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At least 325 records · Page 18Linked to original sources
USGS2026

Software to support remote sensing of river discharge based on critical flow theory

Water resource management requires accurate observations of streamflow but standard field methods for measuring river discharge ( Q ) are costly and can be hazardous for equipment and personnel. Remote sensing has become a viable alternative, but many image-based techniques require field data for calibration and depth and velocity can seldom be mapped with a single sensor. A new approach based on critical flow theory, in contrast, allows both of these attributes to be inferred from readily available image data. This technique only pertains to sites with standing waves, called undular hydraulic jumps (UHJs), but a recent investigation demonstrated its potential to provide accurate discharge estimates. This paper introduces software designed to facilitate Inferring Q from UHJs Identified in River Images (InQUIRI). The package includes modules for retrieving data from image servers, making the measurements of wavelength and width required to calculate discharge, inferring a representative wavelength from a profile digitized along a wave train, combining multiple estimates to obtain an ensemble median discharge, and assessing accuracy via comparison to gage records from the U.S. Geological Survey. By making these steps easier to implement, InQUIRI enables users to apply the workflow to a variety of UHJ-containing images. Accumulating more case studies, some successful and others less so, would help constrain the range of applicability of the critical flow approach and foster development of refined guidelines for selecting and measuring waves. The software described herein could play an important role in promoting informed use of this new technique for non-contact streamflow measurement.

Arizona, Colorado, New Mexico, Utah↗
USGS2021

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria . Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency’s Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

Ecological Indicators↗
USGS2008

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Ecological Modelling↗
USGS2005

Integration of CO2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: potential for quantitative spatial extrapolation

Aim  Extrapolation of tower CO 2 fluxes will be greatly facilitated if robust relationships between flux components and remotely sensed factors are established. Long-term measurements at five Northern Great Plains locations were used to obtain relationships between CO 2 fluxes and photosynthetically active radiation ( Q ), other on-site factors, and Normalized Difference Vegetation Index ( NDVI ) from the SPOT VEGETATION data set. Location  CO 2 flux data from the following stations and years were analysed: Lethbridge, Alberta 1998–2001; Fort Peck, MT 2000, 2002; Miles City, MT 2000–01; Mandan, ND 1999–2001; and Cheyenne, WY 1997–98. Results  Analyses based on light-response functions allowed partitioning net CO 2 flux ( F ) into gross primary productivity ( P g ) and ecosystem respiration ( R e ). Weekly averages of daytime respiration, γ day , estimated from light responses were closely correlated with weekly averages of measured night-time respiration, γ night ( R 2 0.64 to 0.95). Daytime respiration tended to be higher than night-time respiration, and regressions of γ day on γ night for all sites were different from 1 : 1 relationships. Over 13 site-years, gross primary production varied from 459 to 2491 g CO 2 m −2 year −1 , ecosystem respiration from 996 to 1881 g CO 2 m −2 year −1 , and net ecosystem exchange from −537 (source) to +610 g CO 2 m −2 year −1 (sink). Maximum daily ecological light-use efficiencies, ɛ d , max = P g /Q , were in the range 0.014 to 0.032 mol CO 2 (mol incident quanta) −1 . Main conclusions  Ten-day average P g was significantly more highly correlated with NDVI than 10-day average daytime flux, P d ( R 2 = 0.46 to 0.77 for P g -NDVI and 0.05 to 0.58 for P d -NDVI relationships). Ten-day average R e was also positively correlated with NDVI , with R 2 values from 0.57 to 0.77. Patterns of the relationships of P g and R e with NDVI and other factors indicate possibilities for establishing multivariate functions allowing scaling-up local fluxes to larger areas using GIS data, temporal NDVI, and other factors.

