SEARCH · USGS Science

Results for “Remote Sensing Information”

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

SourceResearch connectionFrom yearThrough year

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

At least 1,009 records · Page 56Linked to original sources

USGS2006

A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska

The U.S. Geological Survey (USGS) has developed a 16-year time series of vegetation condition information for the conterminous United States and Alaska using 1 km Advanced Very High Resolution Radiometer (AVHRR) data. The AVHRR data have been processed using consistent methods that account for radiometric variability due to calibration uncertainty, the effects of the atmosphere on surface radiometric measurements obtained from wide field-of-view observations, and the geometric registration accuracy. The conterminous United States and Alaska data sets have an atmospheric correction for water vapor, ozone, and Rayleigh scattering and include a cloud mask derived using the Clouds from AVHRR (CLAVR) algorithm. In comparison with other AVHRR time series data sets, the conterminous United States and Alaska data are processed using similar techniques. The primary difference is that the conterminous United States and Alaska data are at 1 km resolution, while others are at 8 km resolution. The time series consists of weekly and biweekly maximum normalized difference vegetation index (NDVI) composites.

Photogrammetric Engineering and Remote Sensing↗

USGS2004

Mapping recent lava flows at Westdahl Volcano, Alaska, using radar and optical satellite imagery

Field mapping of young lava flows at Aleutian volcanoes is logistically difficult, and the utility of optical images from aircraft or satellites for this purpose is greatly reduced by persistent cloud cover. These factors have hampered earlier estimates of the areas and volumes of three young lava flows at Westdahl Volcano, including its most recent (1991–1992) flow. We combined information from synthetic aperture radar (SAR) images with multispectral Landsat-7 data to differentiate the 1991–1992 flow from the 1964 flow and a pre-1964 flow, and to calculate the flow areas (8.4, 9.2, and 7.3 km 2 , respectively). By differencing a digital elevation model (DEM) from the 1970–1980s with a DEM from the Shuttle Radar Topography Mission (SRTM) in February 2000, we estimated the average thickness of the 1991–1992 flow to be 13 m, which reasonably agrees with field observations (5–10 m). Lava-flow maps produced in this way can be used to facilitate field mapping and flow-hazards assessment, and to study magma-supply dynamics and thus to anticipate future eruptive activity. Based on the recurrence interval of recent eruptions and the results of this study, the next eruption at Westdahl may occur before the end of this decade.

Alaska↗

USGS2013

Absolute radiometric calibration of Landsat using a pseudo invariant calibration site

Pseudo invariant calibration sites (PICS) have been used for on-orbit radiometric trending of optical satellite systems for more than 15 years. This approach to vicarious calibration has demonstrated a high degree of reliability and repeatability at the level of 1-3% depending on the site, spectral channel, and imaging geometries. A variety of sensors have used this approach for trending because it is broadly applicable and easy to implement. Models to describe the surface reflectance properties, as well as the intervening atmosphere have also been developed to improve the precision of the method. However, one limiting factor of using PICS is that an absolute calibration capability has not yet been fully developed. Because of this, PICS are primarily limited to providing only long term trending information for individual sensors or cross-calibration opportunities between two sensors. This paper builds an argument that PICS can be used more extensively for absolute calibration. To illustrate this, a simple empirical model is developed for the well-known Libya 4 PICS based on observations by Terra MODIS and EO-1 Hyperion. The model is validated by comparing model predicted top-of-atmosphere reflectance values to actual measurements made by the Landsat ETM+ sensor reflective bands. Following this, an outline is presented to develop a more comprehensive and accurate PICS absolute calibration model that can be Système international d'unités (SI) traceable. These initial concepts suggest that absolute calibration using PICS is possible on a broad scale and can lead to improved on-orbit calibration capabilities for optical satellite sensors.

