This data release is part of a larger data release including data collected in the pursuit of identifying pre- and post-colonial riparian ecosystems found throughout Anne Arundel County, Maryland, USA. A single raster file is included, and represents a topological classification of the entire county according to a hydrologically conditioned Digital Elevation Model (DEM). These data were generated through the use of r.geomorphon, a GRASS GIS toolkit, to classify local terrain conditions into one of ten distinct landforms called geomorphons.

This data release includes data collected in the pursuit of identifying pre- and post-colonial riparian ecosystems found throughout Anne Arundel County, Maryland, USA. Two comma-delimited tables are included. The first documents pollen records at various depths identified within sediment samples taken throughout the county's floodplains. The other describes morphological information identifying tree species found buried in-situ throughout the county.

This data release includes data collected in the pursuit of identifying pre- and post-colonial riparian ecosystems found throughout Anne Arundel County, Maryland, USA. A single raster file is included, and represents a topological classification of the entire county according to a hydrologically conditioned Digital Elevation Model (DEM). Ten shapefiles are also included, nine of which represent the depths of various soil layers as identified by ground-penetrating radar for two detailed study sites within Anne Arundel County. A tenth documents the depths cored at both detailed study sites, and their spatial locations. Finally, twenty comma-delimited tables are included in this release with fifteen tables documenting pollen records at various depths identified within sediment samples taken throughout the country's flood plains. The remaining five tables include the following: general summary information for all sample sites; radiocarbon dates associated with woody material within the sediment cores; morphological information identifying tree species found buried in-situ throughout the county; visible, near-infrared, and X-Ray Fluorescence (XRF) information associated with each core; and a data dictionary for the previous information.

This data release includes data collected in the pursuit of identifying pre- and post-colonial riparian ecosystems found throughout Anne Arundel County, Maryland, USA. A single raster file is included, and represents a topological classification of the entire county according to a hydrologically conditioned Digital Elevation Model (DEM). Ten shapefiles are also included, nine of which represent the depths of various soil layers as identified by ground-penetrating radar for two detailed study sites within Anne Arundel County. A tenth documents the depths cored at both detailed study sites, and their spatial locations. Finally, twenty comma-delimited tables are included in this release with fifteen tables documenting pollen records at various depths identified within sediment samples taken throughout the country's flood plains. The remaining five tables include the following: general summary information for all sample sites; radiocarbon dates associated with woody material within the sediment cores; morphological information identifying tree species found buried in-situ throughout the county; visible, near-infrared, and X-Ray Fluorescence (XRF) information associated with each core; and a data dictionary for the previous information.

This data release includes data collected in the pursuit of identifying pre- and post-colonial riparian ecosystems found throughout Anne Arundel County, Maryland, USA. The single shapefile included documents the depths cored at both detailed study sites, and their spatial locations. Seventeen comma-delimited tables are included. Fourteen record pollen records at various depths identified within sediment cores taken at this study's two detailed investigation sites: Main Creek, near Pasadena, MD, and Bacon Ridge Branch, near Crownsville, MD. The remaining three include: radiocarbon dates associated with woody material identified in these sediment cores, visible, near-infrared, and X-ray Fluorescence (XRF) information associated with each sediment core respectively, and a data dictionary for the visible, near-infrared, and XRF data for clarity.

This dataset contains geomorphic metrics across 32 cross-sections at four catchments within the Clarksburg Special Protection Area in Montgomery County, Maryland. These data were derived from raw cross-sectional data collected by the Montgomery County, Maryland Department of Environmental Protection. Geomorphic metrics include channel area, bed location, channel depth, channel width, and bank movement for each bank. The catchment types included in this assessment were a majority agricultural catchment which began to be developed in 2016, a forested "control" catchment, and two urbanizing catchments with a high density of stormwater best management practices, in which cross-sectional surveys were collected pre-, during, and post-construction.