Global Ecology and Biogeography↗
USGS2019

Pre‐fire vegetation drives post‐fire outcomes in sagebrush ecosystems: Evidence from field and remote sensing data

Understanding the factors that influence vegetation responses to disturbance is important because vegetation is the foundation of food resources, wildlife habitat, and ecosystem properties and processes. We integrated vegetation cover data derived from field plots and remotely sensed Landsat images in two focal areas over a 37‐yr period (1979–2016) to investigate how historical changes to community composition influence contemporary responses of vegetation to fire in sagebrush ecosystems in the Great Basin. Our objectives were (1) to quantify the magnitude and direction of change in the cover of native and exotic plant functional groups in relation to their exposure to fire; (2) to relate plant community changes to their historical composition, exposure to fire, and environmental conditions; and (3) to test for consistency of trends revealed by vegetation cover data derived from field plots and Landsat images. Historical (1979–1981) field data originated from 298 locations, Landsat‐derived data and contemporary (2011–2016) field data originated from 448 locations, and an expanded set of locations were included in some analyses of Landsat‐derived data. We found that areas burned by fire since the 1980s had higher annual herbaceous cover than unburned areas both historically and contemporarily. Models revealed a significant interaction between historical community composition and exposure to fire, which suggests that plots with historically high herbaceous cover were more susceptible to burning. Trends revealed by field and Landsat‐derived cover data were only partially consistent, potentially due in part to methods used to predict cover values from Landsat images, and the time period over which each data set was collected. Our results suggest that burned areas historically occupied by sagebrush‐dominated plant communities may have been invaded by exotic annuals prior to burning, possibly because of prior land uses, and after burning, have now transitioned to a persistent herbaceous‐dominated state. This type of state transition has important consequences for forage quality, wildlife habitat, soil nutrients, and future disturbances, such as drought and wildfire.

Oregon, Nevada↗
USGS2019

Measurement of cyanobacteria bloom magnitude using satellite remote sensing

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatio-temporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Ocean Land Color Imager (OLCI) on Sentinel-3 data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Florida, Ohio↗
USGS2025

Comparisons of shoreline positions from satellite-derived and traditional field- and remote-sensing techniques

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Alaska, Florida, Massachusetts, Washington↗
USGS2003

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Journal of Geophysical Research E: Planets↗
USGS2008

Modeling multi-layer effects in passive microwave remote sensing of dry snow using Dense Media Radiative Transfer Theory (DMRT) based on quasicrystalline approximation

The Dense Media Radiative Transfer theory (DMRT) of Quasicrystalline Approximation of Mie scattering by sticky particles is used to study the multiple scattering effects in layered snow in microwave remote sensing. Results are illustrated for various snow profile characteristics. Polarization differences and frequency dependences of multilayer snow model are significantly different from that of the single-layer snow model. Comparisons are also made with CLPX data using snow parameters as given by the VIC model. ?? 2007 IEEE.

Conference Paper↗
USGS2010

Lake temperature and ice cover regimes in the Alaskan Subarctic and Arctic: Integrated monitoring, remote sensing, and modeling

Lake surface regimes are fundamental attributes of lake ecosystems and their interaction with the land and atmosphere. High latitudes may be particularly sensitive to climate change, however, adequate baselines for these lakes are often lacking. In this study, we couple monitoring, remote sensing, and modeling techniques to generate baseline datasets of lake surface temperature and ice cover in the Alaskan Subarctic and Arctic. No detectable trends were observed during this study period, but a number of interesting patterns were noted among lakes and between regions. The largest Arctic lake was relatively unresponsive to air temperature, while the largest Subarctic lake was very responsive likely because it is fed by glacial runoff. Mean late summer water temperatures were higher than air temperatures with differences ranging from 1.7 to 5.4°C in Subarctic lakes and from 2.4 to 3.2°C in Arctic lakes. The warmest mean summer water temperature in both regions was in 2004, with the exception of Subarctic glacially fed lake that was highest in 2005. Ice-out timing had high coherence within regions and years, typically occurring in late May in Subarctic and in early-July in Arctic lakes. Ice-on timing was more dependent on lake size and depth, often varying among lakes within a region. Such analyses provide an important baseline of lake surface regimes at a time when there is increasing interest in high-latitude water ecosystems and resources during an uncertain climate future.

