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.

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To improve understanding of the distribution of important, ephemeral wetland habitats across the Great Plains, we documented the occurrence and distribution of surface water in playa wetland complexes for four different years across the Great Plains Landscape Conservation Cooperative (GPLCC) region. Years of research on playas has yielded multiple mechanisms and projections for sub-regions of the LCC area, but a complete, region-wide inventory and assessment has not been completed. This information is important because it informs habitat and population managers about the timing and location of habitat availability. Data representing the presence of water, percent of the area inundated with water, and the spatial distribution of playa wetlands with water, with an accurate time-stamp, are needed for a host of resource inventory, monitoring and research applications. For example, the distribution of inundated wetlands, represents the distribution of available habitat for resident shorebirds and water birds, stop-over habitats for migratory birds, connectivity and clustering of wetland habitats, and surface water recharge to the Ogallala aquifer; there is considerable variability in the distribution of playa wetlands holding water through time. Clear documentation of these spatially and temporally intricate processes will provide data required to assess connections between multiple environmental drivers, such as climate, land use, soil, and topography and the probability of inundation. Data presented here document the area covered by water according to archived Landsat TM data. Classifications representing 4 years of imagery (1989, 1996, 2004 and 2011) are provided.

This USGS Data Release represents geospatial data sets which were created to produce an Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the conterminous United States (2014-2018). The following listed image products were generated 1) Annual spatial datasets (rasters) from 2014 to 2018 each containing 4 bands (Band 1: Unvegetated land fraction; Band 2: Vegetated land fraction; Band 3: Water fraction; Band 4: UVVR clipped into 3 coastal regions (Atlantic (ATL) Gulf of Mexico (GOM) and Pacific (PAC). 2) Calibration/Validation Datasets - datasets which were used in the calibration and validation of the above datasets 3) Mean of masked, multiyear composite - Mean vegetated fraction in coastal wetlands in each region 4) Standard deviation of masked, multiyear composite - Standard deviation of the vegetated fraction in coastal wetlands in each region 5) Unvegetated to Vegetated Ratio (UVVR) based on masked, multiyear composite - Unvegetated to Vegetated Ratio in coastal wetlands in each region The data release was produced in compliance with the new 'open data' requirements as a way to make the scientific products associated with USGS research efforts and publications available to the public.

This dataset contains data used in the associated publication in the International Journal of Remote Sensing.Wilson, Natalie R., and Laura M. Norman. 2018. “Analysis of Vegetation Recovery Surrounding a Restored Wetland Using the Normalized Difference Infrared Index (NDII) and Normalized Difference Vegetation Index (NDVI).” International Journal of Remote Sensing 39 (10): 3243–74. https://doi.org/10.1080/01431161.2018.1437297.The geodatabase contains four feature classes: AOI, MajorZone, MinorZone, and Green2007.Publication abstract: Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.