期刊
GISCIENCE & REMOTE SENSING
卷 51, 期 6, 页码 695-709出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/15481603.2014.983338
关键词
Brazilian Amazon; nonparametric classification algorithms; successional vegetation; ALOS PALSAR; Landsat
资金
- Zhejiang A&F University's Research and Development Fund for the talent startup project [2013FR052]
- Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration at Zhejiang AF University
- Center for Global Change and Earth Observations at Michigan State University
- National High Technology Research and Development Program of China [2013AA122802]
- National Basic Research Program of China [2010CB950900]
Research 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.
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