中文摘要：

结合野外调查数据和Landsat5TM影像,分析6种地形校正方法（Teillet-回归,Cosine,C,SCS,SCS＋C和Minnaert）对雷竹林地上生物量遥感估算的影响。结果表明：1）除Cosine和SCS方法存在校正过度现象,其余4种地形校正方法均具有良好的校正效果;2）6种地形校正方法均可提高TM4,TM5与地上生物量的相关性,且RVI,NDVI和SAVI这3种植被指数与生物量之间的相关性也得到改善;3）与原始影像相比,6种地形校正后的遥感数据都能不同程度地提高雷竹林生物量估算精度,以Teillet-回归校正后所建雷竹林地上部分生物量估算模型精度最高,相关系数从0.441提高到0.687,RMSE降低17%左右;4）尽管Cosine校正最大程度地提高了TM4,TM5与地上生物量的相关关系,但Cosine方法存在校正过度问题,Cosine校正后雷竹林地上生物量遥感估算模型精度反而略低于Teillet-回归校正;5）虽然地形校正可提高植被指数与雷竹林地上生物量之间的相关性,但所选5种植被指数均未入选雷竹林地上生物量遥感估算模型的变量,这与雷竹林较高的密度有关。

英文摘要：

Based on combined data of a field survey and Landsat 5 Thematic Mapper（TM） images,we studied effects of six topographic correction models,including Teillet-regression,Cosine,C,SCS,SCS＋C,and Minnaert,on aboveground forest biomass estimation of Phyllostachys praecox.Results showed：Except for Cosine and SCS correction methods,the rest four methods provided satisfactory correction results;Six topographic correction methods all improved correlations between TM data（TM4 and TM5） and aboveground biomass,as well as the correlations between aboveground biomass and three vegetation indices（RVI,NDVI and SAVI）;Compared to the original image,the calibrated images by using the six correction methods improved to a certain degree biomass estimation performance,in particular,the Teillet correction method provided the highest accuracy,the correlation coefficient increased from 0.441 to 0.687 and the RMSE decreased by around 17%;Although Cosine correction method much improved the correlation coefficients between TM data（TM4 and TM5） and aboveground biomass,the Cosine-based correction result had lower estimation accuracy than the Teillet-based correction method did due to overcorrection with the Cosine-based method;Although the calibration methods improved the correlations between vegetation indices and Phyllostachys praecox aboveground biomass,the five vegetation indices were not selected as variables used in the models,this is probably due to the high density of Phyllostachys praecox.