Geology Reports⌕ Search

SEARCH · Geology Reports

Results for “Remote Sensing”

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 109 records · Page 6Linked to original sources
USGS2021

Urban heat island and its regional impacts using remotely sensed thermal data – A review of recent developments and methodology

Many novel research algorithms have been developed to analyze urban heat island (UHI) and UHI regional impacts (UHIRIP) with remotely sensed thermal data tables. We present a comprehensive review of some important aspects of UHI and UHIRIP studies that use remotely sensed thermal data, including concepts, datasets, methodologies, and applications. We focus on reviewing progress on multi-sensor image selection, preprocessing, computing, gap filling, image fusion, deep learning, and developing new metrics. This literature review shows that new satellite sensors and valuable methods have been developed for calculating land surface temperature (LST) and UHI intensity, and for assessing UHIRIP. Additionally, some of the limitations of using remotely sensed data to analyze the LST, UHI, and UHI intensity are discussed. Finally, we review a variety of applications in UHI and UHIRIP analyses. The assimilation of time-series remotely sensed data with the application of data fusion, gap filling models, and deep learning using the Google Cloud platform and Google Earth Engine platform also has the potential to improve the estimation accuracy of change patterns of UHI and UHIRIP over long time periods.

Land↗
USGS2022

Optimizing satellite resources for the global assessment and mitigation of volcanic hazards—Suggestions from the USGS Powell Center Volcano Remote Sensing Working Group

A significant number of the world’s approximately 1,400 subaerial volcanoes with Holocene eruptions are unmonitored by ground-based sensors yet constitute a potential hazard to nearby residents and infrastructure, as well as air travel and global commerce. Data from an international constellation of more than 60 current satellite instruments provide a cost-effective means of tracking activity and potentially forecasting hazards at volcanoes around the world. These data span the electromagnetic spectrum: ultraviolet, optical, infrared, and microwave (synthetic aperture radar). They can measure volcanic thermal and gas emissions, ground displacement, and surface and topographic change, providing information that addresses one of the grand challenges in volcanology—to overcome our incomplete understanding of the relation between volcanic unrest and eruption, which is currently based on only a few well-studied volcanoes. Although the potential of volcano remote sensing has been recognized for decades, there are many hurdles to clear before remote sensing data can be used fully by all volcano observatories. These include: (1) the limited temporal and spatial coverage of active volcanoes by satellites and the delayed distribution of those data; (2) the lack of background data acquired at all volcanoes; and (3) limited access to, and utilization of, remote sensing data in some areas owing to a lack of expertise, licensing, user-friendly formats, data access portals, or computational infrastructure. While remote sensing data will never replace ground-based monitoring, a joint observation strategy provides a powerful means of assessing volcanic activity before, during, and after hazardous eruptions, especially given the unique spatial, temporal, and spectral perspective provided by remote measurements. A coordinated international remote sensing observation strategy for volcanoes—similar to one used by the cryosphere community—along with a volcano space task group to maximize the utility of satellite data for volcano monitoring would be highly beneficial. Such a vision could facilitate (1) global coordination of satellite observations (as done for polar regions) for background monitoring and eruption response, (2) open data that can be rapidly distributed during crises, (3) communication tools and forums for discussion of satellite data, (4) integrated ground and satellite databases of unrest, and (5) global capacity building.

Scientific Investigations Report↗
USGS1993

Compositing multitemporal remote sensing data sets

To eliminate cloud and atmosphere-affected pixels, the compositing of multi temporal remote sensing data sets is done by selecting the maximum vale of the normalized different vegetation index (NDVI) within a compositing period. The NDVI classifier, however, is strongly affected by surface type and anisotropic properties, sensor viewing geometries, and atmospheric conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external conditions. Consequently, the composited, multi temporal, remote sensing data contain substantial noise from these external effects. To improve the accuracy of compositing products, two key approaches can be taken: one is to refine the compositing classifier (NDVI) and the other is to improve existing compositing algorithms. In this project, an alternative classifier was developed and an alternative pixel selection criterion was proposed for compositing. The new classifier and the alternative compositing algorithm were applied to an advanced very high resolution radiometer data set of different biome types in the United States. The results were compared with the maximum value compositing and the best index slope extraction algorithms. The new approaches greatly reduced the high frequency noises related to the external factors and repainted more reliable data. The results suggest that the geometric-optical canopy properties of specific biomes may be needed in compositing. Limitations of the new approaches include the dependency of pixel selection on the length of the composite period and data discontinuity.

