@article {10.3844/ajeassp.2018.387.396,
article_type = {journal},
title = {The Use of Remote Sensing Techniques in Detecting and Predicting Forest Vegetation Change Using MODIS Satellite Data, Golestan, Iran},
author = {Karimi, Akram and Abdollahi, Sara and Kabiri Balajadeh, Hamid Reza and Ostad-Ali-Askari, Kaveh and Eslamian, Saeid and Singh, Vijay P.},
volume = {11},
number = {1},
year = {2018},
month = {Mar},
pages = {387-396},
doi = {10.3844/ajeassp.2018.387.396},
url = {https://thescipub.com/abstract/ajeassp.2018.387.396},
abstract = {It is so important to be aware of quantitative and qualitative characteristics of changes for the environment, land use planning and sustainable development. Detecting the changes in the condition of an issue is done by time difference observations. Change detection refers to the process of detecting time changes of an object through different time observations. The detection of changes by satellite imageries has come recently to the focus of attention due to their comprehensive and integrated characteristics and their ability to monitor changes during long periods as well as their application to monitoring and controlling changes in the forest ecosystems. The vegetation maps are now used to generate required information for macro and micro planning. This study was done to monitor the changes in the forest of Golestan province in the past and also to investigate the possibility of its future forecast using the Land Change Modeler (LMC). A forest type map was first prepared to monitor changes in forests of Golestan province from 2000 to 2015. The images taken in 12 months during 2000 and 2015 were collected form MODIS satellite imageries to monitor the forest. After pre-processing and preparing the time series in two sections, the forest changes were considered using the Normalized Difference Vegetation Index and Moisture Stress Index (NDVI and MSI). The changes were then classified by indices, including the lands with excellent, very good, medium and poor coverage and compared together. The accuracy of the classification results was assessed using the field maps. The best result was found in the average time series data and the use of the NDVI index was prioritized over other indices. The forecast of changes by 2030 as 0.8629 Kappa and the model results for 2030 indicated a decline of 70000 in the land with high and very high capacities and a further increase in land with a low capacity.},
journal = {American Journal of Engineering and Applied Sciences},
publisher = {Science Publications}
}