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At least 1,045 records · Page 58Linked to original sources
USGS2024

Water-level changes impact angler effort in a large lake: Implications for climate change

Climate change is expected to influence aquatic habitats and associated fish populations, yet we know little about the impact on recreational anglers. Our goal was to explore whether interannual fluctuations in waterbody surface area and other explanatory variables could be used as indicators of changes in angler fishing effort. Our approach leveraged a combination of remotely sensed waterbody surface area, environmental and fish population data, and onsite angler survey monitoring data for Devils Lake, North Dakota, USA during the open-water fishing period (May 1st to August 31st) for 9 years (1992–2021). The information was used to develop a dynamic waterbody size-angler effort model. Changes in waterbody surface area reliably predicted changes in angler effort (r 2 = 0.60). Increases in waterbody surface area led to increases in angler effort, and decreases in waterbody surface area led to decreases in angler effort. Our findings show promise that remotely sensed fluctuations in waterbody surface area could be used as an indicator of interannual angler effort dynamics. Dynamic waterbody size-angler effort models could provide managers the ability to predict changes in angler effort via climate-related hydrological cycles that affect the size and distribution of waterbodies on the landscape.

North Dakota↗
USGS2015

Agencies collaborate, develop a cyanobacteria assessment network

Cyanobacteria are a genetically diverse group of photosynthetic microorganisms that occupy a broad range of habitats on land and water all over the world. They release toxins that can cause lung and skin irritation, alter the taste and odor of potable water, and cause human and animal illness. Cyanobacteria blooms occur worldwide, and climate change may increase the frequency, duration, and extent of these bloom events. Rapid detection of potentially harmful blooms is essential to protect humans and animals from exposure. Information about potential for exposure, such as bloom duration, frequency, and extent, is especially critical for developing environmental management decisions during periods of limited resources and funding. The National Research Council (NRC) report Exposure Science in the 21st Century suggested that effectively assessing and mitigating exposures requires techniques for rapid measurement of a stressor, such as an algal bloom, across diverse geographic, temporal, and biologic scales (e.g., various bloom concentrations) and an enhanced infrastructure to address threats [ NRC , 2012]. The report specifically calls for approaches that use diverse information, such as satellite remote sensing, to identify and understand exposures that may pose a threat to ecosystems or human health. A collaborative effort integrates the work of the U.S. Environmental Protection Agency (EPA), NASA, the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Geological Survey (USGS) to provide an approach for using satellite ocean color capabilities in U.S. fresh and brackish water quality management decisions. The overarching goal of this collaborative project is to detect and quantify cyanobacteria blooms using satellite data records in order to support the environmental management and public use of U.S. lakes and reservoirs. Satellite remote sensing tools may enable policy makers and environmental managers to assess the sustainability of watershed ecosystems and the services they provide, now and in the future. Satellite technology allows us to develop early-warning indicators of cyanobacteria blooms at the local scale while maintaining continuous national coverage.

Eos, Earth and Space Science News↗
USGS2026

Subsurface water ice mapping on Mars: A probabilistic approach

Subsurface water ice deposits on Mars are an important resource for potential future human exploration. They are also an indicator of the planet’s past climate. However, the distribution of subsurface water ice in Mars’s midlatitudes is uncertain because spacecraft imagery cannot directly observe subsurface ice in most cases. Various spacecraft remote sensing instruments are sensitive to subsurface water ice, including thermal imaging spectrometers, radar sounders, and neutron spectrometers. Geomorphic analyses of images can also implicate subsurface ice. Building upon the data products from the Mars Subsurface Water Ice Mapping project, we provide a probabilistic framework to jointly interpret existing data and estimate the likelihood of subsurface water ice in the Martian midlatitudes between 60 ∘ S and 60 ∘ N with uncertainty. Broadly, we find that near-surface ice is likely present poleward of ∼45 ∘ in both the northern and southern hemispheres. However, closer to the equator, existing remote sensing data cannot uniquely constrain the presence of subsurface water ice. Our probabilistic results provide a framework for quantifying the abundance of ice on Mars, and our uncertainty estimates allow future analysis and exploration to target regions of high uncertainty.