Pecora 12 Symposium↗
USGS2016

Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll -a (Chl -a ) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl -a . Where cyanobacteria dominate, Chl -a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl -a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.

Harmful Algae↗
USGS2012

Using computational modeling of river flow with remotely sensed data to infer channel bathymetry

As part of an ongoing investigation into the use of computational river flow and morphodynamic models for the purpose of correcting and extending remotely sensed river datasets, a simple method for inferring channel bathymetry is developed and discussed. The method is based on an inversion of the equations expressing conservation of mass and momentum to develop equations that can be solved for depth given known values of vertically-averaged velocity and water-surface elevation. The ultimate goal of this work is to combine imperfect remotely sensed data on river planform, water-surface elevation and water-surface velocity in order to estimate depth and other physical parameters of river channels. In this paper, the technique is examined using synthetic data sets that are developed directly from the application of forward two-and three-dimensional flow models. These data sets are constrained to satisfy conservation of mass and momentum, unlike typical remotely sensed field data sets. This provides a better understanding of the process and also allows assessment of how simple inaccuracies in remotely sensed estimates might propagate into depth estimates. The technique is applied to three simple cases: First, depth is extracted from a synthetic dataset of vertically averaged velocity and water-surface elevation; second, depth is extracted from the same data set but with a normally-distributed random error added to the water-surface elevation; third, depth is extracted from a synthetic data set for the same river reach using computed water-surface velocities (in place of depth-integrated values) and water-surface elevations. In each case, the extracted depths are compared to the actual measured depths used to construct the synthetic data sets (with two- and three-dimensional flow models). Errors in water-surface elevation and velocity that are very small degrade depth estimates and cannot be recovered. Errors in depth estimates associated with assuming water-surface velocities equal to depth-integrated velocities are substantial, but can be reduced with simple corrections.

Conference Paper↗
USGS2002

Geographic techniques and recent applications of remote sensing to landscape-water quality studies

This article overviews recent advances in studies of landscape-water quality relationships using remote sensing techniques. With the increasing feasibility of using remotely-sensed data, landscape-water quality studies can now be more easily performed on regional, multi-state scales. The traditional method of relating land use and land cover to water quality has been extended to include landscape pattern and other landscape information derived from satellite data. Three items are focused on in this article: 1) the increasing recognition of the importance of larger-scale studies of regional water quality that require a landscape perspective; 2) the increasing importance of remotely sensed data, such as the imagery-derived normalized difference vegetation index (NDVI) and vegetation phenological metrics derived from time-series NDVI data; and 3) landscape pattern. In some studies, using landscape pattern metrics explained some of the variation in water quality not explained by land use/cover. However, in some other studies, the NDVI metrics were even more highly correlated to certain water quality parameters than either landscape pattern metrics or land use/cover proportions. Although studies relating landscape pattern metrics to water quality have had mixed results, this recent body of work applying these landscape measures and satellite-derived metrics to water quality analysis has demonstrated their potential usefulness in monitoring watershed conditions across large regions.

Water, Air, & Soil Pollution↗
USGS2025

Remote sensing of chlorophyll a and temperature to support algal bloom monitoring in Blue Mesa Reservoir, Colorado

We present methods to reconstruct historical chlorophyll a and surface water temperatures from satellite-based remote sensing products for Blue Mesa Reservoir, Colorado, to support algal bloom monitoring. A machine learning model was trained to construct chlorophyll a concentrations from Sentinel-2 satellite imagery and in situ measurements of chlorophyll a concentrations (out of bag RMSE = 1.9 μg/L, R 2 = 0.63) and reconstruct summertime chlorophyll a concentrations over the entire reservoir from 2016 through 2023. Concurrently, we developed an approach to retrieve remotely sensed water temperatures from the Landsat collection 2 provisional surface temperature product (MAE = 0.6°C) and reconstructed summertime surface water temperature records from 2000 through 2023. Finally, we demonstrate how the reconstructed chlorophyll a and temperature records can yield insight on reservoir dynamics. The chlorophyll a records indicate that algal blooms have a consistent spatial pattern across multiple years, initiating in the eastern end of the reservoir and spreading to the west over time. Water temperatures increased at a linearized rate of 0.3°C per decade from 2000 through 2023 and were inversely proportional to reservoir water surface elevation. Finally, mean summer remotely sensed chlorophyll a concentration had a moderately positive correlation with mean summer remotely sensed water temperature.