IEEE Transactions on Geoscience and Remote Sensing↗

USGS2024

Observing systems, modeling, and forecasting

Predicting harmful algal blooms (HABs) requires integrating physical, chemical, and biological data collected from observing networks and then assimilating these data into models, which are used to generate forecasts. In 2005, the Harmful Algal Research and Response: A National Environmental Science Strategy 2005-2015 (HARRNESS, 2005) made recommendations on how to improve HAB modeling and forecasting over the next decade. Key HARRNESS recommendations related to sensing, networking, and modeling HABs included: ● Support the development and validation of new and improved technologies for remote cell and toxin detection, and for modeling and forecasting, ● Improve coordination of monitoring/ and modeling efforts, both at national and regional levels, ● Improve the use of networking technologies for monitoring and modeling efforts, ● Conduct sustained time series measurements of the biotic, chemical, and physical environments impacted by HABs, ● Develop food web models on the ecosystem fate and effects of toxins, ● Develop and improve species-specific models that link to physical-biological models. Here we review HAB observing, modeling, and forecasting advances and technologies and recommend research and management priorities for the next decade and beyond. Our report encompasses sensing technologies, sensor networking and data management, models and forecasts, and the paths to operationalize forecasts. Continued improvements of deployable sensors are foundational to improving early warning indicators, models, and forecasts, which are only as good as the underlying data. Sensing technology has advanced considerably in the last decade; for example, more capable fluorometric pigment sensors can track changes in bloom biomass in real-time. Additionally, automated imaging/classifying systems to identify and quantify key harmful algal (HA) taxa are being routinely deployed. However, deployable toxin sensors are available for only some HAB toxins and continue to be identified as a critical need by researchers and managers. As more and improved sensors and technologies become available, the data quality associated with each sensor needs to be assessed. Data quality encompasses the reliability, accuracy, and uncertainty associated with sensor-generated data. These data need to be of known quality so that researchers, managers, and end-users can reliably determine if the information is appropriate for their intended applications. Many of the data quality recommendations from HARRNESS (2005) are still relevant and have been reiterated within the management community. Understanding and documenting data quality, and when applicable, standardizing best practices for sensor use, continue to be recommended.

Report↗

USGS2007

Ground-water recharge in the arid and semiarid southwestern United States

Ground-water recharge in the arid and semiarid southwestern United States results from the complex interplay of climate, geology, and vegetation across widely ranging spatial and temporal scales. Present-day recharge tends to be narrowly focused in time and space. Widespread water-table declines accompanied agricultural development during the twentieth century, demonstrating that sustainable ground-water supplies are not guaranteed when part of the extracted resource represents paleorecharge. Climatic controls on ground-water recharge range from seasonal cycles of summer monsoonal and winter frontal storms to multimillennial cycles of glacial and interglacial periods. Precipitation patterns reflect global-scale interactions among the oceans, atmosphere, and continents. Large-scale climatic influences associated with El Niño and Pacific Decadal Oscillations strongly, but irregularly, control weather in the study area, so that year-to-year variations in precipitation and ground-water recharge are large and difficult to predict. Proxy data indicate geologically recent periods of naturally occurring multidecadal droughts unlike any in the modern instrumental record. Any anthropogenically induced climate change will likely reduce ground-water recharge through diminished snowpack at higher elevations. Future changes in El Niño and monsoonal patterns, both crucial to precipitation in the study area, are highly uncertain in current models. Current land-use modifications influence ground-water recharge through vegetation, irrigation, and impermeable area. High mountain ranges bounding the study area—the San Bernadino Mountains and Sierra Nevada to the west, and the Wasatch and southern Colorado Rocky Mountains to the east—provide external geologic controls on ground-water recharge. Internal geologic controls stem from tectonic processes that led to numerous, variably connected alluvial-filled basins, exposure of extensive Paleozoic aquifers in mountainous recharge areas, and distinct modes of recharge in the Colorado Plateau and Basin and Range subregions. The chapters in this professional paper present (first) an overview of climatic and hydrogeologic framework (chapter A), followed by a regional analysis of ground-water recharge across the entire study area (chapter B). These are followed by an overview of site-specific case studies representing different subareas of the geographically diverse arid and semiarid southwestern United States (chapter C); the case studies themselves follow in chapters D–K. The regional analysis includes detailed hydrologic modeling within the framework of a high-resolution geographic-information system (GIS). Results from the regional analysis are used to explore both the distribution of ground-water recharge for mean climatic conditions as well as the influence of two climatic patterns—the El Niño-Southern Oscillation and Pacific Decadal Oscillation—that impart a high degree of variability to the hydrologic cycle. Individual case studies employ a variety of geophysical and geochemical techniques to investigate recharge processes and relate the processes to local geologic and climatic conditions. All of the case studies made use of naturally occurring tracers to quantify recharge. Thermal and geophysical techniques that were developed in the course of the studies are presented in appendices. The quantification of ground-water recharge in arid settings is inherently difficult due to the generally low amount of recharge, its spatially and temporally spotty nature, and the absence of techniques for directly measuring fluxes entering the saturated zone from the unsaturated zone. Deep water tables in arid alluvial basins correspond to thick unsaturated zones that produce up to millennial time lags between changes in hydrologic conditions at the land surface and subsequent changes in recharge to underlying ground water. Recent advances in physical, chemical, isotopic, and modeling techniques have fostered new types of recharge assessments. Chemical and isotopic techniques include an increasing variety of environmental tracers that are useful and robust. Physically based techniques include the use of heat as a tracer and computationally intensive geophysical imaging tools for characterizing hydrologic conditions in the unsaturated zone. Modeling-based techniques include spatially distributed water-budget computations using high-resolution remotely sensed and ground-based geographic data. Application of these techniques to arid and semiarid settings in the southwestern United States reveals distinct patterns of recharge corresponding to geologic setting, climatic and vegetative history, and land use. Analysis of recharge patterns shows that large expanses of alluvial basin floors are drying out under current climatic conditions, with little to no recharge to underlying ground water. Ground-water recharge occurs mainly beneath upland catchments in which thin soils overlie permeable bedrock, ephemeral channels in which flow may average only several hours per year, and active agricultural areas. The chapters in this professional paper represent a coordinated attempt to develop a better understanding of one of the Nation's most critical yet difficult-to-quantify renewable resources.

