Orchard identification using landform and landscape factors based on a spatial-temporal classification framework
作者:Li, Y (Li, Yi)[ 1,2 ] ; Gong, JH (Gong, Jianhua)[ 1,2 ] ; Ibrahim, AN (Ibrahim, Abdoul Nasser)[ 2 ] ; Shan, ZJ (Shan, Zhongjin)[ 3 ] ; Li, M (Li, Ming)[ 3 ] ; An, LP (An, Leping)[ 4 ] ; Ye, L (Ye, Lei)[ 1 ] ; Zhang, RP (Zhang, Ruiping)[ 3 ]
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷: 35  期: 6  页: 2118-2135
DOI: 10.1080/01431161.2014.887235
出版年: MAR 19 2014

摘要
Ecological restoration measures have been undertaken in loess hilly and gully regions since the 1970s to prevent soil loss and to improve the ecological environment in those regions. Orchard construction was the main ecological measure undertaken in the Luo-Yu-Gou watershed, and in this article we propose a coupled maximum a posteriori decision rule and Markov random field (MAP-MRF) framework for orchard identification based on landform and landscape factors. Support vector machine (SVM) classification was first performed to obtain initial classification results for the years 2003 and 2008. A series of factors including landform factor, landscape factor, and the spatial-temporal neighbourhood factor are used to obtain land-cover change information including the change in orchard class. Finally, field experiments were carried out in the case study region of the Luo-Yu-Gou watershed, and based on the experimental results, it was found that the quantity error and the allocation error of the classification results for 2008 were 0.0441 and 0.1037, respectively.

通讯作者地址: Gong, JH (通讯作者)
Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth, Beijing 100101, Peoples R China
[ 2 ] Zhejiang CAS Applicat Ctr Geoinformat, Hangzhou 314100, Zhejiang, Peoples R China
[ 3 ] Jiashan Meteorol Bur, Jiashan 314100, Zhejiang, Peoples R China
[ 4 ] YRCC, Tianshui Soil & Water Conservat Expt Stn, Qinghai 741000, Gansu, Peoples R China