基于遥感的区域尺度森林地上生物量估算研究

段祝庚; 赵旦; 曾源; 赵玉金; 吴炳方; 朱建军

doi:10.13203/j.whugis20140709

基于遥感的区域尺度森林地上生物量估算研究

段祝庚^1,2,3,
赵旦^2,4,
曾源^2,,
赵玉金²,
吴炳方²,
朱建军¹

1.
中南大学地球科学与信息物理学院, 湖南长沙, 410083;
2.
中国科学院遥感与数字地球研究所数字地球重点实验室, 北京, 100094;
3.
中南林业科技大学理学院, 湖南长沙, 410004;
4.
北京师范大学地理学与遥感科学学院, 遥感科学国家重点实验室, 北京, 100875

基金项目: 国家自然科学基金资助项目(41201351,41401508);中国科学院战略性先导科技专项资助项目(XDA05050108);遥感科学国家重点实验室开放基金资助项目(OFSLRSS201417)。

详细信息

作者简介:
段祝庚,博士生,副教授,主要研究方向为机载LiDAR。E-mail:dzg47336628@163.com

通讯作者:
曾源,博士,副研究员。E-mail:zengyuan@radi.ac.cn

中图分类号: P237.9
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出版历程
- 收稿日期: 2014-09-21
- 发布日期: 2015-10-04

Estimation of the Forest Aboveground Biomass at Regional Scale Based on Remote Sensing

1.
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2.
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
3.
School of Sciences, Central South University of Forestry and Technology, Changsha 410004, China;
4.
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China

Funds: The National Natural Science Foundation of China, Nos. 41201351, 41401508; the Strategic Priority Research Program - Climate Change, No. XDA05050108; the Open Fund of State Key Laboratory of Remote Sensing Science, No. OFSLRSS201417.

摘要

摘要: 森林是陆地生态系统最大的碳库,精确估算森林生物量是陆地碳循环研究的关键。首先从机载LiDAR数据中提取高度和密度统计量,采用逐步回归模型进行典型样区生物量估算;然后利用机载LiDAR数据估算的生物量作为样本数据,与多光谱遥感数据Landsat8 OLI的波段反射率及植被指数建立回归模型,实现区域尺度森林地上生物量估算。实验结果显示,机载LiDAR数据估算的鼎湖山样区生物量与地面实测生物量的相关性R²达0.81,生物量RMSE为40.85 t/ha,说明机载LiDAR点云数据的高度和密度统计量与生物量存在较高的相关性。以机载LiDAR数据估算的生物量为样本数据,结合多光谱遥感数据Landsat8 OLI估算粤西北地区的森林地上生物量,精度验证结果为:R²为0.58,RMSE为36.9 t/ha;针叶林、阔叶林和针阔叶混交林等3种不同森林类型生物量的估算结果为:R²分别为0.51(n=251)、0.58(n=235)和0.56(n=241),生物量RMSE分别为24.1 t/ha、31.3 t/ha和29.9 t/ha,估算精度相差不大。总体上看,利用遥感数据可以开展区域尺度的森林地上生物量估算,为森林固碳监测提供有力的参考数据。
- 森林地上生物量 /
- 机载LiDAR /
- Landsat8 OLI /
- 鼎湖山 /
- 粤西北
Abstract: Forested areas are the largest carbon pool in terrestrial ecosystems. Thus, a key link in terrestrial carbon pool research is estimating the forest biomass accurately. In this study, canopy height and density indices were calculated from LiDAR point cloud data. Statistical models between the biomass calculated from field data and LiDAR-derived variables were built. A stepwise regression was used for variable selection and the maximum coefficient of determination (R²). Techniques for improving variable selection were applied to select the LiDAR-derived variables to be included in the models. Lastly, the forest aboveground biomass as estimated by field data and LiDAR data, was regarded as sample data. The forest aboveground biomass calculated from LiDAR data, band reflectance and vegetation indices of Landsat8 OLI were used to establish the regression model for estimating the forest aboveground biomass at a regional scale. The result shows that: the correlation (R²) between the biomass estimated by LiDAR data and the biomass calculated from field inventory data was 0.81, and the RMSE of biomass is 40.85 t/ha, which means canopy height indices and density indices of airborne LiDAR point cloud data has a strong relationship with biomass. The biomass was estimated by airborne LiDAR data and Landsat8 OLI for coniferous forest, broad-leaved forest and coniferous and broadleaf mixed forest. The estimated correlation results showed that R² was 0.51 (n=251), 0.58 (n=235) and 0.58 (n=241) respectively, and the RMSE for biomass was 24.1 t/ha, 31.3 t/ha and 29.9 t/ha respectively. The resulting estimated biomass for three different forest types is pretty much the same. On the whole, it is feasible and reliable to estimate forest aboveground biomass at regional scale based on remote sensing. The estimated biomass can provide useful data for the monitoring of forest ecosystem carbon fixation.
- forest aboveground biomass /
- airborne light detection and ranging /
- Landsat8 OLI /
- Dinghu Mountain /
- northwest Guangdong Province

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