The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLVI-4/W6-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 141–146, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-141-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4/W6-2021, 141–146, 2021
https://doi.org/10.5194/isprs-archives-XLVI-4-W6-2021-141-2021

  18 Nov 2021

18 Nov 2021

MODELING AND FORECASTING CUMULATIVE EVI ANOMALIES USING SARIMA FOR BIOPHYSICAL MONITORING: A CASE STUDY IN THE PHILIPPINES

A. J. L. Diccion and J. Z. Duran A. J. L. Diccion and J. Z. Duran
  • School of Advanced Studies- Environmental Engineering Program, Saint Louis University, Baguio City, Philippines

Keywords: cumulative EVI anomalies, modeling, forecasting, SARIMA, biophysical monitoring, remote sensing

Abstract. Understanding changes in vegetation cover that affect the biophysical conditions of a region can help in formulating policies to address current and future problems of terrestrial ecosystems such as deforestation and environmental degradation. This study focuses on developing a model that forecasts the cumulative Enhanced Vegetation Index (EVI) anomalies as a tool for biophysical conditions monitoring in the Philippines. Satellite data from MODIS MYD13Q1 V6, which contains vegetation index per pixel at 16-day intervals with a resolution of 250 meters, were utilized. The cumulative EVI anomalies per instant were calculated in Google Earth Engine by aggregating the difference of a specific data point in 2011–2020 to a reference EVI mean computed from 2001–2010. The Error-Trend-Seasonality model shows that the cumulative EVI anomalies graph is non-stationary with an upward trend and seasonality. The upward trend of the cumulative EVI anomalies indicates the improvement of vegetation in the Philippines. To check the stationarity of the cumulative EVI anomalies data, the Augmented Dickey-Fuller test was utilized and the model was generated using Seasonal Autoregressive Integrated Moving Average model. Based on the analysis, the best-fit model for the cumulative EVI anomalies is SARIMA (1,1,0)(1,1,1)12 with a mean absolute percentage error (MAPE) of 13.26%. Thus, the proposed model can be used as a tool for biophysical assessment by monitoring and forecasting changes in vegetation and contribute to attaining the UN Sustainable Development Goals 2 and 15 – ‘Eliminating Hunger’ and ‘Life on Land’.