1- Shahid Beheshti University
2- Shahid Beheshti University ,a_milan@sbu.ac.ir
2- Shahid Beheshti University ,
Abstract: (54 Views)
Vegetation cover is one of the key elements in maintaining ecological balance and the sustainability of natural resources, playing an effective role in regulating the water cycle, reducing soil erosion, and preserving biodiversity. This cover interacts directly with surface soil moisture, and its changes can indicate ecological transformations in sensitive environments such as Lake Urmia. Examining the correlation between vegetation cover and surface moisture is essential for a better understanding of environmental trends and the sustainable management of water resources. In this study, using deep learning methods and the analysis of remote sensing indices NDVI and NDWI, changes in vegetation cover and surface moisture in four regions around Lake Urmia from 2015 to 2024 were investigated, and the 2025 values were predicted. Modeling was conducted using deep neural networks, and its accuracy was evaluated using the Root Mean Square Error (RMSE) criterion. Statistical analysis of the results showed that the southern and western regions experienced the most fluctuations. Furthermore, the correlation coefficient between NDVI and NDWI across all regions was negative and significant (r between -0.83 and -0.96, p < 0.01), indicating the negative impact of increasing vegetation cover on surface moisture. These findings, while confirming a strong inverse relationship, highlight the importance of integrating vegetation cover and moisture data in long-term monitoring, predicting ecological changes, and adopting effective management strategies for the restoration of Lake Urmia.
Type of Study: Original Research |
Subject:
Remote sensing and GIS
Received: 2026/01/8 | Accepted: 2026/05/25
Received: 2026/01/8 | Accepted: 2026/05/25
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