Professional Paper↗

USGS2011

Using multi-source satellite data for lake level modelling in ungauged basins: A case study for Lake Turkana, East Africa

Managing limited surface water resources is a great challenge in areas where ground-based data are either limited or unavailable. Direct or indirect measurements of surface water resources through remote sensing offer several advantages of monitoring in ungauged basins. A physical based hydrologic technique to monitor lake water levels in ungauged basins using multi-source satellite data such as satellite-based rainfall estimates, modelled runoff, evapotranspiration, a digital elevation model, and other data is presented. This approach is applied to model Lake Turkana water levels from 1998 to 2009. Modelling results showed that the model can reasonably capture all the patterns and seasonal variations of the lake water level fluctuations. A composite lake level product of TOPEX/Poseidon, Jason-1, and ENVISAT satellite altimetry data is used for model calibration (1998-2000) and model validation (2001-2009). Validation results showed that model-based lake levels are in good agreement with observed satellite altimetry data. Compared to satellite altimetry data, the Pearson's correlation coefficient was found to be 0.81 during the validation period. The model efficiency estimated using NSCE is found to be 0.93, 0.55 and 0.66 for calibration, validation and combined periods, respectively. Further, the model-based estimates showed a root mean square error of 0.62 m and mean absolute error of 0.46 m with a positive mean bias error of 0.36 m for the validation period (2001-2009). These error estimates were found to be less than 15 % of the natural variability of the lake, thus giving high confidence on the modelled lake level estimates. The approach presented in this paper can be used to (a) simulate patterns of lake water level variations in data scarce regions, (b) operationally monitor lake water levels in ungauged basins, (c) derive historical lake level information using satellite rainfall and evapotranspiration data, and (d) augment the information provided by the satellite altimetry systems on changes in lake water levels. ?? Author(s) 2011.

Hydrology and Earth System Sciences Discussions↗

USGS2023

Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.

