The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Diff...The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.展开更多
叶面积指数(Leaf Area Index,LAI)是定量研究陆地生态系统物质和能量交换的一个重要结构参数,具有重要的研究意义。针对HJ-1A卫星HSI数据,利用野外实测LAI值,探讨利用HJ-1A星HSI数据反演叶面积指数的可行性。选用比值植被指数(RVI)、归...叶面积指数(Leaf Area Index,LAI)是定量研究陆地生态系统物质和能量交换的一个重要结构参数,具有重要的研究意义。针对HJ-1A卫星HSI数据,利用野外实测LAI值,探讨利用HJ-1A星HSI数据反演叶面积指数的可行性。选用比值植被指数(RVI)、归一化植被指数(NDVI)及改良型土壤调整植被指数(MSAVI)3种植被指数,与实测叶面积指数进行回归分析,构建回归模型。研究结果表明,基于影像提取的RVI、NDVI和MSAVI 3种植被指数均与叶面积指数有较好的定量关系。其中,MSAVI的拟合结果最优,其回归确定性系数为0.622。验证模型的确定性系数为0.547,均方根误差RMSE为0.202,说明实测和模拟LAI值之间具有较好的变化一致性。最后基于HJ-1A星HSI影像和MSAVI的估测模型生成研究区叶面积指数空间分布图。展开更多
基金supported by the United Kingdom(UK)Darwin Initiative(28-003).
文摘The South Aral Seabed is an extreme dryland ecosystem undergoing rapid transformation yet remains misrepresented or absent in global land cover datasets.Conventional vegetation indices,specifically the Normalized Difference Vegetation Index(NDVI),perform poorly in such environments due to their limited ability to distinguish sparse vegetation from highly reflective saline and sandy soils.This study evaluated the effectiveness of the Modified Soil Adjusted Vegetation Index(MSAVI)for improving land cover classification in the South Aral Seabed and conducted a decadal analysis of land cover change between 2013 and 2023 using Landsat 8 imagery(30 m resolution).A spectral index-based classification framework was developed,combining MSAVI with the Normalized Difference Water Index(NDWI)and Salinity Index 1(SI1)to reduce spectral confusion between vegetation,saline soils,and surface water.The MSAVI-based classification achieved an overall accuracy of 77.96%(Kappa coefficient=0.71),supported by 313 field-collected validation points from 2023.While the multi-index approach enabled finer discrimination of ecologically important classes,particularly separating salt pans from solonchak soils,it resulted in a lower overall accuracy(73.80%),highlighting a trade-off between class separability and classification performance.Land cover change analysis revealed a highly dynamic landscape,with 52.96%of the study area transitioning between classes over the decade.Transformed areas(16,893 km2)exceeded stable zones(15,004 km2),driven primarily by rapid desiccation and salinization.Solonchak soils increased at an annual rate of 5.58%,while surface water bodies declined by 4.83%per year.Concurrently,sparse or distressed vegetation increased by 1.43%annually,reflecting ongoing afforestation efforts.This study provides the first MSAVI-based and medium-resolution land cover baseline for the South Aral Seabed and demonstrates that soil-adjusted vegetation indices are essential for reliable dryland classification where conventional indices fail.The proposed spectral index framework offers a replicable methodology applicable to other global drylands facing similar land degradation and restoration challenges.
文摘叶面积指数(Leaf Area Index,LAI)是定量研究陆地生态系统物质和能量交换的一个重要结构参数,具有重要的研究意义。针对HJ-1A卫星HSI数据,利用野外实测LAI值,探讨利用HJ-1A星HSI数据反演叶面积指数的可行性。选用比值植被指数(RVI)、归一化植被指数(NDVI)及改良型土壤调整植被指数(MSAVI)3种植被指数,与实测叶面积指数进行回归分析,构建回归模型。研究结果表明,基于影像提取的RVI、NDVI和MSAVI 3种植被指数均与叶面积指数有较好的定量关系。其中,MSAVI的拟合结果最优,其回归确定性系数为0.622。验证模型的确定性系数为0.547,均方根误差RMSE为0.202,说明实测和模拟LAI值之间具有较好的变化一致性。最后基于HJ-1A星HSI影像和MSAVI的估测模型生成研究区叶面积指数空间分布图。
