The polygons in this shapefile accompany the paper, “Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions,” by Sadinski et al. (submitted). The paper describes relations between climate dynamics and key ecological conditions and processes on wetland-upland landscapes in > 30 sites distributed across four study areas in the midwestern United States. The variables studied included both ground- and satellite-based measures. The polygons in this shapefile pertain to the latter and provide the boundaries of 4-km2 blocks that subsume the field sites and provide a landscape perspective for the timing and duration of snow, evapotranspiration, and photosynthetic activity from 2008 to 2012 from January through August. The data for these variables are provided in a separate tabular file (xxx insert reference link xxx) that links to this shapefile by a shared field called BlockSite. Citation: Sadinski, W., Gallant, A.L., Roth, M., Brown, J., Senay, G., Brininger, W., and Stoker, J., Submitted 2017, Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions: PLOS ONE.

Geospatial corner coordinates for 33 landscape blocks used as units of analyses for research described in the paper, "Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes."

This file includes satellite sensor-derived data on evapotranspiration, vegetation response (normalized difference vegetation index), and snow and weather-station data used to derive weekly growing degree units and total precipitation used in analyses described in the paper, "Challenges in complementing data from ground-based sensors with satellite-derived products to measure ecological changes in relation to climate – lessons from temperate wetland-upland landscapes."

We prepared a water phenology product from the Landsat Level-3 Dynamic Surface Water Extent (DSWE) science product, which provides a water classification for each Landsat scene at 30 m resolution (Jones 2015, Jones 2019). After masking clouds, cloud shadows, and snow, the DSWE product identities 4 categories of water confidence: 1. Not Water; 2. Open Water, High Confidence; 3. Open Water, Moderate Confidence; 4. Partial Surface Water, Conservative; and 5. Partial Surface Water, Aggressive. We reviewed and summarized the DSWE products for 1999–2018 and prepared two metrics of surface water occurrence (defined as the percentage of the observations when surface water was present): 1. June/July/August, 1999–2018, Conservative Surface Water Occurrence (treating the Partial Surface Water, Aggressive class as Not Water); and 2. June/July/August, 1999–2018, Aggressive Surface Water Occurrence (treating all 4 categories of water confidence as Water).

The comma-delimited fields in this dataset provide values for the remotely sensed variables analyzed for landscape blocks described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). The field labeled “BlockSite” links the records in this file with a set of boundaries in a shapefile called “Study_Block_Boundaries.shp” The records represent weekly measurements of normalized difference vegetation index (BlockNDVI) values and total evapotranspiration (BlockETmm), as well as the annual snow-off date (BlockDOYsnowfree) for the study blocks from January through August from 2008 to 2012.

This dataset provides multispectral reflectance imagery (green at 550 nm, red at 660 nm, red edge at 735 nm, and near-infrared at 790 nm), normalized difference vegetation index (NDVI), and digital surface and terrain models for a 0.5 km2 area surrounding Big Trail Lake (BTL) in the Goldstream Creek Valley north of Fairbanks, Alaska. These high spatial resolution maps (13 cm x 13 cm) were generated by unmanned aerial vehicle (UAV) imagery collected on 2019-08-04. Raw images (n=908) were combined into mosaic layers that incorporated ground control points with centimeter accuracy. These layers were then used to generate vegetation, water body, and elevation maps and then combined with in situ measurements of methane flux to improve upscaling models of greenhouse gas emissions.

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 dataset consists of several measures of landscape characteristics, each of which is summarized from raster data within spatial polygons. These spatial polygons represent the land area upstream of sampled stream reaches. These stream reaches were sampled by the Maryland Department of Natural Resources for the Maryland Biological Stream Survey program during survey rounds one, two, and four. Landscape characteristics summarized here are either represented by continuous or discrete raster layers which are summarized as the average value of a given characteristic (continuous data) or the area occupied by each class (discrete data). The continuous datasets summarized included percentage of area occupied by tree canopy (for the years 2011 and 2016) and urban land cover (for the years 2001, 2006, 2011, and 2016); the percentage of the surficial geology made up of various chemical constituents (including aluminum oxide, calcium oxide, ferric oxide, potassium oxide, magnesium oxide, sodium oxide, nitrogen, phosphorus pentoxide, silicon dioxide, and sulfur); surficial geology physical characteristics (including hydraulic conductivity and uniaxial compressive strength); top soil characteristics (percentage of layer made up of clay, sand, and silt as well as soil K-factor/soil erodibility); topographic conditions (including elevation and topographic wetness index); annual and monthly climate conditions for the years 1994-1997,1999-2004, and 2013-2018 (average air temperature and total precipitation). The discrete datasets summarized included land cover classifications for the years 2001, 2004, 2006, 2008, 2011, 2016; land cover change index values for the period between 2001 and 2016.

This comma-delimited dataset provides values for the remotely sensed status of snow on/off analyzed for field study sites described in the paper, "Multi-year data from satellite- and ground-based sensors show details and scale matter in assessing climate’s effects on wetland surface water, amphibians, and landscape conditions," by Sadinski et al. (submitted). These data provide an indication of snow presence at the spatial resolution of a 500-m square cell for each eight-day interval beginning in January and ending at the start July of each year from 2008-2012. The source for the data was the MOD10A2 snow product from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor. We extracted data for the cells associated with 35 field study sites for which we subsequently determined the timing of spring snow-free conditions. The data field labeled "BlockSite" links these values geospatially to a data field of the same name in an ESRI shapefile titled "Study_Block_Boundaries.shp" that delineates study blocks containing the field sites. Refer to the paper by Sadinski et al. (submitted) for details of the analyses performed.

These data were compiled to visualize the extent of Lake Powell at various elevation levels. These data represent water surface elevations for Lake Powell at levels critical to the operation of Glen Canyon Dam, at 5 foot intervals from the "Equalization Tier" ("Full Pool") to "Dead Pool", and at maximum and minimum elevations each water year throughout Glen Canyon Dam's operating history. These data were created for Lake Powell in Arizona and Utah. These data were created by the U.S. Geological Survey, Southwest Biological Science Center, Grand Canyon Monitoring & Research Center by reclassifying "Modified topobathymetric elevation data for Lake Powell" (Jones and Root, 2021) at discrete elevation levels and converting them into vector format. These data can be used to visualize locations or resources in Lake Powell at various elevation levels as it continues to change.