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A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon
Lu, Dengsheng1,2; Li, Guiying2; Moran, Emilio2; Kuang, Wenhui3
2014-11-02
Source PublicationGISCIENCE & REMOTE SENSING
Volume51Issue:6Pages:695-709
AbstractResearch on separation of successional stages has been an active topic for the past two decades because successional vegetation plays an important role in the carbon budget and restoration of soil fertility in the Brazilian Amazon. This article examines classification of successional stages by conducting a comparative analysis of classification algorithms (maximum likelihood classifier - MLC, artificial neural network - ANN, K-nearest neighbour - KNN, support vector machine - SVM, classification tree analysis - CTA, and object-based classification - OBC) on varying remote-sensing data-sets (Landsat and ALOS PALSAR). Through this research we obtained the following four major conclusions: (1) Landsat data provide higher classification accuracy than ALOS PALSAR data, and individual PALSAR data cannot effectively separate successional stages; (2) Fusion of Landsat and PALSAR data provides better classification than individual sensor data; (3) Depending on the data-set, the best classification algorithm varies, MLC and CTA are recommended for Landsat or fusion images; and KNN is recommended for the combination of Landsat and PALSAR data as extra bands; (4) the MLC based on fusion images is recommended for vegetation classification in the moist tropical region when sufficiently representative training samples are available.
SubtypeArticle
KeywordBrazilian Amazon Nonparametric Classification Algorithms Successional Vegetation Alos Palsar Landsat
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
WOS Subject ExtendedPhysical Geography ; Remote Sensing
WOS KeywordLAND-COVER CLASSIFICATION ; REGENERATING TROPICAL FOREST ; OBJECT-BASED CLASSIFICATION ; THEMATIC MAPPER IMAGERY ; REMOTELY-SENSED DATA ; SECONDARY FORESTS ; SPATIAL-RESOLUTION ; EASTERN AMAZONIA ; SENSING DATA ; TM DATA
Indexed BySCI
Language英语
WOS SubjectGeography, Physical ; Remote Sensing
WOS IDWOS:000346292400006
PublisherTAYLOR & FRANCIS LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/68436
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLu, Dengsheng
Affiliation1.Zhejiang A&F Univ, Sch Environm & Resource Sci, Zhejiang Prov Key Lab Carbon Cycling Forest Ecosy, Hangzhou 311300, Zhejiang, Peoples R China
2.Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 USA
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Lu, Dengsheng,Li, Guiying,Moran, Emilio,et al. A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon[J]. GISCIENCE & REMOTE SENSING,2014,51(6):695-709.
APA Lu, Dengsheng,Li, Guiying,Moran, Emilio,&Kuang, Wenhui.(2014).A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon.GISCIENCE & REMOTE SENSING,51(6),695-709.
MLA Lu, Dengsheng,et al."A comparative analysis of approaches for successional vegetation classification in the Brazilian Amazon".GISCIENCE & REMOTE SENSING 51.6(2014):695-709.
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