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

Journal of Radar and Optical Remote Sensing and GIS

مقاله 4، دوره 1، شماره 3.4، اسفند 2018، صفحه 38-54   

نویسندگان

sedigheh emami^{* 1}؛ esmail emami²

¹Ms in GIS, remote sensing,Yazd Branch, Islamic Azad University, Yazd, Iran

²Graduate student University of electric power systems of the Islamic trends free khomeynishahr

چکیده

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.

کلیدواژه‌ها

NDVI؛ Sentinel 2؛ Cellular Automata Markov and logistic regression