Murraylands and Riverland Landscape Board Annual Report Data

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.

Timeseries data from 'MURRAY RIVER NEAR THE MOUTH' (ca_hydro_07FB002)

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

No notes provided

Timeseries data from 'MURIEL LAKE NEAR GURNEYVILLE' (ca_hydro_06AC007)

This tabular data set represents the percent of land cover classes in the riparian zone from the 2019 edition of the National Land Cover databases for the years 2001, 2004, 2006, 2008, 2011, 2013, 2016 and 2019 compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. These databases can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the "National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021)" databases for the years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 produced by the United States Geological Survey (DeWitt, 2021). The source data for riparian areas is the "U.S. Forest Service national riparian areas base map for the conterminous United States in 2019" (Abood and others, 2022). Final data values units are percent. The "NLCD 2019 Land Cover Conterminous United States" databases (NLCD 2019) are a 16-class (additional four classes in Alaska only) land cover classification scheme that has been applied consistently across all 50 United States and Puerto Rico at a spatial resolution of 30 meters. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018).