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).

The first basin-wide map of large stands of invasive Phragmites australis (common reed) in the coastal zone was created through a collaboration between the U.S. Geological Survey and Michigan Tech Research Institute (Bourgeau-Chavez et al 2013). This data set represents a revised version of that map and was created using multi-temporal PALSAR data and Landsat images from 2016-2017. In addition to Phragmites distribution, the data sets shows several land cover types including urban, agriculture, forest, shrub, emergent wetland, forested wetland, and some based on the dominant plant species (e.g., Schoenoplectus, Typha). The classified map was validated using over 400 field visits.

The first basin-wide map of large stands of invasive Phragmites australis (common reed) in the coastal zone was created through a collaboration between the U.S. Geological Survey and Michigan Tech Research Institute (Bourgeau-Chavez et al 2013). This data set represents a revised version of that map and was created using multi-temporal PALSAR data and Landsat images from 2016-2017. In addition to Phragmites distribution, the data sets shows several land cover types including urban, agriculture, forest, shrub, emergent wetland, forested wetland, and some based on the dominant plant species (e.g., Schoenoplectus, Typha). The classified map was validated using over 400 field visits.

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 for monitoring riparian zone trends and changes in the Navajo Nation as part of a study to document riparian ecosystem health and its water use in support of potential restoration efforts. The objective of our study was to monitor the short and medium-term effects on the riparian vegetation in relation to evapotranspiration changes, drought, and other hydrological processes, along some critical riparian zones in the Navajo Nation. These data represent time series of vegetation greenness and water use for the years 2013 to 2023. These data were collected from the spaceborne mission Landsat 8 which carries the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors for an area within the Navajo Nation in northeastern Arizona. The specific regions of interest were focused on some Culturally Important Riparian Areas (CIRAS), including Buell Park/Black Creek Headwaters, Ganado/Pueblo Colorado Wash, Grand Falls, Oraibi Headwaters, Pasture Canyon, and Tappan Springs. Landsat data are collected and distributed by the U.S. Geological Survey. The acquired imagery was filtered for quality and reprocessed by the Vegetation Index and Phenology Lab at the University of Arizona, to generate vegetation indices and evapotranspiration trends for these riparian corridors. These data summarize the time series over the 11-year study. Three vegetation indices (VIs) are computed and reported: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Two-Band Enhanced Vegetation Index (EVI2). NDVI and EVI datasets were taken directly from Landsat products, EVI2 was calculated in the VIP Lab. Time series for daily Actual Evapotranspiration (ETa), in millimeters per day, were estimated from both EVI and EVI2 data using an ET empirical model (Nagler and others, 2013). These data can be used to study the trends in vegetation greenness, productivity, and water use, using VIs and ET respectively. These estimations can be linked to other variables or causes, and used to assess the effect climate change is having in this arid region in the period from 2013 to 2023.

These data were compiled for monitoring riparian zone trends and changes in the Navajo Nation as part of a study to document riparian ecosystem health and its water use in support of potential restoration efforts. The objective of our study was to monitor the short and medium-term effects on the riparian vegetation in relation to evapotranspiration changes, drought, and other hydrological processes, along some critical riparian zones in the Navajo Nation. These data represent time series of vegetation greenness and water use for the years 2013 to 2023. These data were collected from the spaceborne mission Landsat 8 which carries the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors for an area within the Navajo Nation in northeastern Arizona. The specific regions of interest were focused on some Culturally Important Riparian Areas (CIRAS), including Buell Park/Black Creek Headwaters, Ganado/Pueblo Colorado Wash, Grand Falls, Oraibi Headwaters, Pasture Canyon, and Tappan Springs. Landsat data are collected and distributed by the U.S. Geological Survey. The acquired imagery was filtered for quality and reprocessed by the Vegetation Index and Phenology Lab at the University of Arizona, to generate vegetation indices and evapotranspiration trends for these riparian corridors. These data summarize the time series over the 11-year study. Three vegetation indices (VIs) are computed and reported: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and the Two-Band Enhanced Vegetation Index (EVI2). NDVI and EVI datasets were taken directly from Landsat products, EVI2 was calculated in the VIP Lab. Time series for daily Actual Evapotranspiration (ETa), in millimeters per day, were estimated from both EVI and EVI2 data using an ET empirical model (Nagler and others, 2013). These data can be used to study the trends in vegetation greenness, productivity, and water use, using VIs and ET respectively. These estimations can be linked to other variables or causes, and used to assess the effect climate change is having in this arid region in the period from 2013 to 2023.

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.

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.

The first basin-wide map of large stands of invasive Phragmites australis (common reed) in the coastal zone was created through a collaboration between the U.S. Geological Survey and Michigan Tech Research Institute (Bourgeau-Chavez et al 2013). This data set represents a revised version of that map and was created using multi-temporal PALSAR data and Landsat images from 2016-2017. In addition to Phragmites distribution, the data sets shows several land cover types including urban, agriculture, forest, shrub, emergent wetland, forested wetland, and some based on the dominant plant species (e.g., Schoenoplectus, Typha). The classified map was validated using over 400 field visits.This map covers the Southern portion of central Lake Erie.