Colorado↗
USGS2026

End-user needs for remote sensing wetlands of the Prairie Pothole Region of North America

The Prairie Pothole Region (PPR) of North America comprises globally important grassland and wetland ecosystems critical for numerous populations of migratory birds. Due to the importance of this region for migratory birds, and particularly waterfowl, and the threats of habitat loss due to intensifying agriculture, there is a mature and diverse system of conservation organizations, agencies, and partnerships that spends hundreds of millions of dollars annually on habitat conservation to support migratory bird populations. Remote sensing can be a powerful tool for observing and evaluating global change at large scales as well as expanding inferences from field studies to the broader landscape with statistical models. However, development and utilization of these tools has lagged behind their demand for several reasons, including concerns over spatial and temporal resolution and accuracy of products; perception of a misalignment with decision-maker needs; technological barriers such as skill sets of conservation professionals, computing resources, data access, and usability. In this report, we summarize the needs of conservation professionals and scientists who use or want to use remote sensing data products to inform science about wetland change and conservation of wetlands in the PPR. We assembled this information through several methods leading up to, during, and following a January 2026 PPR Wetland Remote Sensing Workshop. The workshop included United States and Canadian scientists, conservation professionals, and policy experts. Our goal was to bring together end-users and remote sensing product developers jointly to explore reducing the lag between product development and utilization of products to inform science and conservation. Specifically, we aimed to identify gaps in wetland remote sensing that limit effective monitoring, management, and conservation in the PPR, and to develop a framework that outlines pathways to address these gaps by fostering collaboration, improving communication networks, encouraging discussion, and building on existing and ongoing efforts. This report summarizes our participants’ descriptions of end-user needs and the outcomes of the workshop.

Prairie Pothole region↗
USGS2024

Global Food Security Support Analysis Data (GFSAD) using remote sensing in support of food and water security in the 21st century

The overarching goal of this chapter is to provide a comprehensive overview of the state-of-art of global cropland mapping procedures using remote sensing as characterized and envisioned by the “Global Food Security Support Analysis Data @ 30 m (GFSAD30)” project working group team. First, the chapter will provide an overview of existing cropland maps and their characteristics along with establishing the gaps in knowledge related to global cropland mapping. Second, definitions of cropland mapping along with key parameters involved in cropland mapping based on their importance in food security analysis, and cropland naming conventions for standardized cropland mapping using remote sensing will be presented. Third, existing methods and approaches for cropland mapping will be discussed. This will include the type of remote sensing data used in cropland mapping and their characteristics along with discussions on the secondary data, field-plot data, and cropland mapping algorithms. Fourth, currently existing global cropland products derived using remote sensing will be presented and discussed. Fifth, a synthesis of all existing products leading to a composite global cropland extent version 1.0 (GCE V1.0) is presented and discussed. Sixth, a way forward for advanced global cropland mapping is visualized.

Book chapter↗
USGS2025

Monitoring cyanobacteria temporal trends in a hypereutrophic lake using remote sensing: From multispectral to hyperspectral

Cyanobacterial harmful algal blooms (cyanoHABs) and associated cyanotoxins are a concern for inland waters. Due to the extensive spatial coverage and frequent availability of satellite images, multispectral remote sensing tools demonstrate utility for monitoring these blooms. The next frontier for remote sensing of cyanoHABs in inland waters is hyperspectral data. Recent and upcoming hyperspectral satellite missions using narrow wavelength imaging spectrometers could have a major impact on advancing our ability to detect, quantify, and characterize cyanobacterial blooms. This study compares multispectral and hyperspectral remote sensing capabilities and processing tools for monitoring cyanoHAB dynamics. We evaluated the temporal trends of cyanoHABs in Clear Lake, California, a hypereutrophic lake with diverse cyanobacteria genera based on 38 sampling events over a five-year monitoring period (2019–2023). We validated the Sentinel-3 Ocean and Land Color Instrument (multispectral) Cyanobacteria Index algorithm for Clear Lake using in situ cyanobacteria measurements, which complemented our field-based evaluation of cyanobacteria trends in Clear Lake. We then demonstrate the advantages of hyperspectral data from both in situ spectroradiometer measurements and full-lake hyperspectral satellite images. We apply the Spectral Mixture Analysis for Surveillance of HABs (SMASH) workflow, a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm, to the hyperspectral images to assess the potential of satellite imaging spectrometer data to identify cyanobacteria genera – the first study to test this tool outside its original study sites. We developed a Clear Lake-specific cyanobacteria spectral library using our field spectroradiometer measurements to improve SMASH performance in Clear Lake, which supports the continued development of this tool.