This dataset contains geomorphic metrics across 32 cross-sections at four catchments within the Clarksburg Special Protection Area in Montgomery County, Maryland. These data were derived from raw cross-sectional data collected by the Montgomery County, Maryland Department of Environmental Protection. Geomorphic metrics include channel area, bed location, channel depth, channel width, and bank movement for each bank. The catchment types included in this assessment were a majority agricultural catchment which began to be developed in 2016, a forested "control" catchment, and two urbanizing catchments with a high density of stormwater best management practices, in which cross-sectional surveys were collected pre-, during, and post-construction.

This data collection includes spatial and tabular datasets related to the delineation and characterization of surface moisture zones (SMZs) in the vicinity of springs mapped in the National Hydrography Dataset (NHD) in southeastern Oregon using time-series analysis of Normalized Difference Vegetation Index (NDVI) data derived from Landsat Thematic Mapper 5 imagery from 1985-2011. The study area is within and adjacent to the Steens Mountain Cooperative Management and Protection Area (CMPA), which is a protected area of approximately 1,732 km2 managed by the Bureau of Land Management (BLM) in Harney County, Oregon. Within or adjacent to the Steens Mountain CMPA, approximately 1,100 springs are mapped in the NHD, however very little hydrologic data exists for these springs. Data in this data release were produced using a set of scripts written in the R programming language, which are also included in this data release (see ‘larger works citation’ to access R scripts and associated metadata). These data processing scripts, data products, and associated metadata provide documentation for a novel remote-sensing based approach to assess the potential resilience of spring-dependent ecosystems to inter-annual changes in water availability. This approach uses time-series analysis of NDVI to (1) delineate SMZs in the vicinity of mapped springs in a semi-arid sage-steppe landscape, (2) derive quantitative indicators of the relative resilience of these SMZs to inter-annual changes in water availability, and (3) synthesize these indicators into an overall resilience score for each cluster of springs. Specifically, for 39 spring clusters in Harney County, Oregon, USA, these scripts process Landsat-derived NDVI and precipitation data from 1985-2011 to derive 7 potential indicators of SMZ resilience to water-cycle changes. For detailed information on the resilience indicators, including their conceptual basis, methods of calculation, and interpretation, see Cartwright and Johnson (2018) and the R scripts and their associated metadata in this data release. References: Cartwright and Johnson (2018), Springs as hydrologic refugia in a changing climate? A remote sensing approach. Ecosphere X(X).

This data collection includes spatial and tabular datasets related to the delineation and characterization of surface moisture zones (SMZs) in the vicinity of springs mapped in the National Hydrography Dataset (NHD) in southeastern Oregon using time-series analysis of Normalized Difference Vegetation Index (NDVI) data derived from Landsat Thematic Mapper 5 imagery from 1985-2011. The study area is within and adjacent to the Steens Mountain Cooperative Management and Protection Area (CMPA), which is a protected area of approximately 1,732 km2 managed by the Bureau of Land Management (BLM) in Harney County, Oregon. Within or adjacent to the Steens Mountain CMPA, approximately 1,100 springs are mapped in the NHD, however very little hydrologic data exists for these springs. Data in this data release were produced using a set of scripts written in the R programming language, which are also included in this data release (see ‘larger works citation’ to access R scripts and associated metadata). These data processing scripts, data products, and associated metadata provide documentation for a novel remote-sensing based approach to assess the potential resilience of spring-dependent ecosystems to inter-annual changes in water availability. This approach uses time-series analysis of NDVI to (1) delineate SMZs in the vicinity of mapped springs in a semi-arid sage-steppe landscape, (2) derive quantitative indicators of the relative resilience of these SMZs to inter-annual changes in water availability, and (3) synthesize these indicators into an overall resilience score for each cluster of springs. Specifically, for 39 spring clusters in Harney County, Oregon, USA, these scripts process Landsat-derived NDVI and precipitation data from 1985-2011 to derive 7 potential indicators of SMZ resilience to water-cycle changes. For detailed information on the resilience indicators, including their conceptual basis, methods of calculation, and interpretation, see Cartwright and Johnson (2018) and the R scripts and their associated metadata in this data release. References: Cartwright and Johnson (2018), Springs as hydrologic refugia in a changing climate? A remote sensing approach. Ecosphere X(X).

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.