Remote Sensing of Environment↗

USGS2016

Users and uses of Landsat 8 satellite imagery—2014 survey results

Executive Summary In 2013, Landsat 8 began adding high quality, global, moderate-resolution imagery to the more than 40-year archive of Landsat imagery. To assess the potential effects of the availability of Landsat 8 imagery on users and their work, the U.S. Geological Survey (USGS) Land Remote Sensing Program (LRS) initiated a survey of Landsat users. The objectives of the survey were to 1. Characterize various Landsat user groups, such as United States (U.S.) and international users and Landsat 8 and non-Landsat 8 users; 2. Identify any differences among user groups in uses and preferences; 3. Measure the importance of and satisfaction with Landsat 8 attributes; 4. Assess the importance to users of the frequency of usable imagery; and 5. Determine any challenges in using Landsat 8. The online survey was sent to 51,617 Landsat users registered with USGS in May 2014. Almost 13,000 people responded to the survey for a response rate of 25 percent (n = 12,966). Current Landsat users (users who had used Landsat in their work in the year prior to the survey) composed 89 percent of the sample (n = 11,549) and past Landsat users composed 11 percent (n = 1,417). The results reported here apply to current Landsat users registered with the USGS Earth Resources Observation and Science (EROS) Center. Users from 161 countries responded to the survey. Of those, 19 percent were citizens or permanent residents of the United States and 81 percent resided in other countries. More than 70 percent of current users had used Landsat 8 in the year prior to the survey. The majority of Landsat 8 users (65 percent) were established users who used Landsat imagery regularly both before and after Landsat 8 imagery became available. The average current Landsat user was male, 36 years old, and highly educated, with 9 years of experience using satellite imagery or geographic information system (GIS) software. Landsat 8 users had, on average, two more years of experience than non-Landsat 8 users. Users were employed predominantly by academic institutions (65 percent), followed by private businesses (13 percent), Federal governments (10 percent), State and local governments (6 percent), and nonprofit organizations (6 percent). Of the Landsat imagery obtained in the past year by current users, on average 31 percent came from a Landsat 8 sensor. An equivalent amount came from the Landsat 7 ETM+ sensor (33 percent); slightly less came from Landsats 4 and 5 TM sensors (27 percent). Much less came from Landsats 1 through 5 MSS sensors (5 percent). Overall, more than a third of users’ work used Landsat imagery (38 percent). Of this work, on average, 37 percent of the work was operational. Landsat 8 users considered a greater proportion of their work operational than non-Landsat 8 users (39 percent compared with 29 percent). Environmental sciences and management were the most commonly selected primary applications (selected by 42 percent of users). Land use/land cover (23 percent) was the second most commonly selected primary application, followed by education (12 percent), agriculture (9 percent), and planning and development (6 percent). Landsat 8 users were asked to rank the importance of certain attributes in determining whether to use Landsat 8 imagery in their work. The archive was ranked most important, followed by cost, spatial resolution, extent of coverage, data quality, and frequency of revisit. Users were asked how satisfied they were with these same attributes as they currently apply to Landsat 8 imagery. On average, users were most satisfied with lack of cost, extent of coverage, data quality, and the archive, but they were satisfied with all attributes. Users were asked how often they needed Landsat imagery to meet various requirements for their primary application. The survey question specifically asked how often users needed usable imagery, which differs from how often they would like the Landsat satellites to acquire an image. Users were asked to identify their needed frequency of usable imagery for the following levels: 1. Threshold level—the minimum frequency of usable imagery needed to be of any value to their primary application. 2. Breakthrough level—the frequency of usable imagery that would result in a significant improvement for their primary application of the imagery. 3. Target level—the frequency of usable imagery that would only provide a limited additional increase in the expected performance for their primary application. To meet the threshold level, three-quarters of users needed usable imagery every 17 days or less frequently. At the breakthrough level, two-thirds of users (64 percent) needed a usable image every 5–16 days. The current constellation of two satellites (Landsat 7 and 8) is capable of meeting the threshold and breakthrough needs of most users at least some of the time, but a single satellite would be highly unlikely to do so. Two-fifths of users (40 percent) felt that usable imagery provided every 4 days or more frequently would meet their target level which the current Landsat constellation cannot provide. Landsat 8 users were significantly more likely than non-Landsat 8 users to need usable imagery more frequently to meet their target levels. Additionally, U.S. Landsat 8 users were significantly more likely than other Landsat users to need usable imagery more frequently in order meet both their breakthrough and target levels. To explore the effect of the availability of Landsat 8 imagery on Landsat imagery use in general, established users (those who had consistently used Landsat imagery both before and after Landsat 8 imagery became available) using Landsat 8 imagery were asked about changes in the amount of Landsat imagery they used. The majority of established users using Landsat 8 imagery (60 percent) reported an average increase of 51 percent in the number of scenes obtained after Landsat 8 imagery became available. Landsat 8 users were asked if they had encountered challenges in using Landsat 8 whereas non-Landsat 8 users were asked if such challenges had played a role in why they were not using Landsat 8 imagery. Although many users did not encounter challenges when using or trying to use Landsat 8 data, slightly less than 30 percent did encounter issues with processing the data to a usable point. The most common issue reported was not being able to create or have access to a surface reflectance corrected product. Other challenges were related to the file sizes of images being too large to download, store, or analyze. There were no statistically significant differences between Landsat 8 and non-Landsat 8 users in terms of challenges encountered when using or trying to use the imagery, which indicates that users were not unduly discouraged by the challenges they may have encountered. When asked about potential consequences of not using Landsat 8, more than half of the non-Landsat 8 users did not report detrimental effects on their work from not using the imagery. Of those who did report detrimental effects, decreased quality of work, decreased scope of work, and increased time spent on work were the most common.

