【摘要】 以广东省广州市从化石门国家森林公园为研究区域，选择4种不同林分类型（针阔混交林、阔叶林、针叶林、竹林）,各林分类型选择3个20 m×20 m地块作为样方；结合激光雷达、多光谱图像、实测数据，构建多元非线性反演模型和多元线性回归模型，估算森林地上生物量，并选择最佳模型进行精度评价。结果表明：（1）依据多源数据建立的4种不同林分类型的多元非线性地上生物量反演模型的精度最高，针阔混交林样地地上生物量预测值为42.79 t·hm^-2、阔叶林样地地上生物量预测值为60.46 t·hm^-2、针叶林样地地上生物量预测值为32.99 t·hm^-2、竹林样地地上生物量预测值为1.92 t·hm^-2。（2）研究区中4种不同林分类型的多元非线性地上生物量反演模型的拟合精度，由大到小依次为竹林（决定系数为0.919）、阔叶林（决定系数为0.813）、针叶林（决定系数为0.786）、针阔混交林（决定系数为0.713）,均符合精度要求。采用多光谱和激光雷达数据结合的方式，能够较精准地提取林分地上生物量信息，可准确估算针阔混交林、阔叶林、针叶林、竹林的地上生物量。更多还原

【Abstract】 Investigating from Shimen National Forest Park, Conghua, Guangzhou City, Guangdong Province, four different forest types（mixed coniferous-broadleaved forest, broadleaved forest, coniferous forest, bamboo forest） were selected as study areas. Three 20 m×20 m plots were chosen for each forest type. Integrated with data from LiDAR, multispectral imagery, and field measurements, we developed multivariate nonlinear inversion models and multivariate linear regression models to estimate aboveground biomass. The optimal model was selected based on accuracy assessment. The results showed that:（1） The multivariate nonlinear inversion models based on multisource data achieved the highest accuracy for estimating aboveground biomass across the four forest types: mixed coniferous-broadleaved forest predicted biomass was 42.79 t·hm^-2, broadleaved forest was 60.46 t·hm^-2, coniferous forest was 32.99 t·hm^-2, and bamboo forest was 1.92 t·hm^-2.（2） Among the four forest types, the multivariate nonlinear inversion models showed decreasing fitting accuracy as follows: bamboo forest（R²=0.919）, broadleaved forest（R²=0.813）, coniferous forest（R²=0.786）, and mixed coniferous-broadleaved forest（R²=0.713）, all meeting accuracy requirements. Integrating multispectral and LiDAR data provided a more precise means to extract aboveground biomass information, facilitating accurate estimation across mixed coniferous-broadleaved, broadleaved, coniferous, and bamboo forests.更多还原