion without additional calibrations. A ROIC prototype that includes the whole proposed functions and data converters was fabricated in a 0.18 μm complementary metal oxide semiconductor (CMOS) process, and its feasibility was experimentally verified to support multiple heterogeneous sensing functions of touch, electrocardiogram, body impedance, and environmental sensors.

SenseCube—a novel inexpensive wireless multisensor for physics lab experimentations

NASA Astrophysics Data System (ADS)

Mehta, Vedant; Lane, Charles D.

2018-07-01

SenseCube is a multisensor capable of measuring many different real-time events and changes in environment. Most conventional sensors used in introductory-physics labs use their own software and have wires that must be attached to a computer or an alternate device to analyze the data. This makes the standard sensors time consuming, tedious, and space-constricted. SenseCube was developed to overcome these limitations. This research was focused on developing a device that is all-encompassing, cost-effective, wireless, and compact, yet can perform the same tasks as the multiple standard sensors normally used in physics labs. It measures more than twenty distinct types of real-time events and transfers the data via Bluetooth. Both Windows and Mac software were developed so that the data from this device can be retrieved and/or saved on either platform. This paper describes the sensor itself, its development, its capabilities, and its cost comparison with standard sensors.

Integrating multisensor satellite data merging and image reconstruction in support of machine learning for better water quality management.

PubMed

Chang, Ni-Bin; Bai, Kaixu; Chen, Chi-Farn

2017-10-01

Monitoring water quality changes in lakes, reservoirs, estuaries, and coastal waters is critical in response to the needs for sustainable development. This study develops a remote sensing-based multiscale modeling system by integrating multi-sensor satellite data merging and image reconstruction algorithms in support of feature extraction with machine learning leading to automate continuous water quality monitoring in environmentally sensitive regions. This new Earth observation platform, termed "cross-mission data merging and image reconstruction with machine learning" (CDMIM), is capable of merging multiple satellite imageries to provide daily water quality monitoring through a series of image processing, enhancement, reconstruction, and data mining/machine learning techniques. Two existing key algorithms, including Spectral Information Adaptation and Synthesis Scheme (SIASS) and SMart Information Reconstruction (SMIR), are highlighted to support feature extraction and content-based mapping. Whereas SIASS can support various data merging efforts to merge images collected from cross-mission satellite sensors, SMIR can overcome data gaps by reconstructing the information of value-missing pixels due to impacts such as cloud obstruction. Practical implementation of CDMIM was assessed by predicting the water quality over seasons in terms of the concentrations of nutrients and chlorophyll-a, as well as water clarity in Lake Nicaragua, providing synergistic efforts to better monitor the aquatic environment and offer insightful lake watershed management strategies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving PERSIANN-CCS rain estimation using probabilistic approach and multi-sensors information

NASA Astrophysics Data System (ADS)

Karbalaee, N.; Hsu, K. L.; Sorooshian, S.; Kirstetter, P.; Hong, Y.

2016-12-01

This presentation discusses the recent implemented approaches to improve the rainfall estimation from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Cloud Classification System (PERSIANN-CCS). PERSIANN-CCS is an infrared (IR) based algorithm being integrated in the IMERG (Integrated Multi-Satellite Retrievals for the Global Precipitation Mission GPM) to create a precipitation product in 0.1x0.1degree resolution over the chosen domain 50N to 50S every 30 minutes. Although PERSIANN-CCS has a high spatial and temporal resolution, it overestimates or underestimates due to some limitations.PERSIANN-CCS can estimate rainfall based on the extracted information from IR channels at three different temperature threshold levels (220, 235, and 253k). This algorithm relies only on infrared data to estimate rainfall indirectly from this channel which cause missing the rainfall from warm clouds and false estimation for no precipitating cold clouds. In this research the effectiveness of using other channels of GOES satellites such as visible and water vapors has been investigated. By using multi-sensors the precipitation can be estimated based on the extracted information from multiple channels. Also, instead of using the exponential function for estimating rainfall from cloud top temperature, the probabilistic method has been used. Using probability distributions of precipitation rates instead of deterministic values has improved the rainfall estimation for different type of clouds.

A real-time automated quality control of rain gauge data based on multiple sensors

NASA Astrophysics Data System (ADS)

qi, Y.; Zhang, J.

2013-12-01

Precipitation is one of the most important meteorological and hydrological variables. Automated rain gauge networks provide direct measurements of precipitation and have been used for numerous applications such as generating regional and national precipitation maps, calibrating remote sensing data, and validating hydrological and meteorological model predictions. Automated gauge observations are prone to a variety of error sources (instrument malfunction, transmission errors, format changes), and require careful quality controls (QC). Many previous gauge QC techniques were based on neighborhood checks within the gauge network itself and the effectiveness is dependent on gauge densities and precipitation regimes. The current study takes advantage of the multi-sensor data sources in the National Mosaic and Multi-Sensor QPE (NMQ/Q2) system and developes an automated gauge QC scheme based the consistency of radar hourly QPEs and gauge observations. Error characteristics of radar and gauge as a function of the radar sampling geometry, precipitation regimes, and the freezing level height are considered. The new scheme was evaluated by comparing an NMQ national gauge-based precipitation product with independent manual gauge observations. Twelve heavy rainfall events from different seasons and areas of the United States are selected for the evaluation, and the results show that the new NMQ product with QC'ed gauges has a more physically spatial distribution than the old product. And the new product agrees much better statistically with the independent gauges.

Towards Linking 3D SAR and Lidar Models with a Spatially Explicit Individual Based Forest Model

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Ranson, J.; Sun, G.; Armstrong, A. H.; Fischer, R.; Huth, A.

2017-12-01

In this study, we present a parameterization of the FORMIND individual-based gap model (IBGM)for old growth Atlantic lowland rainforest in La Selva, Costa Rica for the purpose of informing multisensor remote sensing techniques for above ground biomass techniques. The model was successfully parameterized and calibrated for the study site; results show that the simulated forest reproduces the structural complexity of Costa Rican rainforest based on comparisons with CARBONO inventory plot data. Though the simulated stem numbers (378) slightly underestimated the plot data (418), particularly for canopy dominant intermediate shade tolerant trees and shade tolerant understory trees, overall there was a 9.7% difference. Aboveground biomass (kg/ha) showed a 0.1% difference between the simulated forest and inventory plot dataset. The Costa Rica FORMIND simulation was then used to parameterize a spatially explicit (3D) SAR and lidar backscatter models. The simulated forest stands were used to generate a Look Up Table as a tractable means to estimate aboveground forest biomass for these complex forests. Various combinations of lidar and radar variables were evaluated in the LUT inversion. To test the capability of future data for estimation of forest height and biomass, we considered data of 1) L- (or P-) band polarimetric data (backscattering coefficients of HH, HV and VV); 2) L-band dual-pol repeat-pass InSAR data (HH/HV backscattering coefficients and coherences, height of scattering phase center at HH and HV using DEM or surface height from lidar data as reference); 3) P-band polarimetric InSAR data (canopy height from inversion of PolInSAR data or use the coherences and height of scattering phase center at HH, HV and VV); 4) various height indices from waveform lidar data); and 5) surface and canopy top height from photon-counting lidar data. The methods for parameterizing the remote sensing models with the IBGM and developing Look Up Tables will be discussed. Results from various remote sensing scenarios will also be presented.

Toward accelerating landslide mapping with interactive machine learning techniques

NASA Astrophysics Data System (ADS)

Stumpf, André; Lachiche, Nicolas; Malet, Jean-Philippe; Kerle, Norman; Puissant, Anne

2013-04-01

Despite important advances in the development of more automated methods for landslide mapping from optical remote sensing images, the elaboration of inventory maps after major triggering events still remains a tedious task. Image classification with expert defined rules typically still requires significant manual labour for the elaboration and adaption of rule sets for each particular case. Machine learning algorithm, on the contrary, have the ability to learn and identify complex image patterns from labelled examples but may require relatively large amounts of training data. In order to reduce the amount of required training data active learning has evolved as key concept to guide the sampling for applications such as document classification, genetics and remote sensing. The general underlying idea of most active learning approaches is to initialize a machine learning model with a small training set, and to subsequently exploit the model state and/or the data structure to iteratively select the most valuable samples that should be labelled by the user and added in the training set. With relatively few queries and labelled samples, an active learning strategy should ideally yield at least the same accuracy than an equivalent classifier trained with many randomly selected samples. Our study was dedicated to the development of an active learning approach for landslide mapping from VHR remote sensing images with special consideration of the spatial distribution of the samples. The developed approach is a region-based query heuristic that enables to guide the user attention towards few compact spatial batches rather than distributed points resulting in time savings of 50% and more compared to standard active learning techniques. The approach was tested with multi-temporal and multi-sensor satellite images capturing recent large scale triggering events in Brazil and China and demonstrated balanced user's and producer's accuracies between 74% and 80%. The assessment also included an experimental evaluation of the uncertainties of manual mappings from multiple experts and demonstrated strong relationships between the uncertainty of the experts and the machine learning model.

The Evolution of Remotely Sensed Precipitation Products for Hydrological Applications with a Focus on the Tropical Rainfall Measurement Mission (TRMM)

NASA Astrophysics Data System (ADS)

Tobin, K. J.; Bennett, M.

2012-12-01

This study examines the evolution of how remotely sensed precipitation products have impacted hydrologic modeling from six basins across the continental United States. Precipitation products include both ground-based (Multisensor Precipitation Estimator - MPE) and space-based products. Two space-based products are from the Tropical Rainfall Measurement Mission (TRMM) and include the real-time TRMM Multi-Satellite Precipitation Analysis (TMPA-RT) and TRMM 3B42 Research product. Precipitation products are compared between early (2004-2008) and late (2008-2010) periods. Additionally, version 6 and the new version 7 of these TRMM products are examined. Watersheds examined were moderately large (1000 to 1,000 square kilometers) and included the San Pedro (Arizona), Cimarron (Oklahoma); Alapaha (Georgia), mid-Nueces (Texas), San Casimiro (Texas), and the mid-Rio Grande basins, which is a bi-national basin that spans the Texas-Mexico border. Precipitation products are used to drive streamflow simulations using the Soil Water Assessment Tool (SWAT). The main results of this study concludes that MPE is a mature remote sensing product that generally supports superior hydrologic simulations based on standard performance metrics such as mass balance error, Nash-Sutcliffe efficiency coefficient, and coefficient of persistence. TRMM products support acceptable simulations and have improved in performance between early and late periods for TMPA-RT (both versions) and version 6 of TRMM 3B42 Research in five out of the six basins examined. This improvement is related to modification of TRMM in January 2009 with the addition of more satellite data and a climatologic bias correction, which greatly improves the real-time TMPA-RT product. Conversely, version 7 of the TRMM 3B42 Research has a positive bias compared to version 6, which is translated into poorer hydrological simulations of streamflow. Future research is urgently needed to determine if the issues observed in this study are indicative of a broader problem associated with the most recent version of TRMM.

Middle-term metropolitan water availability index assessment based on synergistic potentials of multi-sensor data

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Yang, Y. Jeffrey; Daranpob, Ammarin

2010-03-01

The impact of recent drought and water pollution episodes results in an acute need to project future water availability to assist water managers in water utility infrastructure management within many metropolitan regions. Separate drought and water quality indices previously developed might not be sufficient for the purpose of such an assessment. This paper describes the development of the "Metropolitan Water Availability Index (MWAI)" and its potential applications in assessing the middle-term water availability at the watershed scale in a fast growing metropolitan region - the Manatee County near Tampa Bay, Florida, U.S.A. The MWAI framework is based on a statistical approach that seeks to reflect the continuous spatial and temporal variations of both water quantity and quality using a simple numerical index. Such a trend analysis will surely result in the final MWAI values for regional water management systems within a specified range. By using remote sensing technologies and data processing techniques, continuous monitoring of spatial and temporal distributions of key water availability variables, such as evapotranspiration (ET) and precipitation, is made achievable. These remote sensing technologies can be ground-based (e.g., radar estimates of rainfall), or based on remote sensing data gathered by aircraft or satellites. Using a middle term historical record, the MWAI was applied to the Manatee County water supplies. The findings clearly indicate that only eight out of twelve months in 2008 had positive MWAI values during the year. Such numerical findings are consistent with the observational evidence of statewide drought events in 2006-2008, which implies the time delay between the ending of severe drought period and the recovery of water availability in MWAI. It is expected that this forward-looking novel water availability forecasting platform will help provide a linkage in methodology between strategic planning, master planning, and the plant operation and adaptations in response to the MWAI implications.

Multisensor Retrieval of Atmospheric Properties.

NASA Astrophysics Data System (ADS)

Boba Stankov, B.

1998-09-01

A new method, Multisensor Retrieval of Atmospheric Properties (MRAP), is presented for deriving vertical profiles of atmospheric parameters throughout the troposphere. MRAP integrates measurements from multiple, diverse, remote sensing, and in situ instruments, the combination of which provides better capabilities than any instrument alone. Since remote sensors can deliver measurements automatically and continuously with high time resolution, MRAP provides better coverage than traditional rawinsondes. MRAP's design is flexible, being capable of incorporating measurements from different instruments in order to take advantage of new or developing advanced sensor technology. Furthermore, new or alternative atmospheric parameters for a variety of applications may be easily added as products of MRAP.A combination of passive radiometric, active radar, and in situ observations provide the best temperature and humidity profile measurements. Therefore, MRAP starts with a traditional, radiometer-based, physical retrieval algorithm provided by the International TOVS (TIROS-N Operational Vertical Sounder) Processing Package (ITPP) that constrains the retrieved profiles to agree with brightness temperature measurements. The first-guess profiles required by the ITPP's iterative retrieval algorithm are obtained by using a statistical inversion technique and ground-based remote sensing measurements. Because the individual ground-based remote sensing measurements are usually of sufficiently high quality, the first-guess profiles by themselves provide a satisfactory solution to establish the atmospheric water vapor and temperature state, and the TOVS data are included to provide profiles with better accuracy at higher levels, MRAP provides a physically consistent mechanism for combining the ground- and space-based humidity and temperature profiles.Data that have been used successfully to retrieve humidity and temperature profiles with MRAP are the following: temperature profiles in the lower troposphere from the ground-based Radio Acoustic Sounding System (RASS); total water vapor measurements from the Global Positioning System; specific humidity gradient profiles from the wind-profiling radar/RASS system; surface meteorological observations from standard instruments; cloud-base heights from a lidar ceilometer; temperature from the Aeronautical Radio, Incorporated Communication, Addressing and Reporting System aboard commercial airlines; and brightness temperature observations from TOVS.Data from the experiment conducted in the late summer of 1995 at Point Loma, California, were used for comparisons of MRAP results and 20 nearby rawinsonde releases to assess the statistical error estimates of MRAP. The temperature profiles had a bias of -0.27°C and a standard deviation of 1.56°C for the entire troposphere. Dewpoint profile retrievals did not have an overall accuracy as high as that of the temperature profiles but they exhibited a markedly improved standard deviation and bias in the lower atmosphere when the wind profiler/RASS specific humidity gradient information was available as a further constraint on the process. The European Centre for Medium-Range Weather Forecasts (ECMWF) model profiles of humidity and temperature for the grid point nearest to the Point Loma site were also used for comparison with the rawinsonde soundings to establish the usefulness of MRAP profiles to the weather forecasting community. The comparison showed that the vertical resolution of the ECMWF model profiles within the planetary boundary layer is not capable of detecting sharp gradients.

On the use of Multisensor and multitemporal data for monitoring risk degradation and looting in archaeological site

NASA Astrophysics Data System (ADS)

Masini, Nicola; Lasaponara, Rosa

2015-04-01

Illegal excavations represent one of the main risks which affect the archaeological heritage all over the world. They cause a massive loss of artefacts but also, and above all, a loss of the cultural context, which makes the subsequent interpretation of archaeological remains very difficult. Remote sensing offers a suitable chance to quantify and analyse this phenomenon, especially in those countries, from Southern America to Middle East, where the surveillance on site is not much effective and time consuming or non practicable due to military or political restrictions. In this paper we focus on the use of GeoEye and Google Earth imagery to quantitatively assess looting in Ventarron (Lambayeque, Peru) that is one of most important archaeological sites in Southern America. Multitemporal satellite images acquired for the study area have been processed by using both autocorrelation statistics and unsupervised classification to highlight and extract looting patterns. The mapping of areas affected by looting offered the opportunity to investigate such areas not previously systematically documented. Reference Lasaponara R.; Giovanni Leucci; Nicola Masini; Raffaele Persico 2014 ": Investigating archaeological looting using very high resolution satellite images and georadar: the experience in Lambayeque in North Peru JASC13-61R1 Cigna Francesca, Deodato Tapete, Rosa Lasaponara and Nicola Masini, 2013 Amplitude Change Detection with ENVISAT ASAR to Image the Cultural Landscape of the Nasca Region, Peru (pages 117-131). Archeological Prospection Article first published online: 21 MAY 2013 | DOI: 10.1002/arp.1451 Tapete Deodato, Francesca Cigna, Nicola Masini and Rosa Lasaponara 2013. Prospection and Monitoring of the Archaeological Heritage of Nasca, Peru, with ENVISAT ASAR Archeological Prospection (pages 133-147) Article first published online: 21 MAY 2013 | DOI: 10.1002/arp.1449 Lasaponara Rosa 2013: Geospatial analysis from space: Advanced approaches for data processing, information extraction and interpretation. Int. J. Applied Earth Observation and Geoinformation 20 Lasaponara . R &N. Masini "Satellite Remote Sensing: A NewTool for Archaeology" Springer February 2012 (http://www.amazon.com/Satellite-Remote-Sensing-Archaeology-Processing/dp/9048188008) Lasaponara, R., Lanorte, A., 2012. Satellite time-series analysis. Int. J. Remote Sens.33 (15), 4649-4652, http://dx.doi.org/10.1080/01431161.2011.638342.

NASA COAST and OCEANIA Airborne Missions Support Ecosystem and Water Quality Research in the Coastal Zone

NASA Technical Reports Server (NTRS)

Guild, Liane; Kudela, Raphael; Hooker, Stanford; Morrow, John; Russell, Philip; Palacios, Sherry; Livingston, John M.; Negrey, Kendra; Torres-Perez, Juan; Broughton, Jennifer

2014-01-01

NASA has a continuing requirement to collect high-quality in situ data for the vicarious calibration of current and next generation ocean color satellite sensors and to validate the algorithms that use the remotely sensed observations. Recent NASA airborne missions over Monterey Bay, CA, have demonstrated novel above- and in-water measurement capabilities supporting a combined airborne sensor approach (imaging spectrometer, microradiometers, and a sun photometer). The results characterize coastal atmospheric and aquatic properties through an end-to-end assessment of image acquisition, atmospheric correction, algorithm application, plus sea-truth observations from state-of-the-art instrument systems. The primary goal is to demonstrate the following in support of calibration and validation exercises for satellite coastal ocean color products: 1) the utility of a multi-sensor airborne instrument suite to assess the bio-optical properties of coastal California, including water quality; and 2) the importance of contemporaneous atmospheric measurements to improve atmospheric correction in the coastal zone. The imaging spectrometer (Headwall) is optimized in the blue spectral domain to emphasize remote sensing of marine and freshwater ecosystems. The novel airborne instrument, Coastal Airborne In-situ Radiometers (C-AIR) provides measurements of apparent optical properties with high dynamic range and fidelity for deriving exact water leaving radiances at the land-ocean boundary, including radiometrically shallow aquatic ecosystems. Simultaneous measurements supporting empirical atmospheric correction of image data are accomplished using the Ames Airborne Tracking Sunphotometer (AATS-14). Flight operations are presented for the instrument payloads using the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter flown over Monterey Bay during the seasonal fall algal bloom in 2011 (COAST) and 2013 (OCEANIA) to support bio-optical measurements of phytoplankton for coastal zone research.

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.

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images

PubMed Central

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-01-01

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures. PMID:26007744

Multisensor Super Resolution Using Directionally-Adaptive Regularization for UAV Images.

PubMed

Kang, Wonseok; Yu, Soohwan; Ko, Seungyong; Paik, Joonki

2015-05-22

In various unmanned aerial vehicle (UAV) imaging applications, the multisensor super-resolution (SR) technique has become a chronic problem and attracted increasing attention. Multisensor SR algorithms utilize multispectral low-resolution (LR) images to make a higher resolution (HR) image to improve the performance of the UAV imaging system. The primary objective of the paper is to develop a multisensor SR method based on the existing multispectral imaging framework instead of using additional sensors. In order to restore image details without noise amplification or unnatural post-processing artifacts, this paper presents an improved regularized SR algorithm by combining the directionally-adaptive constraints and multiscale non-local means (NLM) filter. As a result, the proposed method can overcome the physical limitation of multispectral sensors by estimating the color HR image from a set of multispectral LR images using intensity-hue-saturation (IHS) image fusion. Experimental results show that the proposed method provides better SR results than existing state-of-the-art SR methods in the sense of objective measures.

Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery

NASA Astrophysics Data System (ADS)

Qiu, Chunping; Schmitt, Michael; Zhu, Xiao Xiang

2018-04-01

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established hand-crafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging.

Towards automatic SAR-optical stereogrammetry over urban areas using very high resolution imagery.

PubMed

Qiu, Chunping; Schmitt, Michael; Zhu, Xiao Xiang

2018-04-01

In this paper we discuss the potential and challenges regarding SAR-optical stereogrammetry for urban areas, using very-high-resolution (VHR) remote sensing imagery. Since we do this mainly from a geometrical point of view, we first analyze the height reconstruction accuracy to be expected for different stereogrammetric configurations. Then, we propose a strategy for simultaneous tie point matching and 3D reconstruction, which exploits an epipolar-like search window constraint. To drive the matching and ensure some robustness, we combine different established hand-crafted similarity measures. For the experiments, we use real test data acquired by the Worldview-2, TerraSAR-X and MEMPHIS sensors. Our results show that SAR-optical stereogrammetry using VHR imagery is generally feasible with 3D positioning accuracies in the meter-domain, although the matching of these strongly hetereogeneous multi-sensor data remains very challenging.

Ocean Virtual Laboratory: A New Way to Explore Multi-Sensor Synergy Demonstrated over the Agulhas Region

NASA Astrophysics Data System (ADS)

Collard, F.; Quartly, G. D.; Konik, M.; Johannessen, J. A.; Korosov, A.; Chapron, B.; Piolle, J.-F.; Herledan, S.; Darecki, M.; Isar, A.; Nafornita, C.

2015-12-01

Ocean Virtual Laboratory is an ESA-funded project to prototype the concept of a single point of access for all satellite remote-sensing data with ancillary model output and in situ measurements for a given region. The idea is to provide easy access for the non-specialist to both data and state-of-the-art processing techniques and enable their easy analysis and display. The project, led by OceanDataLab, is being trialled in the region of the Agulhas Current, as it contains signals of strong contrast (due to very energetic upper ocean dynamics) and special SAR data acquisitions have been recorded there. The project also encourages the take up of Earth Observation data by developing training material to help those not in large scientific or governmental organizations make the best use of what data are available. The website for access is: http://ovlproject.oceandatalab.com/

Multisensor comparison of ice concentration estimates in the marginal ice zone

NASA Technical Reports Server (NTRS)

Burns, B. A.; Cavalieri, D. J.; Gloersen, P.; Keller, M. R.; Campbell, W. J.

1987-01-01

Aircraft remote sensing data collected during the 1984 summer Marginal Ice Zone Experiment in the Fram Strait are used to compare ice concentration estimates derived from synthetic aperture radar (SAR) imagery, passive microwave imagery at several frequencies, aerial photography, and spectral photometer data. The comparison is carried out not only to evaluate SAR performance against more established techniques but also to investigate how ice surface conditions, imaging geometry, and choice of algorithm parameters affect estimates made by each sensor.Active and passive microwave sensor estimates of ice concentration derived using similar algorithms show an rms difference of 13 percent. Agreement between each microwave sensor and near-simultaneous aerial photography is approximately the same (14 percent). The availability of high-resolution microwave imagery makes it possible to ascribe the discrepancies in the concentration estimates to variations in ice surface signatures in the scene.

Sense and avoid technology for unmanned aircraft systems

NASA Astrophysics Data System (ADS)

McCalmont, John; Utt, James; Deschenes, Michael; Taylor, Michael; Sanderson, Richard; Montgomery, Joel; Johnson, Randal S.; McDermott, David

2007-04-01

The Sensors Directorate of the Air Force Research Laboratory (AFRL), in conjunction with the Global Hawk Systems Group, the J-UCAS System Program Office and contractor Defense Research Associates, Inc. (DRA) is conducting an Advanced Technology Demonstration (ATD) of a sense-and-avoid capability with the potential to satisfy the Federal Aviation Administration's (FAA) requirement for Unmanned Aircraft Systems (UAS) to provide "an equivalent level of safety, comparable to see-and-avoid requirements for manned aircraft". This FAA requirement must be satisfied for UAS operations within the national airspace. The Sense-and-Avoid, Phase I (Man-in-the-Loop) and Phase II (Autonomous Maneuver) ATD demonstrated an on-board, wide field of regard, multi-sensor visible imaging system operating in real time and capable of passively detecting approaching aircraft, declaring potential collision threats in a timely manner and alerting the human pilot located in the remote ground control station or autonomously maneuvered the aircraft. Intruder declaration data was collected during the SAA I & II Advanced Technology Demonstration flights conducted during December 2006. A total of 27 collision scenario flights were conducted and analyzed. The average detection range was 6.3 NM and the mean declaration range was 4.3 NM. The number of false alarms per engagement has been reduced to approximately 3 per engagement.

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications †

PubMed Central

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Muroyama, Masanori

2017-01-01

Robot tactile sensation can enhance human–robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as “sensor platform LSI”) as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated. PMID:29061954

A Tactile Sensor Network System Using a Multiple Sensor Platform with a Dedicated CMOS-LSI for Robot Applications.

PubMed

Shao, Chenzhong; Tanaka, Shuji; Nakayama, Takahiro; Hata, Yoshiyuki; Bartley, Travis; Nonomura, Yutaka; Muroyama, Masanori

2017-08-28

Robot tactile sensation can enhance human-robot communication in terms of safety, reliability and accuracy. The final goal of our project is to widely cover a robot body with a large number of tactile sensors, which has significant advantages such as accurate object recognition, high sensitivity and high redundancy. In this study, we developed a multi-sensor system with dedicated Complementary Metal-Oxide-Semiconductor (CMOS) Large-Scale Integration (LSI) circuit chips (referred to as "sensor platform LSI") as a framework of a serial bus-based tactile sensor network system. The sensor platform LSI supports three types of sensors: an on-chip temperature sensor, off-chip capacitive and resistive tactile sensors, and communicates with a relay node via a bus line. The multi-sensor system was first constructed on a printed circuit board to evaluate basic functions of the sensor platform LSI, such as capacitance-to-digital and resistance-to-digital conversion. Then, two kinds of external sensors, nine sensors in total, were connected to two sensor platform LSIs, and temperature, capacitive and resistive sensing data were acquired simultaneously. Moreover, we fabricated flexible printed circuit cables to demonstrate the multi-sensor system with 15 sensor platform LSIs operating simultaneously, which showed a more realistic implementation in robots. In conclusion, the multi-sensor system with up to 15 sensor platform LSIs on a bus line supporting temperature, capacitive and resistive sensing was successfully demonstrated.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

NASA Astrophysics Data System (ADS)

Midekisa, Alemayehu; Senay, Gabriel B.; Wimberly, Michael C.

2014-11-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.

Estimating terrestrial aboveground biomass estimation using lidar remote sensing: a meta-analysis

NASA Astrophysics Data System (ADS)

Zolkos, S. G.; Goetz, S. J.; Dubayah, R.

2012-12-01

Estimating biomass of terrestrial vegetation is a rapidly expanding research area, but also a subject of tremendous interest for reducing carbon emissions associated with deforestation and forest degradation (REDD). The accuracy of biomass estimates is important in the context carbon markets emerging under REDD, since areas with more accurate estimates command higher prices, but also for characterizing uncertainty in estimates of carbon cycling and the global carbon budget. There is particular interest in mapping biomass so that carbon stocks and stock changes can be monitored consistently across a range of scales - from relatively small projects (tens of hectares) to national or continental scales - but also so that other benefits of forest conservation can be factored into decision making (e.g. biodiversity and habitat corridors). We conducted an analysis of reported biomass accuracy estimates from more than 60 refereed articles using different remote sensing platforms (aircraft and satellite) and sensor types (optical, radar, lidar), with a particular focus on lidar since those papers reported the greatest efficacy (lowest errors) when used in the a synergistic manner with other coincident multi-sensor measurements. We show systematic differences in accuracy between different types of lidar systems flown on different platforms but, perhaps more importantly, differences between forest types (biomes) and plot sizes used for field calibration and assessment. We discuss these findings in relation to monitoring, reporting and verification under REDD, and also in the context of more systematic assessment of factors that influence accuracy and error estimation.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge datasets (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2014-10-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over CONUS for the period 2002-2012. This comparison effort includes satellite multi-sensor datasets (bias-adjusted TMPA 3B42, near-real time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation datasets are compared with surface observations from the Global Historical Climatology Network (GHCN-Daily) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (± 6%). However, differences at the RFC are more important in particular for near-real time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near real time counterpart 3B42RT. However, large biases remained for 3B42 over the Western US for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in day-1) over the Northwest. Furthermore, the conditional analysis and the contingency analysis conducted illustrated the challenge of retrieving extreme precipitation from remote sensing estimates.

Ionospheric Remote Sensing using GPS Radio Occultation and Ultraviolet Photometry aboard the ISS

NASA Astrophysics Data System (ADS)

Budzien, S. A.; Powell, S. P.; O'Hanlon, B.; Humphreys, T.; Bishop, R. L.; Stephan, A. W.; Gross, J.; Chakrabarti, S.

2017-12-01

The GPS Radio Occultation and Ultraviolet Photometer Co-located (GROUP-C) experiment launched to the International Space Station (ISS) on February 19, 2017 as part of the Space Test Program Houston #5 payload (STP-H5). After early orbit testing, GROUP-C began routine science operations in late April. GROUP-C includes a high-sensitivity far-ultraviolet photometer measuring horizontal nighttime ionospheric gradients and an advanced software-defined GPS receiver providing ionospheric electron density profiles, scintillation measurements, and lower atmosphere profiles. GROUP-C and a companion experiment, the Limb-Imaging Ionospheric and Thermospheric Extreme-Ultraviolet Spectrograph (LITES), offer a unique capability to study spatial and temporal variability of the thermosphere and ionosphere using multi-sensor approaches, including ionospheric tomography. Data are collected continuously across low- and mid-latitudes as the ISS orbit precesses through all local times every 60 days. The GROUP-C GPS sensor routinely collects dual-frequency GPS occultations, makes targeted raw signal captures of GPS and Galileo occultations, and includes multiple antennas to characterize multipath in the ISS environment. The UV photometer measures the 135.6 nm ionospheric recombination airglow emision along the nightside orbital track. We present the first analysis of ionospheric observations, discuss the challenges and opportunities of remote sensing from the ISS platform, and explore how these new data help address questions regarding the complex and dynamic features of the low and middle latitude ionosphere-thermosphere relevant to the upcoming GOLD and ICON missions.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

USGS Publications Warehouse

Midekisa, Alemayehu; Senay, Gabriel; Wimberly, Michael C.

2014-01-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.

Multi-sensor image registration based on algebraic projective invariants.

PubMed

Li, Bin; Wang, Wei; Ye, Hao

2013-04-22

A new automatic feature-based registration algorithm is presented for multi-sensor images with projective deformation. Contours are firstly extracted from both reference and sensed images as basic features in the proposed method. Since it is difficult to design a projective-invariant descriptor from the contour information directly, a new feature named Five Sequential Corners (FSC) is constructed based on the corners detected from the extracted contours. By introducing algebraic projective invariants, we design a descriptor for each FSC that is ensured to be robust against projective deformation. Further, no gray scale related information is required in calculating the descriptor, thus it is also robust against the gray scale discrepancy between the multi-sensor image pairs. Experimental results utilizing real image pairs are presented to show the merits of the proposed registration method.

Ground-based microwave radar and optical lidar signatures of volcanic ash plumes: models, observations and retrievals

NASA Astrophysics Data System (ADS)

Mereu, Luigi; Marzano, Frank; Mori, Saverio; Montopoli, Mario; Cimini, Domenico; Martucci, Giovanni

2013-04-01

The detection and quantitative retrieval of volcanic ash clouds is of significant interest due to its environmental, climatic and socio-economic effects. Real-time monitoring of such phenomena is crucial, also for the initialization of dispersion models. Satellite visible-infrared radiometric observations from geostationary platforms are usually exploited for long-range trajectory tracking and for measuring low level eruptions. Their imagery is available every 15-30 minutes and suffers from a relatively poor spatial resolution. Moreover, the field-of-view of geostationary radiometric measurements may be blocked by water and ice clouds at higher levels and their overall utility is reduced at night. Ground-based microwave radars may represent an important tool to detect and, to a certain extent, mitigate the hazard from the ash clouds. Ground-based weather radar systems can provide data for determining the ash volume, total mass and height of eruption clouds. Methodological studies have recently investigated the possibility of using ground-based single-polarization and dual-polarization radar system for the remote sensing of volcanic ash cloud. A microphysical characterization of volcanic ash was carried out in terms of dielectric properties, size distribution and terminal fall speed, assuming spherically-shaped particles. A prototype of volcanic ash radar retrieval (VARR) algorithm for single-polarization systems was proposed and applied to S-band and C-band weather radar data. The sensitivity of the ground-based radar measurements decreases as the ash cloud is farther so that for distances greater than about 50 kilometers fine ash might be not detected anymore by microwave radars. In this respect, radar observations can be complementary to satellite, lidar and aircraft observations. Active remote sensing retrieval from ground, in terms of detection, estimation and sensitivity, of volcanic ash plumes is not only dependent on the sensor specifications, but also on the range and ash cloud distribution. The minimum detectable signal can be increased, for a given system and ash plume scenario, by decreasing the observation range and increasing the operational frequency using a multi-sensor approach, but also exploiting possible polarimetric capabilities. In particular, multi-wavelengths lidars can be complementary systems useful to integrate radar-based ash particle measurement. This work, starting from the results of a previous study and from above mentioned issues, is aimed at quantitatively assessing the optimal choices for microwave and millimeter-wave radar systems with a dual-polarization capability for real-time ash cloud remote sensing to be used in combination with an optical lidar. The physical-electromagnetic model of ash particle distributions is systematically reviewed and extended to include non-spherical particle shapes, vesicular composition, silicate content and orientation phenomena. The radar and lidar scattering and absorption response is simulated and analyzed in terms of self-consistent polarimetric signatures for ash classification purposes and correlation with ash concentration and mean diameter for quantitative retrieval aims. A sensitivity analysis to ash concentration, as a function of sensor specifications, range and ash category, is carried out trying to assess the expected multi-sensor multi-spectral system performances and limitations. The multi-sensor multi-wavelength polarimetric model-based approach can be used within a particle classification and estimation scheme, based on the VARR Bayesian metrics. As an application, the ground-based observation of the Eyjafjallajökull volcanic ash plume on 15-16 May 2010, carried out at the Atmospheric Research Station at Mace Head, Carna (Ireland) with MIRA36 35-GHz Ka-Band Doppler cloud radar and CHM15K lidar/ceilometer at 1064-nm wavelength, has been considered. Results are discussed in terms of retrievals and intercomparison with other ground-based and satellite-based sensors.

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.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Evaluation and cross-comparison of vegetation indices for crop monitoring from sentinel-2 and worldview-2 images

NASA Astrophysics Data System (ADS)

Psomiadis, Emmanouil; Dercas, Nicholas; Dalezios, Nicolas R.; Spyropoulos, Nikolaos V.

2017-10-01

Farmers throughout the world are constantly searching for ways to maximize their returns. Remote Sensing applications are designed to provide farmers with timely crop monitoring and production information. Such information can be used to identify crop vigor problems. Vegetation indices (VIs) derived from satellite data have been widely used to assess variations in the physiological state and biophysical properties of vegetation. However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target. Therefore, multi-sensor VI capability and effectiveness are critical but complicated issues in the application of multi-sensor vegetation observations. Various factors such as the atmospheric conditions during acquisition, sensor and geometric characteristics, such as viewing angle, field of view, and sun elevation influence direct comparability of vegetation indicators among different sensors. In the present study, two experimental areas were used which are located near the villages Nea Lefki and Melia of Larissa Prefecture in Thessaly Plain area, containing a wheat and a cotton crop, respectively. Two satellite systems with different spatial resolution, WorldView-2 (W2) and Sentinel-2 (S2) with 2 and 10 meters pixel size, were used. Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) were calculated and a statistical comparison of the VIs was made to designate their correlation and dependency. Finally, several other innovative indices were calculated and compared to evaluate their effectiveness in the detection of problematic plant growth areas.

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

NASA Technical Reports Server (NTRS)

Schenker, Paul S. (Editor)

1992-01-01

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Modelling coastal processes by means of innovative integration of remote sensing and modelling analysis

NASA Astrophysics Data System (ADS)

Taramelli, A.; Zanuttigh, B.; Zucca, F.; Dejana, M.; Valentini, E.