Planetary Science Journal↗
USGS2022

Temperature variations in the northern Gulf of Alaska across synoptic to century-long time scales

Surface and subsurface moored buoy, ship-based, remotely sensed, and reanalysis datasets are used to investigate thermal variability of northern Gulf of Alaska (NGA) nearshore, coastal, and offshore waters over synoptic to century-long time scales. NGA sea surface temperature (SST) showed a larger positive trend of 0.22 ± 0.10 °C per decade over 1970–2021 compared to 0.10 ± 0.03 °C per decade over 1900–2021. Over synoptic time scales, SST covariance between two stations is small (<10%) when separation exceeds 100 km, while stations separated by 500 km retain 50% of their co-variability for seasonal and longer fluctuations. Relative to in situ sensor data, remotely sensed SST data has limited accuracy in some NGA settings, capturing 60–70% of the daily SST anomaly in coastal and offshore waters, but often <25% nearshore. North Pacific and NGA leading modes of SST variability leave 25–50% of monthly variance unresolved. Analysis of the 2014–2016 Pacific marine heatwave shows that NGA coastal surface temperatures warmed contemporaneously with offshore waters through 2013, but deep inner shelf waters (200–250 m) exhibited delayed warming. Offshore surface waters cooled from 2014 to 2016, while shelf waters continued to warm from the combined effects of local air-sea and advective heat fluxes. We find that annually averaged Sitka air temperature is a leading predictor (r 2 = 0.37, p < 0.05) for following-year NGA coastal water column temperature. Our results can inform future environmental monitoring designs, assist forward-looking projections of marine conditions, and show the importance of in situ measurements for nearshore studies that require knowledge of thermal conditions over time scales of days and weeks.

Alaska↗
USGS2026

Spectral characteristics and mapping of lithium-rich playas in the Basin and Range Province, western United States

This report presents a comprehensive set of prospectivity maps for lithium in brines located within the playas of the Basin and Range Province of western United States. The prospectivity assessment is based on criteria established in the descriptive deposit model outlined in a previous U.S. Geological Survey report, with a focus on regional remote sensing methodologies. The playas within the study area have been systematically ranked according to several key factors, including size, the presence and abundance of source rocks, vegetation as an indicator of water availability, documented prospects, and data products derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Notably, the deposits in the playa of Clayton Valley make it stand out as the sole lithium brine-producing playa in the United States. The ASTER sensor provides optimal regional coverage, characterized by a 60-kilometer swath width and unique multispectral capabilities that are not available with other remote sensing instruments. This report and the accompanying maps are intended to serve as resources for further multidisciplinary assessments of lithium or other elements or commodities where prospectivity can be inferred from the presence of proxies such as alteration minerals.

Arizona, Baja California, California, Chihuahua, I↗
USGS2011

Evaluating the potential for remote bathymetric mapping of a turbid, sand-bed river: 1. Field spectroscopy and radiative transfer modeling

Remote sensing offers an efficient means of mapping bathymetry in river systems, but this approach has been applied primarily to clear-flowing, gravel bed streams. This study used field spectroscopy and radiative transfer modeling to assess the feasibility of spectrally based depth retrieval in a sand-bed river with a higher suspended sediment concentration (SSC) and greater water turbidity. Attenuation of light within the water column was characterized by measuring the amount of downwelling radiant energy at different depths and calculating a diffuse attenuation coefficient, K d . Attenuation was strongest in blue and near-infrared bands due to scattering by suspended sediment and absorption by water, respectively. Even for red wavelengths with the lowest values of K d , only a small fraction of the incident light propagated to the bed, restricting the range of depths amenable to remote sensing. Spectra recorded above the water surface were used to establish a strong, linear relationship ( R 2 = 0.949) between flow depth and a simple band ratio; even under moderately turbid conditions, depth remained the primary control on reflectance. Constraints on depth retrieval were examined via numerical modeling of radiative transfer within the atmosphere and water column. SSC and sensor radiometric resolution limited both the maximum detectable depth and the precision of image-derived depth estimates. Thus, although field spectra indicated that the bathymetry of turbid channels could be remotely mapped, model results implied that depth retrieval in sediment-laden rivers would be limited to shallow depths (on the order of 0.5 m) and subject to a significant degree of uncertainty.

Water Resources Research↗
USGS1984

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Conference Paper↗
USGS2012

Integration of new geologic mapping and satellite-derived quartz mapping yields insights into the structure of the Roberts Mountains allochthon applicable to assessments for concealed Carlin-type gold deposits

Geologic mapping and remote sensing across north-central Nevada enable recognition of a thick sheet of Middle and Upper Ordovician Valmy Formation quartzite that structurally overlies folded and faulted Ordovician through Devonian stratigraphic units of the Roberts Mountains allochthon. In the northern Independence Mountains and nearby Double Mountain area, the Valmy Formation is in fault contact with Ordovician through Silurian, predominantly clastic, sedimentary rocks of the Roberts Mountains allochthon that were deformed prior to, or during, emplacement of the Valmy thrust sheet. Similar structural relations are recognized discontinuously for 200 kilometers along the strike of the Roberts Mountains allochthon in mapping guided by regional remote-sensing-based (ASTER) quartz maps. Overall thicknesses of deformed Roberts Mountains allochthon units between the base of the Valmy and the top of underlying carbonate rocks that host large Carlin-type gold deposits varies on the order of hundreds of meters but is not known to exceed 700 meters. The base of the Valmy thrust sheet is a complimentary datum in natural resource exploration and mineral resource assessment for concealed Carlin-type gold deposits.

