Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data

	Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data
	Cao, Chunxiang 1; Ni, Xiliang 1; Wang, Xuejun 1; Lu, Shilei 1; Zhang, Yuxing 1; Dang, Yongfeng 1; Singh, Ramesh P.1
刊名	International Journal of Remote Sensing
	2016
卷号	37 期号:5 页码:1210-1222
关键词	TOTAL SUSPENDED MATTER ATMOSPHERIC CORRECTION OCEAN COLOR LAKE TAIHU COASTAL WATERS YUQIAO RESERVOIR SEAWIFS IMAGERY CHLOROPHYLL-A TAMPA-BAY ALGORITHM
通讯作者	Cao, Chunxiang (caocx@radi.ac.cn)
英文摘要	Most terrestrial carbon is stored in forest biomass, which plays an important role in local, regional, and global climate change. Monitoring of forests and their status, and accurate estimation of forest biomass are important in mitigating the impacts of climate change. Empirical models developed using remote-sensing and field-measured forest data are commonly used to estimate forest biomass. In the present study, we used a mechanistic model to estimate height and biomass in the Three Gorges reservoir region (China) based on the allometric scale and resource limits (ASRL) model. The forests in the Three Gorges reservoir region are important and unique in view of the vertical distribution of vegetation and mixed needleleaf. Detailed information about the forest in this region is available from the Geoscience Laser Altimeter System (GLAS) and field measurements from 714 forest plots. The ASRL model parameters were adjusted using GLAS-derived forest tree height to reduce the deviation between modelled and observed forest height. The predicted maximum forest tree height from the optimized ASRL model was compared to measured tree heights, and a good correlation (R2 = 0.566) was found. The allometric scale function between forest height and diameter at breast height (DBH) is developed and the maximum forest tree height from the optimized ASRL model transferred to DBH. Moreover, the forest biomass was estimated from DBH according to the allometric scale function that was determined using DBH and biomass data. The results of maximum forest biomass using the ASRL model and the allometric scale function show a good accuracy (R2 = 0.887) in the Three Gorges reservoir region. Here, we present the forest biomass estimation approach following allometric theory for accurate estimation of maximum forest tree height and biomass. The proposed approach can be applied to forest species in all types of environmental conditions. © 2016 Taylor & Francis.
学科主题	Remote Sensing; Imaging Science & Photographic Technology
类目[WOS]	Remote Sensing ; Imaging Science & Photographic Technology
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:20161202139505
内容类型	期刊论文
源URL	[http://ir.radi.ac.cn/handle/183411/39321]
专题	遥感与数字地球研究所_SCI/EI期刊论文_期刊论文
作者单位	1. State Key Laboratory of Remote Sensing Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 2. State Forestry Administration Planning Design and Research Institute, Beijing, China 3. School of Life and Environmental Sciences, Schmid College of Science and Technology, Chapman University, Orange 4.CA, United States
推荐引用方式 GB/T 7714	Cao, Chunxiang,Ni, Xiliang,Wang, Xuejun,et al. Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data[J]. International Journal of Remote Sensing,2016,37(5):1210-1222.
APA	Cao, Chunxiang.,Ni, Xiliang.,Wang, Xuejun.,Lu, Shilei.,Zhang, Yuxing.,...&Singh, Ramesh P..(2016).Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data.International Journal of Remote Sensing,37(5),1210-1222.
MLA	Cao, Chunxiang,et al."Allometric scaling theory-based maximum forest tree height and biomass estimation in the Three Gorges reservoir region using multi-source remote-sensing data".International Journal of Remote Sensing 37.5(2016):1210-1222.