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At least 739 records · Page 41Linked to original sources
USGS2007

Estimating soil erosion using the USPED model and consecutive remotely sensed land cover observations

Intensified soil erosion contributes to the degradation of ecosystems. Better estimation of soil erosion across landscapes is a necessary part of understanding ecosystem biogeochemical cycles and ecosystem sustainability. In this study, we used the Unit Stream Power-based Erosion Deposition (USPED) model to estimate the lateral movement of soils across Fort Benning, a military training installation in western Georgia, USA. A land cover weight factor was used in the calculation of surface flow accumulation. The simulation results were compared with observations of the total suspended sediments in stream water for ten watersheds, and showed a significant linear relationship (R 2 = 0.72). Erosion estimates of the ten watersheds are also related to the land disturbance index that is a measure of the intensity of military training disturbances. Results suggest that the USPED model is an effective tool to quantify erosion and deposition at military installations.

Georgia↗
USGS1995

Evaluations of duck habitat and estimation of duck population sizes with a remote-sensing-based system

During 1987-90, we used high-altitude photography, aerial videography, counts, and models to estimate sizes of breeding populations of dabbling ducks (Anatinae) and duck production and to identify duck habitat on U.S. Fish and Wildlife Service land and easements and on private land in the prairie pothole region of the United States. The study area contained about 3.1 million wetland basins (28,490 km2). Wetland area (ha per km2) was highest on service-owned land; wetland-basin density was greatest on service easements. Temporary and seasonal wetlands were underrepresented and lakes were overrepresented on service-owned land. Seventy-eight percent of all basins were less than 0.41 ha. Cropland dominated private land. Pond density decreased from 4.4/km2 in 1987 to 3.4/km2 in 1990 and pond area, from 7.2 ha/km2 to 2.7 ha/km2. The density of the blue-winged teal was greatest (3.4 pairs/km2) and was followed in magnitude by those of the mallard (2.1 pairs/km2), the gadwall (1.8 pairs/km2), the northern pintail (0.8 pairs/km2), and the redhead (0.8 pairs/km2). Duck density was consistently highest on service-owned land. The decline of breeding-population sizes in 1987-90 closely corresponded to losses of pond numbers and pond area. The density of breeding pairs per pond was inversely related to pond density, suggesting that breeding ducks tended to concentrate on the remaining ponds as drought intensified. The production of recruits followed the same pattern as breeding-population sizes. We estimated that 2.5% of the ducklings hatched on service-owned land, which was 1.3% of the study area; 19.6% hatched on service easements, which were 14.2% of the study area; and 77.9% hatched on private land, which was 84.6% of the study area. Various sources of bias and sampling error and improvements to the system are discussed.

Biological Science Report↗
USGS1963

Photography and imagery: a clarification of terms

The increased use of pictorial displays of data in the fields of photogrammetry and photo interpretation has led to some confusion of terms, not so much b photogrammetrists as bu users and interpreters of pictorial data. The terms "remote sensing" and "remote sensing of environment" are being used as general terms to describe "the measurement of some property of an object without having the measuring device physically in contact with the object" (Parker, 1962). Measurements of size and shape by photogrammetric and optical means are common examples of remote sensing and therefore require no elaboration. Other techniques of remote sensing of electromagnetic radiation in and beyond the limits of the visible spectrum require some explanation and differentiation from the techniques used in the visible spectrum. The following definitions of "photography" and "imagery" are proposed to clarify these two terms in hope that this will lead to more precise understanding and explanation of the processes.

Photogrammetric Engineering↗
USGS2007

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

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.

Journal of Applied Ecology↗
USGS2001

Remote rainfall sensing for landslide hazard analysis

Methods of assessing landslide hazards and providing warnings are becoming more advanced as remote sensing of rainfall provides more detailed temporal and spatial data on rainfall distribution. Two recent landslide disasters are examined noting the potential for using remotely sensed rainfall data for landslide hazard analysis. For the June 27, 1995, storm in Madison County, Virginia, USA, National Weather Service WSR-88D Doppler radar provided rainfall estimates based on a relation between cloud reflectivity and moisture content on a 1 sq. km. resolution every 6 minutes. Ground-based measurements of rainfall intensity and precipitation total, in addition to landslide timing and distribution, were compared with the radar-derived rainfall data. For the December 14-16, 1999, storm in Vargas State, Venezuela, infrared sensing from the GOES-8 satellite of cloud top temperatures provided the basis for NOAA/NESDIS rainfall estimates on a 16 sq. km. resolution every 30 minutes. These rainfall estimates were also compared with ground-based measurements of rainfall and landslide distribution. In both examples, the remotely sensed data either overestimated or underestimated ground-based values by up to a factor of 2. The factors that influenced the accuracy of rainfall data include spatial registration and map projection, as well as prevailing wind direction, cloud orientation, and topography.

Open-File Report↗
USGS2018

A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m 2 , 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2024

Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications

Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.

Remote Sensing↗
USGS2024

Joint Agency Commercial Imagery Evaluation (JACIE)

The Joint Agency Commercial Imagery Evaluation (JACIE) was formed to leverage resources from several Federal agencies for the characterization of remote sensing data and to share those results across the remote sensing community (U.S. Geological Survey, 2024). Remote sensing data and the quality of that data are vital to (1) understanding the physical world and (2) supporting the science and engineering applications that strive to advance that understanding. The growing number of remotely sensed data sources offers users more choices. Understanding the characteristics and capabilities of current and new data sources, along with the quality of data they provide, is an important function of the multi-agency JACIE team. By performing data-quality analysis of civil and commercial remote sensing data and information products, the JACIE team provides the remote sensing community with awareness and independent verification of image data quality.

Fact Sheet↗
USGS2026

Global performance of remote sensing-based and reanalysis-driven models to estimate open water evaporation

Evaporation plays an essential role in the water cycle, influencing local and regional climates while directly impacting water availability in lakes. However, directly measuring evaporation over water bodies remains challenging due to the high costs of installing and maintaining the required in situ instrumentation. Although several remote sensing algorithms have been providing evaporation estimates, the lack of a global validation hinders our understanding of their relative uncertainties and performances across different regions. Here, we analyze the performance of a suite of models that leverage satellite data and meteorological reanalysis to estimate evaporation over lakes worldwide. We compare 3 remote sensing-based models, 1 reanalysis-driven model and 1 ensemble approach, using in situ observations from 27 lakes representing a diverse range of geographic and climatic regions. Our results demonstrate that, overall, the ensemble outperformed any individual model in terms of accuracy, with a RMSE and a bias of 1.3 and 0.3 mm day −1 , respectively. These findings highlight the benefits of using an ensemble approach to estimate open water evaporation with satellite-based models at the global scale, leveraging the unique strengths of each model. For the individual models, differences in the representation of heat storage changes and advection effects led to lower values of RMSE and bias, depending on the location and depth of the lakes. This study sets the path for future improvement of open water evaporation algorithms globally, while remote sensing techniques are proven satisfactory to monitoring of water loss in lakes globally, an essential step toward effective large-scale water resources management.

Water Resources Research↗
USGS2020

A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery

Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.

Maryland↗
USGS1981

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

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.

Open-File Report↗