IMHE OpenIR  > 数字山地与遥感应用中心
基于粒子群一最小二乘法的glas波形分解及树高反演方法
Alternative TitleAn Approach to Decompose ICESat/GLAS Data Waveform and Estimate Canopy Height Based on PSOLSM Method
卢学辉; 李爱农; 雷光斌; 边金虎; 靳华安
Corresponding Author李爱农
2017
Source Publication地理与地理信息科学
ISSN1672-0504
Volume33Issue:3Pages:22-29
Abstract

树高是陆地碳循环研究中的一个重要的生物物理参数。ICESat/GLAS系统是第一个实现全球连续观测的星载激光雷达系统,由于其具有光斑覆盖面积大、穿透力强的特征,在树高等森林结构参数反演方面具有显著的优势。该文设计了一种基于粒子群一最小二乘法的GLAS波形分解及树高反演方法,采用该方法对贡嘎山地区和茂县山区坡度小于10°的GLAS激光点波形进行了高斯分解和树高反演工作,拟合波形与原始波形吻合良好;树高估算结果与采用统计模型估算的树高非常接近(贡嘎山:R~2=0. 80,RMSE=5. 30 m;茂县:R~2=0. 96,RMSE= 3. 11 m)。研究结果表明该方法能很好应用于GLAS波形分解,并获得较好的树高反演结果。未来可以针对该方法在不同坡度地区的适用性和树高反演精度问题开展进一步的研究。

Other Abstract

Canopy height is an important biophysical parameter for terrestrial carbon cycle study. The Geoscience Laser Altimeter System (GLAS) onboard NASA's Ice,Cloud and land Elevation Satellite (ICESat) provides a new potential for estimating forest canopy height worldwide. Analysis of the returned waveform over area of low topography allows for the direct retrieval of canopy heights. This study presented a new waveform decomposition method based on particle swarm optimization-least square method (PSO-LSM). This method decomposed the GLAS waveform into a series of Gaussian components. The initial parameters of Gaussian decomposition were obtained using particle swarm optimization algorithm,and then they were optimized using the Levenburg-Marquardt method. The first peak from ground direction, the intensity of which exceeded the tenth of maximum waveform intensity,was identified as ground peak. Eventually, the canopy height was estimated from vertical difference between the signal's start and the ground peak. The newly proposed method was applied to Gongga and Maoxian mountain forest. The results indicated that the sum of Gaussian components could be an accurate representation of the original waveform. And the estimated canopy height matched well with the height calculated by the empirical model established by Hayashi et al. in 2013 (Gongga mountain:R~2=0. 80,RMSE= 5. 30 m,Maoxian: R~2=0. 96,RMSE= 3. 11 m). However,the applicability and accuracy of the approach in areas with different slopes need to be further studied.

Keyword波形分解 粒子群算法 最小二乘法 树高
Subject AreaS758
DOIj.issn.1672-0504.2017.03.005
Indexed ByCSCD ; 北大中文核心
Language中文
CSCD IDCSCD:6000627
Funding Organization国家自然科学基金项目(41631180) ; 中国科学院国际合作局对外合作重点项目(GJHZ201320) ; 中国科学院委托研究与专项咨询服务课题(KFJ-EW-STS-020-02)
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/19020
Collection数字山地与遥感应用中心
Affiliation1.中国科学院水利部成都山地灾害与环境研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
卢学辉,李爱农,雷光斌,等. 基于粒子群一最小二乘法的glas波形分解及树高反演方法[J]. 地理与地理信息科学,2017,33(3):22-29.
APA 卢学辉,李爱农,雷光斌,边金虎,&靳华安.(2017).基于粒子群一最小二乘法的glas波形分解及树高反演方法.地理与地理信息科学,33(3),22-29.
MLA 卢学辉,et al."基于粒子群一最小二乘法的glas波形分解及树高反演方法".地理与地理信息科学 33.3(2017):22-29.
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