Open-File Report↗

USGS2005

Topography and Landforms of Ecuador

EXPLANATION The digital elevation model of Ecuador represented in this data set was produced from over 40 individual tiles of elevation data from the Shuttle Radar Topography Mission (SRTM). Each tile was downloaded, converted from its native Height file format (.hgt), and imported into a geographic information system (GIS) for additional processing. Processing of the data included data gap filling, mosaicking, and re-projection of the tiles to form one single seamless digital elevation model. For 11 days in February of 2000, NASA, the National Geospatial-Intelligence Agency (NGA), the German Aerospace Center (DLR), and the Italian Space Agency (ASI) flew X-band and C-band radar interferometry onboard the Space Shuttle Endeavor. The mission covered the Earth between 60?N and 57?S and will provide interferometric digital elevation models (DEMs) of approximately 80% of the Earth's land mass when processing is complete. The radar-pointing angle was approximately 55? at scene center. Ascending and descending orbital passes generated multiple interferometric data scenes for nearly all areas. Up to eight passes of data were merged to form the final processed SRTM DEMs. The effect of merging scenes averages elevation values recorded in coincident scenes and reduces, but does not completely eliminate, the amount of area with layover and terrain shadow effects. The most significant form of data processing for the Ecuador DEM was gap-filling areas where the SRTM data contained a data void. These void areas are a result of radar shadow, layover, standing water, and other effects of terrain, as well as technical radar interferometry phase unwrapping issues. To fill these gaps, topographic contours were digitized from 1:50,000 - scale topographic maps which date from the mid-late 1980's (Souris, 2001). Digital contours were gridded to form elevation models for void areas and subsequently were merged with the SRTM data through GIS and remote sensing image-processing techniques. The data contained in this publication includes a gap filled, countrywide SRTM DEM of Ecuador projected in Universal Transverse Mercator (UTM) Zone 17 North projection, Provisional South American, 1956, Ecuador datum and a non gap filled SRTM DEM of the Galapagos Islands projected in UTM Zone 15 North projection. Both the Ecuador and Galapagos Islands DEMs are available as an ESRI Grid, stored as ArcInfo Export files (.e00), and in Erdas Imagine (IMG) file formats with a 90 meter pixel resolution. Also included in this publication are high and low resolution Adobe Acrobat (PDF) files of topography and landforms maps in Ecuador. The high resolution map should be used for printing and display, while the lower resolution map can be used for quick viewing and reference purposes.