2011-12-01

Coastal marine and inland landforms are dynamic systems undergoing adjustments in form at different time and space scales in response to varying conditions external to the system. Coastal emerged and shallow submerged nearshore areas, affected by short-term perturbations, return to their pre-disturbance morphology and generally reach a dynamic equilibrium. Worldwide in the last century we have experienced in increased coastal inundation, erosion and ecosystem losses. However, erosion can result from a number of other factors, such as altered wind and current patterns, high-energy waves, and reduced fluvial sediment inputs. Direct impacts of human activities, including reclamation of coastal wetlands, deforestation, damming, channelization, diversions of coastal waterways, construction of seawalls and other structures, alter circulation patterns. Also indirect human impacts such as land-uses changes through time (eg. from agricultural to industrial use) have affected coastal ecosystems. The objective of this research is to propose innovative remote sensing applications to monitor specific coastal processes in order to use them within a physical modelling to quantify and model their time evolution. The research was applied in two dynamic and densely populated deltas and coastal areas (the Po and the Plymouth delta) by combining multi-sensor spaceborne remote sensing (SAR and OPTICAL) to physical modelling. The main results are: a) deformation and spatiotemporal variations maps in coastal morphology with a special focus to point out the temporal subsidence evolution, b) inter and intra-annual change detection maps that are both used a to feed a coastal physical modelling (MIKE 21). The basic strategy was to highlight the different components of the coastal system environment through: 1) deformation and spatio-temporal variations maps of coastal morphology, by the use of time-stack from 1992 up today of ESA SAR data (ERS-1/2 and ENVISAT-ASAR sensors) were used to produce deformation maps and to point out the temporal evolution and 2) multitemporal hyperspectral endmembers fractions map of coastal morphology, 3) numerical model well-established through remote sensed based procedures and results in order to produce spatio-temporal scenario in coastal areas. The objective was to locate and characterize important coastal indicators for different regions using multitemporal data from the multi-hyperspectral sensors, as well as topographic elevation, SAR and derived products (eg. coherence) data. The identification of different indicators was based on land spectral properties, topography/landforms (low topography), disturbed areas (agricultural, construction), and vegetation distribution. Moreover, the indicators were assessed at seasonal and interannual time scales over two temporal decades horizons starting from 1990 and 2000.

Environmental monitoring of El Hierro Island submarine volcano, by combining low and high resolution satellite imagery

NASA Astrophysics Data System (ADS)

Eugenio, F.; Martin, J.; Marcello, J.; Fraile-Nuez, E.

2014-06-01

El Hierro Island, located at the Canary Islands Archipelago in the Atlantic coast of North Africa, has been rocked by thousands of tremors and earthquakes since July 2011. Finally, an underwater volcanic eruption started 300 m below sea level on October 10, 2011. Since then, regular multidisciplinary monitoring has been carried out in order to quantify the environmental impacts caused by the submarine eruption. Thanks to this natural tracer release, multisensorial satellite imagery obtained from MODIS and MERIS sensors have been processed to monitor the volcano activity and to provide information on the concentration of biological, chemical and physical marine parameters. Specifically, low resolution satellite estimations of optimal diffuse attenuation coefficient (Kd) and chlorophyll-a (Chl-a) concentration under these abnormal conditions have been assessed. These remote sensing data have played a fundamental role during field campaigns guiding the oceanographic vessel to the appropriate sampling areas. In addition, to analyze El Hierro submarine volcano area, WorldView-2 high resolution satellite spectral bands were atmospherically and deglinted processed prior to obtain a high-resolution optimal diffuse attenuation coefficient model. This novel algorithm was developed using a matchup data set with MERIS and MODIS data, in situ transmittances measurements and a seawater radiative transfer model. Multisensor and multitemporal imagery processed from satellite remote sensing sensors have demonstrated to be a powerful tool for monitoring the submarine volcanic activities, such as discolored seawater, floating material and volcanic plume, having shown the capabilities to improve the understanding of submarine volcanic processes.

UC Irvine CHRS iRain - An Integrated System for Global Real-time Precipitation Observation

NASA Astrophysics Data System (ADS)

Tran, H.; Nguyen, P.; Huynh, P.; Palacios, T.; Braithwaite, D.; Hsu, K. L.; Sorooshian, S.

2016-12-01

CHRS iRain developed by the Center for Hydrometeorology and Remote Sensing (CHRS), University of California, Irvine is an integrated system for global real-time rainfall observation and visualization using multiple data sources from satellites, radars, gauges, and crowd sourcing. Its backbone is the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System (PERSIANN-CCS, Hong et al. 2004). Apart from using traditional PERSIANN technique (Hsu et al. 1997), the PERSIANN-CCS also applies image processing and pattern recognition techniques, which significantly improve its accuracy as well as its temporal and spatial resolution (in hourly and 4 km x 4 km respectively). Although satellite-based precipitation products are developing fast, they are still relatively new compared with other precipitation observations by traditional measuring methods, such as radar or rain gauges. CHRS iRain also provides hourly precipitation information from NCEP Stage IV multi-sensor (radar + gauges) products and gauges with over 2000 NOAA River Forecast Center stations. On the website, users can retrieve data of the most recent 72 hour precipitation over different spatial regions regarding their own interests such as grid coordinate, rectangle, watershed, basin, political division, and country. CHRS iRain is a useful tool that provides important global rainfall information for water resources management and decision making for natural disasters such as flash floods, urban flooding, and river flooding. ACKNOWLEDGMENTSWe would like to acknowledge NASA, NOAA Office of Hydrologic Development (OHD) National Weather Service (NWS), Cooperative Institue for Climate and Satellites (CICS), Army Research Office (ARO), ICIWaRM, and UNESCO for supporting this research.

Quantitative assessment of Urmia Lake water using spaceborne multisensor data and 3D modeling.

PubMed

Jeihouni, Mehrdad; Toomanian, Ara; Alavipanah, Seyed Kazem; Hamzeh, Saeid

2017-10-18

Preserving aquatic ecosystems and water resources management is crucial in arid and semi-arid regions for anthropogenic reasons and climate change. In recent decades, the water level of the largest lake in Iran, Urmia Lake, has decreased sharply, which has become a major environmental concern in Iran and the region. The efforts to revive the lake concerns the amount of water required for restoration. This study monitored and assessed Urmia Lake status over a period of 30 years (1984 to 2014) using remotely sensed data. A novel method is proposed that generates a lakebed digital elevation model (LBDEM) for Urmia Lake based on time series images from Landsat satellites, water level field measurements, remote sensing techniques, GIS, and 3D modeling. The volume of water required to restore the Lake water level to that of previous years and the ecological water level was calculated based on LBDEM. The results indicate a marked change in the area and volume of the lake from its maximum water level in 1998 to its minimum level in 2014. During this period, 86% of the lake became a salt desert and the volume of the lake water in 2013 was just 0.83% of the 1998 volume. The volume of water required to restore Urmia Lake from benchmark status (in 2014) to ecological water level (1274.10 m) is 12.546 Bm 3 , excluding evaporation. The results and the proposed method can be used by national and international environmental organizations to monitor and assess the status of Urmia Lake and support them in decision-making.

Evaluation of precipitation estimates over CONUS derived from satellite, radar, and rain gauge data sets at daily to annual scales (2002-2012)

NASA Astrophysics Data System (ADS)

Prat, O. P.; Nelson, B. R.

2015-04-01

We use a suite of quantitative precipitation estimates (QPEs) derived from satellite, radar, and surface observations to derive precipitation characteristics over the contiguous United States (CONUS) for the period 2002-2012. This comparison effort includes satellite multi-sensor data sets (bias-adjusted TMPA 3B42, near-real-time 3B42RT), radar estimates (NCEP Stage IV), and rain gauge observations. Remotely sensed precipitation data sets are compared with surface observations from the Global Historical Climatology Network-Daily (GHCN-D) and from the PRISM (Parameter-elevation Regressions on Independent Slopes Model). The comparisons are performed at the annual, seasonal, and daily scales over the River Forecast Centers (RFCs) for CONUS. Annual average rain rates present a satisfying agreement with GHCN-D for all products over CONUS (±6%). However, differences at the RFC are more important in particular for near-real-time 3B42RT precipitation estimates (-33 to +49%). At annual and seasonal scales, the bias-adjusted 3B42 presented important improvement when compared to its near-real-time counterpart 3B42RT. However, large biases remained for 3B42 over the western USA for higher average accumulation (≥ 5 mm day-1) with respect to GHCN-D surface observations. At the daily scale, 3B42RT performed poorly in capturing extreme daily precipitation (> 4 in. day-1) over the Pacific Northwest. Furthermore, the conditional analysis and a contingency analysis conducted illustrated the challenge in retrieving extreme precipitation from remote sensing estimates.

Optimization of Self-Directed Target Coverage in Wireless Multimedia Sensor Network

PubMed Central

Yang, Yang; Wang, Yufei; Pi, Dechang; Wang, Ruchuan

2014-01-01

Video and image sensors in wireless multimedia sensor networks (WMSNs) have directed view and limited sensing angle. So the methods to solve target coverage problem for traditional sensor networks, which use circle sensing model, are not suitable for WMSNs. Based on the FoV (field of view) sensing model and FoV disk model proposed, how expected multimedia sensor covers the target is defined by the deflection angle between target and the sensor's current orientation and the distance between target and the sensor. Then target coverage optimization algorithms based on expected coverage value are presented for single-sensor single-target, multisensor single-target, and single-sensor multitargets problems distinguishingly. Selecting the orientation that sensor rotated to cover every target falling in the FoV disk of that sensor for candidate orientations and using genetic algorithm to multisensor multitargets problem, which has NP-complete complexity, then result in the approximated minimum subset of sensors which covers all the targets in networks. Simulation results show the algorithm's performance and the effect of number of targets on the resulting subset. PMID:25136667

The SARVIEWS Project: Automated SAR Processing in Support of Operational Near Real-time Volcano Monitoring

NASA Astrophysics Data System (ADS)

Meyer, F. J.; Webley, P. W.; Dehn, J.; Arko, S. A.; McAlpin, D. B.; Gong, W.

2016-12-01

Volcanic eruptions are among the most significant hazards to human society, capable of triggering natural disasters on regional to global scales. In the last decade, remote sensing has become established in operational volcano monitoring. Centers like the Alaska Volcano Observatory rely heavily on remote sensing data from optical and thermal sensors to provide time-critical hazard information. Despite this high use of remote sensing data, the presence of clouds and a dependence on solar illumination often limit their impact on decision making. Synthetic Aperture Radar (SAR) systems are widely considered superior to optical sensors in operational monitoring situations, due to their weather and illumination independence. Still, the contribution of SAR to operational volcano monitoring has been limited in the past due to high data costs, long processing times, and low temporal sampling rates of most SAR systems. In this study, we introduce the automatic SAR processing system SARVIEWS, whose advanced data analysis and data integration techniques allow, for the first time, a meaningful integration of SAR into operational monitoring systems. We will introduce the SARVIEWS database interface that allows for automatic, rapid, and seamless access to the data holdings of the Alaska Satellite Facility. We will also present a set of processing techniques designed to automatically generate a set of SAR-based hazard products (e.g. change detection maps, interferograms, geocoded images). The techniques take advantage of modern signal processing and radiometric normalization schemes, enabling the combination of data from different geometries. Finally, we will show how SAR-based hazard information is integrated in existing multi-sensor decision support tools to enable joint hazard analysis with data from optical and thermal sensors. We will showcase the SAR processing system using a set of recent natural disasters (both earthquakes and volcanic eruptions) to demonstrate its robustness. We will also show the benefit of integrating SAR with data from other sensors to support volcano monitoring. For historic eruptions at Okmok and Augustine volcano, both located in the North Pacific, we will demonstrate that the addition of SAR can lead to a significant improvement in activity detection and eruption forecasting.

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.

A vector radiative transfer model for coupled atmosphere and ocean systems based on successive order of scattering method.

PubMed

Zhai, Peng-Wang; Hu, Yongxiang; Trepte, Charles R; Lucker, Patricia L

2009-02-16

A vector radiative transfer model has been developed for coupled atmosphere and ocean systems based on the Successive Order of Scattering (SOS) Method. The emphasis of this study is to make the model easy-to-use and computationally efficient. This model provides the full Stokes vector at arbitrary locations which can be conveniently specified by users. The model is capable of tracking and labeling different sources of the photons that are measured, e.g. water leaving radiances and reflected sky lights. This model also has the capability to separate florescence from multi-scattered sunlight. The delta - fit technique has been adopted to reduce computational time associated with the strongly forward-peaked scattering phase matrices. The exponential - linear approximation has been used to reduce the number of discretized vertical layers while maintaining the accuracy. This model is developed to serve the remote sensing community in harvesting physical parameters from multi-platform, multi-sensor measurements that target different components of the atmosphere-oceanic system.

Combination of Tls Point Clouds and 3d Data from Kinect v2 Sensor to Complete Indoor Models

NASA Astrophysics Data System (ADS)

Lachat, E.; Landes, T.; Grussenmeyer, P.

2016-06-01

The combination of data coming from multiple sensors is more and more applied for remote sensing issues (multi-sensor imagery) but also in cultural heritage or robotics, since it often results in increased robustness and accuracy of the final data. In this paper, the reconstruction of building elements such as window frames or door jambs scanned thanks to a low cost 3D sensor (Kinect v2) is presented. Their combination within a global point cloud of an indoor scene acquired with a terrestrial laser scanner (TLS) is considered. If the added elements acquired with the Kinect sensor enable to reach a better level of detail of the final model, an adapted acquisition protocol may also provide several benefits as for example time gain. The paper aims at analyzing whether the two measurement techniques can be complementary in this context. The limitations encountered during the acquisition and reconstruction steps are also investigated.

Detection of Neolithic Settlements in Thessaly (Greece) Through Multispectral and Hyperspectral Satellite Imagery

PubMed Central

Alexakis, Dimitrios; Sarris, Apostolos; Astaras, Theodoros; Albanakis, Konstantinos

2009-01-01

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 – 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution. PMID:22399961

Multi-Mission Remote Sensing of Suspended Particulate Matter and Diffuse Attenuation Coefficient in the Yangtze Estuarine and Coastal Waters

NASA Astrophysics Data System (ADS)

Yu, X.; Salama, S.; Shen, F.

2016-08-01

During the Dragon-3 project (ID: 10555) period, we developed and improved the atmospheric correction algorithms (AC) and retrieval models of suspended sediment concentration ( ) and diffuse attenuation coefficient ( ) for the Yangtze estuarine and coastal waters. The developed models were validated by measurements with consistently stable and fairly accurate estimations, reproducing reasonable distribution maps of and over the study area. Spatial-temporal variations of were presented and the mechanisms of the sediment transport were discussed. We further examined the compatibility of the developed AC algorithms and retrieval model and the consistency of satellite products for multi-sensor such as MODIS/Terra/Aqua, MERIS/Envisat, MERSI/ FY-3 and GOCI. The inter-comparison of multi- sensor suggested that different satellite products can be combined to increase revisit frequency and complement a temporal gap of time series satellites that may exist between on-orbit and off- orbit, facilitating a better monitor on the spatial- temporal dynamics of .

Tracking an Oil Tanker Collision and Spilled Oils in the East China Sea Using Multisensor Day and Night Satellite Imagery

NASA Astrophysics Data System (ADS)

Sun, Shaojie; Lu, Yingcheng; Liu, Yongxue; Wang, Mengqiu; Hu, Chuanmin

2018-04-01

Satellite remote sensing is well known to play a critical role in monitoring marine accidents such as oil spills, yet the recent SANCHI oil tanker collision event in January 2018 in the East China Sea indicates that traditional techniques using synthetic aperture radar or daytime optical imagery could not provide timely and adequate coverage. In this study, we show the unprecedented value of Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and Day/Night Band data in tracking the oil tanker's drifting pathway and locations when all other means are not as effective for the same purpose. Such pathway and locations can also be reproduced with a numerical model, with root-mean-square error of <15 km. While high-resolution optical imagery after 4 days of the tanker's sinking reveals much larger oil spill area (>350 km2) than previous reports, the impact of the spilled condensate oil on the marine environment requires further research.

Realtime Gas Emission Monitoring at Hazardous Sites Using a Distributed Point-Source Sensing Infrastructure.

PubMed

Manes, Gianfranco; Collodi, Giovanni; Gelpi, Leonardo; Fusco, Rosanna; Ricci, Giuseppe; Manes, Antonio; Passafiume, Marco

2016-01-20

This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant's critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events.

Detection of neolithic settlements in thessaly (Greece) through multispectral and hyperspectral satellite imagery.

PubMed

Alexakis, Dimitrios; Sarris, Apostolos; Astaras, Theodoros; Albanakis, Konstantinos

2009-01-01

Thessaly is a low relief region in Greece where hundreds of Neolithic settlements/tells called magoules were established from the Early Neolithic period until the Bronze Age (6,000 - 3,000 BC). Multi-sensor remote sensing was applied to the study area in order to evaluate its potential to detect Neolithic settlements. Hundreds of sites were geo-referenced through systematic GPS surveying throughout the region. Data from four primary sensors were used, namely Landsat ETM, ASTER, EO1 - HYPERION and IKONOS. A range of image processing techniques were originally applied to the hyperspectral imagery in order to detect the settlements and validate the results of GPS surveying. Although specific difficulties were encountered in the automatic classification of archaeological features composed by a similar parent material with the surrounding landscape, the results of the research suggested a different response of each sensor to the detection of the Neolithic settlements, according to their spectral and spatial resolution.

Cross-calibration between airborne SAR sensors

NASA Technical Reports Server (NTRS)

Zink, Manfred; Olivier, Philippe; Freeman, Anthony

1993-01-01

As Synthetic Aperture Radar (SAR) system performance and experience in SAR signature evaluation increase, quantitative analysis becomes more and more important. Such analyses require an absolute radiometric calibration of the complete SAR system. To keep the expenditure on calibration of future multichannel and multisensor remote sensing systems (e.g., X-SAR/SIR-C) within a tolerable level, data from different tracks and different sensors (channels) must be cross calibrated. The 1989 joint E-SAR/DC-8 SAR calibration campaign gave a first opportunity for such an experiment, including cross sensor and cross track calibration. A basic requirement for successful cross calibration is the stability of the SAR systems. The calibration parameters derived from different tracks and the polarimetric properties of the uncalibrated data are used to describe this stability. Quality criteria for a successful cross calibration are the agreement of alpha degree values and the consistency of radar cross sections of equally sized corner reflectors. Channel imbalance and cross talk provide additional quality in case of the polarimetric DC-8 SAR.

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters - Part 2: Aerosols

NASA Astrophysics Data System (ADS)

Wind, Galina; da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-07-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a "simulated radiance" product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land-ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Multi-Sensor Cloud and Aerosol Retrieval Simulator and Remote Sensing from Model Parameters . Part 2; Aerosols

NASA Technical Reports Server (NTRS)

Wind, Galina; Da Silva, Arlindo M.; Norris, Peter M.; Platnick, Steven; Mattoo, Shana; Levy, Robert C.

2016-01-01

The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a simulated radiance product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers. This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled. In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model sub grid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms. Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (MOYD04). TheMOYD04 product is of course normally produced from MOYD021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a MOYD021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source. We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Remote sensing of chlorophyll in the Baltic Sea at basin scale from 1997 to 2012 using merged multi-sensor data

NASA Astrophysics Data System (ADS)

Pitarch, Jaime; Volpe, Gianluca; Colella, Simone; Krasemann, Hajo; Santoleri, Rosalia

2016-03-01

A 15-year (1997-2012) time series of chlorophyll a (Chl a) in the Baltic Sea, based on merged multi-sensor satellite data was analysed. Several available Chl a algorithms were sea-truthed against the largest in situ publicly available Chl a data set ever used for calibration and validation over the Baltic region. To account for the known biogeochemical heterogeneity of the Baltic, matchups were calculated for three separate areas: (1) the Skagerrak and Kattegat, (2) the central Baltic, including the Baltic Proper and the gulfs of Riga and Finland, and (3) the Gulf of Bothnia. Similarly, within the operational context of the Copernicus Marine Environment Monitoring Service (CMEMS) the three areas were also considered as a whole in the analysis. In general, statistics showed low linearity. However, a bootstrapping-like assessment did provide the means for removing the bias from the satellite observations, which were then used to compute basin average time series. Resulting climatologies confirmed that the three regions display completely different Chl a seasonal dynamics. The Gulf of Bothnia displays a single Chl a peak during spring, whereas in the Skagerrak and Kattegat the dynamics are less regular and composed of highs and lows during winter, progressing towards a small bloom in spring and a minimum in summer. In the central Baltic, Chl a follows a dynamics of a mild spring bloom followed by a much stronger bloom in summer. Surface temperature data are able to explain a variable fraction of the intensity of the summer bloom in the central Baltic.<

Ionospheric-thermospheric UV tomography: 3. A multisensor technique for creating full-orbit reconstructions of atmospheric UV emission

NASA Astrophysics Data System (ADS)

Hei, Matthew A.; Budzien, Scott A.; Dymond, Kenneth F.; Nicholas, Andrew C.; Paxton, Larry J.; Schaefer, Robert K.; Groves, Keith M.

2017-07-01

We present the Volume Emission Rate Tomography (VERT) technique for inverting satellite-based, multisensor limb and nadir measurements of atmospheric ultraviolet emission to create whole-orbit reconstructions of atmospheric volume emission rate. The VERT approach is more general than previous ionospheric tomography methods because it can reconstruct the volume emission rate field irrespective of the particular excitation mechanisms (e.g., radiative recombination, photoelectron impact excitation, and energetic particle precipitation in auroras); physical models are then applied to interpret the airglow. The technique was developed and tested using data from the Special Sensor Ultraviolet Limb Imager and Special Sensor Ultraviolet Spectrographic Imager instruments aboard the Defense Meteorological Satellite Program F-18 spacecraft and planned for use with upcoming remote sensing missions. The technique incorporates several features to optimize the tomographic solutions, such as the use of a nonnegative algorithm (Richardson-Lucy, RL) that explicitly accounts for the Poisson statistics inherent in optical measurements, capability to include extinction effects due to resonant scattering and absorption of the photons from the lines of sight, a pseudodiffusion-based regularization scheme implemented between iterations of the RL code to produce smoother solutions, and the capability to estimate error bars on the solutions. Tests using simulated atmospheric emissions verify that the technique performs well in a variety of situations, including daytime, nighttime, and even in the challenging terminator regions. Lastly, we consider ionospheric nightglow and validate reconstructions of the nighttime electron density against Advanced Research Project Agency (ARPA) Long-range Tracking and Identification Radar (ALTAIR) incoherent scatter radar data.

Mapping snow cover using multi-source satellite data on big data platforms

NASA Astrophysics Data System (ADS)

Lhermitte, Stef

2017-04-01

Snowmelt is an important and dynamically changing water resource in mountainous regions around the world. In this framework, remote sensing data of snow cover data provides an essential input for hydrological models to model the water contribution from remote mountain areas and to understand how this water resource might alter as a result of climate change. Traditionally, however, many of these remote sensing products show a trade-off between spatial and temporal resolution (e.g., 16-day Landsat at 30m vs. daily MODIS at 500m resolution). With the advent of Sentinel-1 and 2 and the PROBA-V 100m products this trade-off can partially be tackled by having data that corresponds more closely to the spatial and temporal variations in snow cover typically observed over complex mountain areas. This study provides first a quantitative analysis of the trade-offs between the state-of-the-art snow cover mapping methodologies for Landsat, MODIS, PROBA-V, Sentinel-1 and 2 and applies them on big data platforms such as Google Earth Engine (GEE), RSS (ESA Research Service & Support) CloudToolbox, and the PROBA-V Mission Exploitation Platform (MEP). Second, it combines the different sensor data-cubes in one multi-sensor classification approach using newly developed spatio-temporal probability classifiers within the big data platform environments. Analysis of the spatio-temporal differences in derived snow cover areas from the different sensors reveals the importance of understanding the spatial and temporal scales at which variations occur. Moreover, it shows the importance of i) temporal resolution when monitoring highly dynamical properties such as snow cover and of ii) differences in satellite viewing angles over complex mountain areas. Finally, it highlights the potential and drawbacks of big data platforms for combining multi-source satellite data for monitoring dynamical processes such as snow cover.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

Intensive time series data exploitation: the Multi-sensor Evolution Analysis (MEA) platform

NASA Astrophysics Data System (ADS)

Mantovani, Simone; Natali, Stefano; Folegani, Marco; Scremin, Alessandro

2014-05-01

The monitoring of the temporal evolution of natural phenomena must be performed in order to ensure their correct description and to allow improvements in modelling and forecast capabilities. This assumption, that is obvious for ground-based measurements, has not always been true for data collected through space-based platforms: except for geostationary satellites and sensors, that allow providing a very effective monitoring of phenomena with geometric scale from regional to global; smaller phenomena (with characteristic dimension lower than few kilometres) have been monitored with instruments that could collect data only with a time interval in the order of several days; bi-temporal techniques have been the most used ones for years, in order to characterise temporal changes and try identifying specific phenomena. The more the number of flying sensor has grown and their performance improved, the more their capability of monitoring natural phenomena at a smaller geographic scale has grown: we can now count on tenth of years of remotely sensed data, collected by hundreds of sensors that are now accessible from a wide users' community, and the techniques for data processing have to be adapted to move toward a data intensive exploitation. Starting from 2008, the European Space Agency has initiated the development of the Multi-sensor Evolution Analysis (MEA) platform (https://mea.eo.esa.int), whose first aim was to permit the access and exploitation of long term remotely sensed satellite data from different platforms: 15 years of global (A)ATSR data together with 5 years of regional AVNIR-2 data were loaded into the system and were used, through a web-based graphic user interface, for land cover change analysis. The MEA data availability has grown during years integrating multi-disciplinary data that feature spatial and temporal dimensions: so far tenths of Terabytes of data in the land and atmosphere domains are available and can be visualized and exploited, keeping the time dimension as the most relevant one (https://mea.eo.esa.int/data_availability.html). MEA is also used as Climate Data gateway in the framework of the FP7 EarthServer Project. In the present work, principles of the MEA platform are presented, emphasizing the general concept and the methods that have been implemented for data access (including OGC standard data access) and exploitation. In order to show its effectiveness, use cases focused on multi-field and multi-temporal data analysis are shown.

Hydrological storage variations in a lake water balance, observed from multi-sensor satellite data and hydrological models.

NASA Astrophysics Data System (ADS)

Singh, Alka; Seitz, Florian; Schwatke, Christian; Guentner, Andreas

2013-04-01

Freshwater lakes and reservoirs account for 74.5% of continental water storage in surface water bodies and only 1.8% resides in rivers. Lakes and reservoirs are a key component of the continental hydrological cycle but in-situ monitoring networks are very limited either because of sparse spatial distribution of gauges or national data policy. Monitoring and predicting extreme events is very challenging in that case. In this study we demonstrate the use of optical remote sensing, satellite altimetry and the GRACE gravity field mission to monitor the lake water storage variations in the Aral Sea. Aral Sea is one of the most unfortunate examples of a large anthropogenic catastrophe. The 4th largest lake of 1960s has been decertified for more than 75% of its area due to the diversion of its primary rivers for irrigation purposes. Our study is focused on the time frame of the GRACE mission; therefore we consider changes from 2002 onwards. Continuous monthly time series of water masks from Landsat satellite data and water level from altimetry missions were derived. Monthly volumetric variations of the lake water storage were computed by intersecting a digital elevation model of the lake with respective water mask and altimetry water level. With this approach we obtained volume from two independent remote sensing methods to reduce the error in the estimated volume through least square adjustment. The resultant variations were then compared with mass variability observed by GRACE. In addition, GARCE estimates of water storage variations were compared with simulation results of the Water Gap Hydrology Model (WGHM). The different observations from all missions agree that the lake reached an absolute minimum in autumn 2009. A marked reversal of the negative trend occured in 2010 but water storage in the lake decreased again afterwards. The results reveal that water storage variations in the Aral Sea are indeed the principal, but not the only contributor to the GRACE signal of mass variations in this region; this is also verified by WGHM simulations. An important implication of this finding is the possibility of GRACE to analyses storage changes in other hydrological compartments (soil moisture, snow and groundwater) once the signal has been reduced for surface water storage changes. Therefore the congruent use of multi-sensor satellite data for hydrological studies proves to be a great source of information for assessing terrestrial water storage variations.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

A Multitemporal, Multisensor Approach to Mapping the Canadian Boreal Forest

NASA Astrophysics Data System (ADS)

Reith, Ernest

The main anthropogenic source of CO2 emissions is the combustion of fossil fuels, while the clearing and burning of forests contribute significant amounts as well. Vegetation represents a major reservoir for terrestrial carbon stocks, and improving our ability to inventory vegetation will enhance our understanding of the impacts of land cover and climate change on carbon stocks and fluxes. These relationships may be an indication of a series of troubling biosphere-atmospheric feedback mechanisms that need to be better understood and modeled. Valuable land cover information can be provided to the global climate change modeling community using advanced remote sensing capabilities such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR). Individually and synergistically, data were successfully used to characterize the complex nature of the Canadian boreal forest land cover types. The multiple endmember spectral mixture analysis process was applied against seasonal AVIRIS data to produce species-level vegetated land cover maps of two study sites in the Canadian boreal forest: Old Black Spruce (OBS) and Old Jack Pine (OJP). The highest overall accuracy was assessed to be at least 66% accurate to the available reference map, providing evidence that high-quality, species-level land cover mapping of the Canadian boreal forest is achievable at accuracy levels greater than other previous research efforts in the region. Backscatter information from multichannel, polarimetric SAR utilizing a binary decision tree-based classification technique methodology was moderately successfully applied to AIRSAR to produce maps of the boreal land cover types at both sites, with overall accuracies at least 59%. A process, centered around noise whitening and principal component analysis features of the minimum noise fraction transform, was implemented to leverage synergies contained within spatially coregistered multitemporal and multisensor AVIRIS and AIRSAR data sets to successfully produce high-accuracy boreal forest land cover maps. Overall land cover map accuracies of 78% and 72% were assessed for OJP and OBS sites, respectively, for either seasonal or multitemporal data sets. High individual land cover accuracies appeared to be independent of site, season, or multisensor combination in the minimum-noise fraction-based approach.

The concurrent multiplicative-additive approach for gauge-radar/satellite multisensor precipitation estimates

NASA Astrophysics Data System (ADS)

Garcia-Pintado, J.; Barberá, G. G.; Erena Arrabal, M.; Castillo, V. M.

2010-12-01

Objective analysis schemes (OAS), also called ``succesive correction methods'' or ``observation nudging'', have been proposed for multisensor precipitation estimation combining remote sensing data (meteorological radar or satellite) with data from ground-based raingauge networks. However, opposite to the more complex geostatistical approaches, the OAS techniques for this use are not optimized. On the other hand, geostatistical techniques ideally require, at the least, modelling the covariance from the rain gauge data at every time step evaluated, which commonly cannot be soundly done. Here, we propose a new procedure (concurrent multiplicative-additive objective analysis scheme [CMA-OAS]) for operational rainfall estimation using rain gauges and meteorological radar, which does not require explicit modelling of spatial covariances. On the basis of a concurrent multiplicative-additive (CMA) decomposition of the spatially nonuniform radar bias, within-storm variability of rainfall and fractional coverage of rainfall are taken into account. Thus both spatially nonuniform radar bias, given that rainfall is detected, and bias in radar detection of rainfall are handled. The interpolation procedure of CMA-OAS is built on the OAS, whose purpose is to estimate a filtered spatial field of the variable of interest through a successive correction of residuals resulting from a Gaussian kernel smoother applied on spatial samples. The CMA-OAS, first, poses an optimization problem at each gauge-radar support point to obtain both a local multiplicative-additive radar bias decomposition and a regionalization parameter. Second, local biases and regionalization parameters are integrated into an OAS to estimate the multisensor rainfall at the ground level. The approach considers radar estimates as background a priori information (first guess), so that nudging to observations (gauges) may be relaxed smoothly to the first guess, and the relaxation shape is obtained from the sequential optimization. The procedure is suited to relatively sparse rain gauge networks. To show the procedure, six storms are analyzed at hourly steps over 10,663 km2. Results generally indicated an improved quality with respect to other methods evaluated: a standard mean-field bias adjustment, an OAS spatially variable adjustment with multiplicative factors, ordinary cokriging, and kriging with external drift. In theory, it could be equally applicable to gauge-satellite estimates and other hydrometeorological variables.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

NASA Astrophysics Data System (ADS)

Maimaitijiang, Maitiniyazi; Ghulam, Abduwasit; Sidike, Paheding; Hartling, Sean; Maimaitiyiming, Matthew; Peterson, Kyle; Shavers, Ethan; Fishman, Jack; Peterson, Jim; Kadam, Suhas; Burken, Joel; Fritschi, Felix

2017-12-01

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a + b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

Integrated Visualization of Multi-sensor Ocean Data across the Web

NASA Astrophysics Data System (ADS)

Platt, F.; Thompson, C. K.; Roberts, J. T.; Tsontos, V. M.; Hin Lam, C.; Arms, S. C.; Quach, N.

2017-12-01

Whether for research or operational decision support, oceanographic applications rely on the visualization of multivariate in situ and remote sensing data as an integral part of analysis workflows. However, given their inherently 3D-spatial and temporally dynamic nature, the visual representation of marine in situ data in particular poses a challenge. The Oceanographic In situ data Interoperability Project (OIIP) is a collaborative project funded under the NASA/ACCESS program that seeks to leverage and enhance higher TRL (technology readiness level) informatics technologies to address key data interoperability and integration issues associated with in situ ocean data, including the dearth of effective web-based visualization solutions. Existing web tools for the visualization of key in situ data types - point, profile, trajectory series - are limited in their support for integrated, dynamic and coordinated views of the spatiotemporal characteristics of the data. Via the extension of the JPL Common Mapping Client (CMC) software framework, OIIP seeks to provide improved visualization support for oceanographic in situ data sets. More specifically, this entails improved representation of both horizontal and vertical aspects of these data, which inherently are depth resolved and time referenced, as well as the visual synchronization with relevant remotely-sensed gridded data products, such as sea surface temperature and salinity. Electronic tagging datasets, which are a focal use case for OIIP, provide a representative, if somewhat complex, visualization challenge in this regard. Critical to the achievement of these development objectives has been compilation of a well-rounded set of visualization use cases and requirements based on a series of end-user consultations aimed at understanding their satellite-in situ visualization needs. Here we summarize progress on aspects of the technical work and our approach.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2014-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2015-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Impacts of Subgrid Heterogeneous Mixing between Cloud Liquid and Ice on the Wegner-Bergeron-Findeisen Process and Mixed-phase Clouds in NCAR CAM5

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, M.; Zhang, D.; Wang, Z.; Wang, Y.

2017-12-01

Mixed-phase clouds are persistently observed over the Arctic and the phase partitioning between cloud liquid and ice hydrometeors in mixed-phase clouds has important impacts on the surface energy budget and Arctic climate. In this study, we test the NCAR Community Atmosphere Model Version 5 (CAM5) with the single-column and weather forecast configurations and evaluate the model performance against observation data from the DOE Atmospheric Radiation Measurement (ARM) Program's M-PACE field campaign in October 2004 and long-term ground-based multi-sensor remote sensing measurements. Like most global climate models, we find that CAM5 also poorly simulates the phase partitioning in mixed-phase clouds by significantly underestimating the cloud liquid water content. Assuming pocket structures in the distribution of cloud liquid and ice in mixed-phase clouds as suggested by in situ observations provides a plausible solution to improve the model performance by reducing the Wegner-Bergeron-Findeisen (WBF) process rate. In this study, the modification of the WBF process in the CAM5 model has been achieved with applying a stochastic perturbation to the time scale of the WBF process relevant to both ice and snow to account for the heterogeneous mixture of cloud liquid and ice. Our results show that this modification of WBF process improves the modeled phase partitioning in the mixed-phase clouds. The seasonal variation of mixed-phase cloud properties is also better reproduced in the model in comparison with the long-term ground-based remote sensing observations. Furthermore, the phase partitioning is insensitive to the reassignment time step of perturbations.

Addressing and Presenting Quality of Satellite Data via Web-Based Services

NASA Technical Reports Server (NTRS)

Leptoukh, Gregory; Lynnes, C.; Ahmad, S.; Fox, P.; Zednik, S.; West, P.

2011-01-01

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project.

A global analysis of the urban heat island effect based on multisensor satellite data

NASA Astrophysics Data System (ADS)

Xiao, J.; Frolking, S. E.; Milliman, T. E.; Schneider, A.; Friedl, M. A.

2017-12-01

Human population is rapidly urbanizing. In much of the world, cities are prone to hotter weather than surrounding rural areas - so-called `urban heat islands' - and this effect can have mortal consequences during heat waves. During the daytime, when the surface energy balance is driven by incoming solar radiation, the magnitude of urban warming is strongly influenced by surface albedo and the capacity to evaporate water (i.e., there is a strong relationship between vegetated land fraction and the ratio of sensible to latent heat loss or Bowen ratio). At nighttime, urban cooling is often inhibited by the thermal inertia of the built environment and anthropogenic heat exhaust from building and transportation energy use. We evaluated a suite of global remote sensing data sets representing a range of urban characteristics against MODIS-derived land-surface temperature differences between urban and surrounding rural areas. We included two new urban datasets in this analysis - MODIS-derived change in global urban extent and global urban microwave backscatter - along with several MODIS standard products and DMSP/OLS nighttime lights time series data. The global analysis spanned a range of urban characteristics that likely influence the magnitude of daytime and/or nighttime urban heat islands - urban size, population density, building density, state of development, impervious fraction, eco-climatic setting. Specifically, we developed new satellite datasets and synthesizing these with existing satellite data into a global database of urban land surface parameters, used two MODIS land surface temperature products to generate time series of daytime and nighttime urban heat island effects for 30 large cities across the globe, and empirically analyzed these data to determine specifically which remote sensing-based characterizations of global urban areas have explanatory power with regard to both daytime and nighttime urban heat islands.

