Abstract
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’.
Highlights
Satellite remote sensing is an effective tool to gain understanding of environmental impacts and challenges that acquires a collection of images over wide areas taking quick measurements
This paper focuses on developing a spatiotemporal model of the cumulative Enhanced Vegetation Index (EVI) anomalies in the Philippines to fully understand how the vegetation cover changes over time as a tool for
There are negative cumulative EVI anomalies values from 2011-2012 which means that during the period, there was an overall reduction of vegetation in the country
Summary
Satellite remote sensing is an effective tool to gain understanding of environmental impacts and challenges that acquires a collection of images over wide areas taking quick measurements. This method has the advantage of assessing large areas, where access in a forested area and collection of field measurements are limited. Sensors installed at the satellite collect information at a certain distance from the earth using the absorbed, reflected, and emitted electromagnetic radiation at different wavelengths of the areas under observation (Vorovencii, 2011). Forest areas in Asia have significantly increased which indicates a thriving ecosystem (UN, 2021)
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