Using Remote Sensing Data and Machine Learning Methods to Estimate Changes in Hyrcanian Forests along the Southern Coasts of the Caspian Sea
2025
Afraz, Mahdi | Omarzadeh, Davoud | Eftekhari, Mobin | Yaghoobzadeh, Mostafa | Haji Elyasi, Ali
The Hyrcanian Forests, located along the southern coastal areas of the Caspian Sea in northern Iran, are of great environmental, economic, and cultural significance. They play crucial roles not only in preserving water resources, soil, plant, and animal diversity but in mitigating adverse impacts of climate change as well. The present study investigated changes in the Hyrcanian forest cover between 2000 and 2017 using the diverse remote sensing data of Normalized Difference Vegetation Index (NDVI), and MODIS Vegetation Continuous Fields (VCF) as well as Sentinel-1, Landsat-5, and Landsat-8 satellite images while the Support Vector Machine (SVM) and Random Forest (RF) methods were employed for classification. The results revealed that approximately 534 square kilometers of the forests had experienced degradation. Moreover, classification accuracy levels were impressive as evidenced by a user accuracy of 93.26% and a Kappa coefficient of 94.62% recorded for SVM and corresponding values of 89.29% 74.63% for RF. Comparison with global forest change datasets confirmed the reliability of the results obtained. The research approach seems to offer promising insights useful for forest conservation management, natural resource planning, and enhanced sustainable utilization of Hyrcanian forests.
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