These data were compiled to provide satellite remote sensing observations of landcover in the vicinity of wetlands fed by geothermal springs in Dixie Meadows, Nevada, USA. Objectives of the study were to map landcover of water, vegetation, and soil between October 5, 2015, and January 21, 2022, using available imagery from the Sentinel-2 mission. The U.S. Geological Survey's Southwest Biological Science Center (SBSC) and Grand Canyon Monitoring and Research Center (GCMRC) processed 110 Sentinel-2 satellite images representing bottom of atmosphere surface reflectance and classified them within Google Earth Engine (GEE) using threshold values of the Green Normalized Difference Vegetation Index (gNDVI) and its inverse relationship to the Normalized Difference Water Index (NDWI). The classified image data represent the area covered by five distinct landcover types: open water; mixed shallow surface water, saturated soil, and vegetation; dense green vegetation; moist soil with sparse or small vegetation; dry soil with sparse upland vegetation. These data can be used to evaluate the areal extent of each of the landcover types classified in this study as well as changes in the areal extent of these landcover types between October 5, 2015, and January 21, 2022. Additionally, these data may be used as baseline conditions to evaluate future changes in the areal extent of landcover owing to land use changes or climatic fluctuations.

These data were compiled to provide satellite remote sensing observations of landcover in the vicinity of wetlands fed by geothermal springs in Dixie Meadows, Nevada, USA. Objectives of the study were to map landcover of water, vegetation, and soil between January 26, 2022 and November 27, 2023 using available imagery from the Sentinel-2 mission, thereby extending previously published data from October 5, 2015 to January 21, 2022 (Bransky et al., 2023). The US Geological Survey's Southwest Biological Science Center (SBSC) and Grand Canyon Monitoring and Research Center (GCMRC) processed 36 Sentinel-2 satellite images representing bottom of atmosphere surface reflectance and classified them within Google Earth Engine (GEE) using threshold values of the Green Normalized Difference Vegetation Index (gNDVI) and its inverse relationship to the Normalized Difference Water Index (NDWI). The classified image data represent the area covered by five distinct landcover types: open water; mixed shallow surface water, saturated soil, and vegetation; dense green vegetation; moist soil with sparse or small vegetation; dry soil with sparse upland vegetation. These data can be used to evaluate the areal extent of each of the landcover types classified in this study as well as changes in the areal extent of these landcover types between January 26, 2022 and November 27, 2023. Additionally, these data may be used as baseline conditions to evaluate future changes in the areal extent of landcover owing to land use changes or climatic fluctuations.

These data were compiled to provide satellite remote sensing observations of landcover in the vicinity of wetlands fed by geothermal springs in Dixie Meadows, Nevada, USA. Objectives of the study were to map landcover of water, vegetation, and soil between January 26, 2022 and November 27, 2023 using available imagery from the Sentinel-2 mission, thereby extending previously published data from October 5, 2015 to January 21, 2022 (Bransky et al., 2023). The US Geological Survey's Southwest Biological Science Center (SBSC) and Grand Canyon Monitoring and Research Center (GCMRC) processed 36 Sentinel-2 satellite images representing bottom of atmosphere surface reflectance and classified them within Google Earth Engine (GEE) using threshold values of the Green Normalized Difference Vegetation Index (gNDVI) and its inverse relationship to the Normalized Difference Water Index (NDWI). The classified image data represent the area covered by five distinct landcover types: open water; mixed shallow surface water, saturated soil, and vegetation; dense green vegetation; moist soil with sparse or small vegetation; dry soil with sparse upland vegetation. These data can be used to evaluate the areal extent of each of the landcover types classified in this study as well as changes in the areal extent of these landcover types between January 26, 2022 and November 27, 2023. Additionally, these data may be used as baseline conditions to evaluate future changes in the areal extent of landcover owing to land use changes or climatic fluctuations.

These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Lake Meredith National Recreation Area, Alibates Flint Quarries National Monument. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Lake Meredith National Recreation Area, Alibates Flint Quarries National Monument. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

This dataset consists of polygons representing land classification areas, 2000 - 2010, for Eagle, Dayton, and Churchill Valleys, west-central Nevada.

This database, compiled by Matthews and Fung (1987), provides information on the distribution and environmental characteristics of natural wetlands. The database was developed to evaluate the role of wetlands in the annual emission of methane from terrestrial sources. The original data consists of five global 1-degree latitude by 1-degree longitude arrays. This subset, for the study area of the Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) in South America, retains all five arrays at the 1-degree resolution but only for the area of interest (i.e., longitude 85 deg to 30 deg W, latitude 25 deg S to 10 deg N). The arrays are (1) wetland data source, (2) wetland type, (3) fractional inundation, (4) vegetation type, and (5) soil type. The data subsets are in both ASCII GRID and binary image file formats.The data base is the result of the integration of three independent digital sources: (1) vegetation classified according to the United Nations Educational Scientific and Cultural Organization (UNESCO) system (Matthews, 1983), (2) soil properties from the Food and Agriculture Organization (FAO) soil maps (Zobler, 1986), and (3) fractional inundation in each 1-degree cell compiled from a global map survey of Operational Navigation Charts (ONC). With vegetation, soil, and inundation characteristics of each wetland site identified, the data base has been used for a coherent and systematic estimate of methane emissions from wetlands and for an analysis of the causes for uncertainties in the emission estimate.The complete global data base is available from NASA/GISS [http://www.giss.nasa.gov] and NCAR data set ds765.5 [http://www.ncar.ucar.edu]; the global vegetation types data are available from ORNL DAAC [http://www.daac.ornl.gov].

We created a single map of surface water presence by intersecting water classes from available land cover products (National Wetland Inventory, Gap Analysis Program, National Land Cover Database, and Dynamic Surface Water Extent) across the U.S. state of Arizona. We derived classified samples for four wetland classes from the harmonized map: water, herbaceous wetlands, wooded wetlands, and non-wetland cover. In Google Earth Engine (GEE) we developed a random forest model that combined the training data with spatially explicit predictor variables of vegetation greenness indices, wetness indices, seasonal index variation, topographic variables, and hydrologic parameters. The final product is a wall-to-wall map of general wetland types covering all of Arizona. Results show that the final model separates the four wetland classes with an overall accuracy of 86.2%. This data release comprises the raster map file (TIF format) resulting from the training data and random forest model. The 30-m resolution map has 4 classes: not water or wetland (class 0), open water (class 1), herbaceous wetland (class 2), and wooded wetland (class 3).

We created a single map of surface water presence by intersecting water classes from available land cover products (National Wetland Inventory, Gap Analysis Program, National Land Cover Database, and Dynamic Surface Water Extent) across the U.S. state of Arizona. We derived classified samples for four wetland classes from the harmonized map: water, herbaceous wetlands, wooded wetlands, and non-wetland cover. In Google Earth Engine (GEE) we developed a random forest model that combined the training data with spatially explicit predictor variables of vegetation greenness indices, wetness indices, seasonal index variation, topographic variables, and hydrologic parameters. The final product is a wall-to-wall map of general wetland types covering all of Arizona. Results show that the final model separates the four wetland classes with an overall accuracy of 86.2%. This data release comprises the raster map file (TIF format) resulting from the training data and random forest model. The 30-m resolution map has 4 classes: not water or wetland (class 0), open water (class 1), herbaceous wetland (class 2), and wooded wetland (class 3).

Wetlands Ecological Integrity Vegetation data from 2007-20121, including data at Florissant Fossil Beds National Monument, Great Sand Dunes National Park, and Rocky Mountain National Park.