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Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit...
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To determine if invasive annual grasses increased around energy developments after the construction phase, we calculated an invasives index using Landsat TM and ETM+ imagery for a 34-year time period (1985-2018) and assessed trends for 1,755 wind turbines (from the U.S. Wind Turbine Database) installed between 1988 and 2013 in the southern California desert. The index uses the maximum normalized difference vegetation index (NDVI) for early season greenness (January-June), and mean NDVI (July-October) for the later dry season. We estimated the relative cover of invasive annuals each year at turbine locations and control sites and tested for changes before and after each turbine was installed. These data were used...
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Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the central Cascade Mountain area, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest patches identified in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras...
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This tabular, machine-readable CSV file contains annual phenometrics at locations in ponderosa pine ecosystems across Arizona and New Mexico that experienced stand-clearing, high-severity fire. The locations represent areas of vegetative recovery towards pre-fire (coniferous/pine) vegetation communities or towards novel grassland, shrubland, or deciduous replacements. Each sampled area is associated with the point location (latitude/longitude) as well as multiple calendar year phenometrics derived from the time-series of normalized difference vegetation index (NDVI) values in the phenology software package Timesat v3.2.
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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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Geotagged photographs have become a useful medium for recording, analyzing, and communicating Earth science phenomena. Despite their utility, many field photographs are not published or preserved in a spatial or accessible format—oftentimes because of confusion about photograph metadata, a lack of stability, or user customization in free photo sharing platforms. After receiving a request to release about 1,210 geotagged geological field photographs of the Grand Canyon region, we set out to publish and preserve the collection in the most robust (and expedient) manner possible (fig. 6). We leveraged and reworked existing metadata, JavaScript, and Python tools and developed a toolkit and proposed workflow to display...
Sagebrush-dominant ecosystems in the western United States are highly vulnerable to climatic variability. To understand how these ecosystems will respond under potential future conditions, we correlated changes in National Land Cover Dataset “Back-in-Time” fractional cover maps from 1985-2018 with Daymet climate data in three federally managed preserves in the sagebrush steppe ecosystem: Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. Future (2018 to 2050) abundance and distribution of vegetation cover were modeled at a 300-m resolution under a business-as-usual climate (BAU) scenario and a Representative Concentration Pathway (RCP) 8.5 climate change...
Categories: Publication; Types: Citation
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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...
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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Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the central Cascade Mountain area, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest patches identified in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras...
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As part of a 2018 Northwest Climate Adaptation and Science Center project, USGS researchers are releasing a series of spatially-explicit land-cover projections for the period 2018-2050 covering part of the northern Great Basin (Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Refuge). The dataset contains an empirically-based business-as-usual (BAU) and an RCP8.5 climate change scenario executed for shrub, herbaceous, and bare cover types. Each scenario is executed 30 times (i.e. Monte Carlo simulations) to account for variability across historical change estimates derived from annual fractional cover maps generated by the National Land Cover Database. The map dates...
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These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The...
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The dataset comprises a Landsat-derived assessment of monthly surface water extent within the study area (California's greater Central Valley). The surface water dataset is based on the algorithm for the Dynamic Surface Water Extent (DSWE) (Jones, 2019), which was adapted to the Google Earth Engine JavaScript environment. The level of spatial aggregation is by level-8 hydrologic unit code (HUC).
The USGS National Land Cover Trends Project has the largest repository of field photos at the USGS (over 33,000 photos). Prior to CDI funding, Land Cover Trends had limited funding to make the national collection of photos available online for researchers, land managers, and citizens. The goal of this CDI project was to add geotags and keywords to the digital copies of each field photo and make the collection searchable and downloadable via the Internet. By funding the effort to integrate Land Cover Trends field photography and online mapping technology, CDI has helped provide access to geographic data needed to conduct science and support policy decisions. Sharing georeferenced photography distributed across the...
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The dataset comprises a Landsat-derived assessment of monthly surface water area within the study area (California's greater Central Valley). The surface water estimates are supplied by the European Commission's Joint Research Centre (JRC) Monthly Water History, v1.0. The level of spatial aggregation is by level-8 hydrologic unit code (HUC).
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To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7%) than the NLCD maps (96.2%). More importantly, the urban maps resulted in lower commission...
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Forests in Washington State generate substantial economic revenue from commercial timber harvesting on private lands. To investigate the rates, causes, and spatial and temporal patterns of forest harvest on private tracts throughout the central Cascade Mountain area, we relied on a new generation of annual land-use/land-cover (LULC) products created from the application of the Continuous Change Detection and Classification (CCDC) algorithm to Landsat satellite imagery collected from 1985 to 2014. We calculated metrics of landscape pattern using patches of intact and harvested forest patches identified in each annual layer to identify changes throughout the time series. Patch dynamics revealed four distinct eras...
Abstract (from Ecohydrology): Conservation of montane meadows is a high priority for land and water managers given their critical role in buffering the effects of climate variability and their vulnerability to increasing temperatures and evaporative demands. Recent advances in cloud computing have provided new opportunities to examine ecological responses to climate variability over the past few decades and at large spatial scales. In this study, we characterized the sensitivities (magnitude and direction of the slope) of meadow vegetation responses to interannual variations in climate. We calculated sensitivity as the regression slope between a 31‐year (1985–2016) time series of Landsat‐derived vegetation indices...
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This subset of the USGS Water Boundary Dataset contains the polygons of the 50 8-digit Hydrologic Units that comprise the greater Central Valley study site. The Watershed Boundary Dataset is a comprehensive set of digital spatial data that represents the surface drainages areas of the United States. The information included with the features includes a feature date, a unique common identifier, name, the feature length or area, and other characteristics. Names and their identifiers are assigned from the Geographic Names Information System. The data also contains relations that encode metadata. The names and definitions of all these feature attributes are in the Federal Standards and Procedures for the National Watershed...


    map background search result map search result map Data - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA Logistic Regression Samples - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA Patch Statistics - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA Data - Removing roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011 Datasets for Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California Monthly summaries of pixel counts in Dynamic Surface Water Extent (DSWE) classes in level-8 HUCs in the greater Central Valley, California Monthly summaries of pixel counts in Joint Research Centre Monthly Water History v1.0 dataset in level-8 HUC in the greater Central Valley, California from 1984 to 2015 Subset of 8-digit Hydrologic Unit Code (HUC) watershed shapefile for the greater Central Valley, California - Data Data supporting Landsat time series assessment of invasive annual grasses following energy development Phenology pattern data indicating recovery trajectories of ponderosa pine forests after high-severity fires Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 Land Cover Trends Dataset, 2000-2011 DSWEmod surface water map composites generated from daily MODIS images - California Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 Data - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA Logistic Regression Samples - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA Patch Statistics - Forest harvest patterns on private lands in the Cascade Mountains, Washington, USA Data supporting Landsat time series assessment of invasive annual grasses following energy development Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps Subset of 8-digit Hydrologic Unit Code (HUC) watershed shapefile for the greater Central Valley, California - Data Datasets for Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California Phenology pattern data indicating recovery trajectories of ponderosa pine forests after high-severity fires Monthly summaries of pixel counts in Dynamic Surface Water Extent (DSWE) classes in level-8 HUCs in the greater Central Valley, California Monthly summaries of pixel counts in Joint Research Centre Monthly Water History v1.0 dataset in level-8 HUC in the greater Central Valley, California from 1984 to 2015 DSWEmod surface water map composites generated from daily MODIS images - California Data - Removing roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011 Land Cover Trends Dataset, 2000-2011