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At least 127 records · Page 7Linked to original sources
USGS2006

Remote sensing observations of landslides and ground deformation from the 2004 Niigata Ken Chuetsu earthquake

In recent years, major developments in remote sensing have made it possible to use these technologies to document the effects of earthquakes. Specifically, high-resolution satellite imagery and three-dimensional laser scanning (LIDAR) can provide important observations of earthquake damage that supplement traditional observations from field reconnaissance. The 2004 Niigata Ken Chuetsu earthquake provided an opportunity to use remote sensing to document the distribution of landslides in the epicentral region through the use of high-resolution satellite imagery and to document the detailed three-dimensional geometries of several failures using LIDAR. The satellite imagery was acquired the day after the earthquake, but at very large acquisition angles that resulted in image distortion. Nonetheless, the satellite imagery accurately identified the landslide distribution in the epicentral region, although the total area of landslides was underestimated by about 25% as compared with traditional aerial reconnaissance because of the large acquisition angle for the satellite imagery. Terrestrial LIDAR was used to collect three-dimensional data at several failure sites, including two large rock slides and a railroad tunnel portal affected by ground deformation. The LIDAR data allowed for precise measurement of failure deformations and geometries, and provided digital terrain models that could be archived and used in future analyses. In the future, satellite imagery and LIDAR, as well as other remote sensing technologies, will play an increasing role in documenting and understanding the effects of earthquakes.

Niigata Prefecture↗
USGS2004

Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land–Air–Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations. The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.

Remote Sensing of Environment↗
USGS2017

Combining remote sensing and water-balance evapotranspiration estimates for the conterminous United States

Evapotranspiration (ET) is a key component of the hydrologic cycle, accounting for ~70% of precipitation in the conterminous U.S. (CONUS), but it has been a challenge to predict accurately across different spatio-temporal scales. The increasing availability of remotely sensed data has led to significant advances in the frequency and spatial resolution of ET estimates, derived from energy balance principles with variables such as temperature used to estimate surface latent heat flux. Although remote sensing methods excel at depicting spatial and temporal variability, estimation of ET independently of other water budget components can lead to inconsistency with other budget terms. Methods that rely on ground-based data better constrain long-term ET, but are unable to provide the same temporal resolution. Here we combine long-term ET estimates from a water-balance approach with the SSEBop (operational Simplified Surface Energy Balance) remote sensing-based ET product for 2000–2015. We test the new combined method, the original SSEBop product, and another remote sensing ET product (MOD16) against monthly measurements from 119 flux towers. The new product showed advantages especially in non-irrigated areas where the new method showed a coefficient of determination R 2 of 0.44, compared to 0.41 for SSEBop or 0.35 for MOD16. The resulting monthly data set will be a useful, unique contribution to ET estimation, due to its combination of remote sensing-based variability and ground-based long-term water balance constraints.

Remote Sensing↗
USGS2012

Remote sensing of sagebrush canopy nitrogen

This paper presents a combination of techniques suitable for remotely sensing foliar Nitrogen (N) in semiarid shrublands – a capability that would significantly improve our limited understanding of vegetation functionality in dryland ecosystems. The ability to estimate foliar N distributions across arid and semi-arid environments could help answer process-driven questions related to topics such as controls on canopy photosynthesis, the influence of N on carbon cycling behavior, nutrient pulse dynamics, and post-fire recovery. Our study determined that further exploration into estimating sagebrush canopy N concentrations from an airborne platform is warranted, despite remote sensing challenges inherent to open canopy systems. Hyperspectral data transformed using standard derivative analysis were capable of quantifying sagebrush canopy N concentrations using partial least squares (PLS) regression with an R 2 value of 0.72 and an R 2 predicted value of 0.42 ( n = 35). Subsetting the dataset to minimize the influence of bare ground (n = 19) increased R 2 to 0.95 ( R 2 predicted = 0.56). Ground-based estimates of canopy N using leaf mass per unit area measurements (LMA) yielded consistently better model fits than ground-based estimates of canopy N using cover and height measurements. The LMA approach is likely a method that could be extended to other semiarid shrublands. Overall, the results of this study are encouraging for future landscape scale N estimates and represent an important step in addressing the confounding influence of bare ground, which we found to be a major influence on predictions of sagebrush canopy N from an airborne platform.

