以亚热带天然次生林为研究对象，借助一个条带的少量LiDAR点云数据和覆盖整个研究区的免费LandsatOLI多光谱数据，并结合地面实测数据，探索森林生物量低成本高精度制图方法。首先，提取了OLI和LiDAR特征变量，并与地上和地下生物量进行相关分析以筛选变量；然后，借助LiDAR数据覆盖区的样地和条带LiDAR数据构建“LiDAR生物量模型”；再从LiDAR反演生物量的结果中进行采样，结合OLI特征变量构建“LiDAR-OLI模型”；最后，与单独使用OLI多光谱数据建立的“OLI估算模型”结果进行比较，分析精度并验证新方法的效果。结果表明，“LiDAR-OLI模型”对地上和地下生物量的模型拟合效果较好且均优于“OLI模型”，且其交叉验证的精度也较高并优于“OLI模型”，从而证明了新方法的可靠性及有效性。本研究为主、被动遥感技术在中小尺度上协同反演森林参数提供了实验基础，也为基于全覆盖免费OLI多光谱数据及条带LiDAR数据的低成本森林生物量制图探索了技术路线。

Accurate estimation of forest biomass is critical for modeling the carbon cycle and mitigating climate changes. Integration of multi-spectral satellite data and airborne LiDAR data can accurately estimate the biomass. However, the application of this strategy is lim-ited in subtropical forests, particularly in China. In this study, a novelapproach was assessed using one strip of LiDAR point cloud and "wallto-wall" Landsat OLI free multi-spectral data combined with field-measured plot data to generate a low-cost and high-accuracy forest biomass map in a subtropical secondary forest in southeast China. Sixty square plots (30 m×30 m) were established across the study site. First, the OLI data were processed by atmospheric and geometric correction, and LiDAR point clouds were extracted from the raw full-waveform LiDAR data. Second, fivesets of OLI and three sets of LiDAR metrics were extracted, and correlation analysis was performed with the field estimates of above-and below-ground biomass foroptimal metrics selection. Third, the "LiDAR biomass model" was fitted to LiDAR metrics extracted from the strip of LiDAR point cloud and the field plots within the strip. The "LiDAR-OLI biomass model" was fitted tothe OLI metrics and forest biomass estimated by the LiDAR data. Finally, the performance of the predictive models and the accuracy of the cross-validation results were evaluated through comparison with the accuracy assessment results of the "OLI biomass model." [Result] The "LiDAR-OLI biomass model" (R² of above-and below-ground biomass estimation=0.69 and 0.56, respectively) exhibited improved performance than the "OLI biomass model" (R² of above-and below-ground biomass estimation=0.69 and 0.56, respectively). The relative biases of above-and below-ground biomass estimation increased by 14% and 15%, respectively. The mean differences in the cross-validation results for the "LiDAR-OLI biomass model" (mean differences in above-and below-ground biomass estimation=-12.9 and -0.15, respectively) were more accurate than the "OLI model" (mean differences in above-and below-ground biomass estimation=-18.99 and -0.33, respectively). The ranges of above-and below-ground biomasses were 49.9-214.6 and 15.6-59.0 t·hm^-2, respectively, in the entire study site. Moreover, the spatial distributions of above-and below-ground biomasses were similar to each other. Forests with high biomass were located in valleys and flat areas, whereas those with low biomass were located in the mountain ridge. This study provides an experimental basis for estimation of medium-scale forest parameters by synergizing active and passive remote sensing technologies. The study also explores the technological route of using one strip of LiDAR point cloud and "wall-to-wall" Landsat OLI free multi-spectral data for biomass mapping. These methods are relatively inexpensive and exhibit potential in supporting management and policies for addressing carbon stocks and understanding the effect of subtropical forest ecosystems under climate changes in China and elsewhere.