USGS Science⌕ Search

SEARCH · USGS Science

Results for “Remote Sensing Information”

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

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

At least 1,081 records · Page 60Linked to original sources
USGS2010

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

International Journal of Remote Sensing↗
USGS2018

A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping.

Conterminous United States↗
USGS2019

Assessing plant production responses to climate across water-limited regions using Google Earth Engine

(Munson) Climate variability and change acting at broad scales can lead to divergent changes in plant production at local scales. Quantifying how production responds to variation in climate at local scales is essential to understand underlying ecological processes and inform land management decision-making, but has historically been limited in spatiotemporal scale based on the use of discrete ground-based measurements or coarse resolution satellite observations. With the advent of cloud-based computing through Google Earth Engine (GEE), production responses to climate can be evaluated across broad landscapes though time at a resolution useful for ecological and land management applications. Here, GEE was employed to synthesize a multi-platform Landsat time series (1988 – 2014) and evaluate relationships between the soil-adjusted vegetation index (a proxy for plant production) and climate across deserts and plant communities of the southwestern U.S. A “climate pivot point” approach was adopted in GEE to assess the trade-off between production responses to increasing wetness and resistances to drought at 30-m resolution. Consistent with a long-term seasonal climate gradient, production was most related to climate variance during the cool-season in the western deserts, during the warm-season in the eastern deserts, and equally related to both seasons within several desert areas. Communities dominated by grasses and deciduous trees displayed large production responses to an increase in wetness and low resistances to water deficit, while shrublands and evergreen woodlands had variable responses and high drought resistances. Production in plant communities that spanned multiple deserts responded differently to seasonal climate variability in each desert. Defining these plant production sensitivities to climate at 30-m resolution in GEE advances forecasts of how long-term climate trajectories may affect carbon storage, wildlife habitat, and the vulnerability of water-limited ecosystems.

Arizona, California, Colorado, Nevada, New Mexico,↗
USGS2007

Map and Database of Probable and Possible Quaternary Faults in Afghanistan

The U.S. Geological Survey (USGS) with support from the U.S. Agency for International Development (USAID) mission in Afghanistan, has prepared a digital map showing the distribution of probable and suspected Quaternary faults in Afghanistan. This map is a key component of a broader effort to assess and map the country's seismic hazards. Our analyses of remote-sensing imagery reveal a complex array of tectonic features that we interpret to be probable and possible active faults within the country and in the surrounding border region. In our compilation, we have mapped previously recognized active faults in greater detail, and have categorized individual features based on their geomorphic expression. We assigned mapped features to eight newly defined domains, each of which contains features that appear to have similar styles of deformation. The styles of deformation associated with each domain provide insight into the kinematics of the modern tectonism, and define a tectonic framework that helps constrain deformational models of the Alpine-Himalayan orogenic belt. The modern fault movements, deformation, and earthquakes in Afghanistan are driven by the collision between the northward-moving Indian subcontinent and Eurasia. The patterns of probable and possible Quaternary faults generally show that much of the modern tectonic activity is related to transfer of plate-boundary deformation across the country. The left-lateral, strike-slip Chaman fault in southeastern Afghanistan probably has the highest slip rate of any fault in the country; to the north, this slip is distributed onto several fault systems. At the southern margin of the Kabul block, the style of faulting changes from mainly strike-slip motion associated with the boundary between the Indian and Eurasian plates, to transpressional and transtensional faulting. North and northeast of the Kabul block, we recognized a complex pattern of potentially active strike-slip, thrust, and normal faults that form a conjugate shear system in a transpressional region of the Trans-Himalayan orogenic belt. The general patterns and orientations of faults and the styles of deformation that we interpret from the imagery are consistent with the styles of faulting determined from focal mechanisms of historical earthquakes. Northwest-trending strike-slip fault zones are cut and displaced by younger, southeast-verging thrust faults; these relations define the interaction between northwest-southeast-oriented contraction and northwest-directed extrusion in the western Himalaya, Pamir, and Hindu Kush regions. Transpression extends into north-central Afghanistan where north-verging contraction along the east-west-trending Alburz-Marmul fault system interacts with northwest-trending strike-slip faults. Pressure ridges related to thrust faulting and extensional basins bounded by normal faults are located at major stepovers in these northwest-trending strike-slip systems. In contrast, young faulting in central and western Afghanistan indicates that the deformation is dominated by extension where strike-slip fault zones transition into regions of normal faults. In addition to these initial observations, our digital map and database provide a foundation that can be expanded, complemented, and modified as future investigations provide more detailed information about the location, characteristics, and history of movement on Quaternary faults in Afghanistan.