Data Series↗

USGS2026

The impacts of cover crop biomass on satellite-based detectability of cover crops in Maryland

Cover crop adoption in the U.S. has increased over the past decades, increasing the need to quantify their performance and environmental benefits. While remote sensing (RS)-based approaches for detecting cover crop presence have been developed, there has been limited research on how cover crops with varied biomass and management practices influence detectability. Using unique field-level cover crop presence and biomass datasets in Maryland, U.S., we investigated how RS-based detectability changes for cover crops with varied aboveground biomass, planting, and termination dates. Specifically, we proposed a time-integrated satellite-based greenness feature from Harmonized Landsat-8 and Sentinel-2 (HLS) time series from 2017 to 2021 to estimate biomass of cover crops and evaluated their detectability using a phenology-based cover crop detection framework. The impacts of cover crop planting and termination dates on cover crop biomass were also analyzed. Our results demonstrated that Normalized Difference Vegetation Index (NDVI) estimated biomass with higher accuracy compared to other vegetation indices, and the time-integrated model estimated biomass with higher accuracy than the single-date “snapshot” linear model (R2 from 0.53 to 0.66 and RMSE from 922 kg/ha to 747 kg/ha). While the snapshot models were species sensitive, the time-integrated models showed strong robustness across different species. Detectability increased with cover crop biomass, as detected cover crops averaged 963.3 ±719.5 kg/ha compared to 297.2 ± 209.0 kg/ha for non-detected cover crops. Detection accuracy reached 96.1% for fields exceeding 500 kg/ha, compared with an overall accuracy of 62.7%. Earlier planting and later termination increased biomass and detectability, with biomass rising by 4.14 kg/ha/day (p < 0.01). This study demonstrates how management practices affect cover crop biomass and detectability via satellite time series and provides insights that can inform management of cover crops and monitoring of their effects on agroecosystems.

Maryland↗

USGS2007

Applications of Radarsat-1 synthetic aperture radar imagery to assess hurricane-related flooding of coastal Louisiana

The Louisiana coast is subjected to hurricane impacts including flooding of human settlements, river channels and coastal marshes, and salt water intrusion. Information on the extent of flooding is often required quickly for emergency relief, repairs of infrastructure, and production of flood risk maps. This study investigates the feasibility of using Radarsat-1 SAR imagery to detect flooded areas in coastal Louisiana after Hurricane Lili, October 2002. Arithmetic differencing and multi-temporal enhancement techniques were employed to detect flooding and to investigate relationships between backscatter and water level changes. Strong positive correlations (R2=0.7-0.94) were observed between water level and SAR backscatter within marsh areas proximate to Atchafalaya Bay. Although variations in elevation and vegetation type did influence and complicate the radar signature at individual sites, multi-date differences in backscatter largely reflected the patterns of flooding within large marsh areas. Preliminary analyses show that SAR imagery was not useful in mapping urban flooding in New Orleans after Hurricane Katrina's landfall on 29 August 2005. ?? 2005 Taylor & Francis.

International Journal of Remote Sensing↗

USGS2015

Mapping forest functional type in a forest-shrubland ecotone using SPOT imagery and predictive habitat distribution modelling

The availability of land cover data at local scales is an important component in forest management and monitoring efforts. Regional land cover data seldom provide detailed information needed to support local management needs. Here we present a transferable framework to model forest cover by major plant functional type using aerial photos, multi-date Système Pour l’Observation de la Terre (SPOT) imagery, and topographic variables. We developed probability of occurrence models for deciduous broad-leaved forest and needle-leaved evergreen forest using logistic regression in the southern portion of the Wyoming Basin Ecoregion. The model outputs were combined into a synthesis map depicting deciduous and coniferous forest cover type. We evaluated the models and synthesis map using a field-validated, independent data source. Results showed strong relationships between forest cover and model variables, and the synthesis map was accurate with an overall correct classification rate of 0.87 and Cohen’s kappa value of 0.81. The results suggest our method adequately captures the functional type, size, and distribution pattern of forest cover in a spatially heterogeneous landscape.