California↗
USGS2013

Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in northern Minnesota

Wetland mapping at the landscape scale using remotely sensed data requires both affordable data and an efficient accurate classification method. Random forest classification offers several advantages over traditional land cover classification techniques, including a bootstrapping technique to generate robust estimations of outliers in the training data, as well as the capability of measuring classification confidence. Though the random forest classifier can generate complex decision trees with a multitude of input data and still not run a high risk of over fitting, there is a great need to reduce computational and operational costs by including only key input data sets without sacrificing a significant level of accuracy. Our main questions for this study site in Northern Minnesota were: (1) how does classification accuracy and confidence of mapping wetlands compare using different remote sensing platforms and sets of input data; (2) what are the key input variables for accurate differentiation of upland, water, and wetlands, including wetland type; and (3) which datasets and seasonal imagery yield the best accuracy for wetland classification. Our results show the key input variables include terrain (elevation and curvature) and soils descriptors (hydric), along with an assortment of remotely sensed data collected in the spring (satellite visible, near infrared, and thermal bands; satellite normalized vegetation index and Tasseled Cap greenness and wetness; and horizontal-horizontal (HH) and horizontal-vertical (HV) polarization using L-band satellite radar). We undertook this exploratory analysis to inform decisions by natural resource managers charged with monitoring wetland ecosystems and to aid in designing a system for consistent operational mapping of wetlands across landscapes similar to those found in Northern Minnesota.

Minnesota↗
USGS2006

Remote sensing for grassland management in the arid Southwest

We surveyed a group of rangeland managers in the Southwest about vegetation monitoring needs on grassland. Based on their responses, the objective of the RANGES (Rangeland Analysis Utilizing Geospatial Information Science) project was defined to be the accurate conversion of remotely sensed data (satellite imagery) to quantitative estimates of total (green and senescent) standing cover and biomass on grasslands and semidesert grasslands. Although remote sensing has been used to estimate green vegetation cover, in arid grasslands herbaceous vegetation is senescent much of the year and is not detected by current remote sensing techniques. We developed a ground truth protocol compatible with both range management requirements and Landsat's 30 m resolution imagery. The resulting ground-truth data were then used to develop image processing algorithms that quantified total herbaceous vegetation cover, height, and biomass. Cover was calculated based on a newly developed Soil Adjusted Total Vegetation Index (SATVI), and height and biomass were estimated based on reflectance in the near infrared (NIR) band. Comparison of the remotely sensed estimates with independent ground measurements produced r2 values of 0.80, 0.85, and 0.77 and Nash Sutcliffe values of 0.78, 0.70, and 0.77 for the cover, plant height, and biomass, respectively. The approach for estimating plant height and biomass did not work for sites where forbs comprised more than 30% of total vegetative cover. The ground reconnaissance protocol and image processing techniques together offer land managers accurate and timely methods for monitoring extensive grasslands. The time-consuming requirement to collect concurrent data in the field for each image implies a need to share the high fixed costs of processing an image across multiple users to reduce the costs for individual rangeland managers.

Rangeland Ecology and Management↗
USGS2007

Integrating remote sensing and ground methods to estimate evapotranspiration

Evapotranspiraton (ET) is the second largest term in the terrestrial water budget after precipitation, and ET is expected to increase with global warming. ET studies are relevant to the plant sciences because over 80% of terrestrial ET is due to transpiration by plants. Remote sensing is the only feasible means for projecting ET over large landscape units. In the past decade or so, new ground and remote sensing tools have dramatically increased our ability to measure ET at the plot scale and to scale it over larger regions. Moisture flux towers and micrometeorological stations have been deployed in numerous natural and agricultural biomes and provide continuous measurements of actual ET or potential ET with an accuracy or uncertainty of 10–30%. These measurements can be scaled to larger landscape units using remotely-sensed vegetation indices (VIs), Land Surface Temperature (LST), and other satellite data. Two types of methods have been developed. Empirical methods use time-series VIs and micrometeorological data to project ET measured on the ground to larger landscape units. Physically-based methods use remote sensing data to determine the components of the surface energy balance, including latent heat flux, which determines ET. Errors in predicting ET by both types of methods are within the error bounds of the flux towers by which they are calibrated or validated. However, the error bounds need to be reduced to 10% or less for applications that require precise wide-area ET estimates. The high fidelity between ET and VIs over agricultural fields and natural ecosystems where precise ground estimates of ET are available suggests that this might be an achievable goal if ground methods for measuring ET continue to improve.