Nevada↗
USGS2021

Intensity of grass invasion negatively correlated with population density and age structure of an endangered dune plant across its range

Invasive species are a global threat to ecosystem biodiversity and function; non-native grass invasion has been particularly problematic in sparsely vegetated ecosystems such as open dunes. Native plant population responses to invasion, however, are infrequently translated to landscape scales, limiting the effectiveness of these data for addressing conservation issues. We quantified population density, total population size, and age class distribution of the federally-endangered plant species Antioch Dunes evening primrose ( Oenothera deltoides subsp. howellii ), at sites along a non-native grass invasion gradient in California, USA. We then scaled relationships between invasion and plant density across the species’ range using spatial models and remote sensing data. Adult and juvenile O. deltoides subsp. howellii densities were more than 10 times higher in non-invaded areas (grids with 10% total plant cover) when compared to highly-invaded areas (grids with 80% total plant cover). The ratio of O. deltoides subsp. howellii juveniles to adults decreased to less than 1 at 54% total cover, highlighting sensitivity of the regeneration niche to invasion. Spatial models mapped hotspots of O. deltoides subsp. howellii abundance and population structure across the landscape at sub-meter scales. Scaling the impacts of increasing invasion on plant species of conservation concern holds promise when coupled with remote sensing approaches, especially in naturally low-cover ecosystems where readily available metrics (e.g., Normalized Difference Vegetation Index) can be used to quantify invasion. These spatial models inform how future invasive species management may influence population size and spatial distribution of species of conservation concern.

California↗
USGS2000

Conservation planning and monitoring avian habitat

Migratory bird conservation plans should not only develop population goals, they also should establish attainable objectives for optimizing avian habitats. Meeting population goals is of paramount importance, but progress toward established habitat objectives can generally be monitored more easily than can progress toward population goals. Additionally, local or regional habitat objectives can be attained regardless of perturbations to avian populations that occur outside the geographic area covered by conservation plans. Assessments of current avian habitats, obtained from remotely sensed data, and the historical distribution of habitats should be used in establishing habitat objectives. Habitat planning and monitoring are best conducted using a geographic information system. Habitat objectives are assigned to three categories: maintaining existing habitat, restoring habitat, and creating new or alternative habitat. Progress toward meeting habitat objectives can be monitored through geographic information systems by incorporating georeferenced information on public lands, private lands under conservation easements, corporate lands under prescribed management, habitat restoration areas, and private lands under alternative management to enhance wildlife values. We recommend that the area and distribution of habitats within the area covered by conservation plans be reassessed from remotely sensed imagery at intervals appropriate to detect predicted habitat changes.

Book chapter↗
USGS2019

Estimating river discharge with swath altimetry: A proof of concept using AirSWOT observations

The forthcoming Surface Water and Ocean Topography (SWOT) satellite mission will provide global measurements of the free surface of large rivers, providing new opportunities for remote sensing‐derived estimates of river discharge in gaged and ungaged basins. SWOT discharge algorithms have been developed and benchmarked using synthetic data but remain untested on real‐world swath altimetry observations. We present the first discharge estimates from AirSWOT, a SWOT‐like airborne Ka‐band radar, using 6 days of measurements over a 40‐km segment of the Willamette River in Oregon, USA. The three evaluated discharge algorithms estimated discharge with normalized root‐mean‐square errors of 10–31% when compared with in situ gage data but were sensitive to an initial estimate of mean annual discharge. Our results show that these discharge algorithms provide reliable discharge estimates on remotely sensed data at SWOT‐like spatial scales while highlighting the need for further algorithm sensitivity tests.