Colorado, Utah, Wyoming↗

USGS2022

The future of coastal monitoring through satellite remote sensing

Satellite remote sensing is transforming coastal science from a “data-poor” field into a “data-rich” field. Sandy beaches are dynamic landscapes that change in response to long-term pressures, short-term pulses, and anthropogenic interventions. Until recently, the rate and breadth of beach change have outpaced our ability to monitor those changes, due to the spatiotemporal limitations of our observational capacity. Over the past several decades, only a handful of beaches worldwide have been regularly monitored with accurate yet expensive in situ surveys. The long-term coastal-change data of these few well-monitored beaches have led to in-depth understanding of many site-specific coastal processes. However, because the best-monitored beaches are not representative of all beaches, much remains unknown about the processes and fate of the other >99% of unmonitored beaches worldwide. The fleet of Earth-observing satellites has enabled multiscale monitoring of beaches, for the very first time, by providing imagery with global coverage and up to daily frequency. The long-standing and ever-expanding archive of satellite imagery will enable coastal scientists to investigate coastal change at sites vulnerable to future sea-level rise, that is, (almost) everywhere. In the past decade, our capability to observe coastal change from space has grown substantially with computing and algorithmic power. Yet, further advances are needed in automating monitoring using machine learning, deep learning, and computer vision to fully leverage this massive treasure trove of data. Extensive monitoring and investigation of the causes and effects of coastal change at the requisite spatiotemporal scales will provide coastal managers with additional, valuable information to evaluate problems and solutions, addressing the potential for widespread beach loss due to accelerated sea-level rise, development, and reduced sediment supply. Monitoring from Earth-observing satellites is currently the only means of providing seamless data with high spatiotemporal resolution at the global scale of the impending impacts of climate change on coastal systems.

Cambridge Prisms: Coastal Futures↗

USGS1988

Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada, and California

Three flightlines of Airborne Imaging Spectrometer (AIS) data, acquired over the northern Grapevine Mountains, Nevada, and California, were used to map minerals associated with hydrothermally altered rocks. The data were processed to remove vertical striping, normalized using an equal area normalization, and reduced to reflectance relative to an average spectrum derived from the data. An algorithm was developed to automatically calculate the absorption band parameters band position, band depth, and band width for the strongest absorption feature in each pixel. These parameters were mapped into an intensity, hue, saturation (IHS) color system to produce a single color image that summarized the absorption band information, This image was used to map areas of potential alteration based upon the predicted relationships between the color image and mineral absorption band. Individual AIS spectra for these areas were then examined to identify specific minerals. Two types of alteration were mapped with the AIS data. Areas of quartz-sericite-pyrite alteration were identified based upon a strong absorption feature near 2.21 μm, a weak shoulder near 2.25 μm, and a weak absorption band near 2.35 μm caused by sericite (fine-grained muscovite). Areas of argillic alteration were defined based on the presence of montmorillonite, identified by a weak to moderate absorption feature near 2.21 μm and the absence of the 2.35 μm band. Montmorillonite could not be identified in mineral mixtures. Calcite and dolomite were identified based on sharp absorption features near 2.34 and 2.32 μm, respectively. Areas of alteration identified using the AIS data corresponded well with areas mapped using field mapping, field reflectance spectra, and laboratory spectral measurements.

California, Nevada↗

USGS1968

Infrared survey of the Pisgah Crater area, San Bernardino County, California - a geologic interpretation