Open-File Report↗
USGS2022

Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Frontiers in Remote Sensing↗
USGS2008

Detection rates of the MODIS active fire product in the United States

MODIS active fire data offer new information about global fire patterns. However, uncertainties in detection rates can render satellite-derived fire statistics difficult to interpret. We evaluated the MODIS 1??km daily active fire product to quantify detection rates for both Terra and Aqua MODIS sensors, examined how cloud cover and fire size affected detection rates, and estimated how detection rates varied across the United States. MODIS active fire detections were compared to 361 reference fires (??? 18??ha) that had been delineated using pre- and post-fire Landsat imagery. Reference fires were considered detected if at least one MODIS active fire pixel occurred within 1??km of the edge of the fire. When active fire data from both Aqua and Terra were combined, 82% of all reference fires were found, but detection rates were less for Aqua and Terra individually (73% and 66% respectively). Fires not detected generally had more cloudy days, but not when the Aqua data were considered exclusively. MODIS detection rates decreased with fire size, and the size at which 50% of all fires were detected was 105??ha when combining Aqua and Terra (195??ha for Aqua and 334??ha for Terra alone). Across the United States, detection rates were greatest in the West, lower in the Great Plains, and lowest in the East. The MODIS active fire product captures large fires in the U.S. well, but may under-represent fires in areas with frequent cloud cover or rapidly burning, small, and low-intensity fires. We recommend that users of the MODIS active fire data perform individual validations to ensure that all relevant fires are included. ?? 2008 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗
USGS2014

Mapping irrigated areas in Afghanistan over the past decade using MODIS NDVI

Agricultural production capacity contributes to food security in Afghanistan and is largely dependent on irrigated farming, mostly utilizing surface water fed by snowmelt. Because of the high contribution of irrigated crops (> 80%) to total agricultural production, knowing the spatial distribution and year-to-year variability in irrigated areas is imperative to monitoring food security for the country. We used 16-day composites of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to create 23-point time series for each year from 2000 through 2013. Seasonal peak values and time series were used in a threshold-dependent decision tree algorithm to map irrigated areas in Afghanistan for the last 14 years. In the absence of ground reference irrigated area information, we evaluated these maps with the irrigated areas classified from multiple snapshots of the landscape during the growing season from Landsat 5 optical and thermal sensor images. We were able to identify irrigated areas using Landsat imagery by selecting as irrigated those areas with Landsat-derived NDVI greater than 0.30–0.45, depending on the date of the Landsat image and surface temperature less than or equal to 310 Kelvin (36.9 ° C). Due to the availability of Landsat images, we were able to compare with the MODIS-derived maps for four years: 2000, 2009, 2010, and 2011. The irrigated areas derived from Landsat agreed well r 2 = 0.91 with the irrigated areas derived from MODIS, providing confidence in the MODIS NDVI threshold approach. The maps portrayed a highly dynamic irrigated agriculture practice in Afghanistan, where the amount of irrigated area was largely determined by the availability of surface water, especially snowmelt, and varied by as much as 30% between water surplus and water deficit years. During the past 14 years, 2001, 2004, and 2008 showed the lowest levels of irrigated area (~ 1.5 million hectares), attesting to the severe drought conditions in those years, whereas 2009, 2012 and 2013 registered the largest irrigated area (~ 2.5 million hectares) due to record snowpack and snowmelt in the region. The model holds promise the ability to provide near-real-time (by the end of the growing seasons) estimates of irrigated area, which are beneficial for food security monitoring as well as subsequent decision making for the country. While the model is developed for Afghanistan, it can be adopted with appropriate adjustments in the derived threshold values to map irrigated areas elsewhere.

