We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. For this study, the vegetation conditions are characterized using a series of remote sensing vegetation indices developing using satellite imagery, including the Normalized Difference Vegetation Index (NDVI) and the Tasseled Cap (TC) Transformation. The NDVI is a commonly used vegetation index that quantifies relative greenness of the vegetation based on the plant’s photosynthetic activity, measured as a ratio between the Near Infrared (NIR) and Red bands (Tucker, 1979). The NDVI equation follows: NDVI = (NIR band - Red band) / (NIR band + Red band). NDVI has a range of -1 to 1, though green vegetation theoretically ranges from 0 to 1. Dense green vegetation is represented with values closer to 1 while barren soil, rock, and less-dense surface vegetation has values closer to 0. Values below 0 often represent water due to its unique reflective characteristics. The TC transformation is an approached used to transform satellite imagery into a collection of spectral metrics that can quantify various aspects of the vegetation and soil surfaces (Kauth and Thomas, 1976). Specifically, the TC transformation develops 6 separate metrics, though we only assess the three primary metrics: (i) brightness (transformation 1), (ii) greenness (transformation 2), and (iii) wetness (transformation 3). The TC transformation metrics are calculated using a series of coefficients multiplied across reflectance values for the suite of Landsat bands, then summed across each metric. No specific range is identified for the TC transformation metrics, though for both brightness and greenness, positive values represent brighter and greener conditions, respectively, while negative values represent wetter conditions for wetness. Because bandwidths differ slightly between Landsat 4, 5, 7 and Landsat 8, we use two sets of coefficients and complete the calculation separately before combining the collections into a single series of images (DeVries et al., 2016; Zhai et al., 2022). For each index, we calculate the Sen's Slope across a series of climate periods (see associated Child Item - 1) Database of Trends in Vegetation Properties and Climate Adaptation Variables -- Standardized Precipitation Evapotranspiration Index Timeseries for the Upper Gila River Watershed: 1985 to 2021). All raster products were developed using the Google Earth Engine (GEE) cloud computing software program for the Upper Gila River watershed.
This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. This Child Item consists of a single XML metadata file and a zipped file containing the raster stacks. The raster stacks are identified by both the season they represent (i.e., spring, late-spring, summer, fall) and the vegetation index (i.e., NDVI, TC brightness, TC greenness, TC wetness). Each band represents Sen's slope value across each climate period, where band 1 represents the Sen's slope across the 1st climate period (i.e., 1985 through 1993), band 2 represents the Sen's slope across the 2nd climate period (i.e., 1993 through 2014), and band 3 represents the Sen's slope across the 3rd climate period (i.e. 2014 through 2021).