Improving Arctic Sea Ice Observations and Data Access to Support Advances in Sea Ice Forecasting

NASA Astrophysics Data System (ADS)

Farrell, S. L.

2017-12-01

The economic and strategic importance of the Arctic region is becoming apparent. One of the most striking and widely publicized changes underway is the declining sea ice cover. Since sea ice is a key component of the climate system, its ongoing loss has serious, and wide-ranging, socio-economic implications. Increasing year-to-year variability in the geographic location, concentration, and thickness of the Arctic ice cover will pose both challenges and opportunities. The sea ice research community must be engaged in sustained Arctic Observing Network (AON) initiatives so as to deliver fit-for-purpose remote sensing data products to a variety of stakeholders including Arctic communities, the weather forecasting and climate modeling communities, industry, local, regional and national governments, and policy makers. An example of engagement is the work currently underway to improve research collaborations between scientists engaged in obtaining and assessing sea ice observational data and those conducting numerical modeling studies and forecasting ice conditions. As part of the US AON, in collaboration with the Interagency Arctic Research Policy Committee (IARPC), we are developing a strategic framework within which observers and modelers can work towards the common goal of improved sea ice forecasting. Here, we focus on sea ice thickness, a key varaible of the Arctic ice cover. We describe multi-sensor, and blended, sea ice thickness data products under development that can be leveraged to improve model initialization and validation, as well as support data assimilation exercises. We will also present the new PolarWatch initiative (polarwatch.noaa.gov) and discuss efforts to advance access to remote sensing satellite observations and improve communication with Arctic stakeholders, so as to deliver data products that best address societal needs.

Vegetation productivity responses to drought on tribal lands in the four corners region of the Southwest USA

NASA Astrophysics Data System (ADS)

El-Vilaly, Mohamed Abd Salam; Didan, Kamel; Marsh, Stuart E.; van Leeuwen, Willem J. D.; Crimmins, Michael A.; Munoz, Armando Barreto

2018-03-01

For more than a decade, the Four Corners Region has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources while exacerbating soil erosion. These persistent droughts threaten ecosystem services, agriculture, and livestock activities, and expose the hypersensitivity of this region to inter-annual climate variability and change. Much of the intermountainWestern United States has sparse climate and vegetation monitoring stations, making fine-scale drought assessments difficult. Remote sensing data offers the opportunity to assess the impacts of the recent droughts on vegetation productivity across these areas. Here, we propose a drought assessment approach that integrates climate and topographical data with remote sensing vegetation index time series. Multisensor Normalized Difference Vegetation Index (NDVI) time series data from 1989 to 2010 at 5.6 km were analyzed to characterize the vegetation productivity changes and responses to the ongoing drought. A multi-linear regression was applied to metrics of vegetation productivity derived from the NDVI time series to detect vegetation productivity, an ecosystem service proxy, and changes. The results show that around 60.13% of the study area is observing a general decline of greenness ( p<0.05), while 3.87% show an unexpected green up, with the remaining areas showing no consistent change. Vegetation in the area show a significant positive correlation with elevation and precipitation gradients. These results, while, confirming the region's vegetation decline due to drought, shed further light on the future directions and challenges to the region's already stressed ecosystems. Whereas the results provide additional insights into this isolated and vulnerable region, the drought assessment approach used in this study may be adapted for application in other regions where surface-based climate and vegetation monitoring record is spatially and temporally limited.

MACS-Mar: a real-time remote sensing system for maritime security applications

NASA Astrophysics Data System (ADS)

Brauchle, Jörg; Bayer, Steven; Hein, Daniel; Berger, Ralf; Pless, Sebastian

2018-04-01

The modular aerial camera system (MACS) is a development platform for optical remote sensing concepts, algorithms and special environments. For real-time services for maritime security (EMSec joint project), a new multi-sensor configuration MACS-Mar was realized. It consists of four co-aligned sensor heads in the visible RGB, near infrared (NIR, 700-950 nm), hyperspectral (HS, 450-900 nm) and thermal infrared (TIR, 7.5-14 µm) spectral range, a mid-cost navigation system, a processing unit and two data links. On-board image projection, cropping of redundant data and compression enable the instant generation of direct-georeferenced high-resolution image mosaics, automatic object detection, vectorization and annotation of floating objects on the water surface. The results were transmitted over a distance up to 50 km in real-time via narrow and broadband data links and were visualized in a maritime situation awareness system. For the automatic onboard detection of floating objects, a segmentation and classification workflow based on RGB, IR and TIR information was developed and tested. The completeness of the object detection in the experiment resulted in 95%, the correctness in 53%. Mostly, bright backwash of ships lead to an overestimation of the number of objects, further refinement using water homogeneity in the TIR, as implemented in the workflow, couldn't be carried out due to problems with the TIR sensor, else distinctly better results could have been expected. The absolute positional accuracy of the projected real-time imagery resulted in 2 m without postprocessing of images or navigation data, the relative measurement accuracy of distances is in the range of the image resolution, which is about 12 cm for RGB imagery in the EMSec experiment.

Exploring the potential of optical remote sensing for oil spill detection in shallow coastal waters--a case study in the Arabian Gulf.

PubMed

Zhao, Jun; Temimi, Marouane; Ghedira, Hosni; Hu, Chuanmin

2014-06-02

Remote sensing provides an effective tool for timely oil pollution response. In this paper, the spectral signature in the optical and infrared domains of oil slicks observed in shallow coastal waters of the Arabian Gulf was investigated with MODIS, MERIS, and Landsat data. Images of the Floating Algae Index (FAI) and estimates of sea currents from hydrodynamic models supported the multi-sensor oil tracking technique. Scenes with and without sunglint were studied as the spectral signature of oil slicks in the optical domain depends upon the viewing geometry and the solar angle in addition to the type of oil and its thickness. Depending on the combination of those factors, oil slicks may exhibit dark or bright contrasts with respect to oil-free waters. Three oil spills events were thoroughly analyzed, namely, those detected on May 26 2000 by Landsat 7 ETM + and MODIS/Terra, on October 21 2007 by MERIS and MODIS, and on August 17 2013 by Landsat 8 and MODIS/Aqua. The oil slick with bright contrast observed by Landsat 7 ETM + on May 26 2000 showed lower temperature than oil-free areas. The spectral Rayleigh-corrected reflectance (R(rc)) signature of oil-covered areas indicated higher variability due to differences in oil fractions while the R(rc) spectra of the oil-free area were persistent. Combined with RGB composites, FAI images showed potentials in differentiating oil slicks from algal blooms. Ocean circulation and wind data were used to track oil slicks and forecast their potential landfall. The developed oil spill maps were in agreement with official records. The synergistic use of satellite observations and hydrodynamic modeling is recommended for establishing an early warning and decision support system for oil pollution response.

Fusion of spatio-temporal UAV and proximal sensing data for an agricultural decision support system

NASA Astrophysics Data System (ADS)

Katsigiannis, P.; Galanis, G.; Dimitrakos, A.; Tsakiridis, N.; Kalopesas, C.; Alexandridis, T.; Chouzouri, A.; Patakas, A.; Zalidis, G.

2016-08-01

Over the last few years, multispectral and thermal remote sensing imagery from unmanned aerial vehicles (UAVs) has found application in agriculture and has been regarded as a means of field data collection and crop condition monitoring source. The integration of information derived from the analysis of these remotely sensed data into agricultural management applications facilitates and aids the stakeholder's decision making. Whereas agricultural decision support systems (DSS) have long been utilised in farming applications, there are still critical gaps to be addressed; as the current approach often neglects the plant's level information and lacks the robustness to account for the spatial and temporal variability of environmental parameters within agricultural systems. In this paper, we demonstrate the use of a custom built autonomous UAV platform in providing critical information for an agricultural DSS. This hexacopter UAV bears two cameras which can be triggered simultaneously and can capture both the visible, near-infrared (VNIR) and the thermal infrared (TIR) wavelengths. The platform was employed for the rapid extraction of the normalized difference vegetation index (NDVI) and the crop water stress index (CWSI) of three different plantations, namely a kiwi, a pomegranate, and a vine field. The simultaneous recording of these two complementary indices and the creation of maps was advantageous for the accurate assessment of the plantation's status. Fusion of UAV and soil scanner system products pinpointed the necessity for adjustment of the irrigation management applied. It is concluded that timely CWSI and NDVI measures retrieved for different crop growing stages can provide additional information and can serve as a tool to support the existing irrigation DSS that had so far been exclusively based on telemetry data from soil and agrometeorological sensors. Additionally, the use of the multi-sensor UAV was found to be beneficial in collecting timely, spatio-temporal information for the fusion with ground-based proximal sensing data. This research work was designed and deployed in the frame of the project "AGRO_LESS: Joint reference strategies for rural activities of reduced inputs".

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

REMOTE SENSING TECHNOLOGIES APPLICATIONS RESEARCH

EPA Science Inventory

Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...

Assessment of Hyperspectral and SAR Remote Sensing for Solid Waste Landfill Management

NASA Astrophysics Data System (ADS)

Ottavianelli, Giuseppe; Hobbs, Stephen; Smith, Richard; Bruno, Davide

2005-06-01

Globally, waste management is one of the most critical environmental concerns that modern society is facing. Controlled disposal to land (landfill) is currently important, and due to the potentially harmful effects of gas emissions and leachate land contamination, the monitoring of a landfill is inherent in all phases of the site's life cycle. Data from satellite platforms can provide key support to a number of landfill management and monitoring practices, potentially reducing operational costs and hazards, and meeting the challenges of the future waste management agenda.The few previous studies performed show the value of EO data for mapping landcover around landfills and monitoring vegetation health. However, these were largely qualitative studies limited to single sensor types. The review of these studies highlights three key aspects. Firstly, with regard to leachate and gas monitoring, space-borne remote sensing has not proved to be a valid tool for an accurate quantitative analysis, it can only support ground remediation efforts based on the expertise of the visual interpreter and the knowledge of the landfill operator. Secondly, the additional research that focuses on landfill detection concentrates only on the images' data dimension (spatial and spectral), paying less attention to the sensor-independent bio- and geo-physical variables and the modelling of remote sensing physical principles for both active and restored landfill sites. These studies show some ambiguity in their results and additional aerial images or ground truth visits are always required to support the results. Thirdly, none of the studies explores the potential of Synthetic Aperture Radar (SAR) remote sensing and SAR interferometric processing to achieve a more robust automatic detection algorithm and extract additional information and knowledge for landfill management.Based on our previous work with ERS radar images and SAR interferometry, expertise in the waste management sector, and practical knowledge of landfill management practices, we propose to evaluate the use of hyperspectral and radar images for landfill monitoring and management. CHRIS offers hyperspectral data of commensurate spatial resolution with Envisat radarimages and thus appears ideally suited for studies using multi-sensor data fusion.The goal of the research is to identify practical ways in which EO data can support landfill management and monitoring, providing quantitative data where possible. Our objectives (based on fieldwork in UK landfills) are (1) to develop robust methods of detecting and mapping landfill sites, (2) to correlate EO data with on-site operational procedures, and (3) to investigate data fusion techniques based on our findings with the separate sensors. Dissemination of the findings will be through scientific journals, professional waste management publications and workshops. It is expected that the research will help the development of techniques which could be applied to monitor waste disposal to land beyond the UK scope of this study, including global monitoring.

A Self-Sustained Wireless Multi-Sensor Platform Integrated with Printable Organic Sensors for Indoor Environmental Monitoring

PubMed Central

Wu, Chun-Chang; Chuang, Wen-Yu; Wu, Ching-Da; Su, Yu-Cheng; Huang, Yung-Yang; Huang, Yang-Jing; Peng, Sheng-Yu; Yu, Shih-An; Lin, Chih-Ting; Lu, Shey-Shi

2017-01-01

A self-sustained multi-sensor platform for indoor environmental monitoring is proposed in this paper. To reduce the cost and power consumption of the sensing platform, in the developed platform, organic materials of PEDOT:PSS and PEDOT:PSS/EB-PANI are used as the sensing films for humidity and CO2 detection, respectively. Different from traditional gas sensors, these organic sensing films can operate at room temperature without heating processes or infrared transceivers so that the power consumption of the developed humidity and the CO2 sensors can be as low as 10 μW and 5 μW, respectively. To cooperate with these low-power sensors, a Complementary Metal-Oxide-Semiconductor (CMOS) system-on-chip (SoC) is designed to amplify and to read out multiple sensor signals with low power consumption. The developed SoC includes an analog-front-end interface circuit (AFE), an analog-to-digital convertor (ADC), a digital controller and a power management unit (PMU). Scheduled by the digital controller, the sensing circuits are power gated with a small duty-cycle to reduce the average power consumption to 3.2 μW. The designed PMU converts the power scavenged from a dye sensitized solar cell (DSSC) module into required supply voltages for SoC circuits operation under typical indoor illuminance conditions. To our knowledge, this is the first multiple environmental parameters (Temperature/CO2/Humidity) sensing platform that demonstrates a true self-powering functionality for long-term operations. PMID:28353680

A Self-Sustained Wireless Multi-Sensor Platform Integrated with Printable Organic Sensors for Indoor Environmental Monitoring.

PubMed

Wu, Chun-Chang; Chuang, Wen-Yu; Wu, Ching-Da; Su, Yu-Cheng; Huang, Yung-Yang; Huang, Yang-Jing; Peng, Sheng-Yu; Yu, Shih-An; Lin, Chih-Ting; Lu, Shey-Shi

2017-03-29

A self-sustained multi-sensor platform for indoor environmental monitoring is proposed in this paper. To reduce the cost and power consumption of the sensing platform, in the developed platform, organic materials of PEDOT:PSS and PEDOT:PSS/EB-PANI are used as the sensing films for humidity and CO₂ detection, respectively. Different from traditional gas sensors, these organic sensing films can operate at room temperature without heating processes or infrared transceivers so that the power consumption of the developed humidity and the CO₂ sensors can be as low as 10 μW and 5 μW, respectively. To cooperate with these low-power sensors, a Complementary Metal-Oxide-Semiconductor (CMOS) system-on-chip (SoC) is designed to amplify and to read out multiple sensor signals with low power consumption. The developed SoC includes an analog-front-end interface circuit (AFE), an analog-to-digital convertor (ADC), a digital controller and a power management unit (PMU). Scheduled by the digital controller, the sensing circuits are power gated with a small duty-cycle to reduce the average power consumption to 3.2 μW. The designed PMU converts the power scavenged from a dye sensitized solar cell (DSSC) module into required supply voltages for SoC circuits operation under typical indoor illuminance conditions. To our knowledge, this is the first multiple environmental parameters (Temperature/CO₂/Humidity) sensing platform that demonstrates a true self-powering functionality for long-term operations.

Tunnel-Site Selection by Remote Sensing Techniques

DTIC Science & Technology

A study of the role of remote sensing for geologic reconnaissance for tunnel-site selection was commenced. For this study, remote sensing was defined...conventional remote sensing . Future research directions are suggested, and the extension of remote sensing to include airborne passive microwave

System and method for evaluating wind flow fields using remote sensing devices

DOEpatents

Schroeder, John; Hirth, Brian; Guynes, Jerry

2016-12-13

The present invention provides a system and method for obtaining data to determine one or more characteristics of a wind field using a first remote sensing device and a second remote sensing device. Coordinated data is collected from the first and second remote sensing devices and analyzed to determine the one or more characteristics of the wind field. The first remote sensing device is positioned to have a portion of the wind field within a first scanning sector of the first remote sensing device. The second remote sensing device is positioned to have the portion of the wind field disposed within a second scanning sector of the second remote sensing device.

Exploring Models and Data for Remote Sensing Image Caption Generation

NASA Astrophysics Data System (ADS)

Lu, Xiaoqiang; Wang, Binqiang; Zheng, Xiangtao; Li, Xuelong

2018-04-01

Inspired by recent development of artificial satellite, remote sensing images have attracted extensive attention. Recently, noticeable progress has been made in scene classification and target detection.However, it is still not clear how to describe the remote sensing image content with accurate and concise sentences. In this paper, we investigate to describe the remote sensing images with accurate and flexible sentences. First, some annotated instructions are presented to better describe the remote sensing images considering the special characteristics of remote sensing images. Second, in order to exhaustively exploit the contents of remote sensing images, a large-scale aerial image data set is constructed for remote sensing image caption. Finally, a comprehensive review is presented on the proposed data set to fully advance the task of remote sensing caption. Extensive experiments on the proposed data set demonstrate that the content of the remote sensing image can be completely described by generating language descriptions. The data set is available at https://github.com/201528014227051/RSICD_optimal

Research on active imaging information transmission technology of satellite borne quantum remote sensing

NASA Astrophysics Data System (ADS)

Bi, Siwen; Zhen, Ming; Yang, Song; Lin, Xuling; Wu, Zhiqiang

2017-08-01

According to the development and application needs of Remote Sensing Science and technology, Prof. Siwen Bi proposed quantum remote sensing. Firstly, the paper gives a brief introduction of the background of quantum remote sensing, the research status and related researches at home and abroad on the theory, information mechanism and imaging experiments of quantum remote sensing and the production of principle prototype.Then, the quantization of pure remote sensing radiation field, the state function and squeezing effect of quantum remote sensing radiation field are emphasized. It also describes the squeezing optical operator of quantum light field in active imaging information transmission experiment and imaging experiments, achieving 2-3 times higher resolution than that of coherent light detection imaging and completing the production of quantum remote sensing imaging prototype. The application of quantum remote sensing technology can significantly improve both the signal-to-noise ratio of information transmission imaging and the spatial resolution of quantum remote sensing .On the above basis, Prof.Bi proposed the technical solution of active imaging information transmission technology of satellite borne quantum remote sensing, launched researches on its system composition and operation principle and on quantum noiseless amplifying devices, providing solutions and technical basis for implementing active imaging information technology of satellite borne Quantum Remote Sensing.

Introduction to the physics and techniques of remote sensing

NASA Technical Reports Server (NTRS)

Elachi, Charles

1987-01-01

This book presents a comprehensive overview of the basics behind remote-sensing physics, techniques, and technology. The physics of wave/matter interactions, techniques of remote sensing across the electromagnetic spectrum, and the concepts behind remote sensing techniques now established and future ones under development are discussed. Applications of remote sensing are described for a wide variety of earth and planetary atmosphere and surface sciences. Solid surface sensing across the electromagnetic spectrum, ocean surface sensing, basic principles of atmospheric sensing and radiative transfer, and atmospheric remote sensing in the microwave, millimeter, submillimeter, and infrared regions are examined.

Multi-Sensor Methods for Mobile Radar Motion Capture and Compensation

NASA Astrophysics Data System (ADS)

Nakata, Robert

Remote sensing has many applications, including surveying and mapping, geophysics exploration, military surveillance, search and rescue and counter-terrorism operations. Remote sensor systems typically use visible image, infrared or radar sensors. Camera based image sensors can provide high spatial resolution but are limited to line-of-sight capture during daylight. Infrared sensors have lower resolution but can operate during darkness. Radar sensors can provide high resolution motion measurements, even when obscured by weather, clouds and smoke and can penetrate walls and collapsed structures constructed with non-metallic materials up to 1 m to 2 m in depth depending on the wavelength and transmitter power level. However, any platform motion will degrade the target signal of interest. In this dissertation, we investigate alternative methodologies to capture platform motion, including a Body Area Network (BAN) that doesn't require external fixed location sensors, allowing full mobility of the user. We also investigated platform stabilization and motion compensation techniques to reduce and remove the signal distortion introduced by the platform motion. We evaluated secondary ultrasonic and radar sensors to stabilize the platform resulting in an average 5 dB of Signal to Interference Ratio (SIR) improvement. We also implemented a Digital Signal Processing (DSP) motion compensation algorithm that improved the SIR by 18 dB on average. These techniques could be deployed on a quadcopter platform and enable the detection of respiratory motion using an onboard radar sensor.

Multidate, multisensor remote sensing reveals high density of carbon-rich mountain peatlands in the páramo of Ecuador.

PubMed

Hribljan, John A; Suarez, Esteban; Bourgeau-Chavez, Laura; Endres, Sarah; Lilleskov, Erik A; Chimbolema, Segundo; Wayson, Craig; Serocki, Eleanor; Chimner, Rodney A

2017-12-01

Tropical peatlands store a significant portion of the global soil carbon (C) pool. However, tropical mountain peatlands contain extensive peat soils that have yet to be mapped or included in global C estimates. This lack of data hinders our ability to inform policy and apply sustainable management practices to these peatlands that are experiencing unprecedented high rates of land use and land cover change. Rapid large-scale mapping activities are urgently needed to quantify tropical wetland extent and rate of degradation. We tested a combination of multidate, multisensor radar and optical imagery (Landsat TM/PALSAR/RADARSAT-1/TPI image stack) for detecting peatlands in a 2715 km 2 area in the high elevation mountains of the Ecuadorian páramo. The map was combined with an extensive soil coring data set to produce the first estimate of regional peatland soil C storage in the páramo. Our map displayed a high coverage of peatlands (614 km 2 ) containing an estimated 128.2 ± 9.1 Tg of peatland belowground soil C within the mapping area. Scaling-up to the country level, páramo peatlands likely represent less than 1% of the total land area of Ecuador but could contain as much as ~23% of the above- and belowground vegetation C stocks in Ecuadorian forests. These mapping approaches provide an essential methodological improvement applicable to mountain peatlands across the globe, facilitating mapping efforts in support of effective policy and sustainable management, including national and global C accounting and C management efforts. Published 2017. This article is a U.S. Government work and is in the public domain in the USA.

An approach for combining airborne LiDAR and high-resolution aerial color imagery using Gaussian processes

NASA Astrophysics Data System (ADS)

Liu, Yansong; Monteiro, Sildomar T.; Saber, Eli

2015-10-01

Changes in vegetation cover, building construction, road network and traffic conditions caused by urban expansion affect the human habitat as well as the natural environment in rapidly developing cities. It is crucial to assess these changes and respond accordingly by identifying man-made and natural structures with accurate classification algorithms. With the increase in use of multi-sensor remote sensing systems, researchers are able to obtain a more complete description of the scene of interest. By utilizing multi-sensor data, the accuracy of classification algorithms can be improved. In this paper, we propose a method for combining 3D LiDAR point clouds and high-resolution color images to classify urban areas using Gaussian processes (GP). GP classification is a powerful non-parametric classification method that yields probabilistic classification results. It makes predictions in a way that addresses the uncertainty of real world. In this paper, we attempt to identify man-made and natural objects in urban areas including buildings, roads, trees, grass, water and vehicles. LiDAR features are derived from the 3D point clouds and the spatial and color features are extracted from RGB images. For classification, we use the Laplacian approximation for GP binary classification on the new combined feature space. The multiclass classification has been implemented by using one-vs-all binary classification strategy. The result of applying support vector machines (SVMs) and logistic regression (LR) classifier is also provided for comparison. Our experiments show a clear improvement of classification results by using the two sensors combined instead of each sensor separately. Also we found the advantage of applying GP approach to handle the uncertainty in classification result without compromising accuracy compared to SVM, which is considered as the state-of-the-art classification method.

[Thematic Issue: Remote Sensing.

ERIC Educational Resources Information Center

Howkins, John, Ed.

1978-01-01

Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…

75 FR 65304 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2010-10-22

... Commercial Remote Sensing (ACCRES); Request for Nominations AGENCY: National Oceanic and Atmospheric... Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was... Atmosphere, on matters relating to the U.S. commercial remote sensing industry and NOAA's activities to carry...

Literature relevant to remote sensing of water quality

NASA Technical Reports Server (NTRS)

Middleton, E. M.; Marcell, R. F.

1983-01-01

References relevant to remote sensing of water quality were compiled, organized, and cross-referenced. The following general categories were included: (1) optical properties and measurement of water characteristics; (2) interpretation of water characteristics by remote sensing, including color, transparency, suspended or dissolved inorganic matter, biological materials, and temperature; (3) application of remote sensing for water quality monitoring; (4) application of remote sensing according to water body type; and (5) manipulation, processing and interpretation of remote sensing digital water data.

Learning Methods of Remote Sensing In the 2013 Curriculum of Secondary School

NASA Astrophysics Data System (ADS)

Lili Somantri, Nandi

2016-11-01

The new remote sensing material included in the subjects of geography in the curriculum of 1994. For geography teachers generation of 90s and over who in college do not get the material remote sensing, for teaching is a tough matter. Most teachers only give a theoretical matter, and do not carry out practical reasons in the lack of facilities and infrastructure of computer laboratories. Therefore, in this paper studies the importance about the method or manner of teaching remote sensing material in schools. The purpose of this paper is 1) to explain the position of remote sensing material in the study of geography, 2) analyze the Geography Curriculum 2013 Subjects related to remote sensing material, 3) describes a method of teaching remote sensing material in schools. The method used in this paper is a descriptive analytical study supported by the literature. The conclusion of this paper that the position of remote sensing in the study of geography is a method or a way to obtain spatial data earth's surface. In the 2013 curriculum remote sensing material has been applied to the study of land use and transportation. Remote sensing methods of teaching must go through a practicum, which starts from the introduction of the theory of remote sensing, data extraction phase of remote sensing imagery to produce maps, both visually and digitally, field surveys, interpretation of test accuracy, and improved maps.

JPRS Report, Science & Technology, China, Remote Sensing Systems, Applications.

DTIC Science & Technology

1991-01-17

Partial Contents: Short Introduction to Nation’s Remote Sensing Units, Domestic Airborne Remote - Sensing System, Applications in Monitoring Natural...Disasters, Applications of Imagery From Experimental Satellites Launched in 1985, 1986, Current Status, Future Prospects for Domestic Remote - Sensing -Satellite...Ground Station, and Radar Remote - Sensing Technology Used to Monitor Yellow River Delta,

[A review on polarization information in the remote sensing detection].

PubMed

Gong, Jie-Qiong; Zhan, Hai-Gang; Liu, Da-Zhao

2010-04-01

Polarization is one of the inherent characteristics. Because the surface of the target structure, internal structure, and the angle of incident light are different, the earth's surface and any target in atmosphere under optical interaction process will have their own characteristic nature of polarization. Polarimetric characteristics of radiation energy from the targets are used in polarization remote sensing detection as detective information. Polarization remote sensing detection can get the seven-dimensional information of targets in complicated backgrounds, detect well-resolved outline of targets and low-reflectance region of objectives, and resolve the problems of atmospheric detection and identification camouflage detection which the traditional remote sensing detection can not solve, having good foreground in applications. This paper introduces the development of polarization information in the remote sensing detection from the following four aspects. The rationale of polarization remote sensing detection is the base of polarization remote sensing detection, so it is firstly introduced. Secondly, the present researches on equipments that are used in polarization remote sensing detection are particularly and completely expatiated. Thirdly, the present exploration of theoretical simulation of polarization remote sensing detection is well detailed. Finally, the authors present the applications research home and abroad of the polarization remote sensing detection technique in the fields of remote sensing, atmospheric sounding, sea surface and underwater detection, biology and medical diagnosis, astronomical observation and military, summing up the current problems in polarization remote sensing detection. The development trend of polarization remote sensing detection technology in the future is pointed out in order to provide a reference for similar studies.

Autocorrelation structure of convective rainfall in semiarid-arid climate derived from high-resolution X-Band radar estimates

NASA Astrophysics Data System (ADS)

Marra, Francesco; Morin, Efrat

2018-02-01

Small scale rainfall variability is a key factor driving runoff response in fast responding systems, such as mountainous, urban and arid catchments. In this paper, the spatial-temporal autocorrelation structure of convective rainfall is derived with extremely high resolutions (60 m, 1 min) using estimates from an X-Band weather radar recently installed in a semiarid-arid area. The 2-dimensional spatial autocorrelation of convective rainfall fields and the temporal autocorrelation of point-wise and distributed rainfall fields are examined. The autocorrelation structures are characterized by spatial anisotropy, correlation distances 1.5-2.8 km and rarely exceeding 5 km, and time-correlation distances 1.8-6.4 min and rarely exceeding 10 min. The observed spatial variability is expected to negatively affect estimates from rain gauges and microwave links rather than satellite and C-/S-Band radars; conversely, the temporal variability is expected to negatively affect remote sensing estimates rather than rain gauges. The presented results provide quantitative information for stochastic weather generators, cloud-resolving models, dryland hydrologic and agricultural models, and multi-sensor merging techniques.

Realtime Gas Emission Monitoring at Hazardous Sites Using a Distributed Point-Source Sensing Infrastructure

PubMed Central

Manes, Gianfranco; Collodi, Giovanni; Gelpi, Leonardo; Fusco, Rosanna; Ricci, Giuseppe; Manes, Antonio; Passafiume, Marco

2016-01-01

This paper describes a distributed point-source monitoring platform for gas level and leakage detection in hazardous environments. The platform, based on a wireless sensor network (WSN) architecture, is organised into sub-networks to be positioned in the plant’s critical areas; each sub-net includes a gateway unit wirelessly connected to the WSN nodes, hence providing an easily deployable, stand-alone infrastructure featuring a high degree of scalability and reconfigurability. Furthermore, the system provides automated calibration routines which can be accomplished by non-specialized maintenance operators without system reliability reduction issues. Internet connectivity is provided via TCP/IP over GPRS (Internet standard protocols over mobile networks) gateways at a one-minute sampling rate. Environmental and process data are forwarded to a remote server and made available to authenticated users through a user interface that provides data rendering in various formats and multi-sensor data fusion. The platform is able to provide real-time plant management with an effective; accurate tool for immediate warning in case of critical events. PMID:26805832

Performance evaluation of an asynchronous multisensor track fusion filter

NASA Astrophysics Data System (ADS)

Alouani, Ali T.; Gray, John E.; McCabe, D. H.

2003-08-01

Recently the authors developed a new filter that uses data generated by asynchronous sensors to produce a state estimate that is optimal in the minimum mean square sense. The solution accounts for communications delay between sensors platform and fusion center. It also deals with out of sequence data as well as latent data by processing the information in a batch-like manner. This paper compares, using simulated targets and Monte Carlo simulations, the performance of the filter to the optimal sequential processing approach. It was found that the new asynchronous Multisensor track fusion filter (AMSTFF) performance is identical to that of the extended sequential Kalman filter (SEKF), while the new filter updates its track at a much lower rate than the SEKF.

Cybernetic Basis and System Practice of Remote Sensing and Spatial Information Science

NASA Astrophysics Data System (ADS)

Tan, X.; Jing, X.; Chen, R.; Ming, Z.; He, L.; Sun, Y.; Sun, X.; Yan, L.

2017-09-01

Cybernetics provides a new set of ideas and methods for the study of modern science, and it has been fully applied in many areas. However, few people have introduced cybernetics into the field of remote sensing. The paper is based on the imaging process of remote sensing system, introducing cybernetics into the field of remote sensing, establishing a space-time closed-loop control theory for the actual operation of remote sensing. The paper made the process of spatial information coherently, and improved the comprehensive efficiency of the space information from acquisition, procession, transformation to application. We not only describes the application of cybernetics in remote sensing platform control, sensor control, data processing control, but also in whole system of remote sensing imaging process control. We achieve the information of output back to the input to control the efficient operation of the entire system. This breakthrough combination of cybernetics science and remote sensing science will improve remote sensing science to a higher level.

Near-earth orbital guidance and remote sensing

NASA Technical Reports Server (NTRS)

Powers, W. F.

1972-01-01

The curriculum of a short course in remote sensing and parameter optimization is presented. The subjects discussed are: (1) basics of remote sensing and the user community, (2) multivariant spectral analysis, (3) advanced mathematics and physics of remote sensing, (4) the atmospheric environment, (5) imaging sensing, and (6)nonimaging sensing. Mathematical models of optimization techniques are developed.

Operational programs in forest management and priority in the utilization of remote sensing

NASA Technical Reports Server (NTRS)

Douglass, R. W.

1978-01-01

A speech is given on operational remote sensing programs in forest management and the importance of remote sensing in forestry is emphasized. Forest service priorities in using remote sensing are outlined.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Thematic Conference on Geologic Remote Sensing, 8th, Denver, CO, Apr. 29-May 2, 1991, Proceedings. Vols. 1 & 2

NASA Technical Reports Server (NTRS)

1991-01-01

The proceedings contain papers discussing the state-of-the-art exploration, engineering, and environmental applications of geologic remote sensing, along with the research and development activities aimed at increasing the future capabilities of this technology. The following topics are addressed: spectral geology, U.S. and international hydrocarbon exporation, radar and thermal infrared remote sensing, engineering geology and hydrogeology, mineral exploration, remote sensing for marine and environmental applications, image processing and analysis, geobotanical remote sensing, and data integration and geographic information systems. Particular attention is given to spectral alteration mapping with imaging spectrometers, mapping the coastal plain of the Congo with airborne digital radar, applications of remote sensing techniques to the assessment of dam safety, remote sensing of ferric iron minerals as guides for gold exploration, principal component analysis for alteration mappping, and the application of remote sensing techniques for gold prospecting in the north Fujian province.

Methods of training the graduate level and professional geologist in remote sensing technology

NASA Technical Reports Server (NTRS)

Kolm, K. E.

1981-01-01

Requirements for a basic course in remote sensing to accommodate the needs of the graduate level and professional geologist are described. The course should stress the general topics of basic remote sensing theory, the theory and data types relating to different remote sensing systems, an introduction to the basic concepts of computer image processing and analysis, the characteristics of different data types, the development of methods for geological interpretations, the integration of all scales and data types of remote sensing in a given study, the integration of other data bases (geophysical and geochemical) into a remote sensing study, and geological remote sensing applications. The laboratories should stress hands on experience to reinforce the concepts and procedures presented in the lecture. The geologist should then be encouraged to pursue a second course in computer image processing and analysis of remotely sensed data.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1993-01-01

Progress report on remote sensing of Earth terrain covering the period from Jan. to June 1993 is presented. Areas of research include: radiative transfer model for active and passive remote sensing of vegetation canopy; polarimetric thermal emission from rough ocean surfaces; polarimetric passive remote sensing of ocean wind vectors; polarimetric thermal emission from periodic water surfaces; layer model with tandom spheriodal scatterers for remote sensing of vegetation canopy; application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated mie scatterers with size distributions and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Remote sensing by satellite - Technical and operational implications for international cooperation

NASA Technical Reports Server (NTRS)

Doyle, S. E.

1976-01-01

International cooperation in the U.S. Space Program is discussed and related to the NASA program for remote sensing of the earth. Satellite remote sensing techniques are considered along with the selection of the best sensors and wavelength bands. The technology of remote sensing satellites is considered with emphasis on the Landsat system configuration. Future aspects of remote sensing satellites are considered.

Hybrid Arrays for Chemical Sensing

NASA Astrophysics Data System (ADS)

Kramer, Kirsten E.; Rose-Pehrsson, Susan L.; Johnson, Kevin J.; Minor, Christian P.

In recent years, multisensory approaches to environment monitoring for chemical detection as well as other forms of situational awareness have become increasingly popular. A hybrid sensor is a multimodal system that incorporates several sensing elements and thus produces data that are multivariate in nature and may be significantly increased in complexity compared to data provided by single-sensor systems. Though a hybrid sensor is itself an array, hybrid sensors are often organized into more complex sensing systems through an assortment of network topologies. Part of the reason for the shift to hybrid sensors is due to advancements in sensor technology and computational power available for processing larger amounts of data. There is also ample evidence to support the claim that a multivariate analytical approach is generally superior to univariate measurements because it provides additional redundant and complementary information (Hall, D. L.; Linas, J., Eds., Handbook of Multisensor Data Fusion, CRC, Boca Raton, FL, 2001). However, the benefits of a multisensory approach are not automatically achieved. Interpretation of data from hybrid arrays of sensors requires the analyst to develop an application-specific methodology to optimally fuse the disparate sources of data generated by the hybrid array into useful information characterizing the sample or environment being observed. Consequently, multivariate data analysis techniques such as those employed in the field of chemometrics have become more important in analyzing sensor array data. Depending on the nature of the acquired data, a number of chemometric algorithms may prove useful in the analysis and interpretation of data from hybrid sensor arrays. It is important to note, however, that the challenges posed by the analysis of hybrid sensor array data are not unique to the field of chemical sensing. Applications in electrical and process engineering, remote sensing, medicine, and of course, artificial intelligence and robotics, all share the same essential data fusion challenges. The design of a hybrid sensor array should draw on this extended body of knowledge. In this chapter, various techniques for data preprocessing, feature extraction, feature selection, and modeling of sensor data will be introduced and illustrated with data fusion approaches that have been implemented in applications involving data from hybrid arrays. The example systems discussed in this chapter involve the development of prototype sensor networks for damage control event detection aboard US Navy vessels and the development of analysis algorithms to combine multiple sensing techniques for enhanced remote detection of unexploded ordnance (UXO) in both ground surveys and wide area assessments.

Remote sensing in operational range management programs in Western Canada

NASA Technical Reports Server (NTRS)

Thompson, M. D.

1977-01-01

A pilot program carried out in Western Canada to test remote sensing under semi-operational conditions and display its applicability to operational range management programs was described. Four agencies were involved in the program, two in Alberta and two in Manitoba. Each had different objectives and needs for remote sensing within its range management programs, and each was generally unfamiliar with remote sensing techniques and their applications. Personnel with experience and expertise in the remote sensing and range management fields worked with the agency personnel through every phase of the pilot program. Results indicate that these agencies have found remote sensing to be a cost effective tool and will begin to utilize remote sensing in their operational work during ensuing seasons.

Designing minimal space telerobotics systems for maximum performance

NASA Technical Reports Server (NTRS)

Backes, Paul G.; Long, Mark K.; Steele, Robert D.

