Remote Sensing Inversion of Vegetation Coverage and Optimization of Vegetation Index Based on MODIS Data in Arid Area

doi:10.6046/gtzyyg.2011.02.21

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Abstract

To further understand the ecologic restoration level after water supplying，the authors studied vegetation coverage （Fv）in the water supply area of the lower reaches of the Tarim River. Correlation equation and two-dimensional scatter plot between Fv and vegetation index （VI） could be obtained based on MODIS data by constructing a variety of VIs in combination with measured Fvs，and the value of Fv could be figured out based on the relevant equation. The results show that there exist good correlations between the VIs such as NDVI，MSAVI，SAVI and EVI and Fvs on the basis of MODIS data. With these VIs，the inversion accuracy of the Fvs in the order from higher to lower is NDVI, EVI, MSAVI and SAVI. The authors suggest using MODIS data to monitor vegetation change in the arid desert area，with the optimal choice of VI being NDVI.

Keywords Knowledge based classification method Land types of the mining area Decision tree Change detection

TP 751.1

Issue Date: 17 June 2011

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GUO Yu-Chuan, HE Ying, LI Xia. Remote Sensing Inversion of Vegetation Coverage and Optimization of Vegetation Index Based on MODIS Data in Arid Area[J]. REMOTE SENSING FOR LAND & RESOURCES,2011, 23(2): 115-118.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2011.02.21 OR https://www.gtzyyg.com/EN/Y2011/V23/I2/115

［1］程红芳，章文波，陈锋.植被覆盖度遥感估算方法研究进展［J］.国土资源遥感，2008（1）：13-17.

［2］邢著荣，冯幼贵，杨贵军，等.基于遥感的植被覆盖度估算方法述评［J］.遥感技术与应用，2009，24（6）：849-854.

［3］邓铭江.塔里木河下游生态输水及植被恢复遥感监测评价［J］.冰川冻土，2007，29(3)：380-386.

［4］闫正龙，黄强，牛宝茹，等.应急输水工程对塔里木河下游地区植被覆盖度的影响［J］.应用生态学报，2008，19（3）：65-67.

［5］徐水师，谭克龙，闫正龙.塔里木河下游植被覆盖动态变化遥感分析［J］.西北大学学报，2010，40（2）：323-326.

［6］刘静，银山，张国盛.毛乌素沙地17年间植被覆盖度变化的遥感监测［J］.干旱区资源与环境，2009，23（7）：162-167.

［7］刘静，银山，张国盛.基于高光谱混合像元分解的干旱地区稀疏植被覆盖度估测［J］.应用生态学报，2010，21（7）：152-158.

［8］戴俣俣，丁贤荣，王文种.基于MODIS影像的植被覆盖度提取研究［J］.遥感应用，2009（2）：67-70.

［9］吴云，曾源，吴炳方.基于MODIS数据的三北防护林工程区植被覆盖度提取与分析［J］.生态学杂志，2009，28(9)：1712-1718.

［10］罗亚，徐建华，岳文泽.基于遥感影像的植被指数研究方法述评［J］.生态科学，2005，24(1)：75-79.

［11］杨嘉，郭铌，贾建华.西北地区MODIS/NDVI与MODIS/EVI对比分析［J］.生态学杂志，2007,25(1)：38-43.

［12］Huete A R．A Soil Adjusted Vegetation Index（SAVI）［J］．Remote Sens Environ,1988,25(3）：295-309.

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