基于常用的4种地形校正模型（Cosine模型、C模型、C+SCS模型、Minnaert模型），以IDL语言为二次开发平台，对黑龙江省帽儿山地区2007年7月21日TM图像进行地形校正，从视觉差异、图像的定量统计特征两方面评价了4种地形校正模型的修正效果，并比较了地形校正后几种遥感因子与森林生物量的相关性,建立了森林生物量的遥感反演模型，分析了不同地形校正模型对森林生物量反演的影响.结果表明：由于K-T变换采用线性变换方式，地形校正后遥感数据与森林生物量的相关性出现了较大波动，应根据地表信息调整变换参数，因此该变换方式不适合与地形校正结合使用；植被指数的信息量在地形校正后明显提高，其与森林生物量的相关性显著增强；4种地形校正模型中，Cosine校正过度，不宜采用，C模型和C+SCS模型通过引入半经验参数，较好地消除了地形效应，Minnaert模型校正后降低了森林生物量估测的误差，有效地提高了遥感反演模型的精度.
Based on four commonly used models (Cosine model, C model, C+SCS model, and Minnaert model), the topographic effects in Landsat-5 image of Maoershan region in Heilongjiang Province acquired on July 21, 2007 were calibrated on the platform of IDL language. The 4 models were validated from the aspects of visual differences and quantitative statistical features of the images. After the correlation analysis on the corrected remote sensing data and the forest biomass data, the biomass retrieving models were constructed. Furthermore, the effects of different topographic factors on the estimation of forest biomass were studied. The results showed that due to its liner presumption, the topographic correction combined with K-T transformation was not suitable for forest biomass estimation, and the correlations between the remote sensing data and the forest biomass fluctuated significantly. The parameters of the transformation needed to be adjusted in accordance with the information of land surface. The information content of vegetation index was significantly increased after topographic correction, and the correlation between vegetation index and forest biomass was enhanced greatly. Among the four models, Cosine model overcorrected the shaded areas in image, C model and C+SCS model had good correction performance by using semi-empirical parameters, while Minnaert model decreased the error of biomass estima
tion and improved the precision of remote sensing retrieving models effectively.