复合植被指数在稀疏高寒草原植被盖度遥感反演中的应用
发布时间：2018-10-23 14:22
【摘要】：基于Landsat 8遥感影像数据,以西藏日喀则经南木林到申扎县的一条样带高寒草原作为研究对象,选择较为常用的归一化植被指数(normalized difference vegetation index,NDVI)、土壤调整植被指数(soil adjusted vegetation index,SAVI)、修改型土壤调整植被指数(modified soil adjusted vegetation index,MSAVI)以及对半干旱区低覆盖植被信息较为敏感的转换型土壤调整植被指数(transformational soil adjusted vegetation index,TSAVI),结合地表反射率改进运算的FCD模型(forest canopy density mapping model)裸土(bare soil index,BI)和阴影指数(shadow index,SI)构建适合低植被覆盖区域的复合植被指数(vegetation bare shadow index,VBSI);基于各植被指数构建像元二分模型,定量反演高寒草原植被盖度;并利用网格法实测的植被盖度分析反演精度。研究结果表明,8种植被指数所构建的像元二分模型对高寒草原植被盖度的反演精度以VBSI(TSAVI)最高,反演精度为85.66%;并证明了基于野外采集的土壤光谱曲线获取的TSAVI所构建的像元二分模型对高寒草原植被盖度信息的提取具有一定的实用性;改进运算的FCD模型裸土和阴影指数能较好地削弱土壤和阴影对植被信息的影响,所构建的复合植被指数对提取稀疏高寒草原植被盖度信息具有重要的实际意义。
[Abstract]:Based on the remote sensing image data of Landsat 8, the alpine steppe in a sample belt from Xigaze, Tibet, through Nanmu Lin to Shenza County, was taken as the research object. Select normalized vegetation index (normalized difference vegetation index,NDVI), soil adjustment vegetation index (soil adjusted vegetation index,SAVI), modified soil adjustment vegetation index (modified soil adjusted vegetation index,MSAVI) and conversion soil which are sensitive to low cover vegetation information in semi-arid area. Adjust vegetation index (transformational soil adjusted vegetation index,TSAVI), combine FCD model (forest canopy density mapping model) bare soil (bare soil index,BI) and shadow index (shadow index,SI) to construct compound vegetation index (vegetation bare shadow index,VBSI). Exponential construction of pixel dichotomy model, Quantitative inversion of vegetation coverage of alpine steppe, and analysis of inversion accuracy of vegetation coverage measured by grid method. The results show that VBSI (TSAVI) is the most accurate model for inversion of vegetation coverage in alpine steppe. The accuracy of inversion is 85.66, and it is proved that the pixel dichotomy model constructed by TSAVI based on the soil spectral curve obtained from field data is practical for extracting vegetation coverage information of alpine steppe. The improved FCD model of bare soil and shadow index can weaken the influence of soil and shadow on vegetation information. The constructed composite vegetation index is of great practical significance in extracting vegetation coverage information of sparse alpine steppe.
【作者单位】： 中国科学院水利部成都山地灾害与环境研究所;中国科学院大学;西南交通大学地球科学与环境工程学院;
【基金】：中国科学院战略性先导科技专项(B类)(XDB03030505)
【分类号】：S812
本文编号：2289505
[Abstract]:Based on the remote sensing image data of Landsat 8, the alpine steppe in a sample belt from Xigaze, Tibet, through Nanmu Lin to Shenza County, was taken as the research object. Select normalized vegetation index (normalized difference vegetation index,NDVI), soil adjustment vegetation index (soil adjusted vegetation index,SAVI), modified soil adjustment vegetation index (modified soil adjusted vegetation index,MSAVI) and conversion soil which are sensitive to low cover vegetation information in semi-arid area. Adjust vegetation index (transformational soil adjusted vegetation index,TSAVI), combine FCD model (forest canopy density mapping model) bare soil (bare soil index,BI) and shadow index (shadow index,SI) to construct compound vegetation index (vegetation bare shadow index,VBSI). Exponential construction of pixel dichotomy model, Quantitative inversion of vegetation coverage of alpine steppe, and analysis of inversion accuracy of vegetation coverage measured by grid method. The results show that VBSI (TSAVI) is the most accurate model for inversion of vegetation coverage in alpine steppe. The accuracy of inversion is 85.66, and it is proved that the pixel dichotomy model constructed by TSAVI based on the soil spectral curve obtained from field data is practical for extracting vegetation coverage information of alpine steppe. The improved FCD model of bare soil and shadow index can weaken the influence of soil and shadow on vegetation information. The constructed composite vegetation index is of great practical significance in extracting vegetation coverage information of sparse alpine steppe.
【作者单位】： 中国科学院水利部成都山地灾害与环境研究所;中国科学院大学;西南交通大学地球科学与环境工程学院;
【基金】：中国科学院战略性先导科技专项(B类)(XDB03030505)
【分类号】：S812
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