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 vegetation is characterized using a series of remote sensing vegetation indices developing using satellite imagery, including the Normalized Difference Vegetation Index (NDVI) and Tasseled Cap (TC) Transformation metrics of brightness, greenness, and wetness. Each of these remote sensing vegetation indices provides a unique characterization of the vegetation properties. For example, NDVI provides a general overview of vegetation condition while the TC Transformation metrics are multi-dimensional and interrelated and can provide distinctive information on conditions within a vegetation ecosystem. The climate conditions are characterized by defining climate periods based on a timeseries of 1-year Standardized Precipitation Evapotranspiration Index (SPEI) values averaged across the Upper Gila River watershed and divided into climate periods (i.e., 1985 through 1993; 1993 through 2014; 2014 through 2021) using the breakpoints algorithm in R Software. Finally, the remote sensing products were developed based on a seasonal framework to obtain more information on the vegetation dynamics. The seasonal framework is defined by averaging multiple images across the following two-month periods: (i) spring (March/April), (ii) late-spring (May/June), (iii) summer (July/August), and (iv) fall (September/October).
This Parent data release consists of five Child Items. The first Child Item includes the SPEI timeseries that was used to identify the climate periods applied in this analysis. The second Child Item consists of 16 rasters with 37 bands apiece, where each raster in this Child Item is identified by a combination of the index (i.e., NDVI, TC brightness, TC greenness, TC wetness) and the season (i.e., spring, late-spring, summer, fall), and each band represents a yearly mean value for all years from 1985 through 2021. The third Child Item also consists of 16 rasters, though with only 3 bands apiece. Similarly, for this Child Item, each raster is identified by a combination of the index (i.e., NDVI, TC brightness, TC greenness, TC wetness) and the season (i.e., spring, late-spring, summer, fall). However, each band represents the linear Sen's slope trend across a different climate period, where band 1 represents the Sen's slope trend across the 1st climate period (i.e., 1985 through 1993), band 2 represents the Sen's slope trend across the 2nd climate period (i.e., 1993 through 2014), and band 3 represents the Sen's slope trend across the 3rd climate period (i.e. 2014 through 2021). The fourth and fifth Child Items both include a series of monthly images, stacked into single rasters, for NDVI and TC greenness covering the length of the 3rd climate period (i.e., 2014 through 2021) that were produced to address a series of case studies more directly. Specifically, the fourth Child Item address a phenological analysis while the fifth Child Item addresses a fire-based case study. All raster products were developed using the Google Earth Engine (GEE) cloud computing software program.