Oregon↗
USGS2023

Ice resource mapping on Mars

This chapter explains the rationale for considering shallowly buried (0 to >5 m depth) water ice in the mid-latitudes of Mars as a resource to support future human missions, and describes a NASA-funded effort to map that ice with existing orbital remote-sensing data. In recent decades, numerous studies have used various datasets to investigate the presence and stability of water ice in the Martian shallow subsurface, with the aim of understanding the planet’s recent climate history. As part of a renewed effort to prepare for human Mars missions, NASA has undertaken a more resource-focused approach. Here we describe the Mars Subsurface Water Ice Mapping (SWIM) team’s efforts to characterize the distribution of buried water-ice resources across all longitudes from 60°S to 60°N latitude through the integration of multiple datasets. Deriving composite measures for the presence of accessible ice from a diverse range of remote sensing techniques with unique resolutions and caveats is a challenging problem. To enable data synthesis, the team developed a methodology that assigns values of ice consistency for mapped detections of hydrogen from a neutron spectrometer, thermal behavior from various thermal spectrometers, multiscale geomorphology from imagery and elevation data, and surface and subsurface echoes from a radar sounder. Faced with diverse sensing depths and footprints for these datasets, the team has been pursuing an optimal approach to best represent multi-dataset ice consistency. The current formulation includes the use of weighting factors tuned to depth zones of interest for resource extraction. In the absence of dedicated ground-truth data, the validity of the team’s efforts is assessed by comparing the maps to the locations of fresh, ice-exposing impacts. The highest ice-consistency values occur within discrete zones poleward of ~40° latitude, where ice is relatively shallow, but positive values extend well into the ~20°–30° latitude zone, which is preferable for landing sites due to engineering considerations.

Book chapter↗
USGS2003

Utility of high-altitude infrared spectral data in mineral exploration: Application to Northern Patagonia Mountains, Arizona

Synoptic views of hydrothermal alteration assemblages are of considerable utility in regional-scale minerals exploration . Recent advances in data acquisition and analysis technologies have greatly enhanced the usefulness of remotely sensed imaging spectroscopy for reliable alteration mineral assemblages mapping. Using NASA's Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor, this study mapped large areas of advanced argillic and phyllic-argillic alteration assemblages in the southeastern Santa Rita and northern Patagonia mountains , Arizona . Two concealed porphyry copper deposits have been identified during past exploration , the Red Mountain and Sunnyside deposits, and related published hydrothermal alteration zoning studies allow the comparison of the results obtained from AVIRIS data to the more traditional field mapping approaches. The AVIRIS mapping compares favorably with field-based studies. An analysis of iron-bearing oxide minerals above a concealed supergene chalcocite deposit at Red Mountain also indicates that remotely sensed data can be of value in the interpretation of leached caps above porphyry copper deposits. In conjunction with other types of geophysical data , AVIRIS mineral maps can be used to discriminate different exploration targets within a region.

Economic Geology↗
USGS2025

Do mule deer surf peaks in forage quality while on summer range?

Many animals track ephemeral peaks in food abundance and quality that propagate across landscapes. Migrating ungulates, in particular, track waves of newly emerging plants from low-elevation winter ranges to high-elevation summer ranges—known as “green-wave surfing.” Because plants lose crude protein and gain insoluble fiber with maturation, ruminants are expected to exploit peaks in forage quality among individual plants (i.e., Forage Maturation Hypothesis). Although ample evidence supports the long-standing hypothesis that migratory ungulates surf peaks in forage quality during migration, the hypothesis that ungulates track peaks in forage quality at a small scale (i.e., microsurf while on summer range) remains less known. We studied a partially migratory population of mule deer ( Odocoileus hemionus ) in Wyoming, USA, to understand whether temperate ungulates optimize the use of high-quality forage as plants grow and senesce on disparate summer ranges. Specifically, we evaluated how crude protein, digestible energy, and relative abundance changed throughout the growing season and whether deer altered their diet to reflect species-specific changes in plant phenology. In support of the Forage Maturation Hypothesis, forage quality declined as large-scale patterns of phenology progressed away from a remotely sensed metric of peak green-up for most plant species on the summer ranges of deer that migrated short (<50 km), medium (50–130 km), and long distances (>130 km). Declining rates in forage quality among plant species were heterogeneous, providing deer with the phenological diversity required to microsurf. Deer changed their diet throughout the growing season and prioritized the consumption of some plants, including Rosa woodsii and Purshia tridentata , as the rank of forage quality increased ( p < 0.01). In light of the complexities common to studies on foraging behavior, our findings suggest that deer may have some potential to microsurf on summer range when heterogeneity in resource phenology is prevalent. Moreover, our findings validate the accuracy of remote sensing in quantifying peak forage quality for plants within sagebrush shrublands and montane habitats.

