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Article

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

1
BIO5 Institute, University of Arizona, Tucson, AZ 85719, USA
2
School of Geography, Development, and Environment, University of Arizona, Tucson, AZ 85719, USA
3
Planet Labs, San Francisco, CA 94107, USA
4
School of Natural Resources & Environment, University of Arizona, Tucson, AZ 85719, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Jagannath Aryal
Remote Sens. 2021, 13(8), 1448; https://doi.org/10.3390/rs13081448
Received: 12 March 2021 / Revised: 2 April 2021 / Accepted: 5 April 2021 / Published: 8 April 2021
(This article belongs to the Special Issue Earth Observations for Ecosystem Resilience)
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 = (IxytµxyT)/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. View Full-Text
Keywords: z-score; satellite remote sensing; NDVI; Google Earth Engine; open science z-score; satellite remote sensing; NDVI; Google Earth Engine; open science
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  • Externally hosted supplementary file 1
    Doi: https://doi.org/10.5281/zenodo.4648947
    Link: https://doi.org/10.5281/zenodo.4648947
    Description: Release 0.1.0 for https://github.com/tyson-swetnam/emsi hosted from Zenodo. Github Repository: https://github.com/tyson-swetnam/emsi Github.io website: https://tyson-swetnam.github.io/emsi
MDPI and ACS Style

Swetnam, T.L.; Yool, S.R.; Roy, S.; Falk, D.A. 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. Remote Sens. 2021, 13, 1448. https://doi.org/10.3390/rs13081448

AMA Style

Swetnam TL, Yool SR, Roy S, Falk DA. 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. Remote Sensing. 2021; 13(8):1448. https://doi.org/10.3390/rs13081448

Chicago/Turabian Style

Swetnam, Tyson L.; Yool, Stephen R.; Roy, Samapriya; Falk, Donald A. 2021. "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" Remote Sens. 13, no. 8: 1448. https://doi.org/10.3390/rs13081448

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