Detecting and predicting vegetation cover changes using sentinel 2 Data (A Case Study: Andika Region)

Abstract

The earth surface is itself a complex system, and land cover variation is a complex<br />process influenced by the interference of variables. In this study, the data of Sentinel 2<br />for 2017 and 2016 were processed and classified to study the changes in the Andika<br />area. After discovering vegetation changes between two images over the mentioned<br />time, vegetation increased by 661.74 hectares. Multiple regressions have been used to<br />identify factors affecting vegetation changes. Multiple regressions can explain the<br />relationship between vegetation changes and the factors affecting them. In order to<br />investigate the factors affecting vegetation change, altitude data, distance from the<br />road, distance from residential areas of the village and river were introduced into<br />regression equation. Since this method uses three parameters such as Pseudo-R2 and<br />Relative Operation Characteristic (ROC(, 0.23, and 0.696 values for the above<br />parameters, which indicates that the model is in good agreement. The results of<br />regression analysis show that linear composition of height variable as independent<br />variables in comparison with other parameters has been able to estimate vegetation<br />change. Subsequently, by using two classified pictures of 2017 and 2016, the amount<br />of vegetation changes was calculated, and Markov chain method was used for 2018<br />forecast changes.