Higher Education Complex of Saravan, Saravan, Iran.
Faculty of Geography, University of Tehran, Tehran, Iran.
PLoS One. 2024 Jul 25;19(7):e0305758. doi: 10.1371/journal.pone.0305758. eCollection 2024.
Wind erosion resulting from soil degradation is a significant problem in Iran's Baluchistan region. This study evaluated the accuracy of remote sensing models in assessing degradation severity through field studies. Sentinel-2 Multispectral Imager's (MSI) Level-1C satellite data was used to map Rutak's degradation severity in Saravan. The relationship between surface albedo and spectral indices (NDVI, SAVI, MSAVI, BSI, TGSI) was assessed. Linear regression establishes correlations between the albedo and each index, producing a degradation severity map categorized into five classes based on albedo and spectral indices. Accuracy was tested with 100 ground control points and field observations. The Mann-Whitney U-Test compares remote sensing models with field data. Results showed no significant difference (P > 0.05) between NDVI, SAVI, and MSAVI models with field data, while BSI and TGSI models exhibited significant differences (P ≤ 0.001). The best model, BSI-NDVI, achieves a regression coefficient of 0.86. This study demonstrates the advantage of remote sensing technology for mapping and monitoring degraded areas, providing valuable insights into land degradation assessment in Baluchistan. By accurately identifying severity levels, informed interventions can be implemented to mitigate wind erosion and combat soil degradation in the region.
伊朗俾路支省的土壤退化导致风蚀，这是一个严重的问题。本研究通过实地研究评估了遥感模型评估退化严重程度的准确性。利用 Sentinel-2 多光谱成像仪（MSI）的 Level-1C 卫星数据对萨拉万的 Rutak 退化严重程度进行制图。评估了地表反照率与光谱指数（NDVI、SAVI、MSAVI、BSI、TGSI）之间的关系。线性回归建立了反照率与每个指数之间的相关性，根据反照率和光谱指数将退化严重程度图分为五类。用 100 个地面控制点和实地观测对精度进行了测试。曼-惠特尼 U 检验比较了遥感模型和实地数据。结果表明，NDVI、SAVI 和 MSAVI 模型与实地数据之间没有显著差异（P > 0.05），而 BSI 和 TGSI 模型则存在显著差异（P ≤ 0.001）。最佳模型 BSI-NDVI 的回归系数为 0.86。本研究证明了遥感技术在制图和监测退化区域方面的优势，为俾路支省土地退化评估提供了有价值的见解。通过准确识别严重程度级别，可以实施有针对性的干预措施，减轻该地区的风蚀和土壤退化。
