USGS Science⌕ Search

SEARCH · USGS Science

Results for “Remote Sensing Information”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 703 records · Page 39Linked to original sources

Digital image comparison by subtracting contextual transformations—percentile rank order differentiation

The common method of digital image comparison by subtraction imposes various constraints on the image contents. Precise registration of images is required to assure proper evaluation of surface locations. The attribute being measured and the calibration and scaling of the sensor are also important to the validity and interpretability of the subtraction result. Influences of sensor gains and offsets complicate the subtraction process. The presence of any uniform systematic transformation component in one of two images to be compared distorts the subtraction results and requires analyst intervention to interpret or remove it. A new technique has been developed to overcome these constraints. Images to be compared are first transformed using the cumulative relative frequency as a transfer function. The transformed images represent the contextual relationship of each surface location with respect to all others within the image. The process of differentiating between the transformed images results in a percentile rank ordered difference. This process produces consistent terrain-change information even when the above requirements necessary for subtraction are relaxed. This technique may be valuable to an appropriately designed hierarchical terrain-monitoring methodology because it does not require human participation in the process.

Photogrammetric Engineering and Remote Sensing↗

DRIFteRS: A dataset of drift invertebrate densities in streams and rivers across western North America, 1997–2024

Prey availability is among the most influential and highly variable determinants of fish growth and freshwater habitat carrying capacity, yet it remains understudied compared to physical habitat variables (Ouellet et al., 2025; Rosenfeld et al., 2014; Weber et al., 2017). We often lack a clear understanding of how much food is available to fishes, how it varies spatially and temporally, and how it influences responses to restoration (Ouellet et al., 2025; Rossi et al., 2024; Wipfli et al., 2010). Drift invertebrates—the primary food source for juvenile salmonids and other drift-foraging fishes—play a pivotal role in these dynamics. To better understand the spatiotemporal variability of drift invertebrate abundance and biomass across the freshwater range of drift-feeding salmonids in western North America, we compiled the DRift Invertebrates For salmonids in River Systems (DRIFteRS) dataset. The dataset encompasses 6125 samples of drift invertebrates, and, for a subset of drift samples, associated benthic invertebrate density data, collected from 1360 reaches on 459 unique rivers and streams spanning 55 river basins considered hydrologically independent (i.e., not nested within the same larger watershed) across British Columbia, Canada, and the U.S. states of Alaska, Arizona, California, Colorado, Idaho, Nevada, New Mexico, Oregon, Utah, Washington, and Wyoming. Sample sites represent a diverse array of river and stream habitats (e.g., headwater, mainstem, side channel), in watersheds with diverse land uses (e.g., urban, wilderness, agricultural), and disturbance histories (e.g., fire, restoration). Collected between 1997 and 2024, the data span the full calendar year and capture daily and seasonal patterns in drift abundance and biomass densities. When paired with water quality and quantity data as well as remotely sensed environmental landscape data, such as land use/land cover, climate, and disturbance history, channel morphology, and riparian vegetation composition, the DRIFteRS dataset can aid in identifying key drivers of drift invertebrate densities and mean body size and support predictive modeling in unsampled locations and times. The dataset may also be used to analyze aquatic-terrestrial resource flows, derive prey-encounter rates and profitability (mean prey size), and inform broader investigations of sit-and-wait foraging ecology, especially when paired with data on drift-foraging predators. For salmonid-focused applications, the dataset can be integrated into habitat evaluation models, including bioenergetic (e.g., Naman et al., 2019) and life cycle models (e.g., Beechie et al., 2023), to improve estimates of habitat capacity and population dynamics for river- and stream-rearing salmonids. Understanding prey availability dynamics is increasingly important, because rising water temperatures increase salmonid metabolic demands (Crozier et al., 2010). Flow regime transitions (i.e., snow or glacier dominated to rain dominated; Beechie et al., 2013), wildfire frequency and intensity (Hessburg et al., 2021), as well as plant community and phenology shifts (Cleland et al., 2007; Franklin et al., 2016) are all predicted to change with rising temperatures and are potential drivers of terrestrial and aquatic invertebrate prey quality and availability in lotic systems. These insights can ultimately inform restoration prioritization and design, helping managers consider food resource implications when evaluating restoration priorities and match habitat improvement to food supply. The data are released under the Creative Commons Attribution 4.0 International license and may be reused with attribution.

