Deriving Regional Crown Closure Using Spectral Mixture Analysis Based on Up-Scaling Endmember Extraction Approach and Validation

作者:Cao, CX (Cao, Chunxiang)[ 1 ] ; Tian, HJ (Tian, Haijing)[ 2 ] ; Zhang, YX (Zhang, Yuxing)[ 3 ] ; Dang, YF (Dang, Yongfeng)[ 3 ] ; Ni, XL (Ni, Xiliang)[ 1 ] ; Xu, YF (Xu, Yunfei)[ 1 ] ; Xu, M (Xu, Min)[ 1 ] ; Li, XW (Li, Xiaowen)[ 1 ] ; Xiang, HB (Xiang, Haibing)[ 4 ] ; Yang, TY (Yang, Tianyu)[ 2 ]

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

卷: 8  期: 6  页: 2560-2568  特刊: SI

DOI: 10.1109/JSTARS.2014.2375877

出版年: JUN 2015

摘要

This paper investigates the retrieval of forest crown closure (CC) from the Landsat Thematic Mapper (TM) data and aerial images with a linear spectral mixture analysis (SMA) method. Anshan is selected as the study area. Two endmember extraction methods were used in this paper: 1) traditional image-based method and 2) up-scaling method. (When we get the fractions of components from a coregistered 0.6-m spatial resolution image, the linear spectral mixture model is applied to unmix the TM image and obtain the required endmembers.) For both methods, four fraction images (sunlit canopy, shaded canopy, sunlit background, shaded background) were calculated by linear spectral mixture model and used to derive CC. Results showed that CC can be fitted best with sum of fractions of sunlit canopy and shaded canopy at S-shaped curve and the up-scaling endmember extraction method is better than traditional image-based endmember extraction method. Finally, the up-scaling endmember extraction method was used to map forest CC in Anshan forested region. The measured forest CC distribution map was used to validate the estimated map. Results show that the estimated CC and measured CC have little difference and the estimated CC is slightly lower. The majority of Anshan forest CC values were between 0.4 and 0.8.

通讯作者地址: Cao, CX (通讯作者)

Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100049, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100049, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100049, Peoples R China

[ 3 ] State Forestry Adm, Acad Forest Inventory & Planning, Beijing 100013, Peoples R China

[ 4 ] China Elect Technol Grp Corp, Res Inst, Hefei 230026, Peoples R China