Remote Sensing of Environment↗
USGS2007

A whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra

To maximize the spectral distinctiveness (information) of the canopy reflectance, an atmospheric correction strategy was implemented to provide accurate estimates of the intrinsic reflectance from the Earth Observing 1 (EO1) satellite Hyperion sensor signal. In rendering the canopy reflectance, an estimate of optical depth derived from a measurement of downwelling irradiance was used to drive a radiative transfer simulation of atmospheric scattering and attenuation. During the atmospheric model simulation, the input whole-terrain background reflectance estimate was changed to minimize the differences between the model predicted and the observed canopy reflectance spectra at 34 sites. Lacking appropriate spectrally invariant scene targets, inclusion of the field and predicted comparison maximized the model accuracy and, thereby, the detail and precision in the canopy reflectance necessary to detect low percentage occurrences of invasive plants. After accounting for artifacts surrounding prominent absorption features from about 400nm to 1000nm, the atmospheric adjustment strategy correctly explained 99% of the observed canopy reflectance spectra variance. Separately, model simulation explained an average of 88%??9% of the observed variance in the visible and 98% ?? 1% in the near-infrared wavelengths. In the 34 model simulations, maximum differences between the observed and predicted reflectances were typically less than ?? 1% in the visible; however, maximum reflectance differences higher than ?? 1.6% (<??2.3%) at more than a few wavelengths were observed at three sites. In the near-infrared wavelengths, maximum reflectance differences remained less than ??3% for 68% of the comparisons (??1 standard deviation) and less than ??6% for 95% of the comparisons (??2 standard deviation). Higher reflectance differences in the visible and near-infrared wavelengths were most likely associated with problems in the comparison, not in the model generation. ?? 2005 US Government.

International Journal of Remote Sensing↗
USGS2013

Controls on variations in MODIS fire radiative power in Alaskan boreal forests: implications for fire severity conditions

Fire activity in the Alaskan boreal forest, though episodic at annual and intra-annual time scales, has experienced an increase over the last several decades. Increases in burned area and fire severity are not only releasing more carbon to the atmosphere, but likely shifting vegetation composition in the region towards greater deciduous dominance and a reduction in coniferous stands. While some recent studies have addressed qualitative differences between large and small fire years in the Alaskan boreal forest, the ecological effects of a greater proportion of burning occurring during large fire years and during late season fires have not yet been examined. Some characteristics of wildfires that can be detected remotely are related to fire severity and can provide new information on spatial and temporal patterns of burning. This analysis focused on boreal wildfire intensity (fire radiative power, or FRP) contained in the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire product from 2003 to 2010. We found that differences in FRP resulted from seasonality and intra-annual variability in fire activity levels, vegetation composition, latitudinal variation, and fire spread behavior. Our studies determined two general categories of active fire detections: new detections associated with the spread of the fire front and residual pixels in areas that had already experienced front burning. Residual pixels had a lower average FRP than front pixels, but represented a high percentage of all pixels during periods of high fire activity (large fire years, late season burning, and seasonal periods of high fire activity). As a result, the FRP from periods of high fire activity was less intense than those from periods of low fire activity. Differences related to latitude were greater than expected, with higher latitudes burning later in the season and at a higher intensity than lower latitudes. Differences in vegetation type indicate that coniferous vegetation is the most fire prone, but deciduous vegetation is not particularly fire resistant, as the proportion of active fire detections in deciduous stands is roughly the same as the fraction of deciduous vegetation in the region. Qualitative differences between periods of high and low fire activity are likely to reflect important differences in fire severity. Large fire years are likely to be more severe, characterized by more late season fires and a greater proportion of residual burning. Given the potential for severe fires to effect changes in vegetation cover, the shift toward a greater proportion of area burning during large fire years may influence vegetation patterns in the region over the medium to long term.

Alaska↗
USGS2017

Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S.