Wyoming↗
USGS2026

Quantifying the subfield heterogeneity in winter cereal cover crop biomass across farms in the Mid-Atlantic Coastal Plain

Winter cover crops can reduce erosion and improve water quality. Ecosystem services generally increase with higher cover crop biomass, but they are known to be variable within the field. We generated a large remotely-sensed biomass dataset from more than 8,000 fields enrolled in a cover crop cost share program to study biomass heterogeneity from fields in the Choptank and Chester-Sassafras watersheds in the Delmarva Peninsula, across four years. We also used soil and topography data to determine drivers of subfield heterogeneity in cover crop biomass. We found that winter cover crop biomass heterogeneity is higher in fields with large cover crops, and that low-lying or poorly-drained positions on the landscape often had lower biomass in the most variable fields. Random forest model performance at predicting subfield cover crop biomass was slightly improved at 9 m compared to 3 m spatial resolution. We demonstrate that remotely-sensed normalized difference vegetation index, soil, and topography data can be used to quantify variation in cover crop biomass at a watershed scale.

Maryland↗
USGS2026

Quantifying post-fire live tree presence and spatial variation using Sentinel-2 time series

Accurate mapping of post-fire surviving trees is important for tracking forest recovery and prioritizing land management decisions. Satellite-based remote sensing is an effective method to assess post-fire forest conditions. Traditionally, differenced satellite-derived burn severity indices are computed by differencing one year pre- and post-fire spectral reflectance values. Differenced burn severity indices are useful for quantifying and mapping the magnitude of ecological change, but their application to detecting and mapping post-fire live trees may not be as appropriate, particularly for delayed tree mortality. Delayed tree mortality (“delayed mortality”) is a phenomenon where trees that initially survive fire then die over an extended period (between one and five years), and it can be challenging to measure and predict. In this study, we demonstrate the potential of mapping delayed mortality using readily available remotely sensed imagery alone. We used random forest models to detect post-fire live trees using 10-m resolution Sentinel-2 data at one-, three-, and five-years post-fire for four fires in the southern Sierra Nevada, California, USA. Using imagery from the National Agriculture Imagery Program (NAIP; 60-cm resolution), we manually classified live tree presence in 6000 Sentinel-2 pixels (500 pixels for each fire-year combination) to calibrate and validate models. Sentinel-2 based model accuracies ranged from 65 % to 86 % with F-scores ranging from 0.52 to 0.86, and their predictions of live pixel area were on average 44 % lower than inferred from more traditional indices such as relative differenced normalized burn ratio (RdNBR). This work represents a promising first step in using freely available post-fire spectral reflectance imagery to detect live trees over an extended period to support post-fire management.

California↗
USGS2023

Causality-informed Bayesian inference for rapid seismic ground failure and building damage estimation

Rapid and accurate estimates of seismic ground failure and building damage are beneficial to efficient emergency response and post-earthquake recovery. Traditional approaches, such as physical and geospatial models, have poor accuracy and resolution due to large uncertainties and the limited availability of informing geospatial layers. The introduction of remote sensing techniques has shown potential in providing supplementary information for rapid hazard estimation by analyzing earthquake-induced correlation changes between pre- and post-event satellite images. However, the changes in satellite images are the result of overlapping ground failure, building damage, and environmental noise, making it challenging to categorize and estimate different seismic hazards and impacts directly from satellite images.Here we design a novel causality-informed Bayesian network that continuously updates seismic ground failure and building damage estimates from satellite images by modeling the physical interdependencies between geospatial features, ground failure, building footprints, building damage, and satellite images. The incorporation of physical interdependencies allows an effective fusion of physical models and rich but noisy information from remote sensing observations and reduces bias and uncertainties in estimations. Our experiments show that integrating satellite images through our Bayesian network improves the accuracy of seismic ground failure and building damage estimations.

Conference Paper↗
USGS2017

A land data assimilation system for sub-Saharan Africa food and water security applications

Seasonal agricultural drought monitoring systems, which rely on satellite remote sensing and land surface models (LSMs), are important for disaster risk reduction and famine early warning. These systems require the best available weather inputs, as well as a long-term historical record to contextualize current observations. This article introduces the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS), a custom instance of the NASA Land Information System (LIS) framework. The FLDAS is routinely used to produce multi-model and multi-forcing estimates of hydro-climate states and fluxes over semi-arid, food insecure regions of Africa. These modeled data and derived products, like soil moisture percentiles and water availability, were designed and are currently used to complement FEWS NET’s operational remotely sensed rainfall, evapotranspiration, and vegetation observations. The 30+ years of monthly outputs from the FLDAS simulations are publicly available from the NASA Goddard Earth Science Data and Information Services Center (GES DISC) and recommended for use in hydroclimate studies, early warning applications, and by agro-meteorological scientists in Eastern, Southern, and Western Africa.

Scientific Data↗