Journal of the American Water Resources Associatio↗
USGS2010

Use of ground-based remotely sensed data for surface energy balance evaluation of a semiarid rangeland

An interdisciplinary field experiment was conducted to study the water and energy balance of a semiarid rangeland watershed in southeast Arizona during the summer of 1990. Two subwatersheds, one grass dominated and the other shrub dominated, were selected for intensive study with ground-based remote sensing systems and hydrometeorological instrumentation. Surface energy balance was evaluated at both sites using direct and indirect measurements of the turbulent fluxes (eddy correlation, variance, and Bowen ratio methods) and using an aerodynamic approach based on remote measurements of surface reflectance and temperature and conventional meteorological information. Estimates of net radiant flux density ( R n ), derived from measurements of air temperature, incoming solar radiation, and surface temperature and radiance compared well with values measured using a net radiometer (mean absolute difference (MAD) ≃ 50 W/m 2 over a range from 115 to 670 W/m 2 ). Soil heat flux density ( G ) was estimated using a relation between G / R n and a spectral vegetation index computed from the red and near-infrared surface reflectance. These G estimates compared well with conventional measurements of G using buried soil heat flux plates (MAD ≃ 20 W/m 2 over a range from −13 to 213 W/m 2 ). In order to account for the effects of sparse vegetation, semiempirical adjustments to the single-layer bulk aerodynamic resistance approach were required for evaluation of sensible heat flux density ( H ). This yielded differences between measurements and remote estimates of H of approximately 33 W/m 2 over a range from 13 to 303 W/m 2 . The resulting estimates of latent heat flux density, LE , were of the same magnitude and trend as measured values; however, a significant scatter was still observed: MAD ≃ 40 W/m 2 over a range from 0 to 340 W/m 2 . Because LE was solved as a residual, there was a cumulative effect of errors associated with remote estimates of R n , G , and H .

Water Resources Research↗
USGS2009

Sample project: establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets

Quantifying rates of forest-cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals. This research extends previous research on global forest-cover dynamics and land-cover change estimation to establish a robust, operational forest monitoring and assessment system. The approach integrates both MODIS and Landsat data to provide timely biome-scale forest change estimation. This is achieved by using annual MODIS change indicator maps to stratify biomes into low, medium and high change categories. Landsat image pairs can then be sampled within these strata and analyzed for estimating area of forest cleared.

The NASA LCLUC Program: an interdisciplinary appro↗
USGS2015

Drought monitoring and assessment: Remote sensing and modeling approaches for the Famine Early Warning Systems Network

Drought monitoring is an essential component of drought risk management. It is usually carried out using drought indices/indicators that are continuous functions of rainfall and other hydrometeorological variables. This chapter presents a few examples of how remote sensing and hydrologic modeling techniques are being used to generate a suite of drought monitoring indicators at dekadal (10-day), monthly, seasonal, and annual time scales for several selected regions around the world. Satellite-based rainfall estimates are being used to produce drought indicators such as standardized precipitation index, dryness indicators, and start of season analysis. The Normalized Difference Vegetation Index is being used to monitor vegetation condition. Several satellite data products are combined using agrohydrologic models to produce multiple short- and long-term indicators of droughts. All the data sets are being produced and updated in near-real time to provide information about the onset, progression, extent, and intensity of drought conditions. The data and products produced are available for download from the Famine Early Warning Systems Network (FEWS NET) data portal at http://earlywarning.usgs.gov . The availability of timely information and products support the decision-making processes in drought-related hazard assessment, monitoring, and management with the FEWS NET. The drought-hazard monitoring approach perfected by the U.S. Geological Survey for FEWS NET through the integration of satellite data and hydrologic modeling can form the basis for similar decision support systems. Such systems can operationally produce reliable and useful regional information that is relevant for local, district-level decision making.

