Skip to main content
Advanced Search

Filters: Tags: {"scheme":"Alexandria Digital Library Feature Type Thesaurus"} (X) > partyWithName: Western Geographic Science Center (X)

6 results (211ms)   

View Results as: JSON ATOM CSV
thumbnail
To determine if invasive annual grasses increased around energy developments after the construction phase, we calculated an invasives index using Landsat TM and ETM+ imagery for a 34-year time period (1985-2018) and assessed trends for 1,755 wind turbines (from the U.S. Wind Turbine Database) installed between 1988 and 2013 in the southern California desert. The index uses the maximum normalized difference vegetation index (NDVI) for early season greenness (January-June), and mean NDVI (July-October) for the later dry season. We estimated the relative cover of invasive annuals each year at turbine locations and control sites and tested for changes before and after each turbine was installed. These data were used...
thumbnail
This tabular, machine-readable CSV file contains annual phenometrics at locations in ponderosa pine ecosystems across Arizona and New Mexico that experienced stand-clearing, high-severity fire. The locations represent areas of vegetative recovery towards pre-fire (coniferous/pine) vegetation communities or towards novel grassland, shrubland, or deciduous replacements. Each sampled area is associated with the point location (latitude/longitude) as well as multiple calendar year phenometrics derived from the time-series of normalized difference vegetation index (NDVI) values in the phenology software package Timesat v3.2.
thumbnail
This dataset represents a summary of potential cropland inundation for the state of California applying high-frequency surface water map composites derived from two satellite remote sensing platforms (Landsat and Moderate Resolution Imaging Spectroradiometer [MODIS]) with high-quality cropland maps generated by the California Department of Water Resources (DWR). Using Google Earth Engine, we examined inundation dynamics in California croplands from 2003 –2020 by intersecting monthly surface water maps (n=216 months) with mapped locations of precipitation amounts, rice, field, truck (which comprises truck, nursery, and berry crops), deciduous (deciduous fruits and nuts), citrus (citrus and subtropical), vineyards,...
thumbnail
As part of a 2018 Northwest Climate Adaptation and Science Center project, USGS researchers are releasing a series of spatially-explicit land-cover projections for the period 2018-2050 covering part of the northern Great Basin (Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Refuge). The dataset contains an empirically-based business-as-usual (BAU) and an RCP8.5 climate change scenario executed for shrub, herbaceous, and bare cover types. Each scenario is executed 30 times (i.e. Monte Carlo simulations) to account for variability across historical change estimates derived from annual fractional cover maps generated by the National Land Cover Database. The map dates...
thumbnail
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...
thumbnail
To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7%) than the NLCD maps (96.2%). More importantly, the urban maps resulted in lower commission...
    map background search result map search result map Data - Removing roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011 Data supporting Landsat time series assessment of invasive annual grasses following energy development Phenology pattern data indicating recovery trajectories of ponderosa pine forests after high-severity fires Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 County-level maps of cropland surface water inundation measured from Landsat and MODIS Wetlands in the state of Arizona Spatially-explicit land-cover scenarios of federal lands in the northern Great Basin: 2018-2050 Data supporting Landsat time series assessment of invasive annual grasses following energy development Wetlands in the state of Arizona Phenology pattern data indicating recovery trajectories of ponderosa pine forests after high-severity fires County-level maps of cropland surface water inundation measured from Landsat and MODIS Data - Removing roads from the National Land Cover Database to create improved urban maps for the United States, 1992-2011