Abstract
In this work we explore three methods for quantifying ecosystem vegetation responses spatially and temporally using Google’s Earth Engine, implementing an Ecosystem Moisture Stress Index (EMSI) to monitor vegetation health in agricultural, pastoral, and natural landscapes across the entire era of spaceborne remote sensing. EMSI is the multitemporal standard (z) score of the Normalized Difference Vegetation Index (NDVI) given as I, for a pixel (x,y) at the observational period t. The EMSI is calculated as: zxyt = (Ixyt − µxyT)/σxyT, where the index value of the observational date (Ixyt) is subtracted from the mean (µxyT) of the same date or range of days in a reference time series of length T (in years), divided by the standard deviation (σxyT), during the same day or range of dates in the reference time series. EMSI exhibits high significance (z > |2.0 ± 1.98σ|) across all geographic locations and time periods examined. Our results provide an expanded basis for detection and monitoring: (i) ecosystem phenology and health; (ii) wildfire potential or burn severity; (iii) herbivory; (iv) changes in ecosystem resilience; and (v) change and intensity of land use practices. We provide the code and analysis tools as a research object, part of the findable, accessible, interoperable, reusable (FAIR) data principles.
Highlights
Human land-use and the rapidly changing climate are creating widespread plant water stress at global scale [1]
We examined three methods of calculation for the multi-temporal standardized z index: (1) at a pixel scale, using time series from Google’s Earth Engine (GEE) pixel extractions of surface reflectance normalized Normalized Difference Vegetation Index (NDVI) at a single locale; (2) at area scale directly in the GEE without extracting data; and (3) at area scale with rasters extracted from GEE and calculated in R using GDAL
Time series for the reference periods of each model ecosystem location are presented in the Appendix A figure series (Figures A1–A8)
Summary
Human land-use and the rapidly changing climate are creating widespread plant water stress at global scale [1]. Monitoring the viability of Earth’s ecosystems is a key objective of the global Earth Observation Systems (EOS) network [2,3]. Ecosystem applications of EOS include measuring and monitoring of the global carbon cycle [3,10,11], wildfire severity [12,13], insect and disease outbreaks [14,15,16], and severity of drought-induced forest morbidity and mortality [17,18,19,20]. GEE users can explore anywhere on the Earth’s surface in near realtime, across the entire history of space-based remote sensing. Researchers can compare observations to trends and changes seen in instrumental data, weather interpolations, and climate projections
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