Forest/non-forest mapping using ENVISAT ASAR data in Northeast China - Details

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author：

Huang, Y. (Huang, Y..) ^[1] | Ling, F. (Ling, F..) ^[2] | Wu, B. (Wu, B..) ^[3] | Bai, L. (Bai, L..) ^[4] | Tian, X. (Tian, X..) ^[5]

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Scopus

Abstract：

Large　scale　forest　mapping　and　change　detection　plays　a　significant　role　in　the　study　of　global　change,　particularly　in　the　research　of　carbon　source　and　sink.　This　paper　presents　results　from　forest/non-forest　classification　using　ENVISAT-ASAR　data.　Both　pixel-based　and　object-based　classification　method　were　developed　for　ASAR　HH/HV　images　acquired　on　a　single　date.　For　the　object-based　classification,　two　different　strategies　were　proposed:　rule-set　and　threshold-ratio.　Using　as　reference　a　land　use　map　derived　from　Landsat　TM　images　acquired　in　2000,　the　accuracy　of　the　forest/non-forest　map　from　ASAR　AP　data　has　been　found　to　meet　the　requirements　of　mapping　the　Northeast　Chinese　forests　at　large　scale.　©　2011　IEEE.

Keyword：

classification; forest; object-based; SAR; segmentation

Community：

[ 1 ] [Huang, Y.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350002, China
[ 2 ] [Ling, F.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350002, China
[ 3 ] [Wu, B.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350002, China
[ 4 ] [Bai, L.]Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China
[ 5 ] [Tian, X.]Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing, 100091, China

Reprint 's Address：

[Huang, Y.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350002, China

Email：

n095520003@fzu.edu.cn

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Source ：

ICSDM 2011 - Proceedings 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services

Year： 2011

Page： 381-385

Language： English

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ESI Highly Cited Papers on the List： 0 Unfold All

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