The national network of weather surveillance radars (NEXRAD) detects birds in flight, and has proven to be a useful remote-sensing tool for ornithological study. We used data collected during Fall 2008 to 2014 by 16 NEXRAD and four terminal Doppler weather radars (TDWR) in the northeastern U.S. to map and study the spatial distribution of landbirds shortly after they leave daytime stopover sites to embark on nocturnal migratory flights. Given observed variability in the precise timing of migratory exodus, we developed a new method to sample the onset of migration at the point of maximum rate of increase in bird densities aloft to consistently sample exodus across radars and days. The mean linear trend in aggregate stopover densities of migrants indicated a 4% decline per year from the 2008 baseline density (29% decline over the seven years). Regionally, coastal Virginia and Maine had the steepest declines. The steepest increases in migrant densities across years occurred within the Delmarva Peninsula and in coastal Connecticut. We used NEXRAD observations to develop models to predict potentially important stopover sites throughout USFWS Region 5. Observed NEXRAD data were positively correlated to observations from TDWR and NASA’s S-Band Dual-Polarimetric Radar (NPOL), though not strongly. Predicted densities increased with increasing hardwood cover across multiple scales and with vegetation productivity. Contrastingly, predicted densities decreased with increasing agricultural, emergent marsh and coniferous land cover, but did not change with fraction of urban cover. Stopover density increased closer to bright areas and the Atlantic coast. Moreover, interactive effects indicated that migrants were more concentrated in forested areas that were both brightly lit and near the Atlantic coast. Large areas of predicted regionally important stopover sites were located along the coastlines of Maine, Long Island Sound, New Jersey, the lower Delmarva Peninsula, within the Adirondack Mountains, Catskill Mountains, and eastern Virginia. We also created maps of classified stopover use during bimonthly periods and at multiple-scales. Migrant densities peaked along the Adirondack Mountains early in September, and along the Atlantic coast in late September with the passage of Neotropical migrants. Stopover densities peaked in the most northern extent of Maine and New England States in late October with the departure of temperate migrants. Ground surveys conducted at 48 forested sites within the Delmarva Peninsula and Tidewater Virginia during Fall 2013 and 2014 revealed that nocturnal migrant densities pooled across species and for 14 individual species, after accounting for temporal phenology in their passage timing, were related to factors operating at multiple scales including food resources (primarily arthropod abundance in understory) and understory shrub density at a patch scale, and latitude and proximity to the Atlantic coast at a regional scale. We integrated field survey and radar data to estimate relative stopover duration and to identify stopover functional types among 45 sites that included data from a past study near the Gulf of Mexico. We identified four functional types spanning the gradient of short rest stops to refueling stops with variable duration of stopover in relation to food abundance. The Mid-Atlantic sites were dominated by rest stops near coastal areas and lacked quick refueling stops due to low overall food abundance. The maps and ecological understanding produced can help inform conservation planning to protect and enhance stopover sites for migratory landbirds in the future.

Report↗
USGS2010

AVHRR composite period selection for land cover classification

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, near-infrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite data-set as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa) of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).

International Journal of Remote Sensing↗
USGS2022

Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985–2017

Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities, has increased the demand for information on forest regrowth and recovery following natural and anthropogenic disturbances (e.g., fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human interpreted annual reference data that were collected independently. The validation results show one-year of underestimation and 6-year standard deviation of error between the reference data and regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.

Conterminous United States↗
USGS2023

Improving the operational simplified surface energy balance evapotranspiration model using the forcing and normalizing operation

