Novel Method for Combining NDVI Time Series Forecasting Models

Forecasting vegetation indices represents a hot topic nowadays because of the climate change impacts challenges. Several time series forecasting models are proposed in the literature. However, researchers are continuously looking for an accurate and more performing one. This paper proposes a novel way to combine forecasting models results. Particularly interested in vegetation changes, our study relies on NDVI time series extracted from MODIS DATA. These temporal profiles describe a forest cover located in the northwestern of Tunisia, an African country. To this aim, normally fitted weights and evidence theory were selected to build an accurate combination model. Our findings highlight: on one hand, the importance of studying the statistical properties of the analyzed data. On the other hand, they shed the light on the ability of increasing the accuracy of forecasting models by combining them.

1. Introduction

Vegetation cover represents an essential element for human being. It provides the main sources whatever natural area or agriculture ones (oxygen, food, habitats, money source…). Therefore, sustainable development goals cover three out of seventeen proposed goals [1]. The second one aims to prevent food scarcity. Therefore, it gives more importance to agriculture. The 13^th sheds the light on the climate action where the green cover plays a key role. Lastly, the 15^th goal represents the life on land which has been largely and drastically altered by human activities.