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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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This data release comprises the data files and code necessary to perform all analyses presented in the associated publication. The *.csv data files are aggregations of water extent on the basis of the European Commission's Joint Research Centre (JRC) Monthly Water History database (v1.0) and the Dynamic Surface Water Extent (DSWE) algorithm. The shapefile dataset contains the study area 8-digit hydrologic unit code (HUC) regions used as the basis for analysis. Html files provide an overview of the study workflow and integrated R notebooks (in .Rmd format) for recreating all project results and plots. The R notebook ingest the necessary data files from their online locations. These data support the following publication:...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The vegetation is characterized using a series of remote sensing vegetation indices developing using satellite imagery, including the Normalized Difference Vegetation Index (NDVI) and Tasseled Cap (TC) Transformation metrics of brightness, greenness, and wetness. Each of these remote sensing vegetation indices provides a unique characterization of the vegetation properties. For example, NDVI provides a general overview...
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This data release comprises the raster data files and code necessary to perform all analyses presented in the associated publication. The 16 TIF raster data files are classified surface water maps created using the Dynamic Surface Water Extent (DSWE) model implemented in Google Earth Engine using published technical documents. The 16 tiles cover the country of Cambodia, a flood-prone country in Southeast Asia lacking a comprehensive stream gauging network. Each file includes 372 bands. Bands represent surface water for each month from 1988 to 2018, and are stacked from oldest (Band 1 - January 1988) to newest (Band 372 - December 2018). DSWE classifies pixels unobscured by cloud, cloud shadow, or snow into five...
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USGS researchers with the Patterns in the Landscape – Analyses of Cause and Effect (PLACE) project are releasing a collection of high-frequency surface water map composites derived from daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Using Google Earth Engine, the team developed customized image processing steps and adapted the Dynamic Surface Water Extent (DSWE) to generate surface water map composites in California for 2003-2019 at a 250-m pixel resolution. Daily maps were merged to create 6, 3, 2, and 1 composite(s) per month corresponding to approximately 5-day, 10-day, 15-day, and monthly products, respectively. The resulting maps are available as downloadable files for each year. Each...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Mapping the spatial dynamics of perceived social value across the landscape can help develop a restoration economy that can support ecosystem services in the region. Many different methods have been used to map perceived social value. We used the Social Values for Ecosystem Services (SolVES) GIS tool, version 3.0, which uses social survey responses and various environmental variables to map social value. In the social survey distributed by the Borderlands Restoration Network (BRN) in May 2017, the respondents were asked to consider twelve different social values and map locations on a map where they perceived those social values to be. Additionally, they were asked to weigh each social value using a total of 100...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines how different fuel treatments influenced burn severity and post-fire vegetative stand dynamics on the San Carlos Apache Reservation, in east-central Arizona, U.S.A. Our methods included the use of multitemporal remote sensing data and cloud computing to evaluate burn severity and post-fire vegetation conditions as well as...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Members from the U.S. Geological Survey (USGS) Patterns in the Landscape - Analyses of Cause and Effect (PLACE) team are releasing monthly surface water maps for the conterminous United States (U.S.) from 2003 through 2019 as 250-meter resolution geoTIFF files. The maps were produced using the Dynamic Surface Water Extent (DSWE) algorithm applied to daily Moderate Resolution Imaging Spectroradiometer (MODIS) imagery (DSWEmod) (Soulard et al., 2021) - see associated items. The DSWEmod model classifies the landscape (i.e., each MODIS pixel) into different classes of surface water based on quantified levels of confidence, including, i) high-confidence surface water (class 1), ii) moderate-confidence surface water (class...
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Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack a ground-based monitoring network, remote sensing allows for accurate and consistent estimates of ETa across a large spatial extent – though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an Ensemble Mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (i.e., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control...
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We developed this spatial database of historic and current floodplains to analyze trends in vegetation conditions, identify areas more at risk of degradation, and assess the relationship between riparian vegetation dynamics and climate conditions.Our study area is the riparian areas along the San Carlos River and Gila River within the San Carlos Apache Reservation and the Upper Gila River Level-4 Hydrologic Unit Code (HUC) watershed. The product was developed by digitizing the active channel and primary riparian floodplain using historic 1935 aerial imagery (Arizona State University GeoData, 2021) and National Agriculture Imagery Program (NAIP) aerial imagery from 2019 (U.S. Department of Agriculture, 2021). This...


map background search result map search result map Dataset for 2013 Creek Fire Research Points, Pre- and Post-Fire Data, U.S. Geological Survey Datasets for Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California Perceived Social Value of the Sonoita Creek Watershed using the Social Values for Ecosystem Services (SolVES) Tool, Arizona, U.S.A. Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps DSWEmod surface water map composites generated from daily MODIS images - California Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2003 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2005 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2006 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2009 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2011 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2012 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2014 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2015 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2016 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2017 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2018 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2019 Database of Trends in Vegetation Properties and Climate Adaptation Variables on the San Carlos Apache Reservation and Upper Gila River Watershed (1935-2021) Database of Riparian Floodplain Boundaries for the San Carlos and Gila Rivers on the San Carlos Apache Reservation and Upper Gila River Watershed (1935 - 2021) Monthly Ensemble Mean Evapotranspiration (EMET) Product for the Los Planes basin in Baja California Sur, Mexico from January 2006 through December 2021: U.S. Geological Survey Data Release Dataset for 2013 Creek Fire Research Points, Pre- and Post-Fire Data, U.S. Geological Survey Perceived Social Value of the Sonoita Creek Watershed using the Social Values for Ecosystem Services (SolVES) Tool, Arizona, U.S.A. Monthly Ensemble Mean Evapotranspiration (EMET) Product for the Los Planes basin in Baja California Sur, Mexico from January 2006 through December 2021: U.S. Geological Survey Data Release Database of Trends in Vegetation Properties and Climate Adaptation Variables on the San Carlos Apache Reservation and Upper Gila River Watershed (1935-2021) Database of Riparian Floodplain Boundaries for the San Carlos and Gila Rivers on the San Carlos Apache Reservation and Upper Gila River Watershed (1935 - 2021) Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps Datasets for Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California DSWEmod surface water map composites generated from daily MODIS images - California Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2003 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2005 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2006 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2009 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2011 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2012 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2014 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2015 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2016 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2017 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2018 Monthly Dynamic Surface Water Extent MODIS (DSWEmod) Images for the Conterminous United States – 2019