Actual evapotranspiration modeling is providing useful information for researchers and resource managers in agriculture and water resources around the world. The performance of models depends on the accuracy of forcing inputs and model parameters. We developed an improved approach to the parameterization of the Operational Simplified Surface Energy Balance (SSEBop) model using the Forcing and Normalizing Operation (FANO). SSEBop has two key model parameters that define the model boundary conditions. The FANO algorithm computes the wet-bulb boundary condition using a linear FANO Equation relating surface temperature, surface psychrometric constant, and the Normalized Difference Vegetation Index (NDVI). The FANO parameterization was implemented on two computing platforms using Landsat and gridded meteorological datasets: (1) Google Earth Engine (GEE) and (2) Earth Resources Observation and Science (EROS) Center Science Processing Architecture (ESPA). Evaluation was conducted by comparing modeled actual evapotranspiration ( ETa ) estimates with AmeriFlux eddy covariance (EC) and water balance ETa from level-8 Hydrologic Unit Code sub-basins in the conterminous United States. FANO brought substantial improvements in model accuracy and operational implementation. Compared to the earlier version (v0.1.7), SSEBop FANO (v0.2.6) reduced grassland bias from 47% to −2% while maintaining comparable bias for croplands (11% versus −7%) against EC data. A water balance-based ETa bias evaluation showed an overall improvement from 7% to −1%. Climatology versus annual gridded reference evapotranspiration ( ETr ) produced comparable ETa results, justifying the use of climatology ETr for the global SSEBop Landsat ETa that is accessible through the ESPA website. Besides improvements in model accuracy, SSEBop FANO increases the spatiotemporal coverage of ET modeling due to the elimination of high NDVI requirements for model parameterization. Because of the existence of potential biases from forcing inputs and model parameters, continued evaluation and bias corrections are necessary to improve the absolute magnitude of ETa for localized water budget applications.

Remote Sensing↗
USGS2023

Elevation-based probabilistic mapping of irregularly flooded wetlands along the northern Gulf of Mexico coast

Irregularly flooded wetlands are found above the mean high water tidal datum and are exposed to tides and saltwater less frequently than daily. These wetlands provide important ecosystem services, such as providing habitat for fish and wildlife, enhancing water quality, ameliorating flooding impacts, supporting coastal food webs, and protecting upslope areas from erosion. Mapping irregularly flooded wetlands is challenging given their expansive coverage and dynamic nature. Furthermore, coastal wetlands are expected to change over the coming century due to sea-level rise and changes in the frequency and intensity of extreme storms. Consequently, coastal managers need baseline information on the spatial distribution of wetlands along with efficient and repeatable methods for observing changes. In this study, we used coastal wetlands from existing land use land cover data, best available lidar-derived digital elevation models, and Monte Carlo simulations to incorporate elevation uncertainty to create a probabilistic map of irregularly flooded wetlands along the northern Gulf of Mexico coast (USA). Our approach integrated findings from a review of coastal wetland elevation error in lidar datasets and an analysis of spatial autocorrelations of wetland elevation. We found a positive correlation ( r = 0.563, p < 0.0001) when comparing the probability estimated from a digital elevation model and in situ elevation observations. The differences in probability had a mean bias error of −0.04 (i.e., digital elevation model-based probability tends to be slightly lower), a mean absolute error of 0.20, and a root mean square error of 0.26. Beyond this overall validation, we explored error metrics for land cover classes and lidar collection details. To quantify areal coverage of the probabilistic output, we classified the probability values into equal bins using an interval of 0.33. The areal coverage of the lowest probability bin (“unlikely”; probability ≤0.33) was separated into the upper and lower portions of the irregularly flooded wetland zone. Of the coastal wetlands along the northern Gulf of Mexico coast about 38% were classified as unlikely and low with the greatest coverage in south Louisiana and the Everglades and around 33% were classified as unlikely and high with the greatest coverage in the Everglades and Texas. The relative coverage within the highest probability bin (“likely”; probability >0.66) covered around 13%, with the greatest coverage in south Florida, south Louisiana, and Texas. The framework developed in this study can be transferred to other coastal wetland areas and updated to observe changes with sea-level rise.

Gulf of Mexico coastline↗
USGS2020

Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database

The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001-2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001-2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this time. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4,778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2017

Lidar aboveground vegetation biomass estimates in shrublands: Prediction, uncertainties and application to coarser scales

Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R 2 of 0.76 and RMSE of 125 g/m 2 for shrub biomass and a pseudo R 2 of 0.74 and RMSE of 141 g/m 2 for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m 2 for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.

