Acta Scientiarum Naturalium Universitatis Pekinensis

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Estimation of Forest Aboveground Biomass by Integrating GLAS and ETM Data

DONG Lixin1,2,3 WU Bingfang3, TANG Shihao1,2   

  1. 1. Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, China Meteorological Administration, Beijing 100081; 2. National Satellite Meteorological Center, Beijing 100081; 3. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101;
  • Received:2010-07-06 Online:2011-07-20 Published:2011-07-20



  1. 1. 中国遥感卫星辐射测量与定标重点开放实验室, 北京 100081; 2. 国家卫星气象中心, 北京 100081; 3. 中国科学院遥感应用研究所, 北京 100101;

Abstract: Based on the algorithm of forest canopy height for GLAS data, the neural net model of above ground biomass in complex terrain conditions was established. The map of forest aboveground biomass from BP neural net model was produced. Overall, forest canopy height and aboveground biomass have higher accuracy. The result of forest canopy height of needle-leaf forest has highest accuracy (R2=0.692). The result of broadleaf forest has higher accuracy (R2=0.5062). The results of forest aboveground biomass are very close to the fields measured results, and are consistent with land cover data in the spatial distribution.

Key words: forest canopy height, above ground biomass, LIDAR, GLAS, ETM

摘要: 在实现大脚印激光雷达GLAS森林冠顶高度反演算法基础上, 建立了复杂地形条件下森林地上生物量神经网络反演模型, 制作了研究区森林地上生物量分布图。总体上, 激光雷达GLAS森林冠顶高度和地上生物量估算精度较高。森林冠顶高度针叶林精度最好(R2=0.692); 阔叶林次之(R2=0.5062); 地上生物量反演结 果与实测结果十分接近, 在空间分布上与土地覆盖分布特征非常一致。

关键词: 森林冠顶高度, 地上生物量, 星载激光雷达, GLAS, ETM

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