Remote Sensing of Environment↗
USGS2023

Integration of remote sensing and field observations in evaluating DSSAT model for estimating maize and soybean growth and yield in Maryland, USA

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data.

Maryland↗
USGS2000

U.S. Geological Survey, remote sensing, and geoscience data: Using standards to serve us all

The U.S. Geological Survey (USGS) advocates the use of standards with geosciences and remotely sensed data and metadata for its own purposes and those of its customers. In activities that range from archiving data to making a product, the incorporation of standards makes these functions repeatable and understandable. More important, when accepted standards are followed, data discovery and sharing can be more efficient and the overall value to society can be expanded. The USGS archives many terabytes of digital geoscience and remotely sensed data. Several million photographs are also available to the research community. To manage these vast holdings and ensure that strict preservation and high usability criteria are observed, the USGS uses standards within the archival, data management, public access and ordering, and data distribution areas. The USGS uses Federal and international standards in performing its role as the U.S. National Satellite Land Remote Sensing Data Archive and in its mission as the long-term archive and production center for aerial photographs and cartographic data covering the United States.

Conference Paper↗
USGS2019

A preliminary assessment of hyperspectral remote sensing technology for mapping submerged aquatic vegetation in the Upper Delaware River National Parks

Hyperspectral remote sensing of submerged aquatic vegetation is a complex and difficult process that is affected by unique constraints on the energy flow profile near and below the water surface. In addition, shallow, winding, lotic systems, such as the Upper Delaware River, present additional remote sensing problems in the form of specular reflectance, variable depth and constituents in the water column and sometimes extremely weak signal strength due to absorption and scattering in the water column that can be statistically overwhelmed by the reflectance from upland vegetation in any individual image scene. Here we test hyperspectral imagery from the Civil Air Patrol’s (CAP), Airborne Real-time Cueing Hyperspectral Enhanced Recon (ARCHER) system in the scenic waters of two National Parks on the Upper Delaware River. A number of unique image processing problems were encountered, including specular reflectance from winding lotic systems, variable depth and flow dynamics of the riverine environment, and disproportionate signal strength from surface reflectance in this riverine environment. This were solved by applying a specular reflectance removal algorithm, applying field data collections to classification results and masking upland vegetation so as to not statistically overwhelm the weak reflectance signal from surface and near-surface water. Much was learned about conducting imaging spectroscopy in such difficult conditions. Significant results include successful mapping of SAV presenece/absence, advantages of upland masking of the reflectance signal, and a numkber of processing approaches that are unique to this environment. In this paper we summarize our results and identify unique issues that must be addressed in this environment

New Jersey, New York, Pennsylvania↗
USGS2010

Advances in estimation methods of vegetation water content based on optical remote sensing techniques

Quantitative estimation of vegetation water content (VWC) using optical remote sensing techniques is helpful in forest fire assessment, agricultural drought monitoring and crop yield estimation. This paper reviews the research advances of VWC retrieval using spectral reflectance, spectral water index and radiative transfer model (RTM) methods. It also evaluates the reliability of VWC estimation using spectral water index from the observation data and the RTM. Focusing on two main definitions of VWC—the fuel moisture content (FMC) and the equivalent water thickness (EWT), the retrieval accuracies of FMC and EWT using vegetation water indices are analyzed. Moreover, the measured information and the dataset are used to estimate VWC, the results show there are significant correlations among three kinds of vegetation water indices (i.e., WSI, NDII, NDWI 1640 , WI/NDVI) and canopy FMC of winter wheat ( n =45). Finally, the future development directions of VWC detection based on optical remote sensing techniques are also summarized.