1992-01-01

The design of the remote site of a local-remote telerobot control system is described which addresses the constraints of limited computational power available at the remote site control system while providing a large range of control capabilities. The Modular Telerobot Task Execution System (MOTES) provides supervised autonomous control, shared control and teleoperation for a redundant manipulator. The system is capable of nominal task execution as well as monitoring and reflex motion. The MOTES system is minimized while providing a large capability by limiting its functionality to only that which is necessary at the remote site and by utilizing a unified multi-sensor based impedance control scheme. A command interpreter similar to one used on robotic spacecraft is used to interpret commands received from the local site. The system is written in Ada and runs in a VME environment on 68020 processors and initially controls a Robotics Research K1207 7 degree of freedom manipulator.

Classification and data acquisition with incomplete data

NASA Astrophysics Data System (ADS)

Williams, David P.

In remote-sensing applications, incomplete data can result when only a subset of sensors (e.g., radar, infrared, acoustic) are deployed at certain regions. The limitations of single sensor systems have spurred interest in employing multiple sensor modalities simultaneously. For example, in land mine detection tasks, different sensor modalities are better-suited to capture different aspects of the underlying physics of the mines. Synthetic aperture radar sensors may be better at detecting surface mines, while infrared sensors may be better at detecting buried mines. By employing multiple sensor modalities to address the detection task, the strengths of the disparate sensors can be exploited in a synergistic manner to improve performance beyond that which would be achievable with either single sensor alone. When multi-sensor approaches are employed, however, incomplete data can be manifested. If each sensor is located on a separate platform ( e.g., aircraft), each sensor may interrogate---and hence collect data over---only partially overlapping areas of land. As a result, some data points may be characterized by data (i.e., features) from only a subset of the possible sensors employed in the task. Equivalently, this scenario implies that some data points will be missing features. Increasing focus in the future on using---and fusing data from---multiple sensors will make such incomplete-data problems commonplace. In many applications involving incomplete data, it is possible to acquire the missing data at a cost. In multi-sensor remote-sensing applications, data is acquired by deploying sensors to data points. Acquiring data is usually an expensive, time-consuming task, a fact that necessitates an intelligent data acquisition process. Incomplete data is not limited to remote-sensing applications, but rather, can arise in virtually any data set. In this dissertation, we address the general problem of classification when faced with incomplete data. We also address the closely related problem of active data acquisition, which develops a strategy to acquire missing features and labels that will most benefit the classification task. We first address the general problem of classification with incomplete data, maintaining the view that all data (i.e., information) is valuable. We employ a logistic regression framework within which we formulate a supervised classification algorithm for incomplete data. This principled, yet flexible, framework permits several interesting extensions that allow all available data to be utilized. One extension incorporates labeling error, which permits the usage of potentially imperfectly labeled data in learning a classifier. A second major extension converts the proposed algorithm to a semi-supervised approach by utilizing unlabeled data via graph-based regularization. Finally, the classification algorithm is extended to the case in which (image) data---from which features are extracted---are available from multiple resolutions. Taken together, this family of incomplete-data classification algorithms exploits all available data in a principled manner by avoiding explicit imputation. Instead, missing data is integrated out analytically with the aid of an estimated conditional density function (conditioned on the observed features). This feat is accomplished by invoking only mild assumptions. We also address the problem of active data acquisition by determining which missing data should be acquired to most improve performance. Specifically, we examine this data acquisition task when the data to be acquired can be either labels or features. The proposed approach is based on a criterion that accounts for the expected benefit of the acquisition. This approach, which is applicable for any general missing data problem, exploits the incomplete-data classification framework introduced in the first part of this dissertation. This data acquisition approach allows for the acquisition of both labels and features. Moreover, several types of feature acquisition are permitted, including the acquisition of individual or multiple features for individual or multiple data points, which may be either labeled or unlabeled. Furthermore, if different types of data acquisition are feasible for a given application, the algorithm will automatically determine the most beneficial type of data to acquire. Experimental results on both benchmark machine learning data sets and real (i.e., measured) remote-sensing data demonstrate the advantages of the proposed incomplete-data classification and active data acquisition algorithms.

PROCEEDINGS OF THE FOURTH SYMPOSIUM ON REMOTE SENSING OF ENVIRONMENT; 12, 13, 14 APRIL 1966.

DTIC Science & Technology

The symposium was conducted as part of a continuing program investigating the field of remote sensing , its potential in scientific research and...information on all aspects of remote sensing , with special emphasis on such topics as needs for remotely sensed data, data management, and the special... remote sensing programs, data acquisition, data analysis and application, and equipment design, were presented. (Author)

Remote sensing and image interpretation

NASA Technical Reports Server (NTRS)

Lillesand, T. M.; Kiefer, R. W. (Principal Investigator)

1979-01-01

A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.

Documenting Liquefaction Failures Using Satellite Remote Sensing and Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Oommen, T.; Baise, L. G.; Gens, R.; Prakash, A.; Gupta, R. P.

2009-12-01

Historically, earthquake induced liquefaction is known to have caused extensive damage around the world. Therefore, there is a compelling need to characterize and map liquefaction after a seismic event. Currently, after an earthquake event, field-based mapping of liquefaction is sporadic and limited due to inaccessibility, short life of the failures, difficulties in mapping large aerial extents, and lack of resources. We hypothesize that as liquefaction occurs in saturated granular soils due to an increase in pore pressure, the liquefaction related terrain changes should have an associated increase in soil moisture with respect to the surrounding non-liquefied regions. The increase in soil moisture affects the thermal emittance and, hence, change detection using pre- and post-event thermal infrared (TIR) imagery is suitable for identifying areas that have undergone post-earthquake liquefaction. Though change detection using TIR images gives the first indication of areas of liquefaction, the spatial resolution of TIR images is typically coarser than the resolution of corresponding visible, near-infrared (NIR), and shortwave infrared (SWIR) images. We hypothesize that liquefaction induced changes in the soil and associated surface effects cause textural and spectral changes in images acquired in the visible, NIR, and SWIR. Although these changes can be from various factors, a synergistic approach taking advantage of the thermal signature variation due to changing soil moisture condition, together with the spectral information from high resolution visible, NIR, and SWIR bands can help to narrow down the locations of post-event liquefaction for regional documentation. In this study, we analyze the applicability of combining various spectral bands from different satellites (Landsat, Terra-MISR, IRS-1C, and IRS-1D) for documenting liquefaction failures associated with the magnitude 7.6 earthquake that occurred in Bhuj, India, in 2001. We combine the various spectral bands by neighborhood correlation image analysis using an artificial intelligence algorithm called support vector machine to remotely identify and document liquefaction failures across a region; and assess the reliability and accuracy of the thermal remote sensing approach in documenting regional liquefaction failures. Finally, we present the applicability of the satellite data analyzed and appropriateness of a multisensor and multispectral approach for documenting liquefaction related failures.

Geotechnical applications of remote sensing and remote data transmission; Proceedings of the Symposium, Cocoa Beach, FL, Jan. 31-Feb. 1, 1986

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

Johnson, A.I.; Pettersson, C.B.

1988-01-01

Papers and discussions concerning the geotechnical applications of remote sensing and remote data transmission, sources of remotely sensed data, and glossaries of remote sensing and remote data transmission terms, acronyms, and abbreviations are presented. Aspects of remote sensing use covered include the significance of lineaments and their effects on ground-water systems, waste-site use and geotechnical characterization, the estimation of reservoir submerging losses using CIR aerial photographs, and satellite-based investigation of the significance of surficial deposits for surface mining operations. Other topics presented include the location of potential ground subsidence and collapse features in soluble carbonate rock, optical Fourier analysis ofmore » surface features of interest in geotechnical engineering, geotechnical applications of U.S. Government remote sensing programs, updating the data base for a Geographic Information System, the joint NASA/Geosat Test Case Project, the selection of remote data telemetry methods for geotechnical applications, the standardization of remote sensing data collection and transmission, and a comparison of airborne Goodyear electronic mapping system/SAR with satelliteborne Seasat/SAR radar imagery.« less

Education in Environmental Remote Sensing: Potentials and Problems.

ERIC Educational Resources Information Center

Kiefer, Ralph W.; Lillesand, Thomas M.

1983-01-01

Discusses remote sensing principles and applications and the status and needs of remote sensing education in the United States. A summary of the fundamental policy issues that will determine remote sensing's future role in environmental and resource managements is included. (Author/BC)

THE EPA REMOTE SENSING ARCHIVE

EPA Science Inventory

What would you do if you were faced with organizing 30 years of remote sensing projects that had been haphazardly stored at two separate locations for years then combined? The EPA Remote Sensing Archive, currently located in Las Vegas, Nevada. contains the remote sensing data and...

Research on remote sensing image pixel attribute data acquisition method in AutoCAD

NASA Astrophysics Data System (ADS)

Liu, Xiaoyang; Sun, Guangtong; Liu, Jun; Liu, Hui

2013-07-01

The remote sensing image has been widely used in AutoCAD, but AutoCAD lack of the function of remote sensing image processing. In the paper, ObjectARX was used for the secondary development tool, combined with the Image Engine SDK to realize remote sensing image pixel attribute data acquisition in AutoCAD, which provides critical technical support for AutoCAD environment remote sensing image processing algorithms.

Bibliography of Remote Sensing Techniques Used in Wetland Research.

DTIC Science & Technology

1993-01-01

remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.... Change detection, Wetland assessment, Remote sensing ,

Kite Aerial Photography as a Tool for Remote Sensing

ERIC Educational Resources Information Center

Sallee, Jeff; Meier, Lesley R.

2010-01-01

As humans, we perform remote sensing nearly all the time. This is because we acquire most of our information about our surroundings through the senses of sight and hearing. Whether viewed by the unenhanced eye or a military satellite, remote sensing is observing objects from a distance. With our current technology, remote sensing has become a part…

Remote sensing for detecting and mapping whitefly (Bemisia tabaci) infestations

USDA-ARS?s Scientific Manuscript database

Remote sensing technology has long been used for detecting insect infestations on agricultural crops. With recent advances in remote sensing sensors and other spatial information technologies such as Global Position Systems (GPS) and Geographic Information Systems (GIS), remote sensing is finding mo...

Reflections on Earth--Remote-Sensing Research from Your Classroom.

ERIC Educational Resources Information Center

Campbell, Bruce A.

2001-01-01

Points out the uses of remote sensing in different areas, and introduces the program "Reflections on Earth" which provides access to basic and instructional information on remote sensing to students and teachers. Introduces students to concepts related to remote sensing and measuring distances. (YDS)

Remote-Sensing Practice and Potential

DTIC Science & Technology

1974-05-01

Six essential processes that must be accomplished if use of a remote - sensing system is to result in useful information are defined as problem...to be useful in remote - sensing projects are described. An overview of the current state-of-the-art of remote sensing is presented.

History and future of remote sensing technology and education

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1980-01-01

A historical overview of the discovery and development of photography, related sciences, and remote sensing technology is presented. The role of education to date in the development of remote sensing is discussed. The probable future and potential of remote sensing and training is described.

Ten ways remote sensing can contribute to conservation

USGS Publications Warehouse

Rose, Robert A.; Byler, Dirck; Eastman, J. Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A.; Laporte, Nadine; Leidner, Allison K.; Leimgruber, Peter; Morisette, Jeffrey T.; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C.; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2014-01-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners’ use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions?

Ten ways remote sensing can contribute to conservation.

PubMed

Rose, Robert A; Byler, Dirck; Eastman, J Ron; Fleishman, Erica; Geller, Gary; Goetz, Scott; Guild, Liane; Hamilton, Healy; Hansen, Matt; Headley, Rachel; Hewson, Jennifer; Horning, Ned; Kaplin, Beth A; Laporte, Nadine; Leidner, Allison; Leimgruber, Peter; Morisette, Jeffrey; Musinsky, John; Pintea, Lilian; Prados, Ana; Radeloff, Volker C; Rowen, Mary; Saatchi, Sassan; Schill, Steve; Tabor, Karyn; Turner, Woody; Vodacek, Anthony; Vogelmann, James; Wegmann, Martin; Wilkie, David; Wilson, Cara

2015-04-01

In an effort to increase conservation effectiveness through the use of Earth observation technologies, a group of remote sensing scientists affiliated with government and academic institutions and conservation organizations identified 10 questions in conservation for which the potential to be answered would be greatly increased by use of remotely sensed data and analyses of those data. Our goals were to increase conservation practitioners' use of remote sensing to support their work, increase collaboration between the conservation science and remote sensing communities, identify and develop new and innovative uses of remote sensing for advancing conservation science, provide guidance to space agencies on how future satellite missions can support conservation science, and generate support from the public and private sector in the use of remote sensing data to address the 10 conservation questions. We identified a broad initial list of questions on the basis of an email chain-referral survey. We then used a workshop-based iterative and collaborative approach to whittle the list down to these final questions (which represent 10 major themes in conservation): How can global Earth observation data be used to model species distributions and abundances? How can remote sensing improve the understanding of animal movements? How can remotely sensed ecosystem variables be used to understand, monitor, and predict ecosystem response and resilience to multiple stressors? How can remote sensing be used to monitor the effects of climate on ecosystems? How can near real-time ecosystem monitoring catalyze threat reduction, governance and regulation compliance, and resource management decisions? How can remote sensing inform configuration of protected area networks at spatial extents relevant to populations of target species and ecosystem services? How can remote sensing-derived products be used to value and monitor changes in ecosystem services? How can remote sensing be used to monitor and evaluate the effectiveness of conservation efforts? How does the expansion and intensification of agriculture and aquaculture alter ecosystems and the services they provide? How can remote sensing be used to determine the degree to which ecosystems are being disturbed or degraded and the effects of these changes on species and ecosystem functions? © 2014 Society for Conservation Biology.

A satellite-driven, client-server hydro-economic model prototype for agricultural water management

NASA Astrophysics Data System (ADS)

Maneta, Marco; Kimball, John; He, Mingzhu; Payton Gardner, W.

2017-04-01

Anticipating agricultural water demand, land reallocation, and impact on farm revenues associated with different policy or climate constraints is a challenge for water managers and for policy makers. While current integrated decision support systems based on programming methods provide estimates of farmer reaction to external constraints, they have important shortcomings such as the high cost of data collection surveys necessary to calibrate the model, biases associated with inadequate farm sampling, infrequent model updates and recalibration, model overfitting, or their deterministic nature, among other problems. In addition, the administration of water supplies and the generation of policies that promote sustainable agricultural regions depend on more than one bureau or office. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. To overcome these limitations, we present a client-server, integrated hydro-economic modeling and observation framework driven by satellite remote sensing and other ancillary information from regional monitoring networks. The core of the framework is a stochastic data assimilation system that sequentially ingests remote sensing observations and corrects the parameters of the hydro-economic model at unprecedented spatial and temporal resolutions. An economic model of agricultural production, based on mathematical programming, requires information on crop type and extent, crop yield, crop transpiration and irrigation technology. A regional hydro-climatologic model provides biophysical constraints to an economic model of agricultural production with a level of detail that permits the study of the spatial impact of large- and small-scale water use decisions. Crop type and extent is obtained from the Cropland Data Layer (CDL), which is multi-sensor operational classification of crops maintained by the United States Department of Agriculture. Because this product is only available for the conterminous United States, the framework is currently only applicable in this region. To obtain information on crop phenology, productivity and transpiration at adequate spatial and temporal frequencies we blend high spatial resolution Landsat information with high temporal fidelity MODIS imagery. The result is a 30 m, 8-day fused dataset of crop greenness that is subsequently transformed into productivity and transpiration by adapting existing forest productivity and transpiration algorithms for agricultural applications. To ensure all involved agencies work with identical information and that end-users are sheltered from the computational burden of storing and processing remote sensing data, this modeling framework is integrated in a client-server architecture based on the Hydra platform (www.hydraplatform.org). Assimilation and processing of resource-intensive remote sensing information, as well as hydrologic and other ancillary data, occur on the server side. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. This architecture ensures that all agencies and teams involved in water management use the same, up-to-date information in their simulations.

A fully automatic tool to perform accurate flood mapping by merging remote sensing imagery and ancillary data

NASA Astrophysics Data System (ADS)

D'Addabbo, Annarita; Refice, Alberto; Lovergine, Francesco; Pasquariello, Guido

2016-04-01

Flooding is one of the most frequent and expansive natural hazard. High-resolution flood mapping is an essential step in the monitoring and prevention of inundation hazard, both to gain insight into the processes involved in the generation of flooding events, and from the practical point of view of the precise assessment of inundated areas. Remote sensing data are recognized to be useful in this respect, thanks to the high resolution and regular revisit schedules of state-of-the-art satellites, moreover offering a synoptic overview of the extent of flooding. In particular, Synthetic Aperture Radar (SAR) data present several favorable characteristics for flood mapping, such as their relative insensitivity to the meteorological conditions during acquisitions, as well as the possibility of acquiring independently of solar illumination, thanks to the active nature of the radar sensors [1]. However, flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground: the presence of many land cover types, each one with a particular signature in presence of flood, requires modelling the behavior of different objects in the scene in order to associate them to flood or no flood conditions [2]. Generally, the fusion of multi-temporal, multi-sensor, multi-resolution and/or multi-platform Earth observation image data, together with other ancillary information, seems to have a key role in the pursuit of a consistent interpretation of complex scenes. In the case of flooding, distance from the river, terrain elevation, hydrologic information or some combination thereof can add useful information to remote sensing data. Suitable methods, able to manage and merge different kind of data, are so particularly needed. In this work, a fully automatic tool, based on Bayesian Networks (BNs) [3] and able to perform data fusion, is presented. It supplies flood maps describing the dynamics of each analysed event, combining time series of images, acquired by different sensors, with ancillary information. Some experiments have been performed by combining multi-temporal SAR intensity images, InSAR coherence and optical data, with geomorphic and other ground information. The tool has been tested on different flood events occurred in the Basilicata region (Italy) during the last years, showing good capabilities of identification of a large area interested by the flood phenomenon, partially overcoming the obstacle constituted by the presence of scattering/coherence classes corresponding to different land cover types, which respond differently to the presence of water and to inundation evolution [1] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711-2722, 2014. [2] L. Pulvirenti et al., IEEE Trans. Geosci. Rem. Sens., Vol. PP, pp. 1- 13, 2015. [3] A. D'Addabbo et al., "A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data," IEEE Trans. Geosci. Rem. Sens., in press.

Role of remote sensing in documenting living resources

NASA Technical Reports Server (NTRS)

Wagner, P. E.; Anderson, R. R.; Brun, B.; Eisenberg, M.; Genys, J. B.; Lear, D. W., Jr.; Miller, M. H.

1978-01-01

Specific cases of known or potentially useful applications of remote sensing in assessing biological resources are discussed. It is concluded that the more usable remote sensing techniques relate to the measurement of population fluctuations in aquatic systems. Sensing of the flora and the fauna of the Bay is considered with emphasis on direct sensing of aquatic plant populations and of water quality. Recommendations for remote sensing projects are given.

Commercial future: making remote sensing a media event

NASA Astrophysics Data System (ADS)

Lurie, Ian

1999-12-01

The rapid growth of commercial remote sensing has made high quality digital sensing data widely available -- now, remote sensing must become and remain a strong, commercially viable industry. However, this new industry cannot survive without an educated consumer base. To access markets, remote sensing providers must make their product more accessible, both literally and figuratively: Potential customers must be able to find the data they require, when they require it, and they must understand the utility of the information available to them. The Internet and the World Wide Web offer the perfect medium to educate potential customers and to sell remote sensing data to those customers. A well-designed web presence can provide both an information center and a market place for companies offering their data for sale. A very high potential web-based market for remote sensing lies in media. News agencies, web sites, and a host of other visual media services can use remote sensing data to provide current, relevant information regarding news around the world. This paper will provide a model for promotion and sale of remote sensing data via the Internet.

77 FR 39220 - Advisory Committee on Commercial Remote Sensing (ACCRES); Charter Renewal

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-02

... Commercial Remote Sensing (ACCRES); Charter Renewal AGENCY: National Oceanic and Atmospheric Administration... Committee on Commercial Remote Sensing (ACCRES) was renewed on March 14, 2012. SUPPLEMENTARY INFORMATION: In... Commercial Remote Sensing (ACCRES) is in the public interest in connection with the performance of duties...

76 FR 66042 - Advisory Committee on Commercial Remote Sensing (ACCRES); Request for Nominations

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-25

... Commercial Remote Sensing (ACCRES); Request for Nominations ACTION: Notice requesting nominations for the Advisory Committee on Commercial Remote Sensing (ACCRES). SUMMARY: The Advisory Committee on Commercial Remote Sensing (ACCRES) was established to advise the Secretary of Commerce, through the Under Secretary...

An introduction to quantitative remote sensing. [data processing

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Russell, J.

1974-01-01

The quantitative approach to remote sensing is discussed along with the analysis of remote sensing data. Emphasis is placed on the application of pattern recognition in numerically oriented remote sensing systems. A common background and orientation for users of the LARS computer software system is provided.

Remote Sensing of Ecology, Biodiversity and Conservation: A Review from the Perspective of Remote Sensing Specialists

PubMed Central

Wang, Kai; Franklin, Steven E.; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS). PMID:22163432

Remote sensing of ecology, biodiversity and conservation: a review from the perspective of remote sensing specialists.

PubMed

Wang, Kai; Franklin, Steven E; Guo, Xulin; Cattet, Marc

2010-01-01

Remote sensing, the science of obtaining information via noncontact recording, has swept the fields of ecology, biodiversity and conservation (EBC). Several quality review papers have contributed to this field. However, these papers often discuss the issues from the standpoint of an ecologist or a biodiversity specialist. This review focuses on the spaceborne remote sensing of EBC from the perspective of remote sensing specialists, i.e., it is organized in the context of state-of-the-art remote sensing technology, including instruments and techniques. Herein, the instruments to be discussed consist of high spatial resolution, hyperspectral, thermal infrared, small-satellite constellation, and LIDAR sensors; and the techniques refer to image classification, vegetation index (VI), inversion algorithm, data fusion, and the integration of remote sensing (RS) and geographic information system (GIS).

Remote Sensing and Reflectance Profiling in Entomology.

PubMed

Nansen, Christian; Elliott, Norman

2016-01-01

Remote sensing describes the characterization of the status of objects and/or the classification of their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be benchtop based, and therefore acquired at a high spatial resolution, or airborne at lower spatial resolution to cover large areas. Despite important challenges, airborne remote sensing technologies will undoubtedly be of major importance in optimized management of agricultural systems in the twenty-first century. Benchtop remote sensing applications are becoming important in insect systematics and in phenomics studies of insect behavior and physiology. This review highlights how remote sensing influences entomological research by enabling scientists to nondestructively monitor how individual insects respond to treatments and ambient conditions. Furthermore, novel remote sensing technologies are creating intriguing interdisciplinary bridges between entomology and disciplines such as informatics and electrical engineering.

Remote Sensing in Geography in the New Millennium: Prospects, Challenges, and Opportunities

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Jensen, John R.; Morain, Stanley A.; Walsh, Stephen J.; Ridd, Merrill K.

1999-01-01

Remote sensing science contributes greatly to our understanding of the Earth's ecosystems and cultural landscapes. Almost all the natural and social sciences, including geography, rely heavily on remote sensing to provide quantitative, and indispensable spatial information. Many geographers have made significant contributions to remote sensing science since the 1970s, including the specification of advanced remote sensing systems, improvements in analog and digital image analysis, biophysical modeling, and terrain analysis. In fact, the Remote Sensing Specialty Group (RSSG) is one of the largest specialty groups within the AAG with over 500 members. Remote sensing in concert with a geographic information systems, offers much value to geography as both an incisive spatial-analytical tool and as a scholarly pursuit that adds to the body of geographic knowledge on the whole. The "power" of remote sensing as a research endeavor in geography lies in its capabilities for obtaining synoptic, near-real time data at many spatial and temporal scales, and in many regions of the electromagnetic spectrum - from microwave, to RADAR, to visible, and reflective and thermal infrared. In turn, these data present a vast compendium of information for assessing Earth attributes and characte6stics that are at the very core of geography. Here we revisit how remote sensing has become a fundamental and important tool for geographical research, and how with the advent of new and improved sensing systems to be launched in the near future, remote sensing will further advance geographical analysis in the approaching New Millennium.

Agricultural Production Monitoring in the Sahel Using Remote Sensing: Present Possibilities and Research Needs

DTIC Science & Technology

1993-01-01

during the agricultural season. Satellite remote sensing can contribute significantly to such a system by collecting information on crops and on...well as techniques to derive biophysical variables from remotely-sensed data. Finally, the integration of these remote - sensing techniques with crop

Method of determining forest production from remotely sensed forest parameters

DOEpatents

Corey, J.C.; Mackey, H.E. Jr.

1987-08-31

A method of determining forest production entirely from remotely sensed data in which remotely sensed multispectral scanner (MSS) data on forest 5 composition is combined with remotely sensed radar imaging data on forest stand biophysical parameters to provide a measure of forest production. A high correlation has been found to exist between the remotely sensed radar imaging data and on site measurements of biophysical 10 parameters such as stand height, diameter at breast height, total tree height, mean area per tree, and timber stand volume.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-01

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

remote - sensing reflectance) can be highly inaccurate if a spectrally constant value is applied (although errors can be reduced by carefully filtering measured raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with results compared with those from remote sensing models and from direct measurements. The agreement from different determinations suggests that reasonable results for remote sensing reflectance of clear

Field Data Collection: an Essential Element in Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Pettinger, L. R.

1971-01-01

Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.

Remote sensing and eLearning 2.0 for school education

NASA Astrophysics Data System (ADS)

Voss, Kerstin; Goetzke, Roland; Hodam, Henryk

2010-10-01

The "Remote Sensing in Schools" project aims at improving the integration of "Satellite remote sensing" into school teaching. Therefore, it is the project's overall objective to teach students in primary and secondary schools the basics and fields of application of remote sensing. Existing results show that many teachers are interested in remote sensing and at same time motivated to integrate it into their teaching. Despite the good intention, in the end, the implementation often fails due to the complexity and poor set-up of the information provided. Therefore, a comprehensive and well-structured learning platform on the topic of remote sensing is developed. The platform shall allow a structured introduction to the topic.

Remote sensing programs and courses in engineering and water resources

NASA Technical Reports Server (NTRS)

Kiefer, R. W.

1981-01-01

The content of typical basic and advanced remote sensing and image interpretation courses are described and typical remote sensing graduate programs of study in civil engineering and in interdisciplinary environmental remote sensing and water resources management programs are outlined. Ideally, graduate programs with an emphasis on remote sensing and image interpretation should be built around a core of five courses: (1) a basic course in fundamentals of remote sensing upon which the more specialized advanced remote sensing courses can build; (2) a course dealing with visual image interpretation; (3) a course dealing with quantitative (computer-based) image interpretation; (4) a basic photogrammetry course; and (5) a basic surveying course. These five courses comprise up to one-half of the course work required for the M.S. degree. The nature of other course work and thesis requirements vary greatly, depending on the department in which the degree is being awarded.

Remote sensing research in geographic education: An alternative view

NASA Technical Reports Server (NTRS)

Wilson, H.; Cary, T. K.; Goward, S. N.

1981-01-01

It is noted that within many geography departments remote sensing is viewed as a mere technique a student should learn in order to carry out true geographic research. This view inhibits both students and faculty from investigation of remotely sensed data as a new source of geographic knowledge that may alter our understanding of the Earth. The tendency is for geographers to accept these new data and analysis techniques from engineers and mathematicians without questioning the accompanying premises. This black-box approach hinders geographic applications of the new remotely sensed data and limits the geographer's contribution to further development of remote sensing observation systems. It is suggested that geographers contribute to the development of remote sensing through pursuit of basic research. This research can be encouraged, particularly among students, by demonstrating the links between geographic theory and remotely sensed observations, encouraging a healthy skepticism concerning the current understanding of these data.

Research on assessment and improvement method of remote sensing image reconstruction

NASA Astrophysics Data System (ADS)

Sun, Li; Hua, Nian; Yu, Yanbo; Zhao, Zhanping

2018-01-01

Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling which can improve efficiency. A method of two-dimensional principal component analysis (2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.

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

USDA-ARS?s Scientific Manuscript database

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

Application of the remote-sensing communication model to a time-sensitive wildfire remote-sensing system

Treesearch

Christopher D. Lippitt; Douglas A. Stow; Philip J. Riggan

2016-01-01

Remote sensing for hazard response requires a priori identification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication...

75 FR 32360 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and.... Abstract NOAA has established requirements for the licensing of private operators of remote-sensing space... Land Remote- Sensing Policy Act of 1992 and with the national security and international obligations of...

78 FR 44536 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and... for the licensing of private operators of remote-sensing space systems. The information in applications and subsequent reports is needed to ensure compliance with the Land Remote- Sensing Policy Act of...

Advancement of China’s Visible Light Remote Sensing Technology In Aerospace,

DTIC Science & Technology

1996-03-19

Aerospace visible light film systems were among the earliest space remote sensing systems to be developed in China. They have been applied very well...makes China the third nation in the world to master space remote sensing technology, it also puts recoverable remote sensing satellites among the first

Polarimetric passive remote sensing of periodic surfaces

NASA Technical Reports Server (NTRS)

Veysoglu, Murat E.; Yueh, H. A.; Shin, R. T.; Kong, J. A.

1991-01-01

The concept of polarimetry in active remote sensing is extended to passive remote sensing. The potential use of the third and fourth Stokes parameters U and V, which play an important role in polarimetric active remote sensing, is demonstrated for passive remote sensing. It is shown that, by the use of the reciprocity principle, the polarimetric parameters of passive remote sensing can be obtained through the solution of the associated direct scattering problem. These ideas are applied to study polarimetric passive remote sensing of periodic surfaces. The solution of the direct scattering problem is obtained by an integral equation formulation which involves evaluation of periodic Green's functions and normal derivative of those on the surface. Rapid evaluation of the slowly convergent series associated with these functions is observed to be critical for the feasibility of the method. New formulas, which are rapidly convergent, are derived for the calculation of these series. The study has shown that the brightness temperature of the Stokes parameter U can be significant in passive remote sensing. Values as high as 50 K are observed for certain configurations.

From planets to crops and back: Remote sensing makes sense

NASA Astrophysics Data System (ADS)

Mustard, John F.

2017-04-01

Remotely sensed data and the instruments that acquire them are core parts of Earth and planetary observation systems. They are used to quantify the Earth's interconnected systems, and remote sensing is the only way to get a daily, or more frequent, snapshot of the status of the Earth. It really is the Earth's stethoscope. In a similar manner remote sensing is the rock hammer of the planetary scientist and the only way comprehensive data sets can be acquired. To risk offending many remotely sensed data acquired across the electromagnetic spectrum, it is the tricorder to explore known and unknown planets. Arriving where we are today in the use of remotely sensed data in the solar system has been a continually evolving synergy between Earth observation, planetary exploration, and fundamental laboratory work.

Remote sensing of on-road vehicle emissions: Mechanism, applications and a case study from Hong Kong

NASA Astrophysics Data System (ADS)

Huang, Yuhan; Organ, Bruce; Zhou, John L.; Surawski, Nic C.; Hong, Guang; Chan, Edward F. C.; Yam, Yat Shing

2018-06-01

Vehicle emissions are a major contributor to air pollution in cities and have serious health impacts to their inhabitants. On-road remote sensing is an effective and economic tool to monitor and control vehicle emissions. In this review, the mechanism, accuracy, advantages and limitations of remote sensing were introduced. Then the applications and major findings of remote sensing were critically reviewed. It was revealed that the emission distribution of on-road vehicles was highly skewed so that the dirtiest 10% vehicles accounted for over half of the total fleet emissions. Such findings highlighted the importance and effectiveness of using remote sensing for in situ identification of high-emitting vehicles for further inspection and maintenance programs. However, the accuracy and number of vehicles affected by screening programs were greatly dependent on the screening criteria. Remote sensing studies showed that the emissions of gasoline and diesel vehicles were significantly reduced in recent years, with the exception of NOx emissions of diesel vehicles in spite of greatly tightened automotive emission regulations. Thirdly, the experience and issues of using remote sensing for identifying high-emitting vehicles in Hong Kong (where remote sensing is a legislative instrument for enforcement purposes) were reported. That was followed by the first time ever identification and discussion of the issue of frequent false detection of diesel high-emitters using remote sensing. Finally, the challenges and future research directions of on-road remote sensing were elaborated.

Remote sensing of natural resources: Quarterly literature review

NASA Technical Reports Server (NTRS)

1976-01-01

A quarterly review of technical literature concerning remote sensing techniques is presented. The format contains indexed and abstracted materials with emphasis on data gathering techniques performed or obtained remotely from space, aircraft, or ground-based stations. Remote sensor applications including the remote sensing of natural resources are presented.

Time Series Remote Sensing in Monitoring the Spatio-Temporal Dynamics of Plant Invasions: A Study of Invasive Saltcedar (Tamarix Spp.)

NASA Astrophysics Data System (ADS)

Diao, Chunyuan

In today's big data era, the increasing availability of satellite and airborne platforms at various spatial and temporal scales creates unprecedented opportunities to understand the complex and dynamic systems (e.g., plant invasion). Time series remote sensing is becoming more and more important to monitor the earth system dynamics and interactions. To date, most of the time series remote sensing studies have been conducted with the images acquired at coarse spatial scale, due to their relatively high temporal resolution. The construction of time series at fine spatial scale, however, is limited to few or discrete images acquired within or across years. The objective of this research is to advance the time series remote sensing at fine spatial scale, particularly to shift from discrete time series remote sensing to continuous time series remote sensing. The objective will be achieved through the following aims: 1) Advance intra-annual time series remote sensing under the pure-pixel assumption; 2) Advance intra-annual time series remote sensing under the mixed-pixel assumption; 3) Advance inter-annual time series remote sensing in monitoring the land surface dynamics; and 4) Advance the species distribution model with time series remote sensing. Taking invasive saltcedar as an example, four methods (i.e., phenological time series remote sensing model, temporal partial unmixing method, multiyear spectral angle clustering model, and time series remote sensing-based spatially explicit species distribution model) were developed to achieve the objectives. Results indicated that the phenological time series remote sensing model could effectively map saltcedar distributions through characterizing the seasonal phenological dynamics of plant species throughout the year. The proposed temporal partial unmixing method, compared to conventional unmixing methods, could more accurately estimate saltcedar abundance within a pixel by exploiting the adequate temporal signatures of saltcedar. The multiyear spectral angle clustering model could guide the selection of the most representative remotely sensed image for repetitive saltcedar mapping over space and time. Through incorporating spatial autocorrelation, the species distribution model developed in the study could identify the suitable habitats of saltcedar at a fine spatial scale and locate appropriate areas at high risk of saltcedar infestation. Among 10 environmental variables, the distance to the river and the phenological attributes summarized by the time series remote sensing were regarded as the most important. These methods developed in the study provide new perspectives on how the continuous time series can be leveraged under various conditions to investigate the plant invasion dynamics.

Forest mensuration with remote sensing: A retrospective and a vision for the future

Treesearch

Randolph H. Wynne

2004-01-01

Remote sensing, while occasionally oversold, has clear potential to reduce the overall cost of traditional forest inventories. Perhaps most important, some of the information needed for more intensive, rather than extensive, forest management is available from remote sensing. These new information needs may justify increased use and the increased cost of remote sensing...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 15 Commerce and Foreign Trade 3 2010-01-01 2010-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Remote Sensing: Analyzing Satellite Images to Create Higher Order Thinking Skills.

ERIC Educational Resources Information Center

Marks, Steven K.; And Others

1996-01-01

Presents a unit that uses remote-sensing images from satellites and other spacecraft to provide new perspectives of the earth and generate greater global awareness. Relates the levels of Bloom's hierarchy to different aspects of the remote sensing unit to confirm that the concepts and principles of remote sensing and related images belong in…

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 15 Commerce and Foreign Trade 3 2011-01-01 2011-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 15 Commerce and Foreign Trade 3 2012-01-01 2012-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 15 Commerce and Foreign Trade 3 2014-01-01 2014-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

15 CFR 960.12 - Data policy for remote sensing space systems.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 15 Commerce and Foreign Trade 3 2013-01-01 2013-01-01 false Data policy for remote sensing space... REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.12 Data policy for remote sensing space systems. (a) In accordance with the Act, if the U.S. Government...

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Applied Remote Sensing Program (ARSP)

NASA Technical Reports Server (NTRS)

Johnson, J. D.; Foster, K. E.; Mouat, D. A.; Miller, D. A.; Conn, J. S.

1976-01-01

The activities and accomplishments of the Applied Remote Sensing Program during FY 1975-1976 are reported. The principal objective of the Applied Remote Sensing Program continues to be designed projects having specific decision-making impacts as a principal goal. These projects are carried out in cooperation and collaboration with local, state and federal agencies whose responsibilities lie with planning, zoning and environmental monitoring and/or assessment in the application of remote sensing techniques. The end result of the projects is the use by the involved agencies of remote sensing techniques in problem solving.

Communicating remote sensing concepts in an interdisciplinary environment

NASA Technical Reports Server (NTRS)

Chung, R.

1981-01-01

Although remote sensing is currently multidisciplinary in its applications, many of its terms come from the engineering sciences, particularly from the field of pattern recognition. Scholars from fields such as the social sciences, botany, and biology, may experience initial difficulty with remote sensing terminology, even though parallel concepts exist in their own fields. Some parallel concepts and terminologies from nonengineering fields, which might enhance the understanding of remote sensing concepts in an interdisciplinary situation are identified. Feedbacks which this analogue strategy might have on remote sensing itself are explored.