Critical Reviews in Plant Science↗
USGS1997

Use of remote-sensing techniques to survey the physical habitat of large rivers

Remote-sensing techniques that can be used to quantitatively characterize the physical habitat in large rivers in the United States where traditional survey approaches typically used in small- and medium-sized streams and rivers would be ineffective or impossible to apply. The state-of-the-art remote-sensing technologies that we discuss here include side-scan sonar, RoxAnn, acoustic Doppler current profiler, remotely operated vehicles and camera systems, global positioning systems, and laser level survey systems. The use of these technologies will permit the collection of information needed to create computer visualizations and hard copy maps and generate quantitative databases that can be used in real-time mode in the field to characterize the physical habitat at a study location of interest and to guide the distribution of sampling effort needed to address other habitat-related study objectives. This report augments habitat sampling and characterization guidance provided by Meador et al. (1993) and is intended for use primarily by U.S. Geological Survey National Water Quality Assessment program managers and scientists who are documenting water quality in streams and rivers of the United States.

Report↗
USGS2020

Determining the drivers of suspended sediment dynamics in tidal marsh-influenced estuaries using high-resolution ocean color remote sensing

Sediment budgets are a critical metric to assess coastal marsh vulnerability to sea-level rise and declining riverine sediment inputs. However, calculating accurate sediment budgets is challenging in tidal marsh-influenced estuaries where suspended sediment concentrations (SSC) typically vary on scales of hours and meters, and where SSC dynamics are driven by a complex and often site-specific interplay of hydrodynamic and meteorological conditions. The mapping of SSC using ocean-color remote sensing is well established and can help capture the spatio-temporal variability needed to determine the dominant drivers regulating sediment budgets. However, the coarse spatial resolution of traditional ocean-color sensors (1-km) generally precludes their use in coastal-marsh estuaries. Here, using the Plum Island Estuary (Massachusetts, USA) as an example, we demonstrate that high-spatial-resolution maps of SSC derived from Landsat-8 Operational Land Imager (OLI) and Sentinel-2A/B Multispectral Instruments (MSI) can be used to determine the main drivers of SSC dynamics in tidal marsh-influenced estuaries, despite the long revisit time of these sensors. Local empirical algorithms between SSC and remote sensing reflectance were derived and applied to a total of 46 clear-sky scenes collected by the OLI and the MSI between 2013 and 2018. The analysis revealed that this 5-year record was sufficient to capture a representative range of meteorological and tidal conditions required to determine the main drivers of SSC dynamics in this mid-latitude system. The interplay between river and tidal flows dominated SSC dynamics in this estuary, whereas wind-driven resuspension had more moderate effects. The SSC were higher during spring because of increased river discharge due to snowmelt. Tidal asymmetry also enhanced sediment resuspension during flood tides, possibly favoring deposition on marsh platforms. Together, water level, water-level rate of change, river discharge and wind speed were able to explain > 60% of the variability in the main-channel thalweg-averaged SSC, thereby facilitating future prediction of SSC from these readily available variables. This study demonstrates that the existing multi-year records of high-resolution remote sensing can provide a representative depiction of SSC dynamics in hydrodynamically-complex and small-scale estuaries that moderate-resolution ocean color remote sensing and in situ measurements are unable to capture.

Massachusetts↗
USGS2019

2019 Joint Agency Commercial Imagery Evaluation—Land remote sensing satellite compendium

The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the first edition of the JACIE compendium, which is planned to be updated and released annually.

Circular↗
USGS2020

2020 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium

The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between five Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the second edition of the JACIE compendium, which is planned to be updated and released annually.

Circular↗
USGS2022

2022 Joint Agency Commercial Imagery Evaluation—Remote sensing satellite compendium

The Joint Agency Commercial Imagery Evaluation (JACIE) is a collaboration between six Federal agencies that are major users and producers of satellite land remote sensing data. In recent years, the JACIE group has observed ever-increasing numbers of remote sensing satellites being launched. This rapidly growing wave of new systems creates a need for a single reference for land remote sensing satellites that provides basic system specifications and linkage to any JACIE assessment that may have been completed on existing systems. This volume has been assembled by the Requirements, Capabilities, and Analysis for Earth Observation Project under the U.S. Geological Survey National Land Imaging Program as a contribution to the JACIE community. This is the third edition of the JACIE compendium, which is planned to be updated and released annually.

Circular↗