【摘要】 森林是全球重要的陆地生态系统,各国普遍采用地面样地调查的方法评估其资源量和生物量。随着激光雷达技术的发展,采用星载大光斑激光雷达估算大区域森林地上生物量将成为另一种选择。为探索利用大光斑激光雷达估算森林地上生物量的方法,提出了一种基于仿真大光斑激光雷达和多层感知器的森林地上生物量估算模型。比较仿真大光斑激光雷达波形参数13种组合拟合森林地上生物量的效果后,认为多层感知器的估测精度高于多元线性回归。与样地实测地上生物量相比,多元线性回归估测结果的偏差范围为-34.96～23.28t/hm~2,多层感知器估测结果的偏差范围更小,为-19.09～20.19t/hm~2。因此,多层感知器估测森林地上生物量的效果优于多元线性回归。更多还原

【Abstract】 Forests are important global terrestrial ecosystems.Sample survey is a commonly used method by countries to assess their forest resources and biomass.With the development of LiDAR technology, spaceborne large-footprint ladar become an option to estimate forest above-ground biomass(AGB) in large areas.In order to develop the method to estimate forest AGB with large-footprint LiDAR,the study proposes an AGB estimation model based on simulated large-footprint LiDAR and multi-layer perceptron.Based on 13 groups of LiDAR waveform parameters, the multi-layer perceptron achieves higher accuracy than multiple linear regression to estimate AGB.Compared with the field measured AGB,the deviation range of the estimated AGB from the multiple linear regression is between-34.96 to 23.28 t/hm~2 and the estimated deviation of the multi-layer perceptron is between-19.09 to 20.19 t/hm~2.Therefore, multi-layer perceptron is better than multiple linear regression in estimating forest AGB.更多还原