People, Places and Pixels: Remote Sensing in the Service of Society

NASA Technical Reports Server (NTRS)

Lulla, Kamlesh

2003-01-01

What is the role of Earth remote sensing and other geospatial technologies in our society? Recent global events have brought into focus the role of geospatial science and technology such as remote sensing, GIS, GPS in assisting the professionals who are responsible for operations such as rescue and recovery of sites after a disaster or a terrorist act. This paper reviews the use of recent remote sensing products from satellites such as IKONOS in these efforts. Aerial and satellite imagery used in land mine detection has been evaluated and the results of this evaluation will be discussed. Synopsis of current and future ISS Earth Remote Sensing capabilities will be provided. The role of future missions in humanitarian use of remote sensing will be explored.

A Multimodal Database for a Home Remote Medical Care Application

NASA Astrophysics Data System (ADS)

Medjahed, Hamid; Istrate, Dan; Boudy, Jerome; Steenkeste, François; Baldinger, Jean-Louis; Dorizzi, Bernadette

The home remote monitoring systems aim to make a protective contribution to the well being of individuals (patients, elderly persons) requiring moderate amounts of support for independent living spaces, and improving their everyday life. Existing researches of these systems suffer from lack of experimental data and a standard medical database intended for their validation and improvement. This paper presents a multi-sensors environment for acquiring and recording a multimodal medical database, which includes physiological data (cardiac frequency, activity or agitation, posture, fall), environment sounds and localization data. It provides graphical interface functions to manage, process and index these data. The paper focuses on the system implementation, its usage and it points out possibilities for future work.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

PubMed

Hsu, Yu-Liang; Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-07-15

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology

PubMed Central

Chou, Po-Huan; Chang, Hsing-Cheng; Lin, Shyan-Lung; Yang, Shih-Chin; Su, Heng-Yi; Chang, Chih-Chien; Cheng, Yuan-Sheng; Kuo, Yu-Chen

2017-01-01

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment. PMID:28714884

Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

NASA Astrophysics Data System (ADS)

Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

2016-12-01

Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general indicators of environmental health, and can detect growth and changes in cities that are displacing historical agricultural zones.

Study on Net Primary Productivity over Complicated Mountainous Area based on Multi-Source Remote Sensing Data

NASA Astrophysics Data System (ADS)

Guan, X.; Shen, H.; Li, X.; Gan, W.

2017-12-01

Mountainous area hosts approximately a quarter of the global land surface, with complex climate and ecosystem conditions. More knowledge about mountainous ecosystem could highly advance our understanding of the global carbon cycle and climate change. Net Primary Productivity (NPP), the biomass increment of plants, is a widely used ecological indicator that can be obtained by remote sensing methods. However, limited by the defective characteristic of sensors, which cannot be long-term with enough spatial details synchronously, the mountainous NPP was far from being understood. In this study, a multi-sensor fusion framework was applied to synthesize a 1-km NPP series from 1982 to 2014 in mountainous southwest China, where elevation ranged from 76m to 6740m. The validation with field-measurements proved this framework greatly improved the accuracy of NPP (r=0.79, p<0.01). The detailed spatial and temporal analysis indicated that NPP variation trends changed from decreasing to increasing with the ascending elevation, as a result of a warmer and drier climate over the region. The correlation of NPP and temperature varied from negative to positive almost at the same elevation break-point of NPP trends, but the opposite for precipitation. This phenomenon was determined by the altitudinal and seasonally uneven allocation of climatic factors, as well as the downward run-off. What is more, it was indicated that the NPP variation showed three distinct stages at the year break-point of 1992 and 2002 over the region. The NPP in low-elevation area varied almost triple more drastic than the high-elevation area for all the three stages, due to the much greater change rate of precipitation. In summary, this study innovatively conducted a long-term and accurate NPP study on the not understood mountainous ecosystem with multi-source data, the framework and conclusions will be beneficial for the further cognition of global climate change.

Satellite remote sensing reveals a positive impact of living oyster reefs on microalgal biofilm development

NASA Astrophysics Data System (ADS)

Echappé, Caroline; Gernez, Pierre; Méléder, Vona; Jesus, Bruno; Cognie, Bruno; Decottignies, Priscilla; Sabbe, Koen; Barillé, Laurent

2018-02-01

Satellite remote sensing (RS) is routinely used for the large-scale monitoring of microphytobenthos (MPB) biomass in intertidal mudflats and has greatly improved our knowledge of MPB spatio-temporal variability and its potential drivers. Processes operating on smaller scales however, such as the impact of benthic macrofauna on MPB development, to date remain underinvestigated. In this study, we analysed the influence of wild Crassostrea gigas oyster reefs on MPB biofilm development using multispectral RS. A 30-year time series (1985-2015) combining high-resolution (30 m) Landsat and SPOT data was built in order to explore the relationship between C. gigas reefs and MPB spatial distribution and seasonal dynamics, using the normalized difference vegetation index (NDVI). Emphasis was placed on the analysis of a before-after control-impact (BACI) experiment designed to assess the effect of oyster killing on the surrounding MPB biofilms. Our RS data reveal that the presence of oyster reefs positively affects MPB biofilm development. Analysis of the historical time series first showed the presence of persistent, highly concentrated MPB patches around oyster reefs. This observation was supported by the BACI experiment which showed that killing the oysters (while leaving the physical reef structure, i.e. oyster shells, intact) negatively affected both MPB biofilm biomass and spatial stability around the reef. As such, our results are consistent with the hypothesis of nutrient input as an explanation for the MPB growth-promoting effect of oysters, whereby organic and inorganic matter released through oyster excretion and biodeposition stimulates MPB biomass accumulation. MPB also showed marked seasonal variations in biomass and patch shape, size and degree of aggregation around the oyster reefs. Seasonal variations in biomass, with higher NDVI during spring and autumn, were consistent with those observed on broader scales in other European mudflats. Our study provides the first multi-sensor RS satellite evidence of the promoting and structuring effect of oyster reefs on MPB biofilms.

The application of remote sensing techniques to the study of ophiolites

NASA Astrophysics Data System (ADS)

Khan, Shuhab D.; Mahmood, Khalid

2008-08-01

Satellite remote sensing methods are a powerful tool for detailed geologic analysis, especially in inaccessible regions of the earth's surface. Short-wave infrared (SWIR) bands are shown to provide spectral information bearing on the lithologic, structural, and geochemical character of rock bodies such as ophiolites, allowing for a more comprehensive assessment of the lithologies present, their stratigraphic relationships, and geochemical character. Most remote sensing data are widely available for little or no cost, along with user-friendly software for non-specialists. In this paper we review common remote sensing systems and methods that allow for the discrimination of solid rock (lithologic) components of ophiolite complexes and their structural relationships. Ophiolites are enigmatic rock bodies which associated with most, if not all, plate collision sutures. Ophiolites are ideal for remote sensing given their widely recognized diversity of lithologic types and structural relationships. Accordingly, as a basis for demonstrating the utility of remote sensing techniques, we briefly review typical ophiolites in the Tethyan tectonic belt. As a case study, we apply integrated remote sensing studies of a well-studied example, the Muslim Bagh ophiolite, located in Balochistan, western Pakistan. On this basis, we attempt to demonstrate how remote sensing data can validate and reconcile existing information obtained from field studies. The lithologic and geochemical diversity of Muslim Bagh are representative of Tethyan ophiolites. Despite it's remote location it has been extensively mapped and characterized by structural and geochemical studies, and is virtually free of vegetative cover. Moreover, integrating the remote sensing data with 'ground truth' information thus offers the potential of an improved template for interpreting remote sensing data sets of other ophiolites for which little or no field information is available.

Remote Sensing and Remote Control Activities in Europe and America: Part 2--Remote Sensing Ground Stations in Europe,

DTIC Science & Technology

1996-04-08

Development tasks and products of remote sensing ground stations in Europe are represented by the In-Sec Corporation and the Schlumberger Industries Corporation. The article presents the main products of these two corporations.

Spatial Distribution of Accuracy of Aerosol Retrievals from Multiple Satellite Sensors

NASA Technical Reports Server (NTRS)

Petrenko, Maksym; Ichoku, Charles

2012-01-01

Remote sensing of aerosols from space has been a subject of extensive research, with multiple sensors retrieving aerosol properties globally on a daily or weekly basis. The diverse algorithms used for these retrievals operate on different types of reflected signals based on different assumptions about the underlying physical phenomena. Depending on the actual retrieval conditions and especially on the geographical location of the sensed aerosol parcels, the combination of these factors might be advantageous for one or more of the sensors and unfavorable for others, resulting in disagreements between similar aerosol parameters retrieved from different sensors. In this presentation, we will demonstrate the use of the Multi-sensor Aerosol Products Sampling System (MAPSS) to analyze and intercompare aerosol retrievals from multiple spaceborne sensors, including MODIS (on Terra and Aqua), MISR, OMI, POLDER, CALIOP, and SeaWiFS. Based on this intercomparison, we are determining geographical locations where these products provide the greatest accuracy of the retrievals and identifying the products that are the most suitable for retrieval at these locations. The analyses are performed by comparing quality-screened satellite aerosol products to available collocated ground-based aerosol observations from the Aerosol Robotic Network (AERONET) stations, during the period of 2006-2010 when all the satellite sensors were operating concurrently. Furthermore, we will discuss results of a statistical approach that is applied to the collocated data to detect and remove potential data outliers that can bias the results of the analysis.

[Estimation of desert vegetation coverage based on multi-source remote sensing data].

PubMed

Wan, Hong-Mei; Li, Xia; Dong, Dao-Rui

2012-12-01

Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study areaAbstract: Taking the lower reaches of Tarim River in Xinjiang of Northwest China as study area and based on the ground investigation and the multi-source remote sensing data of different resolutions, the estimation models for desert vegetation coverage were built, with the precisions of different estimation methods and models compared. The results showed that with the increasing spatial resolution of remote sensing data, the precisions of the estimation models increased. The estimation precision of the models based on the high, middle-high, and middle-low resolution remote sensing data was 89.5%, 87.0%, and 84.56%, respectively, and the precisions of the remote sensing models were higher than that of vegetation index method. This study revealed the change patterns of the estimation precision of desert vegetation coverage based on different spatial resolution remote sensing data, and realized the quantitative conversion of the parameters and scales among the high, middle, and low spatial resolution remote sensing data of desert vegetation coverage, which would provide direct evidence for establishing and implementing comprehensive remote sensing monitoring scheme for the ecological restoration in the study area.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

An Approach of Registration between Remote Sensing Image and Electronic Chart Based on Coastal Line

NASA Astrophysics Data System (ADS)

Li, Ying; Yu, Shuiming; Li, Chuanlong

Remote sensing plays an important role marine oil spill emergency. In order to implement a timely and effective countermeasure, it is important to provide exact position of oil spills. Therefore it is necessary to match remote sensing image and electronic chart properly. Variance ordinarily exists between oil spill image and electronic chart, although geometric correction is applied to remote sensing image. It is difficult to find the steady control points on sea to make exact rectification of remote sensing image. An improved relaxation algorithm was developed for finding the control points along the coastline since oil spills occurs generally near the coast. A conversion function is created with the least square, and remote sensing image can be registered with the vector map based on this function. SAR image was used as the remote sensing data and shape format map as the electronic chart data. The results show that this approach can guarantee the precision of the registration, which is essential for oil spill monitoring.

The U.S. Geological Survey land remote sensing program

USGS Publications Warehouse

Saunders, T.; Feuquay, J.; Kelmelis, J.A.

2003-01-01

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Combining Sense and Intelligence for Smart Structures

NASA Technical Reports Server (NTRS)

2002-01-01

IFOS developed the I*Sense technology with assistance from a NASA Langley Research Center SBIR contract. NASA and IFOS collaborated to create sensing network designs that have high sensitivity, low power consumption, and significant potential for mass production. The joint- research effort led to the development of a module that is rugged, compact and light-weight, and immune to electromagnetic interference. These features make the I*Sense multisensor arrays favorable for smart structure applications, including smart buildings, bridges, highways, dams, power plants, ships, and oil tankers, as well as space vehicles, space stations, and other space structures. For instance, the system can be used as an early warning and detection device, with alarms being set to monitor the maximum allowable strain and stress values at various points of a given structure.

Microwave Remote Sensing Modeling of Ocean Surface Salinity and Winds Using an Empirical Sea Surface Spectrum

NASA Technical Reports Server (NTRS)

Yueh, Simon H.

2004-01-01

Active and passive microwave remote sensing techniques have been investigated for the remote sensing of ocean surface wind and salinity. We revised an ocean surface spectrum using the CMOD-5 geophysical model function (GMF) for the European Remote Sensing (ERS) C-band scatterometer and the Ku-band GMF for the NASA SeaWinds scatterometer. The predictions of microwave brightness temperatures from this model agree well with satellite, aircraft and tower-based microwave radiometer data. This suggests that the impact of surface roughness on microwave brightness temperatures and radar scattering coefficients of sea surfaces can be consistently characterized by a roughness spectrum, providing physical basis for using combined active and passive remote sensing techniques for ocean surface wind and salinity remote sensing.

Online catalog access and distribution of remotely sensed information

NASA Astrophysics Data System (ADS)

Lutton, Stephen M.

1997-09-01

Remote sensing is providing voluminous data and value added information products. Electronic sensors, communication electronics, computer software, hardware, and network communications technology have matured to the point where a distributed infrastructure for remotely sensed information is a reality. The amount of remotely sensed data and information is making distributed infrastructure almost a necessity. This infrastructure provides data collection, archiving, cataloging, browsing, processing, and viewing for applications from scientific research to economic, legal, and national security decision making. The remote sensing field is entering a new exciting stage of commercial growth and expansion into the mainstream of government and business decision making. This paper overviews this new distributed infrastructure and then focuses on describing a software system for on-line catalog access and distribution of remotely sensed information.

Remote Sensing and the Environment.

ERIC Educational Resources Information Center

Osmers, Karl

1991-01-01

Suggests using remote sensing technology to help students make sense of the natural world. Explains that satellite information allows observation of environmental changes over time. Identifies possible student projects based on remotely sensed data. Recommends obtaining the assistance of experts and seeking funding through effective project…

Monitoring tropical forest degradation using time series analysis of Landsat and Sentinel-2 data

NASA Astrophysics Data System (ADS)

Bullock, E.; Woodcock, C. E.

2017-12-01

Tropical forest loss is expected to be contribute 5 to 15% of anthropogenic carbon emissions in the coming century. The wide range of expected emissions is indicative of the large uncertainties that exist in the terrestrial carbon cycle. Total carbon loss from forest conversion consists of loss from deforestation plus loss from degradation. There have been significant improvements in the ability to relate plot-level estimates of carbon stocks to remote sensing-derived calculations of deforestation to estimate total carbon emissions from forest loss. These approaches, however, have been limited in their ability to assess the magnitude, extent, and overall impact of forest degradation. The causes of tropical degradation include selective logging, fuel wood collection, fires, and the development of forest plantations. This study demonstrates a newly developed methodology for detecting subtle changes in forest structure and condition using time series analysis of Landsat and Sentinel-2 data. The research shows how the ability to detect small changes in forest biomass, in addition to changes in forest composition, can be improved by incorporating historical context and multi-sensor data fusion. Results are demonstrated from two climatically unique tropical forests in Thailand and Brazil.

Identifying Corresponding Patches in SAR and Optical Images With a Pseudo-Siamese CNN

NASA Astrophysics Data System (ADS)

Hughes, Lloyd H.; Schmitt, Michael; Mou, Lichao; Wang, Yuanyuan; Zhu, Xiao Xiang

2018-05-01

In this letter, we propose a pseudo-siamese convolutional neural network (CNN) architecture that enables to solve the task of identifying corresponding patches in very-high-resolution (VHR) optical and synthetic aperture radar (SAR) remote sensing imagery. Using eight convolutional layers each in two parallel network streams, a fully connected layer for the fusion of the features learned in each stream, and a loss function based on binary cross-entropy, we achieve a one-hot indication if two patches correspond or not. The network is trained and tested on an automatically generated dataset that is based on a deterministic alignment of SAR and optical imagery via previously reconstructed and subsequently co-registered 3D point clouds. The satellite images, from which the patches comprising our dataset are extracted, show a complex urban scene containing many elevated objects (i.e. buildings), thus providing one of the most difficult experimental environments. The achieved results show that the network is able to predict corresponding patches with high accuracy, thus indicating great potential for further development towards a generalized multi-sensor key-point matching procedure. Index Terms-synthetic aperture radar (SAR), optical imagery, data fusion, deep learning, convolutional neural networks (CNN), image matching, deep matching

Secure Fusion Estimation for Bandwidth Constrained Cyber-Physical Systems Under Replay Attacks.

PubMed

Chen, Bo; Ho, Daniel W C; Hu, Guoqiang; Yu, Li; Bo Chen; Ho, Daniel W C; Guoqiang Hu; Li Yu; Chen, Bo; Ho, Daniel W C; Hu, Guoqiang; Yu, Li

2018-06-01

State estimation plays an essential role in the monitoring and supervision of cyber-physical systems (CPSs), and its importance has made the security and estimation performance a major concern. In this case, multisensor information fusion estimation (MIFE) provides an attractive alternative to study secure estimation problems because MIFE can potentially improve estimation accuracy and enhance reliability and robustness against attacks. From the perspective of the defender, the secure distributed Kalman fusion estimation problem is investigated in this paper for a class of CPSs under replay attacks, where each local estimate obtained by the sink node is transmitted to a remote fusion center through bandwidth constrained communication channels. A new mathematical model with compensation strategy is proposed to characterize the replay attacks and bandwidth constrains, and then a recursive distributed Kalman fusion estimator (DKFE) is designed in the linear minimum variance sense. According to different communication frameworks, two classes of data compression and compensation algorithms are developed such that the DKFEs can achieve the desired performance. Several attack-dependent and bandwidth-dependent conditions are derived such that the DKFEs are secure under replay attacks. An illustrative example is given to demonstrate the effectiveness of the proposed methods.

Earth Observation Satellites and Chinese Applications

NASA Astrophysics Data System (ADS)

Li, D.

In this talk existing and future Earth observation satellites are briefly described These satellites include meteorological satellites ocean satellites land resources satellites cartographic satellites and gravimetric satellites The Chinese government has paid and will pay more attention to and put more effort into enhancing Chinese earth observation satellite programs in the next fifteen years The utilization of these satellites will effectively help human beings to solve problems it faces in areas such as population natural resources and environment and natural hazards The author will emphasize the originality of the scientific and application aspects of the Chinese program in the field of Earth observations The main applications include early warning and prevention of forest fires flooding and drought disaster water and ocean ice disasters monitoring of landslides and urban subsidence investigation of land cover change and urban expansion as well as urban and rural planning The author introduces the most up-to-date technology used by Chinese scientists including fusion and integration of multi-sensor multi-platform optical and SAR data of remote sensing Most applications in China have obtained much support from related international organizations and universities around the world These applications in China are helpful for economic construction and the efficient improvement of living quality

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.; Newhouse, M. E.

1974-01-01

Remote sensing studies in Virginia and Chesapeake Bay areas to investigate soil and plant conditions via remote sensing technology are reported ant the results given. Remote sensing techniques and interactions are also discussed. Specific studies on the effects of soil moisture and organic matter on energy reflection of extensively occurring Sassafras soils are discussed. Greenhouse and field studies investigating the effects of chlorophyll content of Irish potatoes on infrared reflection are presented. Selected ground truth and environmental monitoring data are shown in summary form. Practical demonstrations of remote sensing technology in agriculture are depicted and future use areas are delineated.

Applications of remote sensing to watershed management

NASA Technical Reports Server (NTRS)

Rango, A.

1975-01-01

Aircraft and satellite remote sensing systems which are capable of contributing to watershed management are described and include: the multispectral scanner subsystem on LANDSAT and the basic multispectral camera array flown on high altitude aircraft such as the U-2. Various aspects of watershed management investigated by remote sensing systems are discussed. Major areas included are: snow mapping, surface water inventories, flood management, hydrologic land use monitoring, and watershed modeling. It is indicated that technological advances in remote sensing of hydrological data must be coupled with an expansion of awareness and training in remote sensing techniques of the watershed management community.

NASA Glenn OHIOVIEW FY01/02 Project

NASA Technical Reports Server (NTRS)

2003-01-01

The results of the research performed by the university principal investigators are herein compiled. OhioView's general goals were: 1) To increase remote sensing education for Ohio s undergraduate and graduate students, and also enhancing curriculum in the mathematics and science for K-12 students using the capabilities of remote sensing; 2) To conduct advanced research to develop novel remote sensing applications, i.e. to turn data into information for more applications; 3) To maximize the use of remote sensing technology by the general public through outreach and the development of tools for more user-friendly access to remote sensing data.

The availability of conventional forms of remotely sensed data

USGS Publications Warehouse

Sturdevant, James A.; Holm, Thomas M.

1982-01-01

For decades Federal and State agencies have been collecting aerial photographs of various film types and scales over parts of the United States. More recently, worldwide Earth resources data acquired by orbiting satellites have inundated the remote sensing community. Determining the types of remotely sensed data that are publicly available can be confusing to the land-resource manager, planner, and scientist. This paper is a summary of the more commonly used types of remotely sensed data (aircraft and satellite) and their public availability. Special emphasis is placed on the National High-Altitude Photography (NHAP) program and future remote-sensing satellites.

NASA's Applied Remote Sensing Training (ARSET) Webinar Series

Atmospheric Science Data Center

2016-07-12

NASA's Applied Remote Sensing Training (ARSET) Webinar Series Tuesday, July 12, 2016 ... you of a free training opportunity: Introduction to Remote Sensing for Air Quality Applications Webinar Series Beginning in ...

Tropospheric Passive Remote Sensing

NASA Technical Reports Server (NTRS)

Keafer, L. S., Jr. (Editor)

1982-01-01

The long term role of airborne/spaceborne passive remote sensing systems for tropospheric air quality research and the identification of technology advances required to improve the performance of passive remote sensing systems were discussed.

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Opportunities and problems in introducing or expanding the teaching of remote sensing in universities

NASA Technical Reports Server (NTRS)

Maxwell, E. L.

1980-01-01

The need for degree programs in remote sensing is considered. Any education program which claims to train remote sensing specialists must include expertise in the physical principles upon which remote sensing is based. These principles dictate the limits of engineering and design, computer analysis, photogrammetry, and photointerpretation. Faculty members must be hired to provide emphasis in those five areas.

Remote sensing of vegetation fires and its contribution to a fire management information system

Treesearch

Stephane P. Flasse; Simon N. Trigg; Pietro N. Ceccato; Anita H. Perryman; Andrew T. Hudak; Mark W. Thompson; Bruce H. Brockett; Moussa Drame; Tim Ntabeni; Philip E. Frost; Tobias Landmann; Johan L. le Roux

2004-01-01

In the last decade, research has proven that remote sensing can provide very useful support to fire managers. This chapter provides an overview of the types of information remote sensing can provide to the fire community. First, it considers fire management information needs in the context of a fire management information system. An introduction to remote sensing then...

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER... electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing

DTIC Science & Technology

2016-07-15

AFRL-AFOSR-JP-TR-2016-0068 Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing Hean-Teik...SUBTITLE Multi-scale Computational Electromagnetics for Phenomenology and Saliency Characterization in Remote Sensing 5a.  CONTRACT NUMBER 5b.  GRANT NUMBER...electromagnetics to the application in microwave remote sensing as well as extension of modelling capability with computational flexibility to study

Basic Remote Sensing Investigations for Beach Reconnaissance.

DTIC Science & Technology

Progress is reported on three tasks designed to develop remote sensing beach reconnaissance techniques applicable to the benthic, beach intertidal...and beach upland zones. Task 1 is designed to develop remote sensing indicators of important beach composition and physical parameters which will...ultimately prove useful in models to predict beach conditions. Task 2 is designed to develop remote sensing techniques for survey of bottom features in

Bridging the Scales from Field to Region with Practical Tools to Couple Time- and Space-Synchronized Data from Flux Towers and Networks with Proximal and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Burba, G. G.; Avenson, T.; Burkart, A.; Gamon, J. A.; Guan, K.; Julitta, T.; Pastorello, G.; Sakowska, K.

2017-12-01

Many hundreds of flux towers are presently operational as standalone projects and as parts of regional networks. However, the vast majority of these towers do not allow straightforward coupling with remote sensing (drone, aircraft, satellite, etc.) data, and even fewer have optical sensors for validation of remote sensing products, and upscaling from field to regional levels. In 2016-2017, new tools to collect, process, and share time-synchronized flux data from multiple towers were developed and deployed globally. Originally designed to automate site and data management, and to streamline flux data analysis, these tools allow relatively easy matching of tower data with remote sensing data: GPS-driven PTP time protocol synchronizes instrumentation within the station, different stations with each other, and all of these to remote sensing data to precisely align remote sensing and flux data in time Footprint size and coordinates computed and stored with flux data help correctly align tower flux footprints and drone, aircraft or satellite motion to precisely align optical and flux data in space Full snapshot of the remote sensing pixel can then be constructed, including leaf-level, ground optical sensor, and flux tower measurements from the same footprint area, closely coupled with the remote sensing measurements to help interpret remote sensing data, validate models, and improve upscaling Additionally, current flux towers can be augmented with advanced ground optical sensors and can use standard routines to deliver continuous products (e.g. SIF, PRI, NDVI, etc.) based on automated field spectrometers (e.g., FloX and RoX, etc.) and other optical systems. Several dozens of new towers already operational globally can be readily used for the proposed workflow. Over 500 active traditional flux towers can be updated to synchronize their data with remote sensing measurements. This presentation will show how the new tools are used by major networks, and describe how this approach can be utilized for matching remote sensing and tower data to aid in ground truthing, improve scientific interactions, and promote joint grant writing and other forms of collaboration between the flux and remote sensing communities.

Monitoring Crop Phenology and Growth Stages from Space: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Gao, F.; Anderson, M. C.; Mladenova, I. E.; Kustas, W. P.; Alfieri, J. G.

2014-12-01

Crop growth stages in concert with weather and soil moisture conditions can have a significant impact on crop yields. In the U.S., crop growth stages and conditions are reported by farmers at the county level. These reports are somewhat subjective and fluctuate between different reporters, locations and times. Remote sensing data provide an alternative approach to monitoring crop growth over large areas in a more consistent and quantitative way. In the recent years, remote sensing data have been used to detect vegetation phenology at 1-km spatial resolution globally. However, agricultural applications at field scale require finer spatial resolution remote sensing data. Landsat (30-m) data have been successfully used for agricultural applications. There are many medium resolution sensors available today or in near future. These include Landsat, SPOT, RapidEye, ASTER and future Sentinel-2 etc. Approaches have been developed in the past several years to integrate remote sensing data from different sensors which may have different sensor characteristics, and spatial and temporal resolutions. This allows us opportunities today to map crop growth stages and conditions using dense time-series remote sensing at field scales. However, remotely sensed phenology (or phenological metrics) is normally derived based on the mathematical functions of the time-series data. The phenological metrics are determined by either identifying inflection (curvature) points or some pre-defined thresholds in the remote sensing phenology algorithms. Furthermore, physiological crop growth stages may not be directly correlated to the remotely sensed phenology. The relationship between remotely sensed phenology and crop growth stages is likely to vary for specific crop types and varieties, growing stages, conditions and even locations. In this presentation, we will examine the relationship between remotely sensed phenology and crop growth stages using in-situ measurements from Fluxnet sites and crop progress reports from USDA NASS. We will present remote sensing approaches and focus on: 1) integrating multiple sources of remote sensing data; and 2) extracting crop phenology at field scales. An example in the U.S. Corn Belt area will be presented and analyzed. Future directions for mapping crop growth stages will be discussed.

Remote Sensing: A Film Review.

ERIC Educational Resources Information Center

Carter, David J.

1986-01-01

Reviews the content of 19 films on remote sensing published between 1973 and 1980. Concludes that they are overly simplistic, notably outdated, and generally too optimistic about the potential of remote sensing from space for resource exploration and environmental problem-solving. Provides names and addresses of more current remote sensing…

Minefield reconnaissance and detector system

DOEpatents

Butler, M.T.; Cave, S.P.; Creager, J.D.; Johnson, C.M.; Mathes, J.B.; Smith, K.J.

1994-04-26

A multi-sensor system is described for detecting the presence of objects on the surface of the ground or buried just under the surface, such as anti-personnel or anti-tank mines or the like. A remote sensor platform has a plurality of metal detector sensors and a plurality of short pulse radar sensors. The remote sensor platform is remotely controlled from a processing and control unit and signals from the remote sensor platform are sent to the processing and control unit where they are individually evaluated in separate data analysis subprocess steps to obtain a probability score for each of the pluralities of sensors. These probability scores are combined in a fusion subprocess step by comparing score sets to a probability table which is derived based upon the historical incidence of object present conditions given that score set. A decision making rule is applied to provide an output which is optionally provided to a marker subprocess for controlling a marker device to mark the location of found objects. 7 figures.

Educational activities of remote sensing archaeology (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hadjimitsis, Diofantos G.; Agapiou, Athos; Lysandrou, Vasilki; Themistocleous, Kyriacos; Cuca, Branka; Nisantzi, Argyro; Lasaponara, Rosa; Masini, Nicola; Krauss, Thomas; Cerra, Daniele; Gessner, Ursula; Schreier, Gunter

2016-10-01

Remote sensing science is increasingly being used to support archaeological and cultural heritage research in various ways. Satellite sensors either passive or active are currently used in a systematic basis to detect buried archaeological remains and to systematic monitor tangible heritage. In addition, airborne and low altitude systems are being used for documentation purposes. Ground surveys using remote sensing tools such as spectroradiometers and ground penetrating radars can detect variations of vegetation and soil respectively, which are linked to the presence of underground archaeological features. Education activities and training of remote sensing archaeology to young people is characterized of highly importance. Specific remote sensing tools relevant for archaeological research can be developed including web tools, small libraries, interactive learning games etc. These tools can be then combined and aligned with archaeology and cultural heritage. This can be achieved by presenting historical and pre-historical records, excavated sites or even artifacts under a "remote sensing" approach. Using such non-form educational approach, the students can be involved, ask, read, and seek to learn more about remote sensing and of course to learn about history. The paper aims to present a modern didactical concept and some examples of practical implementation of remote sensing archaeology in secondary schools in Cyprus. The idea was built upon an ongoing project (ATHENA) focused on the sue of remote sensing for archaeological research in Cyprus. Through H2020 ATHENA project, the Remote Sensing Science and Geo-Environment Research Laboratory at the Cyprus University of Technology (CUT), with the support of the National Research Council of Italy (CNR) and the German Aerospace Centre (DLR) aims to enhance its performance in all these new technologies.

Remote Sensing and the Earth.

ERIC Educational Resources Information Center

Brosius, Craig A.; And Others

This document is designed to help senior high school students study remote sensing technology and techniques in relation to the environmental sciences. It discusses the acquisition, analysis, and use of ecological remote data. Material is divided into three sections and an appendix. Section One is an overview of the basics of remote sensing.…

Microwave remote sensing of snowpack properties

NASA Technical Reports Server (NTRS)

Rango, A. (Editor)

1980-01-01

Topic concerning remote sensing capabilities for providing reliable snow cover data and measurement of snow water equivalents are discussed. Specific remote sensing technqiues discussed include those in the microwave region of the electromagnetic spectrum.

Commerical Remote Sensing Data Contract

USGS Publications Warehouse

,

2005-01-01

The U. S. Geological Survey's (USGS) Commercial Remote Sensing Data Contracts (CRSDCs) provide government agencies with access to a broad range of commercially available remotely sensed airborne and satellite data. These contracts were established to support The National Map partners, other Federal Civilian agency programs, and Department of Defense programs that require data for the United States and its territories. Experience shows that centralized procurement of remotely sensed data leads to considerable cost savings to the Federal government through volume discounts, reduction of redundant contract administrative costs, and avoidance of duplicate purchases. These contracts directly support the President's Commercial Remote Sensing Space Policy, signed in 2003, by providing a centralized mechanism for civil agencies to acquire commercial remote sensing products to support their mission needs in an efficient and coordinated way. CRSDC administration is provided by the USGS Mid-Continent Mapping Center in Rolla, Missouri.

Object-oriented recognition of high-resolution remote sensing image

NASA Astrophysics Data System (ADS)

Wang, Yongyan; Li, Haitao; Chen, Hong; Xu, Yuannan

2016-01-01

With the development of remote sensing imaging technology and the improvement of multi-source image's resolution in satellite visible light, multi-spectral and hyper spectral , the high resolution remote sensing image has been widely used in various fields, for example military field, surveying and mapping, geophysical prospecting, environment and so forth. In remote sensing image, the segmentation of ground targets, feature extraction and the technology of automatic recognition are the hotspot and difficulty in the research of modern information technology. This paper also presents an object-oriented remote sensing image scene classification method. The method is consist of vehicles typical objects classification generation, nonparametric density estimation theory, mean shift segmentation theory, multi-scale corner detection algorithm, local shape matching algorithm based on template. Remote sensing vehicles image classification software system is designed and implemented to meet the requirements .

Experimental Sea Slicks in the Marsen (Maritime Remote Sensing) Exercise.

DTIC Science & Technology

1980-10-30

Experimental slicks with various surface properties were generated in the North Sea as part of the MARSEN (Maritime Remote Sensing ) exercise. The one...with remote sensing instrumentation. Because of the numerous effects of surface films on air-sea interfacial processes, these experiments were designed...information was obtained on the influence of sea surface films on the interpretation of signals received by remote sensing systems. Criteria for the

SYMPOSIUM ON REMOTE SENSING IN THE POLAR REGIONS

DTIC Science & Technology

The Arctic Institute of North America long has been interested in encouraging full and specific attention to applications of remote sensing to polar...research problems. The major purpose of the symposium was to acquaint scientists and technicians concerned with remote sensing with some of the...special problems of the polar areas and, in turn, to acquaint polar scientists with the potential of the use of remote sensing . The Symposium therefore was

REMOTE SENSING IN OCEANOGRAPHY.

DTIC Science & Technology

remote sensing from satellites. Sensing of oceanographic variables from aircraft began with the photographing of waves and ice. Since then remote measurement of sea surface temperatures and wave heights have become routine. Sensors tested for oceanographic applications include multi-band color cameras, radar scatterometers, infrared spectrometers and scanners, passive microwave radiometers, and radar imagers. Remote sensing has found its greatest application in providing rapid coverage of large oceanographic areas for synoptic and analysis and

Methods of Determining Playa Surface Conditions Using Remote Sensing

DTIC Science & Technology

1987-10-08

NO. 11. TITLE (include Security Classification) METHODS OF DETERMINING PLAYA SURFACE CONDITIONS USING REMOTE SENSING 12. PERSONAL AUTHOR(S) J. PONDER...PLAYA SURFACE CONDITIONS USING REMOTE SENSING J. Ponder Henley U. S. Army Engineer Topographic Laboratories Fort Belvoir, Virginia 22060-5546 "ABSTRACT...geochemistry, hydrology and remote sensing but all of these are important to the understanding of these unique geomorphic features. There is a large body

Needs Assessment for the Use of NASA Remote Sensing Data in the Development and Implementation of Estuarine and Coastal Water Quality Standards

NASA Technical Reports Server (NTRS)

Spiering, Bruce; Underwood, Lauren; Ellis, Chris; Lehrter, John; Hagy, Jim; Schaeffer, Blake

2010-01-01

The goals of the project are to provide information from satellite remote sensing to support numeric nutrient criteria development and to determine data processing methods and data quality requirements to support nutrient criteria development and implementation. The approach is to identify water quality indicators that are used by decision makers to assess water quality and that are related to optical properties of the water; to develop remotely sensed data products based on algorithms relating remote sensing imagery to field-based observations of indicator values; to develop methods to assess estuarine water quality, including trends, spatial and temporal variability, and seasonality; and to develop tools to assist in the development and implementation of estuarine and coastal nutrient criteria. Additional slides present process, criteria development, typical data sources and analyses for criteria process, the power of remote sensing data for the process, examples from Pensacola Bay, spatial and temporal variability, pixel matchups, remote sensing validation, remote sensing in coastal waters, requirements for remotely sensed data products, and needs assessment. An additional presentation examines group engagement and information collection. Topics include needs assessment purpose and objectives, understanding water quality decision making, determining information requirements, and next steps.

Commercial use of remote sensing in agriculture: a case study

NASA Astrophysics Data System (ADS)

Gnauck, Gary E.

1999-12-01

Over 25 years of research have clearly shown that an analysis of remote sensing imagery can provide information on agricultural crops. Most of this research has been funded by and directed toward the needs of government agencies. Commercial use of agricultural remote sensing has been limited to very small-scale operations supplying remote sensing services to a few selected customers. Datron/Transco Inc. undertook an internally funded remote sensing program directed toward the California cash crop industry (strawberries, lettuce, tomatoes, other fresh vegetables and cotton). The objectives of this program were twofold: (1) to assess the need and readiness of agricultural land managers to adopt remote sensing as a management tool, and (2) determine what technical barriers exist to large-scale implementation of this technology on a commercial basis. The program was divided into three phases: Planning, Engineering Test and Evaluation, and Commercial Operations. Findings: Remote sensing technology can deliver high resolution multispectral imagery with rapid turnaround, that can provide information on crop stress insects, disease and various soil parameters. The limiting factors to the use of remote sensing in agriculture are a lack of familiarization by the land managers, difficulty in translating 'information' into increased revenue or reduced cost for the land manager, and the large economies of scale needed to make the venture commercially viable.