Alaska, Arizona, British Columbia, California, Col↗

Lunar Apennine-Hadley region: Geological implications of earth-based radar and infrared measurements

Recently completed high-resolution radar maps of the moon contain information on the decimeter-scale structure of the surface. When this information is combined with eclipse thermal-enhancement data and with high-resolution Lunar Orbiter photography, the surface morphology is revealed in some detail. A geological history for certain features and subareas can be developed, which provides one possible framework for the interpretation of the findings from the Apollo 15 landing. Frequency of decimeter- and meter-size blocks in and around lunar craters, given by the remote-sensed data, supports a multilayer structure in the Palus Putredinis mare region, as well as a great age for the bordering Apennine Mountains scarp.

Science↗

A circumpolar monitoring framework for polar bears

Polar bears ( Ursus maritimus ) occupy remote regions that are characterized by harsh weather and limited access. Polar bear populations can only persist where temporal and spatial availability of sea ice provides adequate access to their marine mammal prey. Observed declines in sea ice availability will continue as long as greenhouse gas concentrations rise. At the same time, human intrusion and pollution levels in the Arctic are expected to increase. A circumpolar understanding of the cumulative impacts of current and future stressors is lacking, long-term trends are known from only a few subpopulations, and there is no globally coordinated effort to monitor effects of stressors. Here, we describe a framework for an integrated circumpolar monitoring plan to detect ongoing patterns, predict future trends, and identify the most vulnerable polar bear subpopulations. We recommend strategies for monitoring subpopulation abundance and trends, reproduction, survival, ecosystem change, human-caused mortality, human–bear conflict, prey availability, health, stature, distribution, behavioral change, and the effects that monitoring itself may have on polar bears. We assign monitoring intensity for each subpopulation through adaptive assessment of the quality of existing baseline data and research accessibility. A global perspective is achieved by recommending high intensity monitoring for at least one subpopulation in each of four major polar bear ecoregions. Collection of data on harvest, where it occurs, and remote sensing of habitat, should occur with the same intensity for all subpopulations. We outline how local traditional knowledge may most effectively be combined with the best scientific methods to provide comparable and complementary lines of evidence. We also outline how previously collected intensive monitoring data may be sub-sampled to guide future sampling frequencies and develop indirect estimates or indices of subpopulation status. Adoption of this framework will inform management and policy responses to changing worldwide polar bear status and trends.

Ursus↗

Decomposition of AVIRIS spectra: Extraction of spectral reflectance, atmospheric, and instrumental components

Presents techniques that use only information contained within a raw, high-spectral-resolution, hyperspectral Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene to estimate and remove additive components (atmospheric scattering and instrument dark current). These techniques allow normalization of multiplicative components (instrument gain, topography, atmospheric transmission) and enhancement, extraction, and identification of relative-reflectance information related to surface composition and mineralogy. The authors' derivation of additive components from raw AVIRIS data is based on an adaptation of Crippen's "regression intersection method (RIM)." As does RIM, the authors use pairs of surface units that are spectrally homogeneous, spatially extensive, and located in rugged terrain. However, their technique utilizes the long-wavelength spectral data of AVIRIS to derive and remove atmospheric scattering components for each unit. AVIRIS data from the Kelso Dunes and Granite Mountain areas of southern California served as spectrally contrasting, topographically modulated surfaces for illustration of this technique. For a given site and wavelength pair, subtraction of the wavelength-dependent additive component from individual bands will remove topographic shading in both sites in band-to-band ratio images. Normalization of all spectra in the scene to the average scene spectrum results in cancellation of multiplicative components and produces a relative-reflectance scene. Absorption features due to mineral absorptions that depart from the average spectrum can be identified in the relative-reflectance AVIRIS product. The validity of these techniques is demonstrated by comparisons between relative-reflectance AVIRIS spectra derived from application of this technique and those derived by using the standard calibration techniques of JPL. Calibrated spectra were extracted from an AVIRIS scene of the Upheaval Dome area of Canyonlands National Park, UT. Results show that surface-reflectance information can be extracted and interpreted in terms of surface mineralogy after application of these techniques to AVIRIS data.

