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

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