Abstract
Spatially-explicit depictions of plant productivity over large areas are critical to monitoring landscapes in highly heterogeneous arid ecosystems. Applying radiometric change detection techniques we sought to determine whether: (1) differences between pre- and post-growing season spectral vegetation index values effectively identify areas of significant change in vegetation; and (2) areas of significant change coincide with altered ecological states. We differenced NDVI values, standardized difference values to Z-scores to identify areas of significant increase and decrease in NDVI, and examined the ecological states associated with these areas. The vegetation index differencing method and translation of growing season NDVI to Z-scores permit examination of change over large areas and can be applied by non-experts. This method identified areas with potential for vegetation/ecological state transition and serves to guide field reconnaissance efforts that may ultimately inform land management decisions for millions of acres of federal lands.
Highlights
Spatially-explicit depictions of plant production that are derived in a consistent and repeatable manner are critical to monitoring landscapes in heterogeneous arid and semi-arid grassland and savanna ecosystems
Change detection techniques applied to remotely sensed data provide opportunities to identify and characterize changes in land surface conditions to assist decision-making for land management and to focus field reconnaissance efforts
We suggest that radiometric change detection techniques can serve as an effective method to evaluate land surface changes in the context of vegetation dynamics predicted by state-and-transition models (STMs) and to identify locations to focus field monitoring efforts
Summary
Spatially-explicit depictions of plant production that are derived in a consistent and repeatable manner are critical to monitoring landscapes in heterogeneous arid and semi-arid grassland and savanna ecosystems (hereafter “rangelands”). Predictions for future climate are increasing temperatures and variability in the amount and timing of rainfall in the water-limited regions of the southwestern USA [1,2]. These changes in conjunction with increasing pressure on resources posed by a rapidly growing human population in this region necessitate effective, consistent, and data-driven tools to guide land management decisions and envision novel scenarios. The USA encompasses approximately 312 million hectares of rangelands, 43% of which is managed by the federal government [3]. The millions of hectares under federal jurisdiction pose a particular challenge to meeting the need for data-driven models that are linked to ecosystem function
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