IEEE Transactions on Geoscience and Remote Sensing↗

A web-based application for exploring potential changes in design peak flow of U.S. urban areas driven by land cover change

Floods have become increasingly prominent in recent decades, especially in urban areas causing devastating effects on lives and livelihoods worldwide. Efficient tools to assess the drivers of floods, such as increasing urbanization could help to minimize flood hazards. A Google Earth Engine (GEE) application was developed to explore the potential changes (1985-2020) in design peak-flow of urban areas across the conterminous United States driven by land cover change. The results indicate a potential increase in peak-flow in urban areas up to 126.6% in 2020 compared to 1985. Out of the total 3535 study urban areas, about 80% (2840) urban areas increased peak-flow and about 19% (654) decreased peak-flow. A general pattern of increasing peak-flow was observed during 1985-2010 and decreasing pattern during 2010-2020, primarily driven by respective increasing (decreasing) and decreasing (increasing) developed areas (croplands). The GEE application pro-vides crucial information by visualizing both spatial and temporal data that could be useful for decision-makers in developing and improving urban stormwater management plans and policies for efficient resource allocations and reducing flood risks.

Journal of Remote Sensing↗

Remote sensing of actual evapotranspiration from croplands

Agriculture accounted for the majority of human water use and for more than 90% of global freshwater consumption during the twentieth century (Hoekstra and Mekonnen, 2012; Shiklomanov, 2000). Streamflow depletion due to enhanced evapotranspiration (ET) from irrigated crops impacts freshwater ecosystems globally (Foley et al., 2005). Water scarcity limits crop production in many arid and semiarid regions, and water is likely to be a key resource limiting food production and food security in the twenty-first century (Foley et al., 2011; Vorosmarty et al., 2000). Despite this, estimates of the location and temporal dynamics of ET from croplands are often uncertain at a variety of spatial and temporal scales. Better information on ET can be useful in several applications at a range of spatial scales, including water resources, agronomy, and meteorology (e.g., Rivas and Caselles, 2004). At the scale of irrigation projects, maps of ET can assist with irrigation scheduling and demand assessment. Measurements of ET are required for monitoring plant water requirements, plant growth, and productivity, as well as for irrigation management and deciding when to carry out cultivation procedures (e.g., Consolli et al., 2006; Glenn et al., 2007; Yang et al., 2010).

Book chapter↗

Too much and not enough data: Challenges and solutions for generating information in freshwater research and monitoring

Evaluating progress toward achieving freshwater conservation and sustainability goals requires transforming diverse types of data into useful information for scientists, managers, and other interest groups. Despite substantial increases in the volume of freshwater data collected worldwide, many regions and ecosystems still lack sufficient data collection and/or data access. We illustrate how these data challenges result from a diverse set of underlying mechanisms and propose solutions that can be applied by individuals or organizations. We discuss creative approaches to address data scarcity, including the use of community science, remote-sensing, environmental sensors, and legacy datasets. We highlight the importance of coordinated data collection efforts among groups and training programs to improve data access. At the institutional level, we emphasize the power of prioritizing data curation, incentivizing data publication, and promoting research that enhances data coverage and representativeness. Some of these strategies involve technological and analytical approaches, but many necessitate shifting the priorities and incentives of organizations such as academic and government research institutions, monitoring groups, journals, and funding agencies. Our overarching goal is to stimulate discussion to narrow the data disparities hindering the understanding of freshwater processes and their change across spatial scales.

Nahuel Huapi Lake, Lake Tahoe↗

Overview of intercalibration of satellite instruments

Inter-calibration of satellite instruments is critical for detection and quantification of changes in the Earth’s environment, weather forecasting, understanding climate processes, and monitoring climate and land cover change. These applications use data from many satellites; for the data to be inter-operable, the instruments must be cross-calibrated. To meet the stringent needs of such applications requires that instruments provide reliable, accurate, and consistent measurements over time. Robust techniques are required to ensure that observations from different instruments can be normalized to a common scale that the community agrees on. The long-term reliability of this process needs to be sustained in accordance with established reference standards and best practices. Furthermore, establishing physical meaning to the information through robust Système International d'unités (SI) traceable Calibration and Validation (Cal/Val) is essential to fully understand the parameters under observation. The processes of calibration, correction, stability monitoring, and quality assurance need to be underpinned and evidenced by comparison with “peer instruments” and, ideally, highly calibrated in-orbit reference instruments. Inter-calibration between instruments is a central pillar of the Cal/Val strategies of many national and international satellite remote sensing organizations. Inter-calibration techniques as outlined in this paper not only provide a practical means of identifying and correcting relative biases in radiometric calibration between instruments but also enable potential data gaps between measurement records in a critical time series to be bridged. Use of a robust set of internationally agreed upon and coordinated inter-calibration techniques will lead to significant improvement in the consistency between satellite instruments and facilitate accurate monitoring of the Earth’s climate at uncertainty levels needed to detect and attribute the mechanisms of change. This paper summarizes the state-of-the-art of post-launch radiometric calibration of remote sensing satellite instruments, through inter-calibration.

