Accurate estimation of understory topography has significant scientific importance for maintaining ecosystem balance and biodiversity conservation. Addressing the issue of inadequate representation of spatial heterogeneity when traditional forest topographic inversion methods consider the entire forest as the inversion unit, this study proposes a differentiated modeling approach for forest types based on refined land cover classification. Taking Puerto Rico and Maryland as study areas, a multi-dimensional feature system is constructed by integrating multi-source remote sensing data: ICESat-2 spaceborne lidar is used to obtain benchmark values for understory terrain, topographic factors such as slope and aspect are extracted based on SRTM data, and vegetation cover characteristics are analyzed using Landsat-8 multispectral imagery. Innovatively, this study incorporates forest type as a classification modeling condition and applies the Random Forest algorithm to build differentiated topographic inversion models. Experimental results indicate that, compared to traditional whole-area modeling methods (RMSE=5.06 m), forest type-based classification modeling significantly improves the accuracy of understory terrain estimation (RMSE=2.94 m), validating the effectiveness of spatial heterogeneity modeling. Further sensitivity analysis reveals that canopy structure parameters (with RMSE variation reaching 4.11 m) exert a stronger regulatory effect on estimation accuracy compared to forest cover, providing important theoretical support for optimizing remote sensing models of forest topography.