The infrared survey of the Pisgah Crater Area, San Bernardino County, California was primarily undertaken to establish parameters by which rock types, structures, and textures peculiar to this locale could be recognized or differentiated. A secondary purpose was to provide an adequate evaluation and calibration of airborne and ground-based instruments used in the survey. Pisgah Crater and its vicinity was chosen as one of the fundamental test sites for the NASA remote sensing program because of its relatively fresh basaltic flows and pyroclastics. Its typical exposure of basalt also made it a possible lunar analogue. A fundamental test site for the purpose of the program is defined as a readily accessible area for which the topography, geology, hydrology, soils, vegetation and other features are relatively well known. All remote sensor instrument teams, i.e. infrared, radar, microwave, and photography, were obligated to use the fundamental test sites for instrument evaluation and to establish terrain identification procedures. Pisgah Crater, nearby Sunshine Cone, and their associated lava flows are in the southern Mojave Desert about 40 miles east-southeast of Barstow, California. (See fig. 1.) U. S. Highway 66 skirts .the northern part of the area and provides access via asphalt-paved and dirt roads to the Crater and to the perimeters of the flows. Pisgah Crater, which is a pumiceous cone, is owned and occasionally quarried by the Atchison, Topeka and Santa Fe Railroad. The remaining part of the area to the south is within the boundary of the Marine Corps Base, Twentynine Palms, California and is currently being used as a gunnery, and bombing range. The proximate area to east, west, and north of Pisgah Crater is public domain. Originally, an area totaling 10 square miles was outlined for detailed study. (See plate 1.) This included an 8 mile long strip extending south- east from and including Pisgah Crater to Lavic Dry Lake, and a 2 mile strip aligned to include a portion of the Sunshine lava flow and the dry lake. Additional aerial infrared imagery of the Sunshine and Pisgah flows along the Pisgah fault proved so interesting and informative that this area is included in the discussion. Infrared surveys were flown February ii through 13, 1965 and August 5 and 9, 1966. The initial survey was flown by the NASA personnel aboard the NASA 926 Convair 240 aircraft. Because of technical problems with the infrared scanners (4.5-5.5 and 8-14 micron bands) and with certain ground instruments, most of the imagery and ground temperature data obtained during the initial survey period was of little value. However, excellent infrared imagery in the 8-14 micron (?) region of the spectrum was acquired by the Geological Survey during the August 1966 survey. The scanner was mounted in a Beech D-18 aircraft provided by the Survey's Water Resources Division. Likewise, more reliable ground data was obtained at this time owing to improved instrumentation and technique. Ground data were taken by Geological Survey personnel including W. A. Fischer, J. D. Friedman, W. R. Hemphill, D. L. Daniels, G. R. Boynton, Po W. Philbin and the author. C. R. Fross operated the infrared scanner during the August, 1966 survey and R. M. Turner was-responsible for photo processing of the infrared imagery. Their assistance is gratefully acknowledged.

Open-File Report↗

USGS2014

Biomass modeling of four water intensiveleading world crops using hyperspectral narrowbands in support of HyspIRI Mission

New satellite missions are expected to record high spectral resolution information globally and consistently for the first time, so it is important to identify modeling techniques that take advantage of these new data. In this paper, we estimate biomass for four major crops using ground-based hyperspectral narrowbands. The spectra and their derivatives are evaluated using three modeling techniques: two-band hyperspectral vegetation indices (HVIs), multiple band-HVIs (MB-HVIs) developed from Sequential Search Methods (SSM), and MB-HVIs developed from Principal Component Regression. Overall, the two-band HVIs and MB-HVIs developed from SSMs using first derivative transformed spectra in the visible blue and green and NIR explained more biomass variability and had lower error than the other approaches or transformations; however a better search criterion needs to be developed in order to reflect the true ability of the two-band HVI approach. Short-Wave Infrared 1 (1000 to 1700 nm) proved less effective, but still important in the final models.

Photogrammetric Engineering and Remote Sensing↗

USGS2010

The utility of estimating net primary productivity over Alaska using baseline AVHRR data

Net primary productivity (NPP) is a fundamental ecological variable that provides information about the health and status of vegetation communities. The Normalized Difference Vegetation Index, or NDVI, derived from the Advanced Very High Resolution Radiometer (AVHRR) is increasingly being used to model or predict NPP, especially over large remote areas. In this article, seven seasonally based metrics calculated from a seven-year baseline NDVI dataset were used to model NPP over Alaska, USA. For each growing season, they included maximum, mean and summed NDVI, total days, product of total days and maximum NDVI, an integral estimate of NDVI and a summed product of NDVI and solar radiation. Field (plot) derived NPP estimates were assigned to 18 land cover classes from an Alaskan statewide land cover database. Linear relationships between NPP and each NDVI metric were analysed at four scales: plot, 1-km, 10-km and 20-km pixels. Results show moderate to poor relationship between any of the metrics and NPP estimates for all data sets and scales. Use of NDVI for estimating NPP may be possible, but caution is required due to data seasonality, the scaling process used and land surface heterogeneity.

Alaska↗

USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