Book chapter↗
USGS2024

National-scale remotely sensed lake trophic state from 1984 through 2020

Lake trophic state is a key ecosystem property that integrates a lake’s physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales.

Scientific Data↗
USGS1986

The Conterminous United States Mineral Assessment Program; background information to accompany folio of geologic, geochemical, geophysical, remote sensing, and mineral resource maps of the Wallace 1 degree x 2 degrees Quadrangle, Montana and Idaho

The Wallace 1? x 2 quadrangle in Montana and Idaho was studied by an interdisciplinary research team that included geologists, geochemists, and geophysicists, as well as specialists in isotopic dating and remote sensing. The basic data resulting from these studies, as well as the final metallic mineral resource assessments, are published as a folio of maps, figures, tables, and accompanying discussions. This circular provides background information on the studies and lists the published components of the resource appraisal. An extensive bibliography lists both specific and general references that apply to this geoscience study of the quadrangle.

Circular↗
USGS2011

Hyperspectral remote sensing of vegetation and agricultural crops: Knowledge gain and knowledge gap after 40 years of research

The focus of this chapter was to summarize the advances made over last 40+ years, as reported in various chapters of this book, in understanding, modeling, and mapping terrestrial vegetation using hyperspectral remote sensing (or imaging spectroscopy) using sensors that are ground-based, truck-mounted, airborne, and spaceborne. As we have seen in various chapters of this book and synthesized in this chapter, the advances made include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracies (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (b), (e) ability to access stress resulting from causes such as management practices, pests and disease, water deficit or excess; , and (f) establishing more sensitive wavebands and indices to detect plant water\moisture content. The advent of spaceborne hyperspectral sensors (e.g., NASA’s Hyperion, ESA’s PROBA, and upcoming NASA’s HyspIRI) and numerous methods and techniques espoused in this book to overcome Hughes phenomenon or data redundancy when handling large volumes of hyperspectral data have generated tremendous interest in advancing our hyperspectral applications knowledge base over larger spatial extent such as region, nation, continent, and globe.

Book chapter↗
USGS2021

Rapid monitoring of the abundance and spread of exotic annual grasses in the western United States using remote sensing and machine learning

Exotic annual grasses (EAG) are one of the most damaging agents of change in western North America. Despite known socio-environmental effects of EAG there remains a need to enhance monitoring capabilities for better informing conservation and management practices. Here, we integrate field observations, remote sensing and climate data with machine-learning techniques to estimate and assess patterns of historical (1985–2019; R 2 = 0.86 ± 0.05; MAE = 6.7 ± 1.4%), present (2020), and future (2025–2040) EAG abundance (30-m) across much of the western United States. Trend analysis revealed that ∼8% and 1% of the landscape experienced significant rises and declines in historical EAG cover, respectively, with hotspots of invasion generally occurring near roads and along low-to-mid elevation gradients with warmer and drier conditions. Accurate simulations of the response of EAG to changing environmental conditions, disturbances and management treatments indicate that ecosystem resistance to invasion is largely controlled by long-term EAG abundance (surrogate for seed bank), time since and frequency of wildfire, and plant community interactions. Ecological thresholds associated with enhanced probabilities of wildfire occurrence and invasion rates indicate that relatively little (10%) EAG cover is needed to heighten these risks. Climate change is expected to push 8% of the landscape across invasion thresholds by 2040, impacting 6% of existing sage-grouse habitat, and we identify where fuel breaks may be placed to reduce wildfire risks and invasion. Spatially detailed, timely, and accurate depictions of past, present, and future EAG abundance are vital for the protection of life and property and the continued stewardship of sagebrush ecosystems.

California, Colorado, Idaho, Nevada, Oregon, Utah,↗