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and...

Advanced Remote Sensing Research

USGS Publications Warehouse

Slonecker, Terrence; Jones, John W.; Price, Susan D.; Hogan, Dianna

2008-01-01

'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

Remote sensing in the coastal and marine environment. Proceedings of the US North Atlantic Regional Workshop

NASA Technical Reports Server (NTRS)

Zaitzeff, J. B. (Editor); Cornillon, P. (Editor); Aubrey, D. A. (Editor)

1980-01-01

Presentations were grouped in the following categories: (1) a technical orientation of Earth resources remote sensing including data sources and processing; (2) a review of the present status of remote sensing technology applicable to the coastal and marine environment; (3) a description of data and information needs of selected coastal and marine activities; and (4) an outline of plans for marine monitoring systems for the east coast and a concept for an east coast remote sensing facility. Also discussed were user needs and remote sensing potentials in the areas of coastal processes and management, commercial and recreational fisheries, and marine physical processes.

Remote sensing of Earth terrain

NASA Technical Reports Server (NTRS)

Kong, J. A.

1992-01-01

Research findings are summarized for projects dealing with the following: application of theoretical models to active and passive remote sensing of saline ice; radiative transfer theory for polarimetric remote sensing of pine forest; scattering of electromagnetic waves from a dense medium consisting of correlated Mie scatterers with size distribution and applications to dry snow; variance of phase fluctuations of waves propagating through a random medium; theoretical modeling for passive microwave remote sensing of earth terrain; polarimetric signatures of a canopy of dielectric cylinders based on first and second order vector radiative transfer theory; branching model for vegetation; polarimetric passive remote sensing of periodic surfaces; composite volume and surface scattering model; and radar image classification.

Brazil's remote sensing activities in the Eighties

NASA Technical Reports Server (NTRS)

Raupp, M. A.; Pereiradacunha, R.; Novaes, R. A.

1985-01-01

Most of the remote sensing activities in Brazil have been conducted by the Institute for Space Research (INPE). This report describes briefly INPE's activities in remote sensing in the last years. INPE has been engaged in research (e.g., radiance studies), development (e.g., CCD-scanners, image processing devices) and applications (e.g., crop survey, land use, mineral resources, etc.) of remote sensing. INPE is also responsible for the operation (data reception and processing) of the LANDSATs and meteorological satellites. Data acquisition activities include the development of CCD-Camera to be deployed on board the space shuttle and the construction of a remote sensing satellite.

Application of remote sensing to state and regional problems. [for Mississippi

NASA Technical Reports Server (NTRS)

Miller, W. F.; Bouchillon, C. W.; Harris, J. C.; Carter, B.; Whisler, F. D.; Robinette, R.

1974-01-01

The primary purpose of the remote sensing applications program is for various members of the university community to participate in activities that improve the effective communication between the scientific community engaged in remote sensing research and development and the potential users of modern remote sensing technology. Activities of this program are assisting the State of Mississippi in recognizing and solving its environmental, resource and socio-economic problems through inventory, analysis, and monitoring by appropriate remote sensing systems. Objectives, accomplishments, and current status of the following individual projects are reported: (1) bark beetle project; (2) state park location planning; and (3) waste source location and stream channel geometry monitoring.

Physics teaching by infrared remote sensing of vegetation

NASA Astrophysics Data System (ADS)

Schüttler, Tobias; Maman, Shimrit; Girwidz, Raimund

2018-05-01

Context- and project-based teaching has proven to foster different affective and cognitive aspects of learning. As a versatile and multidisciplinary scientific research area with diverse applications for everyday life, satellite remote sensing is an interesting context for physics education. In this paper we give a brief overview of satellite remote sensing of vegetation and how to obtain your own, individual infrared remote sensing data with affordable converted digital cameras. This novel technique provides the opportunity to conduct individual remote sensing measurement projects with students in their respective environment. The data can be compared to real satellite data and is of sufficient accuracy for educational purposes.

Application of remote sensing to water resources problems

NASA Technical Reports Server (NTRS)

Clapp, J. L.

1972-01-01

The following conclusions were reached concerning the applications of remote sensing to water resources problems: (1) Remote sensing methods provide the most practical method of obtaining data for many water resources problems; (2) the multi-disciplinary approach is essential to the effective application of remote sensing to water resource problems; (3) there is a correlation between the amount of suspended solids in an effluent discharged into a water body and reflected energy; (4) remote sensing provides for more effective and accurate monitoring, discovery and characterization of the mixing zone of effluent discharged into a receiving water body; and (5) it is possible to differentiate between blue and blue-green algae.

Regional Mapping of Coupled Fluxes of Carbon and Water Using Multi-Sensor Fusion Techniques

NASA Astrophysics Data System (ADS)

Schull, M. A.; Anderson, M. C.; Semmens, K. A.; Yang, Y.; Gao, F.; Hain, C.; Houborg, R.

2014-12-01

In an ever-changing climate there is an increasing need to measure the fluxes of water, energy and carbon for decision makers to implement policies that will help mitigate the effects of climate change. In an effort to improve drought monitoring, water resource management and agriculture assessment capabilities, a multi-scale and multi-sensor framework for routine mapping of land-surface fluxes of water and energy at field to regional scales has been established. The framework uses the ALEXI (Atmosphere Land Exchange Inverse)/DisALEXI (Disaggregated ALEXI) suite of land-surface models forced by remotely sensed data from Landsat, MODIS (MODerate resolution Imaging Spectroradiometer), and GOES (Geostationary Operational Environmental Satellite). Land-surface temperature (LST) can be an effective substitute for in-situ surface moisture observations and a valuable metric for constraining land-surface fluxes at sub-field scales. The adopted multi-scale thermal-based land surface modeling framework facilitates regional to local downscaling of water and energy fluxes by using a combination of shortwave reflective and thermal infrared (TIR) imagery from GOES (4-10 km; hourly), MODIS (1 km; daily), and Landsat (30-100 m; bi-weekly). In this research the ALEXI/DisALEXI modeling suite is modified to incorporate carbon fluxes using a stomatal resistance module, which replaces the Priestley-Taylor latent heat approximation. In the module, canopy level nominal light-use-efficiency (βn) is the parameter that modulates the flux of water and carbon in and out of the canopy. Leaf chlorophyll (Chl) is a key parameter for quantifying variability in photosynthetic efficiency to facilitate the spatial distribution of coupled carbon and water retrievals. Spatial distribution of Chl are retrieved from Landsat (30 m) using a surface reflectance dataset as input to the REGularized canopy reFLECtance (REGFLEC) tool. The modified ALEXI/DisALEXI suite is applied to regions of rain fed and irrigated soybean and maize agricultural landscapes within the continental U.S. and flux estimates are compared with flux tower observations.

Multi-temporal Soil Erosion Modelling over the Mt Kenya Region with Multi-Sensor Earth Observation Data

NASA Astrophysics Data System (ADS)

Symeonakis, Elias; Higginbottom, Thomas

2015-04-01

Accelerated soil erosion is the principal cause of soil degradation across the world. In Africa, it is seen as a serious problem creating negative impacts on agricultural production, infrastructure and water quality. Regarding the Mt Kenya region, specifically, soil erosion is a serious threat mainly due to unplanned and unsustainable practices linked to tourism, agriculture and rapid population growth. The soil types roughly correspond with different altitudinal zones and are generally very fertile due to their volcanic origin. Some of them have been created by eroding glaciers while others are due to millions of years of fluvial erosion. The soils on the mountain are easily eroded once exposed: when vegetation is removed, the soil quickly erodes down to bedrock by either animals or humans, as tourists erode paths and local people clear large swaths of forested land for agriculture, mostly illegally. It is imperative, therefore, that a soil erosion monitoring system for the Mt Kenya region is in place in order to understand the magnitude of, and be able to respond to, the increasing number of demands on this renewable resource. In this paper, we employ a simple regional-scale soil erosion modelling framework based on the Thornes model and suggest an operational methodology for quantifying and monitoring water runoff and soil erosion using multi-sensor and multi-temporal remote sensing data in a GIS framework. We compare the estimates of this study with general data on the severity of soil erosion over Kenya and with measured rates of soil loss at different locations over the area of study. The results show that the measured and estimated rates of erosion are generally similar and within the same order of magnitude. They also show that, over the last years, erosion rates are increasing in large parts of the region at an alarming rate, and that mitigation measures are needed to reverse the negative effects of uncontrolled socio-economic practices.

SUPERFUND REMOTE SENSING SUPPORT

EPA Science Inventory

This task provides remote sensing technical support to the Superfund program. Support includes the collection, processing, and analysis of remote sensing data to characterize hazardous waste disposal sites and their history. Image analysis reports, aerial photographs, and assoc...

Remote Sensing and the Earth

NASA Technical Reports Server (NTRS)

Brosius, C. A.; Gervin, J. C.; Ragusa, J. M.

1977-01-01

A text book on remote sensing, as part of the earth resources Skylab programs, is presented. The fundamentals of remote sensing and its application to agriculture, land use, geology, water and marine resources, and environmental monitoring are summarized.

Operational Use of Remote Sensing within USDA

NASA Technical Reports Server (NTRS)

Bethel, Glenn R.

2007-01-01

A viewgraph presentation of remote sensing imagery within the USDA is shown. USDA Aerial Photography, Digital Sensors, Hurricane imagery, Remote Sensing Sources, Satellites used by Foreign Agricultural Service, Landsat Acquisitions, and Aerial Acquisitions are also shown.

Investigation related to multispectral imaging systems

NASA Technical Reports Server (NTRS)

Nalepka, R. F.; Erickson, J. D.

1974-01-01

A summary of technical progress made during a five year research program directed toward the development of operational information systems based on multispectral sensing and the use of these systems in earth-resource survey applications is presented. Efforts were undertaken during this program to: (1) improve the basic understanding of the many facets of multispectral remote sensing, (2) develop methods for improving the accuracy of information generated by remote sensing systems, (3) improve the efficiency of data processing and information extraction techniques to enhance the cost-effectiveness of remote sensing systems, (4) investigate additional problems having potential remote sensing solutions, and (5) apply the existing and developing technology for specific users and document and transfer that technology to the remote sensing community.

An object-based storage model for distributed remote sensing images

NASA Astrophysics Data System (ADS)

Yu, Zhanwu; Li, Zhongmin; Zheng, Sheng

2006-10-01

It is very difficult to design an integrated storage solution for distributed remote sensing images to offer high performance network storage services and secure data sharing across platforms using current network storage models such as direct attached storage, network attached storage and storage area network. Object-based storage, as new generation network storage technology emerged recently, separates the data path, the control path and the management path, which solves the bottleneck problem of metadata existed in traditional storage models, and has the characteristics of parallel data access, data sharing across platforms, intelligence of storage devices and security of data access. We use the object-based storage in the storage management of remote sensing images to construct an object-based storage model for distributed remote sensing images. In the storage model, remote sensing images are organized as remote sensing objects stored in the object-based storage devices. According to the storage model, we present the architecture of a distributed remote sensing images application system based on object-based storage, and give some test results about the write performance comparison of traditional network storage model and object-based storage model.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Multisensor Network System for Wildfire Detection Using Infrared Image Processing

PubMed Central

Bosch, I.; Serrano, A.; Vergara, L.

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated. PMID:23843734

Multisensor network system for wildfire detection using infrared image processing.

PubMed

Bosch, I; Serrano, A; Vergara, L

2013-01-01

This paper presents the next step in the evolution of multi-sensor wireless network systems in the early automatic detection of forest fires. This network allows remote monitoring of each of the locations as well as communication between each of the sensors and with the control stations. The result is an increased coverage area, with quicker and safer responses. To determine the presence of a forest wildfire, the system employs decision fusion in thermal imaging, which can exploit various expected characteristics of a real fire, including short-term persistence and long-term increases over time. Results from testing in the laboratory and in a real environment are presented to authenticate and verify the accuracy of the operation of the proposed system. The system performance is gauged by the number of alarms and the time to the first alarm (corresponding to a real fire), for different probability of false alarm (PFA). The necessity of including decision fusion is thereby demonstrated.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

DARLA: Data Assimilation and Remote Sensing for Littoral Applications

NASA Astrophysics Data System (ADS)

Jessup, A.; Holman, R. A.; Chickadel, C.; Elgar, S.; Farquharson, G.; Haller, M. C.; Kurapov, A. L.; Özkan-Haller, H. T.; Raubenheimer, B.; Thomson, J. M.

2012-12-01

DARLA is 5-year collaborative project that couples state-of-the-art remote sensing and in situ measurements with advanced data assimilation (DA) modeling to (a) evaluate and improve remote sensing retrieval algorithms for environmental parameters, (b) determine the extent to which remote sensing data can be used in place of in situ data in models, and (c) infer bathymetry for littoral environments by combining remotely-sensed parameters and data assimilation models. The project uses microwave, electro-optical, and infrared techniques to characterize the littoral ocean with a focus on wave and current parameters required for DA modeling. In conjunction with the RIVET (River and Inlets) Project, extensive in situ measurements provide ground truth for both the remote sensing retrieval algorithms and the DA modeling. Our goal is to use remote sensing to constrain data assimilation models of wave and circulation dynamics in a tidal inlet and surrounding beaches. We seek to improve environmental parameter estimation via remote sensing fusion, determine the success of using remote sensing data to drive DA models, and produce a dynamically consistent representation of the wave, circulation, and bathymetry fields in complex environments. The objectives are to test the following three hypotheses: 1. Environmental parameter estimation using remote sensing techniques can be significantly improved by fusion of multiple sensor products. 2. Data assimilation models can be adequately constrained (i.e., forced or guided) with environmental parameters derived from remote sensing measurements. 3. Bathymetry on open beaches, river mouths, and at tidal inlets can be inferred from a combination of remotely-sensed parameters and data assimilation models. Our approach is to conduct a series of field experiments combining remote sensing and in situ measurements to investigate signature physics and to gather data for developing and testing DA models. A preliminary experiment conducted at the Field Research Facility at Duck, NC in September 2010 focused on assimilation of tower-based electo-optical, infrared, and radar measurements in predictions of longshore currents. Here we provide an overview of our contribution to the RIVET I experiment at New River Inlet, NC in May 2012. During the course of the 3-week measurement period, continuous tower-based remote sensing measurements were made using electro-optical, infrared, and radar techniques covering the nearshore zone and the inlet mouth. A total of 50 hours of airborne measurements were made using high-resolution infrared imagers and a customized along track interferometric synthetic aperture radar (ATI SAR). The airborne IR imagery provides kilometer-scale mapping of frontal features that evolve as the inlet flow interacts with the oceanic wave and current fields. The ATI SAR provides maps of the two-dimensional surface currents. Near-surface measurements of turbulent velocities and surface waves using SWIFT drifters, designed to measures near-surface properties relevant to remote sensing, complimented the extensive in situ measurements by RIVET investigators.

A remote sensing and GIS-enabled asset management system (RS-GAMS).

DOT National Transportation Integrated Search

2013-04-01

Under U.S. Department of Transportation (DOT) Commercial Remote Sensing and : Spatial Information (CRS&SI) Technology Initiative 2 of the Transportation : Infrastructure Construction and Condition Assessment, an intelligent Remote Sensing and : GIS-b...

Remote Sensing.

ERIC Educational Resources Information Center

Williams, Richard S., Jr.; Southworth, C. Scott

1983-01-01

The Landsat Program became the major event of 1982 in geological remote sensing with the successful launch of Landsat 4. Other 1982 remote sensing accomplishments, research, publications, (including a set of Landsat worldwide reference system index maps), and conferences are highlighted. (JN)

Remote sensing utility in a disaster struck urban environment

NASA Technical Reports Server (NTRS)

Rush, M.; Holguin, A.; Vernon, S.

1974-01-01

A project to determine the ways in which remote sensing can contribute to solutions of urban public health problems in time of natural disaster is discussed. The objectives of the project are to determine and describe remote sensing standard operating procedures for public health assistance during disaster relief operations which will aid the agencies and organizations involved in disaster intervention. Proposed tests to determine the validity of the remote sensing system are reported.

Removal of Surface-Reflected Light for the Measurement of Remote-Sensing Reflectance from an Above-Surface Platform

DTIC Science & Technology

2010-12-06

raw data). To remove surface-reflected light in field measurements of remote sensing reflectance, a spectral optimization approach was applied, with...results compared with those from remote - sensing models and from direct measurements. The agreement from different determinations suggests that...reasonable results for remote sensing reflectance of clear blue water to turbid brown water are obtainable from above-surface measurements, even under conditions of high waves.

Bibliography of Remote Sensing Techniques Used in Wetland Research

DTIC Science & Technology

1993-01-01

8217 is investigating the application of remote sensing technology for detecting changes in wetland environments. This report documents a bibliographic...search conducted as part of that work unit on applications of remote sensing techniques in wetland research. Results were used to guide research...efforts on the use of remote sensing technology for wetland change detection and assessment. The citations are presented in three appendixes, organized by wetland type, sensor type, and author.

Use of Openly Available Satellite Images for Remote Sensing Education

NASA Astrophysics Data System (ADS)

Wang, C.-K.

2011-09-01

With the advent of Google Earth, Google Maps, and Microsoft Bing Maps, high resolution satellite imagery are becoming more easily accessible than ever. It have been the case that the college students may already have wealth experiences with the high resolution satellite imagery by using these software and web services prior to any formal remote sensing education. It is obvious that the remote sensing education should be adjusted to the fact that the audience are already the customers of remote sensing products (through the use of the above mentioned services). This paper reports the use of openly available satellite imagery in an introductory-level remote sensing course in the Department of Geomatics of National Cheng Kung University as a term project. From the experience learned from the fall of 2009 and 2010, it shows that this term project has effectively aroused the students' enthusiastic toward Remote Sensing.

Strategies for using remotely sensed data in hydrologic models

NASA Technical Reports Server (NTRS)

Peck, E. L.; Keefer, T. N.; Johnson, E. R. (Principal Investigator)

1981-01-01

Present and planned remote sensing capabilities were evaluated. The usefulness of six remote sensing capabilities (soil moisture, land cover, impervious area, areal extent of snow cover, areal extent of frozen ground, and water equivalent of the snow cover) with seven hydrologic models (API, CREAMS, NWSRFS, STORM, STANFORD, SSARR, and NWSRFS Snowmelt) were reviewed. The results indicate remote sensing information has only limited value for use with the hydrologic models in their present form. With minor modifications to the models the usefulness would be enhanced. Specific recommendations are made for incorporating snow covered area measurements in the NWSRFS Snowmelt model. Recommendations are also made for incorporating soil moisture measurements in NWSRFS. Suggestions are made for incorporating snow covered area, soil moisture, and others in STORM and SSARR. General characteristics of a hydrologic model needed to make maximum use of remotely sensed data are discussed. Suggested goals for improvements in remote sensing for use in models are also established.

A preliminary study of the statistical analyses and sampling strategies associated with the integration of remote sensing capabilities into the current agricultural crop forecasting system

NASA Technical Reports Server (NTRS)

Sand, F.; Christie, R.

1975-01-01

Extending the crop survey application of remote sensing from small experimental regions to state and national levels requires that a sample of agricultural fields be chosen for remote sensing of crop acreage, and that a statistical estimate be formulated with measurable characteristics. The critical requirements for the success of the application are reviewed in this report. The problem of sampling in the presence of cloud cover is discussed. Integration of remotely sensed information about crops into current agricultural crop forecasting systems is treated on the basis of the USDA multiple frame survey concepts, with an assumed addition of a new frame derived from remote sensing. Evolution of a crop forecasting system which utilizes LANDSAT and future remote sensing systems is projected for the 1975-1990 time frame.

Archimedean Witness: The Application of Remote Sensing as an Aid to Human Rights Prosecutions

NASA Astrophysics Data System (ADS)

Walker, James Robin

The 21st century has seen a significant increase in the use of remote sensing technology in the international human rights arena for the purposes of documenting crimes against humanity. The nexus between remote sensing, human rights activism, and international criminal prosecutions sits at a significant crossroads within geographic thought, calling attention to the epistemological and geopolitical implications that stem from the "view from nowhere" afforded by satellite imagery. Therefore, this thesis is divided into three sections. The first looks at the geographical questions raised by the expansion of remote sensing use in the context of international activism. The second explores the complications inherent in the presentation of remote sensing data as evidence of war crimes. Building upon the first two, the third section is a case study in alternate forms of analysis, aimed at expanding the utility of remote sensing data in international criminal prosecutions.

[Small unmanned aerial vehicles for low-altitude remote sensing and its application progress in ecology.

PubMed

Sun, Zhong Yu; Chen, Yan Qiao; Yang, Long; Tang, Guang Liang; Yuan, Shao Xiong; Lin, Zhi Wen

2017-02-01

Low-altitude unmanned aerial vehicles (UAV) remote sensing system overcomes the deficiencies of space and aerial remote sensing system in resolution, revisit period, cloud cover and cost, which provides a novel method for ecological research on mesoscale. This study introduced the composition of UAV remote sensing system, reviewed its applications in species, population, community and ecosystem ecology research. Challenges and opportunities of UAV ecology were identified to direct future research. The promising research area of UAV ecology includes the establishment of species morphology and spectral characteristic data base, species automatic identification, the revelation of relationship between spectral index and plant physiological processes, three-dimension monitoring of ecosystem, and the integration of remote sensing data from multi resources and multi scales. With the development of UAV platform, data transformation and sensors, UAV remote sensing technology will have wide application in ecology research.

Remote Sensing Approach to Drought Monitoring to Inform Range Management at the Hopi Tribe and Navajo Nation

NASA Astrophysics Data System (ADS)

El Vilaly, M. M.; Van Leeuwen, W. J.; Didan, K.; Marsh, S. E.; Crimmins, , M. A.

2012-12-01

The Hopi Tribe and Navajo Nation are situated in the Northeastern corner of Arizona in the Colorado River Plateau. For more than a decade, the area has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources while exacerbating soil erosion. Moreover, these persistent droughts threaten ecosystem services, agriculture, and livestock production activities, and make this region sensitive to inter-annual climate variability and change. The limited hydroclimatic observations, bolstered by numerous anecdotal drought impact reports, indicate that the region has been suffering through an almost 15-year long drought which is threatening its socio-economic development. The objective of this research is to employ remote sensing data to monitor the ongoing drought and inform management and decision-making. The overall goals of this study are to develop a common understanding of the current status of drought across the area in order to understand the existing seasonal and inter-annual relationships between climate variability and vegetation dynamics. To analyze and investigate vegetation responses to climate variability, land use practices, and environmental factors in Hopi and Navajo nation during the last 22 years, a drought assessment framework was developed that integrates climate and topographical data with land surface remote sensing time series data. Multi-sensor Normalized Difference Vegetation Index time series data were acquired from the vegetation index and phenology project (vip.arizona.edu) from 1989 to 2010 at 5.6 km, were analyzed to characterize the intra-annual changes of vegetation, seasonal phenology and inter-annual vegetation response to climate variability and environmental factors. Due to the low number of retrieval obtained from TIMESAT software, we developed a new framework that can maximize the number of retrieval. Four vegetation development stages, annual integrated NDVI (Net Primary Production (NPP)), minimum annual NDVI, maximum annual NDVI, and annual amplitude, were extracted using that new framework. A multi-linear regression has been applied to these vegetation phenology metrics as well as to the relationship between pheno-metrics and environmental variables, to detect potential vegetation changes and to examine the existing relationship between vegetation dynamics and rainfall and elevation gradients. The results suggest that vegetation behavior is foremost governed by rainfall gradients (R-square =0.74). Trend analyses confirmed that around 80 percent of pixels showed a general decline of greenness with confidence level of 95% (p< 0.05), while 4 percent showed a general greening up. Vegetation in the area showed a significant and strong relationship with elevation and precipitation gradients. This correlation was more prominent at mid-elevations, which could be explained by the snowmelt dynamics and hydrological redistribution of water at that elevation. These tools, methods and results can be used to aid in monitoring and understanding climate change and variability impacts on vegetation productivity, ecosystem services, and water resources of the region, and to inform decision-makers and range managers at Hopi Tribe and Navajo nation. Keywords: drought, remote sensing, time series, vegetation dynamics, Hopi Tribe and Navajo Nations

Using in-field and remote sensing techniques for the monitoring of small-scale permafrost decline in Northern Quebec

NASA Astrophysics Data System (ADS)

May, Inga; Kim, Jun Su; Spannraft, Kati; Ludwig, Ralf; Hajnsek, Irena; Bernier, Monique; Allard, Michel

2010-05-01

Permafrost-affected soils represent about 45% of Canadian arctic and subarctic regions. Under the recently recorded changed climate conditions, the areas located in the discontinuous permafrost zones are likely to belong to the most impacted environments. Degradations of Palsas and lithalsas as being the most distinct permafrost landforms as well as an extension of wetlands have been observe during the past decades by several research teams all over the northern Arctic. These alterations, caused by longer an warmer thawing periods, are expected to become more and more frequent in the future. The effects on human beings and on the surrounding sensitive ecosystems are presumed to be momentous and of high relevance. Hence, there is a high demand for new techniques that are able to detect, and possibly even predict, the behavior of the permafrost within a changing environment. The presented study is part of an international research collaboration between LMU, INRS and UL within the framework of ArcticNet. The project intends to develop a monitoring system strongly based on remote sensing imagery and GIS-based data analysis, using a test site located in northern Quebec (Umiujaq, 56°33' N, 76°33' W). It shall be investigated to which extent the interpretation of satellite imagery is feasible to partially substitute costly and difficult geophysical point measurements, and to provide spatial knowledge about the major factors that control permafrost dynamics and ecosystem change. In a first step, these factors, mainly expected to be determined from changes in topography, vegetation cover and snow cover, are identified and validated by means of several consecutive ground truthing initiatives supporting the analysis of multi-sensoral time series of remotely sensed information. Both sources are used to generate and feed different concepts for modeling permafrost dynamics by ways of parameter retrieval and data assimilation. On this poster, the outcomes of the first project year (2009) are highlighted. The main focus during this year was to figure out whether small-scale topographical changes caused by seasonal thawing and freezing processes, are detectable by means of SAR-interferometry. For this purpose, repeat passes of interferometric products were computed from the multi-temporal image pairs of Germany's X-band SAR sensor TerraSar-X. These are then compared with in-situ measurements surveyed by high precision differential GPS, taken during the field measurements in April and August of 2009. Thus, the first methodological research question is to prove that the results from the interferogram analysis correspond to the findings of field surveys. The results are promising as topographical changes could be observed with the D-GPS as well as on the interferograms. Due to the amount of factors influencing the remote sensed data, the analysis of the information contained in the data in order to make quantitative statements still remains an effort. Nevertheless it is very likely that, after further investigation to fully understand the radar-signal, this procedure is indeed reliable and efficient, and may be applied to a long-term and interannual assessment of permafrost dynamics in the sub-arctic.

International Models and Methods of Remote Sensing Education and Training.

ERIC Educational Resources Information Center

Anderson, Paul S.

A classification of remote sensing courses throughout the world, the world-wide need for sensing instruction, and alternative instructional methods for meeting those needs are discussed. Remote sensing involves aerial photointerpretation or the use of satellite and other non-photographic imagery; its focus is to interpret what is in the photograph…

A Review and Analysis of Remote Sensing Capability for Air Quality Measurements as a Potential Decision Support Tool Conducted by the NASA DEVELOP Program

NASA Technical Reports Server (NTRS)

Ross, A.; Richards, A.; Keith, K.; Frew, C.; Boseck, J.; Sutton, S.; Watts, C.; Rickman, D.

2007-01-01

This project focused on a comprehensive utilization of air quality model products as decision support tools (DST) needed for public health applications. A review of past and future air quality measurement methods and their uncertainty, along with the relationship of air quality to national and global public health, is vital. This project described current and future NASA satellite remote sensing and ground sensing capabilities and the potential for using these sensors to enhance the prediction, prevention, and control of public health effects that result from poor air quality. The qualitative uncertainty of current satellite remotely sensed air quality, the ground-based remotely sensed air quality, the air quality/public health model, and the decision making process is evaluated in this study. Current peer-reviewed literature suggests that remotely sensed air quality parameters correlate well with ground-based sensor data. A satellite remote-sensed and ground-sensed data complement is needed to enhance the models/tools used by policy makers for the protection of national and global public health communities

Theme section for 36th International Symposium for Remote Sensing of the Environment in Berlin

NASA Astrophysics Data System (ADS)

Trinder, John; Waske, Björn

2016-09-01

The International Symposium for Remote Sensing of the Environment (ISRSE) is the longest series of international conferences held on the topic of Remote Sensing, commencing in Ann Arbor, Michigan USA in 1962. While the name of the conference has changed over the years, it is regularly held approximately every 2 years and continues to be one of the leading international conferences on remote sensing. The latest of these conferences, the 36th ISRSE, was held in Berlin, Germany from 11 to 15 May 2015. All complete papers from the conference are available in the ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences at http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/index.html.

THE REMOTE SENSING DATA GATEWAY

EPA Science Inventory

The EPA Remote Sensing Data Gateway (RSDG) is a pilot project in the National Exposure Research Laboratory (NERL) to develop a comprehensive data search, acquisition, delivery and archive mechanism for internal, national and international sources of remote sensing data for the co...

A remote sensing and GIS-enabled asset management system (RS-GAMS) : phase 2.

DOT National Transportation Integrated Search

2014-04-01

Under the U.S. Department of Transportation (DOT) Commercial Remote Sensing and Spatial : Information (CRS&SI) Technology Initiative 2 of the Transportation Infrastructure Construction : and Condition Assessment, an intelligent Remote Sensing and GIS...

Remote sensing applications program

NASA Technical Reports Server (NTRS)

1984-01-01

The activities of the Mississippi Remote Sensing Center are described in addition to technology transfer and information dissemination, remote sensing topics such as timber identification, water quality, flood prevention, land use, erosion control, animal habitats, and environmental impact studies are also discussed.

Remote Sensing Terminology in a Global and Knowledge-Based World

NASA Astrophysics Data System (ADS)

Kancheva, Rumiana

The paper is devoted to terminology issues related to all aspects of remote sensing research and applications. Terminology is the basis for a better understanding among people. It is crucial to keep up with the latest developments and novelties of the terminology in advanced technology fields such as aerospace science and industry. This is especially true in remote sensing and geoinformatics which develop rapidly and have ever extending applications in various domains of science and human activities. Remote sensing terminology issues are directly relevant to the contemporary worldwide policies on information accessibility, dissemination and utilization of research results in support of solutions to global environmental challenges and sustainable development goals. Remote sensing and spatial information technologies are an integral part of the international strategies for cooperation in scientific, research and application areas with a particular accent on environmental monitoring, ecological problems natural resources management, climate modeling, weather forecasts, disaster mitigation and many others to which remote sensing data can be put. Remote sensing researchers, professionals, students and decision makers of different counties and nationalities should fully understand, interpret and translate into their native language any term, definition or acronym found in papers, books, proceedings, specifications, documentation, and etc. The importance of the correct use, precise definition and unification of remote sensing terms refers not only to people working in this field but also to experts in a variety of disciplines who handle remote sensing data and information products. In this paper, we draw the attention on the specifics, peculiarities and recent needs of compiling specialized dictionaries in the area of remote sensing focusing on Earth observations and the integration of remote sensing with other geoinformation technologies such as photogrammetry, geodesy, GIS, etc. Our belief is that the elaboration of bilingual and multilingual dictionaries and glossaries in this spreading, most technically advanced and promising field of human expertise is of great practical importance. The work on an English-Bulgarian Dictionary of Remote Sensing Terms is described including considerations on its scope, structure, information content, sellection of terms, and etc. The vision builds upon previous national and international experience and makes use of ongoing activities on the subject. Any interest in cooperation and initiating suchlike collaborative projects is welcome and highly appreciated.

A Bayesian Approach to Estimating Detection Performance in a Multi-Sensor Environment

DTIC Science & Technology

2014-09-01

National Defence, 2014 © Sa Majesté la Reine (en droit du Canada), telle que représentée par le ministre de la Défense nationale, 2014 DRDC...emphasis can be seen in CF publications such as the Maritime Capability Planning Guidance (MCPG) 2010 [3]. To make effective use of these new remote...fixed installation, ship, aircraft, or satellite. Ships of a certain size or type are required by the International Maritime Organization (IMO) to

Indicators of international remote sensing activities

NASA Technical Reports Server (NTRS)

Spann, G. W.

1977-01-01

The extent of worldwide remote sensing activities, including the use of satellite and high/medium altitude aircraft data was studied. Data were obtained from numerous individuals and organizations with international remote sensing responsibilities. Indicators were selected to evaluate the nature and scope of remote sensing activities in each country. These indicators ranged from attendance at remote sensing workshops and training courses to the establishment of earth resources satellite ground stations and plans for the launch of earth resources satellites. Results indicate that this technology constitutes a rapidly increasing component of environmental, land use, and natural resources investigations in many countries, and most of these countries rely on the LANDSAT satellites for a major portion of their data.

Free acquisition and dissemination of data through remote sensing. [Landsat program legal aspects

NASA Technical Reports Server (NTRS)

Hosenball, S. N.

1976-01-01

Free acquisition and dissemination of data through remote sensing is discussed with reference to the Landsat program. The role of the Scientific and Technical Subcommittee of the U.N. General Assembly's Committee on the Peaceful Uses of Outer Space has made recommendations on the expansion of existing ground stations and on the establishment of an experimental center for training in remote sensing. The working group for the legal subcommittee of the same U.N. committee indicates that there are common elements in the three drafts on remote sensing submitted to it: a call for international cooperation and the belief that remote sensing should be conducted for the benefit of all mankind.

Some fundamental concepts in remote sensing

NASA Technical Reports Server (NTRS)

1982-01-01

The term remote sensing is defined as well as ideas such as class, pattern, feature, pattern recognition, feature extraction, and theme. The electromagnetic spectrum is examined especially those wavelength regions available to remote sensing. Relevant energy and wave propagation laws are discussed and the characteristics of emitted and reflected radiation and their detection are investigated. The identification of classes by their spectral signatures, the multispectral approach, and the principal types of sensors and platforms used in remote sensing are also considered.

LWIR Microgrid Polarimeter for Remote Sensing Studies

DTIC Science & Technology

2010-02-28

Polarimeter for Remote Sensing Studies 5b. GRANT NUMBER FA9550-08-1-0295 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 1. Scott Tyo 5e. TASK...and tested at the University of Arizona, and preliminary images are shown in this final report. 15. SUBJECT TERMS Remote Sensing , polarimetry 16...7.0 LWIR Microgrid Polarimeter for Remote Sensing Studies J. Scott Tyo College of Optical Sciences University of Arizona Tucson, AZ, 85721 tyo

Remote sensing new model for monitoring the east Asian migratory locust infections based on its breeding circle

NASA Astrophysics Data System (ADS)

Han, Xiuzhen; Ma, Jianwen; Bao, Yuhai

2006-12-01

Currently the function of operational locust monitor system mainly focused on after-hazards monitoring and assessment, and to found the way effectively to perform early warning and prediction has more practical meaning. Through 2001, 2002 two years continuously field sample and statistics for locusts eggs hatching, nymph growth, adults 3 phases observation, sample statistics and calculation, spectral measurements as well as synchronically remote sensing data processing we raise the view point of Remote Sensing three stage monitor the locust hazards. Based on the point of view we designed remote sensing monitor in three stages: (1) during the egg hitching phase remote sensing can retrieve parameters of land surface temperature (LST) and soil moisture; (2) during nymph growth phase locust increases appetite greatly and remote sensing can calculate vegetation index, leaf area index, vegetation cover and analysis changes; (3) during adult phase the locust move and assembly towards ponds and water ditches as well as less than 75% vegetation cover areas and remote sensing combination with field data can monitor and predicts potential areas for adult locusts to assembly. In this way the priority of remote sensing technology is elaborated effectively and it also provides technique support for the locust monitor system. The idea and techniques used in the study can also be used as reference for other plant diseases and insect pests.

NASA Remote Sensing Research as Applied to Archaeology

NASA Technical Reports Server (NTRS)

Giardino, Marco J.; Thomas, Michael R.

2002-01-01

The use of remotely sensed images is not new to archaeology. Ever since balloons and airplanes first flew cameras over archaeological sites, researchers have taken advantage of the elevated observation platforms to understand sites better. When viewed from above, crop marks, soil anomalies and buried features revealed new information that was not readily visible from ground level. Since 1974 and initially under the leadership of Dr. Tom Sever, NASA's Stennis Space Center, located on the Mississippi Gulf Coast, pioneered and expanded the application of remote sensing to archaeological topics, including cultural resource management. Building on remote sensing activities initiated by the National Park Service, archaeologists increasingly used this technology to study the past in greater depth. By the early 1980s, there were sufficient accomplishments in the application of remote sensing to anthropology and archaeology that a chapter on the subject was included in fundamental remote sensing references. Remote sensing technology and image analysis are currently undergoing a profound shift in emphasis from broad classification to detection, identification and condition of specific materials, both organic and inorganic. In the last few years, remote sensing platforms have grown increasingly capable and sophisticated. Sensors currently in use, or nearing deployment, offer significantly finer spatial and spectral resolutions than were previously available. Paired with new techniques of image analysis, this technology may make the direct detection of archaeological sites a realistic goal.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

USGS Publications Warehouse

Zimmermann, N.E.; Edwards, T.C.; Moisen, Gretchen G.; Frescino, T.S.; Blackard, J.A.

2007-01-01

1. Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. 2. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. 3. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. 4. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. 5. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. ?? 2007 The Authors.

