On the Use of Standardized Multi-Temporal Indices for Monitoring Disturbance and Ecosystem Moisture Stress across Multiple Earth Observation Systems in the Google Earth Engine

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: z(xyt) = (I-xyt - mu(xyT))/sigma(xyT), where the index value of the observational date (I-xyt) is subtracted from the mean (mu(xyT)) of the same date or range of days in a reference time series of length T (in years), divided by the standard deviation (sigma(xyT)), during the same day or range of dates in the reference time series. EMSI exhibits high significance (z > |2.0 +/- 1.98 sigma|) 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.

Automated summary

This study explores 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. The results show that EMSI exhibits high significance across all geographic locations and time periods, providing a broader basis for monitoring ecosystem phenology, wildfire potential, herbivory, changes in ecosystem resilience, and land use practices.