IEEE Transactions on Geoscience and Remote Sensing↗

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

ISPRS Journal of Photogrammetry and Remote Sensing↗

Survey of geographical information system and image processing software

The Global Resource Information Database—a part of the United Nations Environment Programme—conducts a bi-annual survey of geographical information system (GIS) and image processing (IP) software. This survey makes information about software products available in developing countries. The 1993 survey showed that the number of installations of GIS, IP, and related software products increased dramatically from 1991 to 1993, mostly in North America and Europe.

International Journal of Remote Sensing↗

Applications of bistatic radar to volcano topography – A review of 10 years of TanDEM-X

The TanDEM-X satellite mission has revolutionized DEM generation from spaceborne synthetic aperture radar. In addition to the primary objective of generating a consistent digital elevation model with global coverage and unprecedented accuracy, the mission has acquired time series of topographic data over several volcanoes, providing an excellent opportunity to test the use of this innovative dataset for volcano monitoring and research. In this article, we review the utilization of the single-pass TanDEM-X data for studying various kinds of volcanic activity, such as basaltic lava flows, the formation and destruction of lava domes and related pyroclastic density currents, and subsurface magma withdrawal and intrusion. We then consider the uses of these data for hazard assessment and forecasting. Our goal is to highlight the importance of timely and repeated topographic information in volcanology, and to suggest the development of future spaceborne bistatic synthetic aperture radar satellite missions, such as ESA's Earth Explorer 10 candidate mission, “Harmony.”

IEEE Journal of Selected Topics in Applied Earth O↗

Extracting topographic structure from digital elevation data for geographic information-system analysis

Software tools have been developed at the U.S. Geological Survey's EROS Data Center to extract topographic structure and to delineate watersheds and overland flow paths from digital elevation models. The tools are specialpurpose FORTRAN programs interfaced with general-purpose raster and vector spatial analysis and relational data base management packages. The first phase of analysis is a conditioning phase that generates three data sets: the original OEM with depressions filled, a data set indicating the flow direction for each cell, and a flow accumulation data set in which each cell receives a value equal to the total number of cells that drain to it. The original OEM and these three derivative data sets can then be processed in a variety of ways to optionally delineate drainage networks, overland paths, watersheds for userspecified locations, sub-watersheds for the major tributaries of a drainage network, or pour point linkages between watersheds. The computer-generated drainage lines and watershed polygons and the pour point linkage information can be transferred to vector-based geographic information systems for futher analysis. Comparisons between these computergenerated features and their manually delineated counterparts generally show close agreement, indicating that these software tools will save analyst time spent in manual interpretation and digitizing.

Photogrammetric Engineering and Remote Sensing↗

Transdisciplinary research supports the sustainability of barrier island systems threatened by climate change

The management of developed barrier islands is often piece-meal and reactionary despite the complex, dynamic nature of these systems, and sustainable practices will become increasingly difficult due to heightened pressures of climate change. Adaptation actions, including nature-based solutions, need to be thoroughly evaluated prior to implementation to understand system-wide impacts and avoid maladaptation. Anarde et al. (2024a), ( https://doi.org/10.1029/2023ef003672 ), Anarde et al. (2024b), ( https://doi.org/10.1029/2023ef004200 ) is the latest important contribution in a growing body of transdisciplinary research that more robustly evaluates the complex physical process-and-response relationship of barrier systems via sophisticated numerical modeling approaches that also interface with socioeconomic models to inform coastal management actions in response to mitigating coastal risk. This new research indicates the importance of coordinated system-scale barrier island management, as strategies to reduce coastal hazard risk in one location will directly affect adjacent communities. Further, this work demonstrates that reducing barrier management interventions may actually promote barrier recovery and sustainability in the face of sea level rise. In addition, recent advances in the analysis and application of remotely sensed data from satellites and oblique aerial photography provide scientists an unprecedented opportunity to track coastal evolution over a wide range of spatial and temporal scales at minimal cost. As sea level rise and changing storm patterns challenge the sustainable management of barrier island systems, integrating these advanced, transdisciplinary tools will enable scientists and coastal practitioners to more thoroughly evaluate coastal adaptation options, efficiently invest limited resources to mitigate coastal hazard risk for communities, support healthy ecosystems, and reduce system-wide impacts.