Remote sensing-based predictors improve distribution models of rare, early successional and broadleaf tree species in Utah

PubMed Central

ZIMMERMANN, N E; EDWARDS, T C; MOISEN, G G; FRESCINO, T S; BLACKARD, J A

2007-01-01

Compared to bioclimatic variables, remote sensing predictors are rarely used for predictive species modelling. When used, the predictors represent typically habitat classifications or filters rather than gradual spectral, surface or biophysical properties. Consequently, the full potential of remotely sensed predictors for modelling the spatial distribution of species remains unexplored. Here we analysed the partial contributions of remotely sensed and climatic predictor sets to explain and predict the distribution of 19 tree species in Utah. We also tested how these partial contributions were related to characteristics such as successional types or species traits. We developed two spatial predictor sets of remotely sensed and topo-climatic variables to explain the distribution of tree species. We used variation partitioning techniques applied to generalized linear models to explore the combined and partial predictive powers of the two predictor sets. Non-parametric tests were used to explore the relationships between the partial model contributions of both predictor sets and species characteristics. More than 60% of the variation explained by the models represented contributions by one of the two partial predictor sets alone, with topo-climatic variables outperforming the remotely sensed predictors. However, the partial models derived from only remotely sensed predictors still provided high model accuracies, indicating a significant correlation between climate and remote sensing variables. The overall accuracy of the models was high, but small sample sizes had a strong effect on cross-validated accuracies for rare species. Models of early successional and broadleaf species benefited significantly more from adding remotely sensed predictors than did late seral and needleleaf species. The core-satellite species types differed significantly with respect to overall model accuracies. Models of satellite and urban species, both with low prevalence, benefited more from use of remotely sensed predictors than did the more frequent core species. Synthesis and applications. If carefully prepared, remotely sensed variables are useful additional predictors for the spatial distribution of trees. Major improvements resulted for deciduous, early successional, satellite and rare species. The ability to improve model accuracy for species having markedly different life history strategies is a crucial step for assessing effects of global change. PMID:18642470

7 CFR 2.29 - Chief Economist.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Committees prior to any release outside the Department. (6) Related to remote sensing. (i) Provide technical... satellite remote sensing activities to assure full consideration and evaluation of advanced technology. (ii) Coordinate administrative, management, and budget information relating to the Department's remote sensing...

Development of sea ice monitoring with aerial remote sensing technology

NASA Astrophysics Data System (ADS)

Jiang, Xuhui; Han, Lei; Dong, Liang; Cui, Lulu; Bie, Jun; Fan, Xuewei

2014-11-01

In the north China Sea district, sea ice disaster is very serious every winter, which brings a lot of adverse effects to shipping transportation, offshore oil exploitation, and coastal engineering. In recent years, along with the changing of global climate, the sea ice situation becomes too critical. The monitoring of sea ice is playing a very important role in keeping human life and properties in safety, and undertaking of marine scientific research. The methods to monitor sea ice mainly include: first, shore observation; second, icebreaker monitoring; third, satellite remote sensing; and then aerial remote sensing monitoring. The marine station staffs use relevant equipments to monitor the sea ice in the shore observation. The icebreaker monitoring means: the workers complete the test of the properties of sea ice, such as density, salinity and mechanical properties. MODIS data and NOAA data are processed to get sea ice charts in the satellite remote sensing means. Besides, artificial visual monitoring method and some airborne remote sensors are adopted in the aerial remote sensing to monitor sea ice. Aerial remote sensing is an important means in sea ice monitoring because of its strong maneuverability, wide watching scale, and high resolution. In this paper, several methods in the sea ice monitoring using aerial remote sensing technology are discussed.

Remote sensor response study in the regime of the microwave radiation-induced magnetoresistance oscillations

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

Ye, Tianyu; Mani, R. G.; Wegscheider, W.

2013-11-04

A concurrent remote sensing and magneto-transport study of the microwave excited two dimensional electron system (2DES) at liquid helium temperatures has been carried out using a carbon detector to remotely sense the microwave activity of the 2D electron system in the GaAs/AlGaAs heterostructure during conventional magneto-transport measurements. Various correlations are observed and reported between the oscillatory magnetotransport and the remotely sensed reflection. In addition, the oscillatory remotely sensed signal is shown to exhibit a power law type variation in its amplitude, similar to the radiation-induced magnetoresistance oscillations.

Review of Remote Sensing Needs and Applications in Africa

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2007-01-01

Remote sensing data has had an important role in identifying and responding to inter-annual variations in the African environment during the past three decades. As a largely agricultural region with diverse but generally limited government capacity to acquire and distribute ground observations of rainfall, temperature and other parameters, remote sensing is sometimes the only reliable measure of crop growing conditions in Africa. Thus, developing and maintaining the technical and scientific capacity to analyze and utilize satellite remote sensing data in Africa is critical to augmenting the continent's local weather/climate observation networks as well as its agricultural and natural resource development and management. The report Review of Remote Sensing Needs and Applications in Africa' has as its central goal to recommend to the US Agency for International Development an appropriate approach to support sustainable remote sensing applications at African regional remote sensing centers. The report focuses on "RS applications" to refer to the acquisition, maintenance and archiving, dissemination, distribution, analysis, and interpretation of remote sensing data, as well as the integration of interpreted data with other spatial data products. The report focuses on three primary remote sensing centers: (1) The AGRHYMET Regional Center in Niamey, Niger, created in 1974, is a specialized institute of the Permanent Interstate Committee for Drought Control in the Sahel (CILSS), with particular specialization in science and techniques applied to agricultural development, rural development, and natural resource management. (2) The Regional Centre for Maiming of Resources for Development (RCMRD) in Nairobi, Kenya, established in 1975 under the auspices of the United Nations Economic Commission for Africa and the Organization of African Unity (now the African Union), is an intergovernmental organization, with 15 member states from eastern and southern Africa. (3) The Regional Remote Sensing Unit (RRSU) in Gaborone, Botswana, began work in June 1988 and operates under the Agriculture Information Management System (AIMS), as part of the Food, Agriculture and Natural Resources (FANR) Directorate, based at the Southern Africa Development Community (SADC) Secretariat.

The Earth Resources Observation Systems data center's training technical assistance, and applications research activities

USGS Publications Warehouse

Sturdevant, J.A.

1981-01-01

The Earth Resources Observation Systems (EROS) Data Center (EDO, administered by the U.S. Geological Survey, U.S. Department of the Interior, provides remotely sensed data to the user community and offers a variety of professional services to further the understanding and use of remote sensing technology. EDC reproduces and sells photographic and electronic copies of satellite images of areas throughout the world. Other products include aerial photographs collected by 16 organizations, including the U.S. Geological Survey and the National Aeronautics and Space Administration. Primary users of the remotely sensed data are Federal, State, and municipal government agencies, universities, foreign nations, and private industries. The professional services available at EDC are primarily directed at integrating satellite and aircraft remote sensing technology into the programs of the Department of the Interior and its cooperators. This is accomplished through formal training workshops, user assistance, cooperative demonstration projects, and access to equipment and capabilities in an advanced data analysis laboratory. In addition, other Federal agencies, State and local governments, universities, and the general public can get assistance from the EDC Staff. Since 1973, EDC has contributed to the accelerating growth in development and operational use of remotely sensed data for land resource problems through its role as educator and by conducting basic and applied remote sensing applications research. As remote sensing technology continues to evolve, EDC will continue to respond to the increasing demand for timely information on remote sensing applications. Questions most often asked about EDC's research and training programs include: Who may attend an EDC remote sensing training course? Specifically, what is taught? Who may cooperate with EDC on remote sensing projects? Are interpretation services provided on a service basis? This report attempts to define the goals and objectives of and policies on the following EDC services: Training Program.User Assistance.Data Analysis Laboratory.Cooperative Demonstration Projects.Research Projects.

Risk profiling of schistosomiasis using remote sensing: approaches, challenges and outlook.

PubMed

Walz, Yvonne; Wegmann, Martin; Dech, Stefan; Raso, Giovanna; Utzinger, Jürg

2015-03-17

Schistosomiasis is a water-based disease that affects an estimated 250 million people, mainly in sub-Saharan Africa. The transmission of schistosomiasis is spatially and temporally restricted to freshwater bodies that contain schistosome cercariae released from specific snails that act as intermediate hosts. Our objective was to assess the contribution of remote sensing applications and to identify remaining challenges in its optimal application for schistosomiasis risk profiling in order to support public health authorities to better target control interventions. We reviewed the literature (i) to deepen our understanding of the ecology and the epidemiology of schistosomiasis, placing particular emphasis on remote sensing; and (ii) to fill an identified gap, namely interdisciplinary research that bridges different strands of scientific inquiry to enhance spatially explicit risk profiling. As a first step, we reviewed key factors that govern schistosomiasis risk. Secondly, we examined remote sensing data and variables that have been used for risk profiling of schistosomiasis. Thirdly, the linkage between the ecological consequence of environmental conditions and the respective measure of remote sensing data were synthesised. We found that the potential of remote sensing data for spatial risk profiling of schistosomiasis is - in principle - far greater than explored thus far. Importantly though, the application of remote sensing data requires a tailored approach that must be optimised by selecting specific remote sensing variables, considering the appropriate scale of observation and modelling within ecozones. Interestingly, prior studies that linked prevalence of Schistosoma infection to remotely sensed data did not reflect that there is a spatial gap between the parasite and intermediate host snail habitats where disease transmission occurs, and the location (community or school) where prevalence measures are usually derived from. Our findings imply that the potential of remote sensing data for risk profiling of schistosomiasis and other neglected tropical diseases has yet to be fully exploited.

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

Remote sensing as a source of data for outdoor recreation planning

NASA Technical Reports Server (NTRS)

Reed, W. E.; Goodell, H. G.; Emmitt, G. D.

1972-01-01

Specific data needs for outdoor recreation planning and the ability of tested remote sensors to provide sources for these data are examined. Data needs, remote sensor capabilities, availability of imagery, and advantages and problems of incorporating remote sensing data sources into ongoing planning data collection programs are discussed in detail. Examples of the use of imagery to derive data for a range of common planning analyses are provided. A selected bibliography indicates specific uses of data in planning, basic background materials on remote sensing technology, and sources of information on environmental information systems expected to use remote sensing to provide new environmental data of use in outdoor recreation planning.

Online Remote Sensing Interface

NASA Technical Reports Server (NTRS)

Lawhead, Joel

2007-01-01

BasinTools Module 1 processes remotely sensed raster data, including multi- and hyper-spectral data products, via a Web site with no downloads and no plug-ins required. The interface provides standardized algorithms designed so that a user with little or no remote-sensing experience can use the site. This Web-based approach reduces the amount of software, hardware, and computing power necessary to perform the specified analyses. Access to imagery and derived products is enterprise-level and controlled. Because the user never takes possession of the imagery, the licensing of the data is greatly simplified. BasinTools takes the "just-in-time" inventory control model from commercial manufacturing and applies it to remotely-sensed data. Products are created and delivered on-the-fly with no human intervention, even for casual users. Well-defined procedures can be combined in different ways to extend verified and validated methods in order to derive new remote-sensing products, which improves efficiency in any well-defined geospatial domain. Remote-sensing products produced in BasinTools are self-documenting, allowing procedures to be independently verified or peer-reviewed. The software can be used enterprise-wide to conduct low-level remote sensing, viewing, sharing, and manipulating of image data without the need for desktop applications.

What is a picture worth? A history of remote sensing

USGS Publications Warehouse

Moore, Gerald K.

1979-01-01

Remote sensing is the use of electromagnetic energy to measure the physical properties of distant objects. It includes photography and geophysical surveying as well as newer techniques that use other parts of the electromagnetic spectrum. The history of remote sensing begins with photography. The origin of other types of remote sensing can be traced to World War II, with the development of radar, sonar, and thermal infrared detection systems. Since the 1960s, sensors have been designed to operate in virtually all of the electromagnetic spectrum. Today a wide variety of remote sensing instruments are available for use in hydrological studies; satellite data, such as Skylab photographs and Landsat images are particularly suitable for regional problems and studies. Planned future satellites will provide a ground resolution of 10–80 m. Remote sensing is currently used for hydrological applications in most countries of the world. The range of applications includes groundwater exploration determination of physical water quality, snowfield mapping, flood-inundation delineation, and making inventories of irrigated land. The use of remote sensing commonly results in considerable hydrological information at minimal cost. This information can be used to speed-up the development of water resources, to improve management practices, and to monitor environmental problems.

Polymeric Smart Skin Materials: Concepts, Materials, and Devices

DTIC Science & Technology

2006-03-31

nanotube actuators for both sensing and active control of surfaces. State-of-the-art OLED and photovoltaic materials have been developed for display...format. 14. SUBJECT TERMS Multi-sensor paints; carbon nanotube materials and devices; OLED , 15. NUMBER OF PAGES nhntovnlthir ndni elp.trAn-nntjc ’vicn...Significant advances in organic light emitting device ( OLED ) materials has also been achieved as is evident from the publications and invention

International Conference on Remote Sensing Applications for Archaeological Research and World Heritage Conservation

NASA Technical Reports Server (NTRS)

2002-01-01

Contents include the following: Monitoring the Ancient Countryside: Remote Sensing and GIS at the Chora of Chersonesos (Crimea, Ukraine). Integration of Remote Sensing and GIS for Management Decision Support in the Pendjari Biosphere Reserve (Republic of Benin). Monitoring of deforestation invasion in natural reserves of northern Madagascar based on space imagery. Cartography of Kahuzi-Biega National Park. Cartography and Land Use Change of World Heritage Areas and the Benefits of Remote Sensing and GIS for Conservation. Assessing and Monitoring Vegetation in Nabq Protected Area, South Sinai, Egypt, using combine approach of Satellite Imagery and Land Surveys. Evaluation of forage resources in semi-arid savannah environments with satellite imagery: contribution to the management of a protected area (Nakuru National Park) in Kenya. SOGHA, the Surveillance of Gorilla Habitat in World Heritage sites using Space Technologies. Application of Remote Sensing to monitor the Mont-Saint-Michel Bay (France). Application of Remote Sensing & GIS for the Conservation of Natural and Cultural Heritage Sites of the Southern Province of Sri Lanka. Social and Environmental monitoring of a UNESCO Biosphere Reserve: Case Study over the Vosges du Nord and Pfalzerwald Parks using Corona and Spot Imagery. Satellite Remote Sensing as tool to Monitor Indian Reservation in the Brazilian Amazonia. Remote Sensing and GIS Technology for Monitoring UNESCO World Heritage Sites - A Pilot Project. Urban Green Spaces: Modern Heritage. Monitoring of the technical condition of the St. Sophia Cathedral and related monastic buildings in Kiev with Space Applications, geo-positioning systems and GIS tools. The Murghab delta palaeochannel Reconstruction on the Basis of Remote Sensing from Space. Acquisition, Registration and Application of IKONOS Space Imagery for the cultural World Heritage site at Mew, Turkmenistan. Remote Sensing and VR applications for the reconstruction of archaeological landscapes. Archaeology through Space: Experience in Indian Subcontinent. The creation of a GIS Archaeological Site Location Catalogue in Yucatan: A Tool to preserve its Cultural Heritage. Mapping the Ancient Anasazi Roads of Southeast Utah. Remote Sensing and GIS Technology for Identification of Conservation and Heritage sites in Urban Planning. Mapping Angkor: For a new appraisal of the Angkor region. Angkor and radar imaging: seeing a vast pre-industrial low-density, dispersed urban complex. Technical and methodological aspects of archaeological CRM integrating high resolution satellite imagery. The contribution of satellite imagery to archaeological survey: an example from western Syria. The use of satellite images, digital elevation models and ground truth for the monitoring of land degradation in the "Cinque Terre" National park. Remote Sensing and GIS Applications for Protection and Conservation of World Heritage Site on the coast - Case Study of Tamil Nadu Coast, India. Multispectral high resolution satellite imagery in combination with "traditional" remote sensing and ground survey methods to the study of archaeological landscapes. The case study of Tuscany. Use of Remotely-Sensed Imagery in Cultural Landscape. Characterisation at Fort Hood, Texas. Heritage Learning and Data Collection: Biodiversity & Heritage Conservation through Collaborative Monitoring & Research. A collaborative project by UNESCO's WHC (World Heritage Center) & The GLOBE Program (Global Learning and Observations to Benefit the Environment). Practical Remote Sensing Activities in an Interdisciplinary Master-Level Space Course.

Exploring Remote Rensing Through The Use Of Readily-Available Classroom Technologies

NASA Astrophysics Data System (ADS)

Rogers, M. A.

2013-12-01

Frontier geoscience research using remotely-sensed satellite observation routinely requires sophisticated and novel remote sensing techniques to succeed. Describing these techniques in an educational format presents significant challenges to the science educator, especially with regards to the professional development setting where a small, but competent audience has limited instructor contact time to develop the necessary understanding. In this presentation, we describe the use of simple and cheaply available technologies, including ultrasonic transducers, FLIR detectors, and even simple web cameras to provide a tangible analogue to sophisticated remote sensing platforms. We also describe methods of curriculum development that leverages the use of these simple devices to teach the fundamentals of remote sensing, resulting in a deeper and more intuitive understanding of the techniques used in modern remote sensing research. Sample workshop itineraries using these techniques are provided as well.

Remote sensing of wetlands

NASA Technical Reports Server (NTRS)

Roller, N. E. G.

1977-01-01

The concept of using remote sensing to inventory wetlands and the related topics of proper inventory design and data collection are discussed. The material presented shows that aerial photography is the form of remote sensing from which the greatest amount of wetlands information can be derived. For extensive, general-purpose wetlands inventories, however, the use of LANDSAT data may be more cost-effective. Airborne multispectral scanners and radar are, in the main, too expensive to use - unless the information that these sensors alone can gather remotely is absolutely required. Multistage sampling employing space and high altitude remote sensing data in the initial stages appears to be an efficient survey strategy for gathering non-point specific wetlands inventory data over large areas. The operational role of remote sensing insupplying inventory data for application to several typical wetlands management problems is illustrated by summary descriptions of past ERIM projects.

Land Remote Sensing Overview

NASA Technical Reports Server (NTRS)

Byrnes, Ray

2007-01-01

A general overview of the USGS land remote sensing program is presented. The contents include: 1) Brief overview of USGS land remote sensing program; 2) Highlights of JACIE work at USGS; 3) Update on NASA/USGS Landsat Data Continuity Mission; and 4) Notes on alternative data sources.

Hydrological Application of Remote Sensing: Surface States -- Snow

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Kelly, Richard E. J.; Foster, James L.; Chang, Alfred T. C.

2004-01-01

Remote sensing research of snow cover has been accomplished for nearly 40 years. The use of visible, near-infrared, active and passive-microwave remote sensing for the analysis of snow cover is reviewed with an emphasis on the work on the last decade.

Remote sensing education in NASA's technology transfer program

NASA Technical Reports Server (NTRS)

Weinstein, R. H.

1981-01-01

Remote sensing is a principal focus of NASA's technology transfer program activity with major attention to remote sensing education the Regional Program and the University Applications Program. Relevant activities over the past five years are reviewed and perspective on future directions is presented.

Analysis of Coastal Dunes: A Remote Sensing and Statistical Approach.

ERIC Educational Resources Information Center

Jones, J. Richard

1985-01-01

Remote sensing analysis and statistical methods were used to analyze the coastal dunes of Plum Island, Massachusetts. The research methodology used provides an example of a student project for remote sensing, geomorphology, or spatial analysis courses at the university level. (RM)

7 CFR 2.72 - Chairman, World Agricultural Outlook Board.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Commodity Estimates Committees prior to any release outside the Department. (4) Related to remote sensing..., developing, and carrying out satellite remote sensing activities to assure full consideration and evaluation... to the Department's remote sensing activities including: (A) Inter- and intra-agency meetings...

Remote sensing and reflectance profiling in entomology

USDA-ARS?s Scientific Manuscript database

Remote sensing is about characterizing the status of objects and/or classifies their identity based on a combination of spectral features extracted from reflectance or transmission profiles of radiometric energy. Remote sensing can be ground-based, and therefore acquired at a high spatial resolutio...

Planning and Implementation of Remote Sensing Experiments.

DTIC Science & Technology

Contents: TEKTITE II experiment-upwelling detection (NASA Mx 138); Design of oceanographic experiments (Gulf of Mexico, Mx 159); Design of oceanographic experiments (Gulf of Mexico, Mx 165); Experiments on thermal pollution; Remote sensing newsletter; Symposium on remote sensing in marine biology and fishery resources.

Ionospheric Profiles from Ultraviolet Remote Sensing

DTIC Science & Technology

1997-09-30

The long-term goal of this project is to obtain ionospheric profiles from ultraviolet remote sensing of the ionosphere from orbiting space platforms... Remote sensing of the nighttime ionosphere is a more straightforward process because of the absence of the complications brought about by daytime

The hydrology of prehistoric farming systems in a central Arizona ecotone

NASA Technical Reports Server (NTRS)

Gumerman, G. J.; Hanson, J. A.; Brew, D.; Tomoff, K.; Weed, C. S.

1975-01-01

The prehistoric land use and water management in the semi-arid Southwest was examined. Remote sensing data, geology, hydrology and biology are discussed along with an evaluation of remote sensing contributions, recommendations for applications, and proposed future remote sensing studies.

Research investigations in and demonstrations of remote sensing applications to urban environmental problems

NASA Technical Reports Server (NTRS)

Hidalgo, J. U.

1975-01-01

The applicability of remote sensing to transportation and traffic analysis, urban quality, and land use problems is discussed. Other topics discussed include preliminary user analysis, potential uses, traffic study by remote sensing, and urban condition analysis using ERTS.

Multi-scale remote sensing of coral reefs

USGS Publications Warehouse

Andréfouët, Serge; Hochberg, E.J.; Chevillon, Christophe; Muller-Karger, Frank E.; Brock, John C.; Hu, Chuanmin

2005-01-01

In this chapter we present how both direct and indirect remote sensing can be integrated to address two major coral reef applications - coral bleaching and assessment of biodiversity. This approach reflects the current non-linear integration of remote sensing for environmental assessment of coral reefs, resulting from a rapid increase in available sensors, processing methods and interdisciplinary collaborations (Andréfouët and Riegl, 2004). Moreover, this approach has greatly benefited from recent collaborations of once independent investigations (e.g., benthic ecology, remote sensing, and numerical modeling).

Remote sensing program

NASA Technical Reports Server (NTRS)

Philipson, W. R. (Principal Investigator)

1983-01-01

Built on Cornell's thirty years of experience in aerial photographic studies, the NASA-sponsored remote sensing program strengthened instruction and research in remote sensing, established communication links within and beyond the university community, and conducted research projects for or with town, county, state, federal, and private organizations in New York State. The 43 completed applied research projects are listed as well as 13 spinoff grants/contracts. The curriculum offered, consultations provided, and data processing facilities available are described. Publications engendered are listed including the thesis of graduates in the remote sensing program.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The data assimilation problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three-dimensional concentration fields from atmospheric diffusion models. General conditions were derived for the reconstructability of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data was developed.

Development of mathematical techniques for the assimilation of remote sensing data into atmospheric models

NASA Technical Reports Server (NTRS)

Seinfeld, J. H. (Principal Investigator)

1982-01-01

The problem of the assimilation of remote sensing data into mathematical models of atmospheric pollutant species was investigated. The problem is posed in terms of the matching of spatially integrated species burden measurements to the predicted three dimensional concentration fields from atmospheric diffusion models. General conditions are derived for the "reconstructability' of atmospheric concentration distributions from data typical of remote sensing applications, and a computational algorithm (filter) for the processing of remote sensing data is developed.

The applicability of remote sensing to Earth biological problems. Part 2: The potential of remote sensing in pest management

NASA Technical Reports Server (NTRS)

Polhemus, J. T.

1980-01-01

Five troublesome insect pest groups were chosen for study. These represent a broad spectrum of life cycles, ecological indicators, pest management strategies, and remote sensing requirements. Background data, and field study results for each of these subjects is discussed for each insect group. Specific groups studied include tsetse flies, locusts, western rangeland grasshoppers, range caterpillars, and mosquitoes. It is concluded that remote sensing methods are aplicable to the pest management of the insect groups studied.

Searches over graphs representing geospatial-temporal remote sensing data

DOEpatents

Brost, Randolph; Perkins, David Nikolaus

2018-03-06

Various technologies pertaining to identifying objects of interest in remote sensing images by searching over geospatial-temporal graph representations are described herein. Graphs are constructed by representing objects in remote sensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Geospatial-temporal graph searches are made computationally efficient by taking advantage of characteristics of geospatial-temporal data in remote sensing images through the application of various graph search techniques.

Antarctic Tabular Iceberg A-24 Movement and Decay Via Satellite Remote Sensing

DTIC Science & Technology

1993-04-02

Austraia. Pulished by ft Amencan Meteormogicat Society. Bost:o, MA. P7.27 ANTARCTIC TABULAR ICEBERG A-24 MOVEMENT AND DECAY VIA SATELLITE REMOTE SENSING AD...2. REMOTE SENSING DATA SOURCES 85 GHz imagery verified that the iceberg began to indicate more than The vis/IR imagery from the one berg existed in...SSM/I Instrument Evaluation, conditions. The corresponding IR data IEEE Trans. Geosci. Remote Sensing , was also of particular interest due Vol. 28, pp

Coastal Remote Sensing Investigations. Volume 2. Beach Environment

DTIC Science & Technology

1980-12-01

1 ’ "■"’.."■•■.» ■ a .1 "llpll CO Ifi o Q- O CO I y Final Report COASTAL REMOTE SENSING INVESTIGATIONS VOLUME 2: BEACH... Remote Sensing Grain Size Soil Moisture Soil Mineralogy Multispectral Scanner iO AUTNACT fCHtfÜBB on merit nJt ij ntinwin and idmlify In hloti...The work reported herein summarizes the final research activity in the Beach Environment Task of a program at ERIM entitled "Coastal Remote Sensing Investigations

Radar Remote Sensing of Waves and Currents in the Nearshore Zone

DTIC Science & Technology

2006-01-01

and application of novel microwave, acoustic, and optical remote sensing techniques. The objectives of this effort are to determine the extent to which...Doppler radar techniques are useful for nearshore remote sensing applications. Of particular interest are estimates of surf zone location and extent...surface currents, waves, and bathymetry. To date, optical (video) techniques have been the primary remote sensing technology used for these applications. A key advantage of the radar is its all weather day-night operability.

Emergence of the Green’s Functions from Noise and Passive Acoustic Remote Sensing of Ocean Dynamics

DTIC Science & Technology

2009-09-30

Acoustic Remote Sensing of Ocean Dynamics Oleg A. Godin CIRES/Univ. of Colorado and NOAA/OAR/Earth System Research Lab., R/PSD99, 325 Broadway...characterization of a time-varying ocean where ambient acoustic noise is utilized as a probing signal. • To develop a passive remote sensing technique for...inapplicable. 3. To quantify degradation of performance of passive remote sensing techniques due to ocean surface motion and other variations of underwater

Active and Passive Remote Sensing of Ice

DTIC Science & Technology

1993-01-26

92 4. TITLE AND SUBTITLE S. FUNDING NUMBERS Active and Passive Remote Sensing of Ice NO0014-89-J-l 107 6. AUTHOR(S) 425f023-08 Prof. J.A. Kong 7... REMOTE SENSING OF ICE Sponsored by: Department of the Navy Office of Naval Research Contract number: N00014-89-J-1107 Research Organization: Center for...J. A. Kong Period covered: October 1, 1988 - November 30, 1992 St ACTIVE AND PASSIVE REMOTE SENSING OF ICE FINAL REPORT This annual report covers

Investigation of the application of remote sensing technology to environmental monitoring

NASA Technical Reports Server (NTRS)

Rader, M. L. (Principal Investigator)

1980-01-01

Activities and results are reported of a project to investigate the application of remote sensing technology developed for the LACIE, AgRISTARS, Forestry and other NASA remote sensing projects for the environmental monitoring of strip mining, industrial pollution, and acid rain. Following a remote sensing workshop for EPA personnel, the EOD clustering algorithm CLASSY was selected for evaluation by EPA as a possible candidate technology. LANDSAT data acquired for a North Dakota test sight was clustered in order to compare CLASSY with other algorithms.

Remote Sensing For Water Resources And Hydrology. Recommended research emphasis for the 1980's

NASA Technical Reports Server (NTRS)

1980-01-01

The problems and the areas of activity that the Panel believes should be emphasized in work on remote sensing for water resources and hydrology in the 1980's are set forth. The Panel deals only with those activities and problems in water resources and hydrology that the Panel considers important, and where, in the Panel's opinion, application of current remote sensing capability or advancements in remote sensing capability can help meet urgent problems and provide large returns in practical benefits.

Research on Method of Interactive Segmentation Based on Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yang, Y.; Li, H.; Han, Y.; Yu, F.

2017-09-01

In this paper, we aim to solve the object extraction problem in remote sensing images using interactive segmentation tools. Firstly, an overview of the interactive segmentation algorithm is proposed. Then, our detailed implementation of intelligent scissors and GrabCut for remote sensing images is described. Finally, several experiments on different typical features (water area, vegetation) in remote sensing images are performed respectively. Compared with the manual result, it indicates that our tools maintain good feature boundaries and show good performance.

Nonlinear Photonic Systems for V- and W-Band Antenna Remoting Applications

DTIC Science & Technology

2016-10-22

for commercial, academic, and military purposes delivering microwaves through fibers to remote areas for wireless sensing , imaging, and detection...academic, and military purposes, which use optical carriers to deliver microwave signals to remote areas for wireless sensing , imaging, and...and military purposes, which use optical carriers to deliver microwave signals to remote areas for wireless sensing , imaging, and detection

First results of ground-based LWIR hyperspectral imaging remote gas detection

NASA Astrophysics Data System (ADS)

Zheng, Wei-jian; Lei, Zheng-gang; Yu, Chun-chao; Wang, Hai-yang; Fu, Yan-peng; Liao, Ning-fang; Su, Jun-hong

2014-11-01

The new progress of ground-based long-wave infrared remote sensing is presented. The LWIR hyperspectral imaging by using the windowing spatial and temporal modulation Fourier spectroscopy, and the results of outdoor ether gas detection, verify the features of LWIR hyperspectral imaging remote sensing and technical approach. It provides a new technical means for ground-based gas remote sensing.

Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR: Chapter 1

USGS Publications Warehouse

Panda, Sudhanshu S.; Rao, Mahesh N.; Thenkabail, Prasad S.; Fitzerald, James E.

2015-01-01

The American Society of Photogrammetry and Remote Sensing defined remote sensing as the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell et al., 1983). Environmental Systems Research Institute (ESRI) in its geographic information system (GIS) dictionary defines remote sensing as “collecting and interpreting information about the environment and the surface of the earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the earth’s surface or from the atmosphere, or by sending signals transmitted from a device and reflected back to it (ESRI, 2014).” The usual source of passive remote sensing data is the measurement of reflected or transmitted electromagnetic radiation (EMR) from the sun across the electromagnetic spectrum (EMS); this can also include acoustic or sound energy, gravity, or the magnetic field from or of the objects under consideration. In this context, the simple act of reading this text is considered remote sensing. In this case, the eye acts as a sensor and senses the light reflected from the object to obtain information about the object. It is the same technology used by a handheld camera to take a photograph of a person or a distant scenic view. Active remote sensing, however, involves sending a pulse of energy and then measuring the returned energy through a sensor (e.g., Radio Detection and Ranging [RADAR], Light Detection and Ranging [LiDAR]). Thermal sensors measure emitted energy by different objects. Thus, in general, passive remote sensing involves the measurement of solar energy reflected from the Earth’s surface, while active remote sensing involves synthetic (man-made) energy pulsed at the environment and the return signals are measured and recorded.

Progress in remote sensing of global land surface heat fluxes and evaporations with a turbulent heat exchange parameterization method

NASA Astrophysics Data System (ADS)

Chen, Xuelong; Su, Bob

2017-04-01

Remote sensing has provided us an opportunity to observe Earth land surface with a much higher resolution than any of GCM simulation. Due to scarcity of information for land surface physical parameters, up-to-date GCMs still have large uncertainties in the coupled land surface process modeling. One critical issue is a large amount of parameters used in their land surface models. Thus remote sensing of land surface spectral information can be used to provide information on these parameters or assimilated to decrease the model uncertainties. Satellite imager could observe the Earth land surface with optical, thermal and microwave bands. Some basic Earth land surface status (land surface temperature, canopy height, canopy leaf area index, soil moisture etc.) has been produced with remote sensing technique, which already help scientists understanding Earth land and atmosphere interaction more precisely. However, there are some challenges when applying remote sensing variables to calculate global land-air heat and water exchange fluxes. Firstly, a global turbulent exchange parameterization scheme needs to be developed and verified, especially for global momentum and heat roughness length calculation with remote sensing information. Secondly, a compromise needs to be innovated to overcome the spatial-temporal gaps in remote sensing variables to make the remote sensing based land surface fluxes applicable for GCM model verification or comparison. A flux network data library (more 200 flux towers) was collected to verify the designed method. Important progress in remote sensing of global land flux and evaporation will be presented and its benefits for GCM models will also be discussed. Some in-situ studies on the Tibetan Plateau and problems of land surface process simulation will also be discussed.

Research Status and Development Trend of Remote Sensing in China Using Bibliometric Analysis

NASA Astrophysics Data System (ADS)

Zeng, Y.; Zhang, J.; Niu, R.

2015-06-01

Remote sensing was introduced into China in 1970s and then began to flourish. At present, China has developed into a big remote sensing country, and remote sensing is increasingly playing an important role in various fields of national economic construction and social development. Based on China Academic Journals Full-text Database and China Citation Database published by China National Knowledge Infrastructure, this paper analyzed academic characteristics of 963 highly cited papers published by 16 professional and academic journals in the field of surveying and mapping from January 2010 to December 2014 in China, which include hot topics, literature authors, research institutions, and fundations. At the same time, it studied a total of 51,149 keywords published by these 16 journals during the same period. Firstly by keyword selection, keyword normalization, keyword consistency and keyword incorporation, and then by analysis of high frequency keywords, the progress and prospect of China's remote sensing technology in data acquisition, data processing and applications during the past five years were further explored and revealed. It can be seen that: highly cited paper analysis and word frequency analysis is complementary on subject progress analysis; in data acquisition phase, research focus is new civilian remote sensing satellite systems and UAV remote sensing system; research focus of data processing and analysis is multi-source information extraction and classification, laser point cloud data processing, objectoriented high resolution image analysis, SAR data and hyper-spectral image processing, etc.; development trend of remote sensing data processing is quantitative, intelligent, automated, and real-time, and the breadth and depth of remote sensing application is gradually increased; parallel computing, cloud computing and geographic conditions monitoring and census are the new research focuses to be paid attention to.

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2003-01-01

In 2002, the U. S. Geological Survey (USGS) launched a program to enhance the acquisition, preservation, and use of remotely sensed data for USGS science programs, as well as for those of cooperators and customers. Remotely sensed data are fundamental tools for studying the Earth's land surface, including coastal and near-shore environments. For many decades, the USGS has been a leader in providing remotely sensed data to the national and international communities. Acting on its historical topographic mapping mission, the USGS has archived and distributed aerial photographs of the United States for more than half a century. Since 1972, the USGS has acquired, processed, archived, and distributed Landsat and other satellite and airborne remotely sensed data products to users worldwide. Today, the USGS operates and manages the Landsats 5 and 7 missions and cooperates with the National Aeronautics and Space Administration (NASA) to define and implement future satellite missions that will continue and expand the collection of moderate-resolution remotely sensed data. In addition to being a provider of remotely sensed data, the USGS is a user of these data and related remote sensing technology. These data are used in natural resource evaluations for energy and minerals, coastal environmental surveys, assessments of natural hazards (earthquakes, volcanoes, and landslides), biological surveys and investigations, water resources status and trends analyses and studies, and geographic and cartographic applications, such as wildfire detection and tracking and as a source of information for The National Map. The program furthers these distinct but related roles by leading the USGS activities in providing remotely sensed data while advancing applications of such data for USGS programs and a wider user community.

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Methods and Applications

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.

1998-01-01

Thermal Infrared (TIR) remote sensing data can provide important measurements of surface energy fluxes and temperatures, which are integral to understanding landscape processes and responses. One example of this is the successful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that satellite-based measurements from TIR remote sensing data can lead to more accurate regional-scale estimates of daily evapotranspiration. With further refinement in analytical techniques and models, the use of TIR data from airborne and satellite sensors could be very useful for parameterizing surface moisture conditions and developing better simulations of landscape energy exchange over a variety of conditions and space and time scales. Thus, TIR remote sensing data can significantly contribute to the observation, measurement, and analysis of energy balance characteristics (i.e., the fluxes and redistribution of thermal energy within and across the land surface) as an implicit and important aspect of landscape dynamics and landscape functioning. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate primarily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references on TIR remote sensing throughout the scientific literature. It is our purpose here to provide evidence from work that has employed TIR remote sensing for analysis of landscape characteristics to illustrate how these data can provide important data for the improved measurement of landscape energy response and energy flux relationships. We examine the direct or indirect use of TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes.

Use of Remote Sensing for Decision Support in Africa

NASA Technical Reports Server (NTRS)

Policelli, Frederick S.

2007-01-01

Over the past 30 years, the scientific community has learned a great deal about the Earth as an integrated system. Much of this research has been enabled by the development of remote sensing technologies and their operation from space. Decision makers in many nations have begun to make use of remote sensing data for resource management, policy making, and sustainable development planning. This paper makes an attempt to provide a survey of the current state of the requirements and use of remote sensing for sustainable development in Africa. This activity has shown that there are not many climate data ready decision support tools already functioning in Africa. There are, however, endusers with known requirements who could benefit from remote sensing data.

