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引用本文:

DOI:

10.11834/jrs.20110124

收稿日期:

2010-04-12

修改日期:

2010-09-20

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石漠化遥感评价因子提取研究
1.中国科学院 亚热带农业生态研究所,亚热带农业生态过程重点实验室,湖南 长沙 410125;2.中国科学院 环江喀斯特生态系统观测研究站,广西 环江 547100;3.中国科学院 对地观测与数字地球科学中心,北京 100080;4.香港中文大学 太空与地球信息科学研究所,香港;5.遥感科学国家重点实验室 中国科学院遥感应用研究所,北京 100101
摘要:

通过地面光谱试验,验证了植被指数和线性光谱分解方法提取石漠化遥感评价因子的有效性,并基于光谱吸收特征发展了石漠化综合指数(karst rocky desertification synthesis indices,KRDSI)。结果表明,植被指数能够比较有效地提取绿色植被盖度,土壤背景对基于植被指数的喀斯特地区绿色植被盖度提取影响很小;植被指数和线性光谱分解技术均不能有效提取表征石漠化信息的非绿色植被覆盖信息,而KRDSI能够直接提取石漠化遥感评价因子。研究表明,利用Hyperion高光谱成像数据,基于植被指数和KRDSI能够比较有效地直接提取石漠化遥感评价因子。

Remote sensing of indicators for evaluating karst rocky desertifi cation
Abstract:

Karst rocky desertifi cation is the most serious problems of land degradation in karst regions, southwest China. Remote sensing technique is the promising method to assess and monitor the degree and extent of karst rocky desertifi cation at large scale. In this study, based on fi eld spectral refl ectance measurements, the traditional vegetation indices (VIs) and linear spectral unmixing (LSU) are assessed to extract the key indicators of karst rocky desertifi cation. Karst rocky desertifi cation synthesis index (KRDSI) has been developed with the unique of spectral features observed in non-vegetation land cover types. The results show that VIs could be used to extract the fractional cover of green vegetation, and they are not sensitive to soil background. Both VIs and LSU can effi ciently extract the fractional cover of non-green vegetation. Compared with LSU, KRDSI shows more consistent results with the fi eld measurement of non-vegetation land cover fractions. This study indicates that evaluation indicators of karst rocky desertifi cation can be extracted from the Hyperion image with the combination of vegetation indices and KRDSI values.

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