Share Email Print

Proceedings Paper

Study on forest above-ground biomass synergy inversion from GLAS and HJ-1 data
Author(s): Zhou Fang; Chunxiang Cao; Wei Ji; Min Xu; Wei Chen
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The need exists to develop a systematic approach to inventory and monitor global forests, both for carbon stock evaluation and for land use change analysis. The use of freely available satellite-based data for carbon stock estimation mitigates both the cost and the spatial limitations of field-based techniques. Spaceborne lidar data have been demonstrated as useful for forest aboveground biomass (AGB) estimation over a wide range of biomass values and forest types. However, the application of these data is limited because of their spatially discrete nature. Spaceborne multispectral sensors have been used extensively to estimate AGB, but these methods have been demonstrated as inappropriate for forest structure characterization in high-biomass mature forests. This study uses an integration of ICESat Geospatial Laser Altimeter System (GLAS) lidar and HJ-1 satellites data to develop methods to estimate AGB in an area of Qilian Mountains, Northwest China. Considering the study area belongs to mountainous terrain, the difficulties of this article are how to extract canopy height from GLAS waveform metrics. Combining with HJ-1 data and ground survey data of the study area, we establish forest biomass estimation model for the GLAS data based on BP neural network model. In order to estimate AGB, the training sample data includes the canopy height extracted from GLAS, LAI, vegetation coverage and several kinds of vegetation indices from HJ-1 data. 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.

Paper Details

Date Published: 21 November 2012
PDF: 8 pages
Proc. SPIE 8524, Land Surface Remote Sensing, 85241D (21 November 2012); doi: 10.1117/12.977449
Show Author Affiliations
Zhou Fang, State Key Lab. of Remote Sensing Science (China)
Graduate School of the Chinese Academy of Sciences (China)
Chunxiang Cao, State Key Lab. of Remote Sensing Science (China)
Wei Ji, State Key Lab. of Remote Sensing Science (China)
Min Xu, State Key Lab. of Remote Sensing Science (China)
Wei Chen, Kyoto Univ. (Japan)

Published in SPIE Proceedings Vol. 8524:
Land Surface Remote Sensing
Dara Entekhabi; Yoshiaki Honda; Haruo Sawada; Jiancheng Shi; Taikan Oki, Editor(s)

© SPIE. Terms of Use
Back to Top