Mohamad Hamza Istanbuly(1) and Mustafa Nur Istanbuly *(1)(2)
(1). Department
of Renewable Natural Resources and Ecology, Faculty of Agriculture
Engineering, University of Aleppo,
Aleppo, Syria.
(2) Department of Environmental Engineering, Faculty of Natural Resources, University of Tehran, Tehran, Iran.
(*Corrsponding author: Mustafa Nur Istanbuly, steevanur@yahoo.com.).
Received: 13/05/2019 Accepted: 07/06/2019
Abstract
The development of remote sensing techniques was accompanied by an evolution in statistical analysis techniques and software used to improve analysis of remote sensing data such as NDVI values. The objective of this study was to analyze the changes in vegetation cover in the Afrin region, Syria, using the NDVI, the R programming language and analysis of the Principal Component Analysis (PCA). The changes in the NDVI values extracted from Landsat satellite images were analyzed for the years of study and the relationship between them and the studied areas. The results showed a clear correlation between the amount of changes in Normalized Difference Vegetation index values by regions and years of study. The changes in the Koy area were not related to the years of study and are from prior years of 2010. For 2010 and 2011, changes in vegetation cover were due to change in the regions (Rajo and Koran), meaning that in these two regions most changes were observed during these two years. For the Basouta region, the changes were in 2010. As for the year 2016, it was not associated with any specific change for a given region and distributed throughout the study area. For kasimli, the change in vegetation is mainly distributed over the years 2013-2014-2015. Belbbol region where change was related to 2014 and 2015. The prediction of the case of forest cover in the Afrin area showed two possibilities: either to remain in this condition and this coverage for the next 10 years or the worst case scenario for the total loss of forest coverage until 2020. The study showed that the use of NDVI is an effective method of monitoring and management Forestry coverings and the merge of novel methods in the analysis of satellite images with modern statistical methods is an effective tool for understanding and interpreting changes in the target forest coverings of the study. This paper showed clearly how it can to merge Time factor with spatial factor to explain forestry covers changing using new methods.
Keyword: Normalized Difference Vegetation Index, Principal component analysis, Remote sensing, Syria.
Full paper in Arabic: PDF