Design and Performance of a Multiwavelength Airborne Polarimetric Lidar for Vegetation Remote Sensing

NASA Astrophysics Data System (ADS)

Tan, Songxin; Narayanan, Ram M.

2004-04-01

The University of Nebraska has developed a multiwavelength airborne polarimetric lidar (MAPL) system to support its Airborne Remote Sensing Program for vegetation remote sensing. The MAPL design and instrumentation are described in detail. Characteristics of the MAPL system include lidar waveform capture and polarimetric measurement capabilities, which provide enhanced opportunities for vegetation remote sensing compared with current sensors. Field tests were conducted to calibrate the range measurement. Polarimetric calibration of the system is also discussed. Backscattered polarimetric returns, as well as the cross-polarization ratios, were obtained from a small forested area to validate the system's ability for vegetation canopy detection. The system has been packaged to fly abroad a Piper Saratoga aircraft for airborne vegetation remote sensing applications.

Remote sensing with unmanned aircraft systems for precision agriculture applications

USDA-ARS?s Scientific Manuscript database

The Federal Aviation Administration is revising regulations for using unmanned aircraft systems (UAS) in the national airspace. An important potential application of UAS may be as a remote-sensing platform for precision agriculture, but simply down-scaling remote sensing methodologies developed usi...

Remote sensing for cotton farming

USDA-ARS?s Scientific Manuscript database

Application of remote sensing technologies in agriculture began with the use of aerial photography to identify cotton root rot in the late 1920s. From then on, agricultural remote sensing has developed gradually until the introduction of precision farming technologies in the late 1980s and biotechno...

Remote sensing for mined area reclamation: Application inventory

NASA Technical Reports Server (NTRS)

1971-01-01

Applications of aerial remote sensing to coal mined area reclamation are documented, and information concerning available data banks for coal producing areas in the east and midwest is given. A summary of mined area information requirements to which remote sensing methods might contribute is included.

Remote sensing applications for transportation and traffic engineering studies: A review of the literature

NASA Technical Reports Server (NTRS)

Epps, J. W.

1973-01-01

Current references were surveyed for the application of remote sensing to traffic and transportation studies. The major problems are presented that concern traffic engineers and transportation managers, and the literature references that discuss remote sensing applications are summarized.

What does remote sensing do for ecology?

NASA Technical Reports Server (NTRS)

Roughgarden, J.; Running, S. W.; Matson, P. A.

1991-01-01

The application of remote sensing to ecological investigations is briefly discussed. Emphasis is given to the recruitment problem in marine population dynamics, the regional analysis of terrestrial ecosystems, and the monitoring of ecological changes. Impediments to the use of remote sensing data in ecology are addressed.

REVIEW OF METHODS FOR REMOTE SENSING OF ATMOSPHERIC EMISSIONS FROM STATIONARY SOURCES

EPA Science Inventory

The report reviews the commercially available and developing technologies for the application of remote sensing to the measurement of source emissions. The term 'remote sensing technology', as applied in the report, means the detection or concentration measurement of trace atmosp...

75 FR 26919 - Charter Renewals

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-13

...: Notice of Renewal of the Advisory Committee on Commercial Remote Sensing Charter. SUMMARY: In accordance... Commercial Remote Sensing (ACCRES) is in the public interest in connection with the performance of duties... Oceans and Atmosphere on matters relating to the U.S. commercial remote-sensing industry and NOAA's...

75 FR 52307 - Submission for OMB Review; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-25

...: National Oceanic and Atmospheric Administration (NOAA). Title: Licensing of Private Remote-Sensing Space... National Satellite Land Remote Sensing Data Archive; 3 hours for the submission of an operational quarterly... and Uses: NOAA has established requirements for the licensing of private operators of remote-sensing...

Atmospheric Correction Algorithm for Hyperspectral Remote Sensing of Ocean Color from Space

DTIC Science & Technology

2000-02-20

Existing atmospheric correction algorithms for multichannel remote sensing of ocean color from space were designed for retrieving water-leaving...atmospheric correction algorithm for hyperspectral remote sensing of ocean color with the near-future Coastal Ocean Imaging Spectrometer. The algorithm uses

Target detection method by airborne and spaceborne images fusion based on past images

NASA Astrophysics Data System (ADS)

Chen, Shanjing; Kang, Qing; Wang, Zhenggang; Shen, ZhiQiang; Pu, Huan; Han, Hao; Gu, Zhongzheng

2017-11-01

To solve the problem that remote sensing target detection method has low utilization rate of past remote sensing data on target area, and can not recognize camouflage target accurately, a target detection method by airborne and spaceborne images fusion based on past images is proposed in this paper. The target area's past of space remote sensing image is taken as background. The airborne and spaceborne remote sensing data is fused and target feature is extracted by the means of airborne and spaceborne images registration, target change feature extraction, background noise suppression and artificial target feature extraction based on real-time aerial optical remote sensing image. Finally, the support vector machine is used to detect and recognize the target on feature fusion data. The experimental results have established that the proposed method combines the target area change feature of airborne and spaceborne remote sensing images with target detection algorithm, and obtains fine detection and recognition effect on camouflage and non-camouflage targets.

A light and faster regional convolutional neural network for object detection in optical remote sensing images

NASA Astrophysics Data System (ADS)

Ding, Peng; Zhang, Ye; Deng, Wei-Jian; Jia, Ping; Kuijper, Arjan

2018-07-01

Detection of objects from satellite optical remote sensing images is very important for many commercial and governmental applications. With the development of deep convolutional neural networks (deep CNNs), the field of object detection has seen tremendous advances. Currently, objects in satellite remote sensing images can be detected using deep CNNs. In general, optical remote sensing images contain many dense and small objects, and the use of the original Faster Regional CNN framework does not yield a suitably high precision. Therefore, after careful analysis we adopt dense convoluted networks, a multi-scale representation and various combinations of improvement schemes to enhance the structure of the base VGG16-Net for improving the precision. We propose an approach to reduce the test-time (detection time) and memory requirements. To validate the effectiveness of our approach, we perform experiments using satellite remote sensing image datasets of aircraft and automobiles. The results show that the improved network structure can detect objects in satellite optical remote sensing images more accurately and efficiently.

Ontology-based classification of remote sensing images using spectral rules

NASA Astrophysics Data System (ADS)

Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

2017-05-01

Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

USGS Publications Warehouse

Baccini, A.; Friedl, M.A.; Woodcock, C.E.; Zhu, Z.

2007-01-01

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor's field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure. 

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

USGS Publications Warehouse

Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.

2010-01-01

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

a Hadoop-Based Distributed Framework for Efficient Managing and Processing Big Remote Sensing Images

NASA Astrophysics Data System (ADS)

Wang, C.; Hu, F.; Hu, X.; Zhao, S.; Wen, W.; Yang, C.

2015-07-01

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

A NDVI assisted remote sensing image adaptive scale segmentation method

NASA Astrophysics Data System (ADS)

Zhang, Hong; Shen, Jinxiang; Ma, Yanmei

2018-03-01

Multiscale segmentation of images can effectively form boundaries of different objects with different scales. However, for the remote sensing image which widely coverage with complicated ground objects, the number of suitable segmentation scales, and each of the scale size is still difficult to be accurately determined, which severely restricts the rapid information extraction of the remote sensing image. A great deal of experiments showed that the normalized difference vegetation index (NDVI) can effectively express the spectral characteristics of a variety of ground objects in remote sensing images. This paper presents a method using NDVI assisted adaptive segmentation of remote sensing images, which segment the local area by using NDVI similarity threshold to iteratively select segmentation scales. According to the different regions which consist of different targets, different segmentation scale boundaries could be created. The experimental results showed that the adaptive segmentation method based on NDVI can effectively create the objects boundaries for different ground objects of remote sensing images.

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

Book Review: Book review

NASA Astrophysics Data System (ADS)

van der Linden, Sebastian

2016-05-01

Compiling a good book on urban remote sensing is probably as hard as the research in this disciplinary field itself. Urban areas comprise various environments and show high heterogeneity in many respects, they are highly dynamic in time and space and at the same time of greatest influence on connected and even tele-connected regions due to their great economic importance. Urban remote sensing is therefore of great importance, yet as manifold as its study area: mapping urban areas (or sub-categories thereof) plays an important (and challenging) role in land use and land cover (change) monitoring; the analysis of urban green and forests is by itself a specialization of ecological remote sensing; urban climatology asks for spatially and temporally highly resolved remote sensing products; the detection of artificial objects is not only a common and important remote sensing application but also a typical benchmark for image analysis techniques, etc. Urban analyses are performed with all available spaceborne sensor types and at the same time they are one of the most relevant fields for airborne remote sensing. Several books on urban remote sensing have been published during the past 10 years, each taking a different perspective. The book Global Urban Monitoring and Assessment through Earth Observation is motivated by the objectives of the Global Urban Observation and Information Task (SB-04) in the GEOSS (Global Earth Observation System of Systems) 2012-2015 workplan (compare Chapter 2) and wants to highlight the global aspects of state-of-the-art urban remote sensing.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE)

PubMed Central

Tian, Xin; Li, Zengyuan; Chen, Erxue; Liu, Qinhuo; Yan, Guangjian; Wang, Jindi; Niu, Zheng; Zhao, Shaojie; Li, Xin; Pang, Yong; Su, Zhongbo; van der Tol, Christiaan; Liu, Qingwang; Wu, Chaoyang; Xiao, Qing; Yang, Le; Mu, Xihan; Bo, Yanchen; Qu, Yonghua; Zhou, Hongmin; Gao, Shuai; Chai, Linna; Huang, Huaguo; Fan, Wenjie; Li, Shihua; Bai, Junhua; Jiang, Lingmei; Zhou, Ji

2015-01-01

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques. PMID:26332035

Multiscale and Multitemporal Urban Remote Sensing

NASA Astrophysics Data System (ADS)

Mesev, V.

2012-07-01

The remote sensing of urban areas has received much attention from scientists conducting studies on measuring sprawl, congestion, pollution, poverty, and environmental encroachment. Yet much of the research is case and data-specific where results are greatly influenced by prevailing local conditions. There seems to be a lack of epistemological links between remote sensing and conventional theoretical urban geography; in other words, an oversight for the appreciation of how urban theory fuels urban change and how urban change is measured by remotely sensed data. This paper explores basic urban theories such as centrality, mobility, materiality, nature, public space, consumption, segregation and exclusion, and how they can be measured by remote sensing sources. In particular, the link between structure (tangible objects) and function (intangible or immaterial behavior) is addressed as the theory that supports the wellknow contrast between land cover and land use classification from remotely sensed data. The paper then couches these urban theories and contributions from urban remote sensing within two analytical fields. The first is the search for an "appropriate" spatial scale of analysis, which is conveniently divided between micro and macro urban remote sensing for measuring urban structure, understanding urban processes, and perhaps contributions to urban theory at a variety of scales of analysis. The second is on the existence of a temporal lag between materiality of urban objects and the planning process that approved their construction, specifically how time-dependence in urban structural-functional models produce temporal lags that alter the causal links between societal and political functional demands and structural ramifications.

Feasibility of remote sensing for detecting thermal pollution. Part 1: Feasibility study. Part 2: Implementation plan. [coastal ecology

NASA Technical Reports Server (NTRS)

Veziroglu, T. N.; Lee, S. S.

1973-01-01

A feasibility study for the development of a three-dimensional generalized, predictive, analytical model involving remote sensing, in-situ measurements, and an active system to remotely measure turbidity is presented. An implementation plan for the development of the three-dimensional model and for the application of remote sensing of temperature and turbidity measurements is outlined.

Remote sensing procurement package: Remote Sensing Industry Directory

NASA Technical Reports Server (NTRS)

1981-01-01

A directory of over 140 firms and organizations which contains detailed information in the types of products, services and equipment which they offer is presented. Also included for each firm or organization are addresses, phone numbers, contact person(s), and experience in the remote sensing field.

Accommodating Student Diversity in Remote Sensing Instruction.

ERIC Educational Resources Information Center

Hammen, John L., III.

1992-01-01

Discusses the difficulty of teaching computer-based remote sensing to students of varying levels of computer literacy. Suggests an instructional method that accommodates all levels of technical expertise through the use of microcomputers. Presents a curriculum that includes an introduction to remote sensing, digital image processing, and…

76 FR 65529 - Agency Information Collection Activities: Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-21

... National Land Remote Sensing Education, Outreach and Research Activity (NLRSEORA). As required by the... Drive MS 517, Reston, VA, 20192 (mail) . SUPPLEMENTARY INFORMATION: Title: National Land Remote Sensing... Remote Sensing Program, therefore it is more appropriate to refer to this effort as an activity rather...

15 CFR 960.11 - Conditions for operation.

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENVIRONMENTAL DATA SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.11 Conditions for... all facilities which comprise the remote sensing space system for the purpose of conducting license... possession, the licensee shall offer such data to the National Satellite Land Remote Sensing Data Archive at...

15 CFR 960.3 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.3 Definitions. For purposes of the regulations in this part, the following terms have the following meanings: Act means the Land Remote Sensing... application for a NOAA license to operate a remote sensing space system. Assistant Administrator means the...

15 CFR 960.2 - Scope.

Code of Federal Regulations, 2010 CFR

2010-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.2 Scope. (a) The Act and the regulations in this... proposes to operate a private remote sensing space system, either directly or through an affiliate or... private remote sensing system. (b) In determining whether substantial connections exist with regard to a...

Western Regional Remote Sensing Conference Proceedings, 1981

NASA Technical Reports Server (NTRS)

1981-01-01

Diverse applications of LANDSAT data, problem solutions, and operational goals are described by remote sensing users from 14 western states. The proposed FY82 federal budget reductions for technology transfer activities and the planned transition of the operational remote sensing system to NOAA's supervision are also considered.

Some Defence Applications of Civilian Remote Sensing Satellite Images

DTIC Science & Technology

1993-11-01

This report is on a pilot study to demonstrate some of the capabilities of remote sensing in intelligence gathering. A wide variety of issues, both...colour images. The procedure will be presented in a companion report. Remote sensing , Satellite imagery, Image analysis, Military applications, Military intelligence.

Active/Passive Remote Sensing of the Ocean Surface at Microwave Frequencies

DTIC Science & Technology

1999-09-30

This report summarizes research activities and results obtained under grant N000l4-99-1-0627 "Active/Passive Remote Sensing of the Ocean Surface at...Measurements were completed during April 1999 by the Microwave Remote Sensing Laboratory at the University of Massachusetts.

15 CFR 960.2 - Scope.

Code of Federal Regulations, 2012 CFR

2012-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.2 Scope. (a) The Act and the regulations in this... proposes to operate a private remote sensing space system, either directly or through an affiliate or... private remote sensing system. (b) In determining whether substantial connections exist with regard to a...

15 CFR 960.2 - Scope.

Code of Federal Regulations, 2014 CFR

2014-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.2 Scope. (a) The Act and the regulations in this... proposes to operate a private remote sensing space system, either directly or through an affiliate or... private remote sensing system. (b) In determining whether substantial connections exist with regard to a...

15 CFR 960.2 - Scope.

Code of Federal Regulations, 2013 CFR

2013-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.2 Scope. (a) The Act and the regulations in this... proposes to operate a private remote sensing space system, either directly or through an affiliate or... private remote sensing system. (b) In determining whether substantial connections exist with regard to a...

15 CFR 960.2 - Scope.

Code of Federal Regulations, 2011 CFR

2011-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.2 Scope. (a) The Act and the regulations in this... proposes to operate a private remote sensing space system, either directly or through an affiliate or... private remote sensing system. (b) In determining whether substantial connections exist with regard to a...

Tools and Methods for the Registration and Fusion of Remotely Sensed Data

NASA Technical Reports Server (NTRS)

Goshtasby, Arthur Ardeshir; LeMoigne, Jacqueline

2010-01-01

Tools and methods for image registration were reviewed. Methods for the registration of remotely sensed data at NASA were discussed. Image fusion techniques were reviewed. Challenges in registration of remotely sensed data were discussed. Examples of image registration and image fusion were given.

Linking remote sensing, land cover and disease.

PubMed

Curran, P J; Atkinson, P M; Foody, G M; Milton, E J

2000-01-01

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

The LARSYS educational package: Instructor's notes. [instructional materials for training people to analyze remotely sensed data

NASA Technical Reports Server (NTRS)

Lindenlaub, J. C.; Davis, S. M.

1974-01-01

Materials are presented for assisting instructors in teaching the LARSYS Educational Package, which is a set of instructional materials to train people to analyze remotely sensed multispectral data. The seven units of the package are described. These units are: quantitative remote sensing, overview of the LARSYS software system, the 2780 remote terminal, demonstration of LARSYS on the 2780 remote terminal, exercises, guide to multispectral data analysis, and a case study using LARSYS for analysis of LANDSAT data.

A new simple concept for ocean colour remote sensing using parallel polarisation radiance

PubMed Central

He, Xianqiang; Pan, Delu; Bai, Yan; Wang, Difeng; Hao, Zengzhou

2014-01-01

Ocean colour remote sensing has supported research on subjects ranging from marine ecosystems to climate change for almost 35 years. However, as the framework for ocean colour remote sensing is based on the radiation intensity at the top-of-atmosphere (TOA), the polarisation of the radiation, which contains additional information on atmospheric and water optical properties, has largely been neglected. In this study, we propose a new simple concept to ocean colour remote sensing that uses parallel polarisation radiance (PPR) instead of the traditional radiation intensity. We use vector radiative transfer simulation and polarimetric satellite sensing data to demonstrate that using PPR has two significant advantages in that it effectively diminishes the sun glint contamination and enhances the ocean colour signal at the TOA. This concept may open new doors for ocean colour remote sensing. We suggest that the next generation of ocean colour sensors should measure PPR to enhance observational capability. PMID:24434904

The University of Kansas Applied Sensing Program: An operational perspective

NASA Technical Reports Server (NTRS)

Martinko, E. A.

1981-01-01

The Kansas applied remote sensing (KARS) program conducts demonstration projects and applied research on remote sensing techniques which enable local, regional, state and federal agency personnel to better utilize available satellite and airborne remote sensing systems. As liason with Kansas agencies for the Earth Resources Laboratory (ERL), Kansas demonstration project, KARS coordinated interagency communication, field data collection, hands-on training, and follow-on technical assistance and worked with Kansas agency personnel in evaluating land cover maps provided by ERL. Short courses are being conducted to provide training in state-of-the-art remote sensing technology for university faculty, state personnel, and persons from private industry and federal government. Topics are listed which were considered in intensive five-day courses covering the acquisition, interpretation, and application of information derived through remote sensing with specific training and hands-on experience in image interpretation and the analysis of LANDSAT data are listed.

Metal oxide based multisensor array and portable database for field analysis of antioxidants

PubMed Central

Sharpe, Erica; Bradley, Ryan; Frasco, Thalia; Jayathilaka, Dilhani; Marsh, Amanda; Andreescu, Silvana

2014-01-01

We report a novel chemical sensing array based on metal oxide nanoparticles as a portable and inexpensive paper-based colorimetric method for polyphenol detection and field characterization of antioxidant containing samples. Multiple metal oxide nanoparticles with various polyphenol binding properties were used as active sensing materials to develop the sensor array and establish a database of polyphenol standards that include epigallocatechin gallate, gallic acid, resveratrol, and Trolox among others. Unique charge-transfer complexes are formed between each polyphenol and each metal oxide on the surface of individual sensors in the array, creating distinct optically detectable signals which have been quantified and logged into a reference database for polyphenol identification. The field-portable Pantone/X-Rite© CapSure® color reader was used to create this database and to facilitate rapid colorimetric analysis. The use of multiple metal-oxide sensors allows for cross-validation of results and increases accuracy of analysis. The database has enabled successful identification and quantification of antioxidant constituents within real botanical extractions including green tea. Formation of charge-transfer complexes is also correlated with antioxidant activity exhibiting electron transfer capabilities of each polyphenol. The antioxidant activity of each sample was calculated and validated against the oxygen radical absorbance capacity (ORAC) assay showing good comparability. The results indicate that this method can be successfully used for a more comprehensive analysis of antioxidant containing samples as compared to conventional methods. This technology can greatly simplify investigations into plant phenolics and make possible the on-site determination of antioxidant composition and activity in remote locations. PMID:24610993

Early results from NASA's SnowEx campaign

NASA Astrophysics Data System (ADS)

Kim, Edward; Gatebe, Charles; Hall, Dorothy; Misakonis, Amy; Elder, Kelly; Marshall, Hans Peter; Hiemstra, Chris; Brucker, Ludovic; Crawford, Chris; Kang, Do Hyuk; De Marco, Eugenia; Beckley, Matt; Entin, Jared

2017-04-01

SnowEx is a multi-year airborne snow campaign with the primary goal of addressing the question: How much water is stored in Earth's terrestrial snow-covered regions? Year 1 (2016-17) focuses on the distribution of snow-water equivalent (SWE) and the snow energy balance in a forested environment. The year 1 primary site is Grand Mesa and the secondary site is the Senator Beck Basin, both in western, Colorado, USA. Ten core sensors on four core aircraft will make observations using a broad suite of airborne sensors including active and passive microwave, and active and passive optical/infrared sensing techniques to determine the sensitivity and accuracy of these potential satellite remote sensing techniques, along with models, to measure snow under a range of forest conditions. SnowEx also includes an extensive range of ground truth measurements—in-situ samples, snow pits, ground based remote sensing measurements, and sophisticated new techniques. A detailed description of the data collected will be given and some early results will be presented. Seasonal snow cover is the largest single component of the cryosphere in areal extent (covering an average of 46M km2 of Earth's surface (31 % of land areas) each year). This seasonal snow has major societal impacts in the areas of water resources, natural hazards (floods and droughts), water security, and weather and climate. The only practical way to estimate the quantity of snow on a consistent global basis is through satellites. Yet, current space-based techniques underestimate storage of snow water equivalent (SWE) by as much as 50%, and model-based estimates can differ greatly vs. estimates based on remotely-sensed observations. At peak coverage, as much as half of snow-covered terrestrial areas involve forested areas, so quantifying the challenge represented by forests is important to plan any future snow mission. Single-sensor approaches may work for certain snow types and certain conditions, but not for others. Snow simply varies too much. Thus, the snow community consensus is that a multi-sensor approach is needed to adequately address global snow, combined with modeling and data assimilation. What remains at issue, then, is how best to combine and use the various sensors in an optimal way. That requires field measurements. NASA's SnowEx airborne campaign is designed to do exactly that. A list of core sensors is as follows. All are from NASA unless otherwise noted. • Radar (volume scattering): European Space Agency's SnowSAR, operated by MetaSensing • Lidar & hyperspectral imager: Airborne Snow Observatory (ASO) • Passive microwave: Airborne Earth Science Microwave Imaging Radiometer (AESMIR) • Bi-directional Reflectance Function (BRDF): the Cloud Absorption Radiometer (CAR) • Thermal Infrared imager • Thermal infrared non-imager from U. Washington • Video camera The ASO suite flew on a King Air, and the other sensors flew on a Navy P-3. In addition, two NASA radars flew on G-III aircraft to test more experimental retrieval techniques: • InSAR altimetry: Glacier and Ice Surface Topography Interferometer (GLISTIN-A) • Radar phase delay: Uninhabited Aerial Vehicle Synthetic Aperture Radar, (UAVSAR)

Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments

USDA-ARS?s Scientific Manuscript database

Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Most image acquisitions from UAS have been in the visible bands, while multispectral remote sensing ap...

Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications.

USGS Publications Warehouse

Clark, R.N.; Roush, T.L.

1984-01-01

Several methods for the analysis of remotely sensed reflectance data are compared, including empirical methods and scattering theories, both of which are important for solving remote sensing problems. The concept of the photon mean path length and the implications for use in modeling reflectance spectra are presented.-from Authors

An overview of the development of remote sensing techniques for the screwworm eradication program

NASA Technical Reports Server (NTRS)

Barnes, C. M.; Forsberg, F. C.

1975-01-01

The current status of remote sensing techniques developed for the screwworm eradication program of the Mexican-American Screwworm Eradication Commission was reported. A review of the type of data and equipment used in the program is presented. Future applications of remote sensing techniques are considered.

Monitoring rice (oryza sativa L.) growth using multifrequency microwave scatterometers

USDA-ARS?s Scientific Manuscript database

Microwave remote sensing can help monitor the land surface water cycle and crop growth. This type of remote sensing has great potential over conventional remote sensing using the visible and infrared regions due to its all-weather day-and-night imaging capabilities. In this investigation, a ground-b...

Conference of Remote Sensing Educators (CORSE-78)

NASA Technical Reports Server (NTRS)

1978-01-01

Ways of improving the teaching of remote sensing students at colleges and universities are discussed. Formal papers and workshops on various Earth resources disciplines, image interpretation, and data processing concepts are presented. An inventory of existing remote sensing and related subject courses being given in western regional universities is included.

Frontiers of Remote Sensing of the Oceans and Troposphere from Air and Space Platforms

NASA Technical Reports Server (NTRS)

1984-01-01

Several areas of remote sensing are addressed including: future satellite systems; air-sea interaction/wind; ocean waves and spectra/S.A.R.; atmospheric measurements (particulates and water vapor); synoptic and weather forecasting; topography; bathymetry; sea ice; and impact of remote sensing on synoptic analysis/forecasting.

Remote sensing of earth terrain

NASA Technical Reports Server (NTRS)

Kong, Jin AU; Yueh, Herng-Aung; Shin, Robert T.

1991-01-01

Abstracts from 46 refereed journal and conference papers are presented for research on remote sensing of earth terrain. The topics covered related to remote sensing include the following: mathematical models, vegetation cover, sea ice, finite difference theory, electromagnetic waves, polarimetry, neural networks, random media, synthetic aperture radar, electromagnetic bias, and others.

Evapotranspiration estimates derived using multi-platform remote sensing in a semiarid region

USDA-ARS?s Scientific Manuscript database

Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed ob...

15 CFR 960.9 - License term.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.9 License term. (a) Each license for... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the...

Calculating Remote Sensing Reflectance Uncertainties Using an Instrument Model Propagated Through Atmospheric Correction via Monte Carlo Simulations

NASA Technical Reports Server (NTRS)

Karakoylu, E.; Franz, B.

2016-01-01

First attempt at quantifying uncertainties in ocean remote sensing reflectance satellite measurements. Based on 1000 iterations of Monte Carlo. Data source is a SeaWiFS 4-day composite, 2003. The uncertainty is for remote sensing reflectance (Rrs) at 443 nm.

Elementary Age Children and Remote Sensing: Research from Project Omega.

ERIC Educational Resources Information Center

Kirman, Joseph M.

1991-01-01

Discusses remote sensing technology use in teaching elementary school students about science and social studies. Reviews findings dealing with the use of remote sensing and considering children's abilities, teacher training, computer applications, gifted children, and sex-related differences. Concludes that children as young as grade three can…

Inquiry-Based Learning in Remote Sensing: A Space Balloon Educational Experiment

ERIC Educational Resources Information Center

Mountrakis, Giorgos; Triantakonstantis, Dimitrios

2012-01-01

Teaching remote sensing in higher education has been traditionally restricted in lecture and computer-aided laboratory activities. This paper presents and evaluates an engaging inquiry-based educational experiment. The experiment was incorporated in an introductory remote sensing undergraduate course to bridge the gap between theory and…

Interactive Online Tools for Enhancing Student Learning Experiences in Remote Sensing

ERIC Educational Resources Information Center

Joyce, Karen E.; Boitshwarelo, Bopelo; Phinn, Stuart R.; Hill, Greg J. E.; Kelly, Gail D.

2014-01-01

The rapid growth in Information and Communications Technologies usage in higher education has provided immense opportunities to foster effective student learning experiences in geography. In particular, remote sensing lends itself to the creative utilization of multimedia technologies. This paper presents a case study of a remote sensing computer…

A Remote-Sensing Mission

ERIC Educational Resources Information Center

Hotchkiss, Rose; Dickerson, Daniel

2008-01-01

Sponsored by NASA and the JASON Education Foundation, the remote Sensing Earth Science Teacher Education Program (RSESTeP) trains teachers to use state-of-the art remote-sensing technology with the idea that participants bring back what they learn and incorporate it into Earth science lessons using technology. The author's participation in the…

15 CFR 960.13 - Prohibitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Prohibitions § 960.13 Prohibitions. It is unlawful for... subsidiary or affiliate to: (a) Operate a private remote sensing space system in such a manner as to...) Operate a private remote sensing space system without possession of a valid license issued under the Act...

Fully Engaging Students in the Remote Sensing Process through Field Experience

ERIC Educational Resources Information Center

Rundquist, Bradley C.; Vandeberg, Gregory S.

2013-01-01

Field data collection is often crucial to the success of investigations based upon remotely sensed data. Students of environmental remote sensing typically learn about the discipline through classroom lectures, a textbook, and computer laboratory sessions focused on the interpretation and processing of aircraft and satellite data. The importance…

Satellites, Remote Sensing, and Classroom Geography for Canadian Teachers.

ERIC Educational Resources Information Center

Kirman, Joseph M.

1998-01-01

Argues that remote sensing images are a powerful tool for teaching geography. Discusses the use of remote sensing images in the classroom and provides a number of sources for them, some free, many on the World Wide Web. Reviews each source's usefulness for different grade levels and geographic topics. (DSK)

77 FR 14951 - Delegations of Authority

Federal Register 2010, 2011, 2012, 2013, 2014

2012-03-14

... reflect changes in the coordination of Departmental remote sensing activities. These responsibilities are... responsible for coordinating USDA remote sensing activities (7 CFR 2.29(a)(6)). Within the Office of the Chief... Outlook Board (WAOB) (7 CFR 2.72(a)(4)). WAOB coordinates USDA remote sensing activities by chairing the...

Active and Passive Remote Sensing of Ice.

DTIC Science & Technology

1984-09-01

This is a report on the progress that has been made in the study of active and passive remote sensing of ice during the period of February 1, 1984...the emissivities as functions of viewing angles and polarizations. They are used to interpret the passive microwave remote sensing data from

Polarimetric Interferometry - Remote Sensing Applications

DTIC Science & Technology

2007-02-01

This lecture is mainly based on the work of S.R. Cloude and presents examples for remote sensing applications Polarimetric SAR Interferometry...PolInSAR). PolInSAR has its origins in remote sensing and was first developed for applications in 1997 using SIRC L-Band data [1,2]. In its original form it

Remote Sensing in Latin America: Technology and Markets for the 1980s

DTIC Science & Technology

1981-08-01

A review is made on the impact of satellite derived remote sensing data in Latin America. Data availability has generated a phenomenal growth in the...The international institutionalization of remote sensing interests in the area is an indicator submitted as a viable force in the continued, future

Active and Passive Remote Sensing of Ice.

DTIC Science & Technology

1985-01-01

This is a report on the progress that has been made in the study of active and passive remote sensing of ice during the period of August 1, 1984...active and passive microwave remote sensing , (2) used the strong fluctuation theory and the fluctuation-dissipation theorem to calculate the brightness

Remote Sensing of Rock Type in the Visible and Near-Infrared,

DTIC Science & Technology

Visible and near-infrared spectra of minerals and rocks have been measured and evaluated in terms of remote sensing applications. The authors...difficult or impossible to use in a generalized remote sensing effort in which the composition of all rocks is to be mapped. Instead, this spectral

15 CFR 960.13 - Prohibitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Prohibitions § 960.13 Prohibitions. It is unlawful for... subsidiary or affiliate to: (a) Operate a private remote sensing space system in such a manner as to...) Operate a private remote sensing space system without possession of a valid license issued under the Act...

15 CFR 960.13 - Prohibitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Prohibitions § 960.13 Prohibitions. It is unlawful for... subsidiary or affiliate to: (a) Operate a private remote sensing space system in such a manner as to...) Operate a private remote sensing space system without possession of a valid license issued under the Act...

15 CFR 960.13 - Prohibitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Prohibitions § 960.13 Prohibitions. It is unlawful for... subsidiary or affiliate to: (a) Operate a private remote sensing space system in such a manner as to...) Operate a private remote sensing space system without possession of a valid license issued under the Act...

15 CFR 960.13 - Prohibitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Prohibitions § 960.13 Prohibitions. It is unlawful for... subsidiary or affiliate to: (a) Operate a private remote sensing space system in such a manner as to...) Operate a private remote sensing space system without possession of a valid license issued under the Act...

Real Estate Assistance

NASA Technical Reports Server (NTRS)

1997-01-01

The Commercial Remote Sensing Program at Stennis Space Center assists numerous companies across the United States, in learning to use remote sensing capabilities to enhance their competitiveness. Through the Visiting Investigator Program, SSC helped Coast Delta Realty in Diamondhead, Miss., incorporate remote sensing and Geogrpahic Information System technology for real estate marketing and management.

Groundwater inventory and monitoring technical guide: Remote sensing of groundwater

USDA-ARS?s Scientific Manuscript database

The application of remotely sensed data in conjunction with in situ data greatly enhances the ability of the USDA Forest Service to meet the demands of field staff, customers, and others for groundwater information. Generally, the use of remotely sensed data to inventory and monitor groundwater reso...

Remote sensing of the Earth from Space: A program in crisis

NASA Technical Reports Server (NTRS)

1985-01-01

The present situation in earth remote sensing, determining why certain problems exist, and trying to find out what can be done to solve these problems are discussed. The conclusion is that operational remote sensing is in disarray. The difficulties involve policy and institutional issues. Recommendations are given.

Application of remote sensing to solution of ecological problems

NASA Technical Reports Server (NTRS)

Adelman, A.

1972-01-01

The application of remote sensing techniques to solving ecological problems is discussed. The three phases of environmental ecological management are examined. The differences between discovery and exploitation of natural resources and their ecological management are described. The specific application of remote sensing to water management is developed.

Conjugate-Gradient Neural Networks in Classification of Multisource and Very-High-Dimensional Remote Sensing Data

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Swain, P. H.; Ersoy, O. K.

1993-01-01

Application of neural networks to classification of remote sensing data is discussed. Conventional two-layer backpropagation is found to give good results in classification of remote sensing data but is not efficient in training. A more efficient variant, based on conjugate-gradient optimization, is used for classification of multisource remote sensing and geographic data and very-high-dimensional data. The conjugate-gradient neural networks give excellent performance in classification of multisource data, but do not compare as well with statistical methods in classification of very-high-dimentional data.

Remote Sensing Information Sciences Research Group, Santa Barbara Information Sciences Research Group, year 3

NASA Technical Reports Server (NTRS)

Estes, J. E.; Smith, T.; Star, J. L.

1986-01-01

Research continues to focus on improving the type, quantity, and quality of information which can be derived from remotely sensed data. The focus is on remote sensing and application for the Earth Observing System (Eos) and Space Station, including associated polar and co-orbiting platforms. The remote sensing research activities are being expanded, integrated, and extended into the areas of global science, georeferenced information systems, machine assissted information extraction from image data, and artificial intelligence. The accomplishments in these areas are examined.

Present and future development of remote sensing in China

NASA Astrophysics Data System (ADS)

Pan, H. R.; Jiang, J. S.; Hu, D. Y.; Wang, C. Y.

This paper summarizes the program that has been established during the past decade and the present situation in remote sensing techniques and applications in China. Special attention is given to the recent results that have been achieved in remote sensing applications, such as the successful applications of aerial photography and satellite images to a wide range of grassland surveys in Xinjians province, and to real time flood monitoring in the Tons-Tins Lake drainage basin in 1985, etc. The paper also touches upon the future trends for developing remote sensing in China.

Spatial information technologies for remote sensing today and tomorrow; Proceedings of the Ninth Pecora Symposium, Sioux Falls, SD, October 2-4, 1984

NASA Technical Reports Server (NTRS)

1984-01-01

Topics discussed at the symposium include hardware, geographic information system (GIS) implementation, processing remotely sensed data, spatial data structures, and NASA programs in remote sensing information systems. Attention is also given GIS applications, advanced techniques, artificial intelligence, graphics, spatial navigation, and classification. Papers are included on the design of computer software for geographic image processing, concepts for a global resource information system, algorithm development for spatial operators, and an application of expert systems technology to remotely sensed image analysis.

The Large Area Crop Inventory Experiment (LACIE). Part 3: A systematic approach to the practical application of remote-sensing technology

NASA Technical Reports Server (NTRS)

Murphy, J. D.; Dideriksen, R. I.

1975-01-01

The application of remote sensing technology by the U.S. Department of Agriculture (USDA) is examined. The activities of the USDA Remote-Sensing User Requirement Task Force which include cataloging USDA requirements for earth resources data, determining those requirements that would return maximum benefits by using remote sensing technology and developing a plan for acquiring, processing, analyzing, and distributing data to satisfy those requirements are described. Emphasis is placed on the large area crop inventory experiment and its relationship to the task force.

Chemical Remote Sensing ’Proof of Concept’,

DTIC Science & Technology

1981-03-31

A122 579 CHEMICAL REMOTE SENSING ;PROOF OF CONCEPT’(U) UTAH 1/I \\ STATE UNIV LOGAN ELECTRO-DYNAMICS LAB BARTSCHI ET AL. 31 MAR 81 SCIENTIFC-8...STANDARDS -I963-A AFGL-TR-81-021 2 CHEMICAL REMOTE SENSING "Proof of Concept" B.Y. Bartschi F. P. DelGreco M. Ahmadjian Electro-Dynamics Laboratories...Applications of remote sensing 2 2.2 Program Development 4 -O 3.1 Optical Layout 6 3.2 Block Diagram of Sensor System 7 3.3 Sensor Facility 10 3.4