Earth's Future↗

Maps showing mineral resource assessment for porphyry and stockwork deposits of copper, molybdenum, and tungsten and for stockwork and disseminated deposits of gold and silver in the Butte 1° x 2° quadrangle, Montana

This report documents the assessment for potential occurrences of undiscovered porphyry and stockwork deposits of copper, molybdenum, and tungsten (porphyry Cu-Mo-W) and stockwork and disseminated deposits of gold and silver (disseminated Au-Ag) in the Butte 1 °X2° quadrangle. The Butte quadrangle, in west-central Montana, is one of the best known mineral producing regions in the U.S. Mining districts in the quadrangle, including the world famous Butte or Summit Valley district, have produced a variety of metallic and nonmetallic mineral commodities valued at more than $6.4 billion (at the time of production). Because of its importance as a mineral producing region, the Butte quadrangle was selected for study by the U.S. Geological Survey under the Conterminous United States Mineral Assessment Program (CUSMAP). Under this program, new data on geology, geochemistry, geophysics, geochronology, mineral resources, and remote sensing were collected and synthesized. The field and laboratory studies were supported, in part, by funding from the Geologic Framework and Synthesis Program and the Wilderness Program. The methods used in this resource assessment for porphyry Cu-Mo-W and disseminated Au-Ag deposits in the quadrangle include a compilation of all data, the development of descriptive occurrence models, and the analysis of data using techniques provided by a Geographic Information System (GIS). This map is one of several maps on the Butte 1 °X2° quadrangle. Other deposit types have been assessed for the Butte quadrangle, and maps (U.S. Geological Survey (USGS) Miscellaneous Investigation Series Maps) for each of the following have been prepared: Vein and replacement deposits of gold, silver, copper, lead, zinc, manganese, and tungsten (Elliott, Wallace, and others, 1992a) and skarn deposits of gold, silver, copper, tungsten, and iron (Elliott and others, 1992b ). Other publications resulting from this study include linear features map (Rowan and others, 1991 ); limonite and hydrothermal alteration map (Rowan and Segal, 1989); mineral occurrence maps (Elliott and others, 1986; Elliott, Loen, and others, 1992); and geologic maps (Wallace, 1987; Wallace and others, 1987).

Montana↗

Characterizing land surface phenology and exotic annual grasses in dryland ecosystems using Landsat and Sentinel-2 data in harmony

Invasive annual grasses, such as cheatgrass (Bromus tectorum L.), have proliferated in dryland ecosystems of the western United States, promoting increased fire activity and reduced biodiversity that can be detrimental to socio-environmental systems. Monitoring exotic annual grass cover and dynamics over large areas requires the use of remote sensing that can support early detection and rapid response initiatives. However, few studies have leveraged remote sensing technologies and computing frameworks capable of providing rangeland managers with maps of exotic annual grass cover at relatively high spatiotemporal resolutions and near real-time latencies. Here, we developed a system for automated mapping of invasive annual grass (%) cover using in situ observations, harmonized Landsat and Sentinel-2 (HLS) data, maps of biophysical variables, and machine learning techniques. A robust and automated cloud, cloud shadow, water, and snow/ice masking procedure (mean overall accuracy >81%) was implemented using time-series outlier detection and data mining techniques prior to spatiotemporal interpolation of HLS data via regression tree models (r = 0.94; mean absolute error (MAE) = 0.02). Weekly, cloud-free normalized difference vegetation index (NDVI) image composites (2016–2018) were used to construct a suite of spectral and phenological metrics (e.g., start and end of season dates), consistent with information derived from Moderate Resolution Image Spectroradiometer (MODIS) data. These metrics were incorporated into a data mining framework that accurately (r = 0.83; MAE = 11) modeled and mapped exotic annual grass (%) cover throughout dryland ecosystems in the western United States at a native, 30-m spatial resolution. Our results show that inclusion of weekly HLS time-series data and derived indicators improves our ability to map exotic annual grass cover, as compared to distribution models that use MODIS products or monthly, seasonal, or annual HLS composites as primary inputs. This research fills a critical gap in our ability to effectively assess, manage, and monitor drylands by providing a framework that allows for an accurate and timely depiction of land surface phenology and exotic annual grass cover at spatial and temporal resolutions that are meaningful to local resource managers.

California, Idaho, Nevada, Oregon↗

The challenges of remote monitoring of wetlands

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?

Remote Sensing↗

Phenological classification of the United States: A geographic framework for extending multi-sensor time-series data

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

Remote Sensing↗