Idaho↗
USGS2000

A diurnal animation of thermal images from a day-night pair

Interpretation of thermal images is often complicated because the physical property information is contained in both the spatial and temporal variations of the data and thermal models are necessary to extract and display this information. A linearized radiative transfer solution to the surface flux has been used to derive a function that is invariant with respect to thermal inertia. This relationship makes it possible to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements (e.g., noon and midnight). An animation can then be constructed from a pair of day-night images to view both the spatial and temporal temperature changes throughout the diurnal cycle. A more complete solution for the invariant function, using the method of Laplace transforms and based on the linearized solution, was introduced. These results indicate that the linear model does not provide a sufficiently accurate estimate. Using standard conditions (latitude 30??, solar declination 0??, acquisition times at noon and midnight), this new relationship was used to predict temperature throughout the diurnal cycle to an rms error of 0.2??C, which is close to the system noise of most thermal scanners. The method was further extended to include the primary effects of topographic slope with similar accuracy. The temperature was computed at 48 equally spaced times in the diurnal cycle with this algorithm using a co-registered day and night TIMS (Thermal Infrared Multispectral Scanner) data pair (330 pixels, 450 lilies) acquired of the Carlin, Nevada, area and a co-registered DEM (Digital Elevation Model). (Any reader can view the results by downloading the animation file from an identified tip site). The results illustrate the power of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences. This 'visual change' approach could significantly increase the use of thermal data for environmental, hazard, and resource studies. Published by Elsevier Science Inc., 2000.A linearized radiative transfer solution of determining the surface flux is proposed to predict the temperature variation at any time in the diurnal cycle using only two distinct measurements. An animation is constructed from a pair of day-night images to view the spatial and temporal temperature changes throughout the diurnal cycle. The results illustrate the effectiveness of animation to display subtle temporal and spatial temperature changes, which can provide clues to structural controls and material property differences.

Remote Sensing of Environment↗
USGS2017

Mapping burned areas using dense time-series of Landsat data

Complete and accurate burned area data are needed to document patterns of fires, to quantify relationships between the patterns and drivers of fire occurrence, and to assess the impacts of fires on human and natural systems. Unfortunately, in many areas existing fire occurrence datasets are known to be incomplete. Consequently, the need to systematically collect burned area information has been recognized by the United Nations Framework Convention on Climate Change and the Intergovernmental Panel on Climate Change, which have both called for the production of essential climate variables (ECVs), including information about burned area. In this paper, we present an algorithm that identifies burned areas in dense time-series of Landsat data to produce the Landsat Burned Area Essential Climate Variable (BAECV) products. The algorithm uses gradient boosted regression models to generate burn probability surfaces using band values and spectral indices from individual Landsat scenes, lagged reference conditions, and change metrics between the scene and reference predictors. Burn classifications are generated from the burn probability surfaces using pixel-level thresholding in combination with a region growing process. The algorithm can be applied anywhere Landsat and training data are available. For this study, BAECV products were generated for the conterminous United States from 1984 through 2015. These products consist of pixel-level burn probabilities for each Landsat scene, in addition to, annual composites including: the maximum burn probability and a burn classification. We compared the BAECV burn classification products to the existing Global Fire Emissions Database (GFED; 1997–2015) and Monitoring Trends in Burn Severity (MTBS; 1984–2013) data. We found that the BAECV products mapped 36% more burned area than the GFED and 116% more burned area than MTBS. Differences between the BAECV products and the GFED were especially high in the West and East where the BAECV products mapped 32% and 88% more burned area, respectively. However, the BAECV products found less burned area than the GFED in regions with frequent agricultural fires. Compared to the MTBS data, the BAECV products identified 31% more burned area in the West, 312% more in the Great Plains, and 233% more in the East. Most pixels in the MTBS data were detected by the BAECV, regardless of burn severity. The BAECV products document patterns of fire similar to those in the GFED but also showed patterns of fire that are not well characterized by the existing MTBS data. We anticipate the BAECV products will be useful to studies that seek to understand past patterns of fire occurrence, the drivers that created them, and the impacts fires have on natural and human systems.

continental United States↗