CHINA TYPICAL FOREST ABOVEGROUND BIOMASS ESTIMATION BY FUSION OF MULTI-PLATFORM DATA

被引：2

作者：

Pang Yong ^{[1
]}

Li Zengyuan ^{[1
]}

Meng Shili ^{[1
,2
]}

Lu Hao ^{[1
]}

Jia Wen ^{[1
]}

Liu Qingwang ^{[1
]}

Li Haikui ^{[1
]}

Lei Yuancai ^{[1
]}

机构：

[1] Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing, Peoples R China

[2] Beijing Normal Univ, Coll Informat Sci & Technol, Beijing, Peoples R China

来源：

2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2016年

基金：

国家高技术研究发展计划(863计划);

关键词：

China forest; aboveground biomass; LiDAR; Field-Airborne-Spaceborne (FAS) observation; multi-platform;

D O I：

10.1109/IGARSS.2016.7729921

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

China has a wide variety of forest types. It is challenging to make a reliable estimation of these forest aboveground biomass (AGB) using geo-spatial technologies. We developed a Field-Airborne-Spaceborne (FAS) comprehensive observation method for AGB estimation. According to forest ecological zones of China, we carried out three FAS campaigns in the Northeast, central, and Southwest of China. Airborne LiDAR data were collected along National Forest Inventory (NFI) plots. Then the airborne LiDAR data were used to estimate AGB after been trained by NFI plots. Then these LiDAR estimated AGB were used to train satellite data for large area biomass mapping. The stratified regression tree modeling method was used in this research. The overall estimation correlation coefficient are better than 0.8.

引用

页码：3557 / 3560

页数：4

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