Science China Technological Sciences↗
USGS1995

History and use of remote sensing for conservation and management of federal lands in Alaska, USA

Remote sensing has been used to aid land use planning efforts for federal public lands in Alaska since the 1940s. Four federal land management agencies-the U.S. Fish and Wildlife Service, US. Bureau of Land Management, US. National Park Service, and U.S. Forest Service-have used aerial photography and satellite imagery to document the extent, type, and condition of Alaska's natural resources. Aerial photographs have been used to collect detailed information over small to medium-sized areas. This standard management tool is obtainable using equipment ranging from hand-held 35-mm cameras to precision metric mapping cameras. Satellite data, equally important, provide synoptic views of landscapes, are digitally manipulatable, and are easily merged with other digital databases. To date, over 109.2 million ha (72%) of Alaska's land cover have been mapped via remote sensing. This information has provided a base for conservation, management, and planning on federal public lands in Alaska.

Alaska↗
USGS1992

Combining remote sensing techniques with simulation modelling for assessing rangeland resources

Simulation modelling and remote sensing techniques can be used to describe ecological processes. When used together, these tools can increase our understanding of the spatial variability of ecosystems. Remotely sensed data can be combined with simulation models for predicting the accumulation of plant biomass, as surrogate variables within the models, or as correlative information for monitoring entire eco‐regions.

Geocarto International↗
USGS2008

Characterizing the marsh dieback spectral response at the plant and canopy level with hyperspectral and temporal remote sensing data

We describe newly developed remote sensing tools to map the localized occurrences and regional distribution of the marsh dieback in coastal Louisiana (Fig. 1). As a final goal of our research and development, we identified what spectral features accompanied the onset of dieback and could be directly linked to the optical signal measured at the satellite. In order to accomplish our research goal, we carried out two interlinked objectives. First, we determined the spectral features within the hyperspectral spectra of the impacted plant that could be linked to the spectral return. This was accomplished by measuring the differences in leaf optical properties of impacted and non impacted marsh plants in such a way that the measured differences could be linked to the dieback onset and progression. The spectral analyses were constrained to selected wavelengths (bands of reflectance data) historically associated with changes in leaf composition and structure caused by changes in the plant biophysical environment. Second, we determined what changes in the canopy reflectance (canopy signal sensed at the satellite) could be linked to dieback onset and progression. Third, we transformed a suite of six Landsat Thematic Mapper images collected before, during, and in the final stages of dieback to maps of dieback occurrences. ??2008 IEEE.

Conference Paper↗
USGS2024

Science target prioritization framework for remote sensing

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

Conference Paper↗
USGS2000

Remote sensing for site characterization

This volume, Remote Sensing for Site Characterization, describes the feasibility of aircraft- and satellite-based methods of revealing environmental-geological problems. A balanced ratio between explanations of the methodological/technical side and presentations of case studies is maintained. The comparison of case studies from North America and Germany show how the respective territorial conditions lead to distinct methodological approaches.

Book↗
USGS2017

Using remotely sensed data to estimate river characteristics including water-surface velocity and discharge

This paper describes a project combining field studies and analyses directed at providing an assessment of the accuracy of remotely sensed methods for determining river characteristics such as velocity and discharge. In particular, we describe a remote sensing method for surface velocities using mid-wave thermal camera videography combined with image analysis. One of the critical problems in this work is determining a method for relating remotely measured water-surface velocities to vertically averaged velocities through a velocity index. We explore three similarity profiles that allow a relationship between surface and vertically averaged velocity to be found either using empirical results or simple roughness-to-depth ratios. To test the approaches we compare them in a situation where vertical structure is known over most of the flow depth through ADCP measurements. By determining best-fit profiles through the ADCP profiles, average values of the velocity index are found for the cross-sections where measurement were made. By comparing these to the predicted velocity indices from the three similarity profiles, we find that, although the differences between the various similarity profiles are substantial, they are smaller than differences associated with local nonuniformity and nonhydrostatic flow. Nevertheless, the velocity indices are accurate to about +/-5%, meaning that remotely sensed vertically averaged velocities can be computed to well within the current accuracy standard for such values when used for river gaging.

Conference Paper↗
USGS2020

Quantifying western U.S. rangelands as fractional components with multi-resolution remote sensing and in situ data

Quantifying western U.S. rangelands as a series of fractional components with remote sensing provides a new way to understand these changing ecosystems. Nine rangeland ecosystem components, including percent shrub, sagebrush ( Artemisia ), big sagebrush, herbaceous, annual herbaceous, litter, and bare ground cover, along with sagebrush and shrub heights, were quantified at 30 m resolution. Extensive ground measurements, two scales of remote sensing data from commercial high-resolution satellites and Landsat 8, and regression tree models were used to create component predictions. In the mapped area (2,993,655 km²), bare ground averaged 45.5%, shrub 15.2%, sagebrush 4.3%, big sagebrush 2.9%, herbaceous 23.0%, annual herbaceous 4.2%, and litter 15.8%. Component accuracies using independent validation across all components averaged R 2 values of 0.46 and an root mean squared error (RMSE) of 10.37, and cross-validation averaged R 2 values of 0.72 and an RMSE of 5.09. Component composition strongly varies by Environmental Protection Agency (EPA) level III ecoregions ( n = 32): 17 are bare ground dominant, 11 herbaceous dominant, and four shrub dominant. Sagebrush physically covers 90,950 km², or 4.3%, of our study area, but is present in 883,449 km², or 41.5%, of the mapped portion of our study area.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2024

Remote sensing for monitoring mine lands and recovery efforts

Under the Bipartisan Infrastructure Law Ecosystem Restoration Program, the U.S. Department of the Interior has invested in assessing and recovering degraded ecosystems to promote healthy human communities and wildlife habitats. One priority established by the program is the need to address degraded ecosystems associated with mine lands, including active, inactive, and abandoned mines. Mine lands occur in every State of the United States and present a range of environmental hazards and safety risks to human communities and wildlife habitats. However, limited information compiled across the United States exists on the whereabouts of mining activities and their potential environmental effects on landscapes. Remote sensing is the process of acquiring information about the landscape from ground platforms, aircraft, or satellites to assess surface characteristics such as topography, vegetation, and soil properties and can therefore provide important cost-effective methods for identifying mining sites and assessing their environmental effects. Based on a literature review of remote sensing applications that assessed land health conditions and monitoring of mine lands, this report highlights important approaches, capabilities, considerations, and case studies using a breadth of techniques. The report identifies considerations for setting appropriate vegetation recovery targets and demonstrates how remote sensing can inform the prioritization, recovery design, and long-term assessments of recovery to help support decision makers and land managers. Applications of remote sensing to mine recovery will be most effective when recovery targets are clearly defined and quantifiable from data collected before mining activity, per the Surface Mining Control and Reclamation Act of 1977 and similar laws. Additionally, the collected data would need to be accessible and maintained in databases for baseline references used to establish these recovery targets.

continental United States↗
USGS2016

Removing sun glint from optical remote sensing images of shallow rivers

Sun glint is the specular reflection of light from the water surface, which often causes unusually bright pixel values that can dominate fluvial remote sensing imagery and obscure the water-leaving radiance signal of interest for mapping bathymetry, bottom type, or water column optical characteristics. Although sun glint is ubiquitous in fluvial remote sensing imagery, river-specific methods for removing sun glint are not yet available. We show that existing sun glint-removal methods developed for multispectral images of marine shallow water environments over-correct shallow portions of fluvial remote sensing imagery resulting in regions of unreliable data along channel margins. We build on existing marine glint-removal methods to develop a river-specific technique that removes sun glint from shallow areas of the channel without overcorrection by accounting for non-negligible water-leaving near-infrared radiance. This new sun glint-removal method can improve the accuracy of spectrally-based depth retrieval in cases where sun glint dominates the at-sensor radiance. For an example image of the gravel-bed Snake River, Wyoming, USA, observed-vs.-predicted R 2 values for depth retrieval improved from 0.66 to 0.76 following sun glint removal. The methodology presented here is straightforward to implement and could be incorporated into image processing workflows for multispectral images that include a near-infrared band.

Earth Surface Processes and Landforms↗
USGS2007

Remote sensing sensors and applications in environmental resources mapping and modeling

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

Sensors↗