Land Use/cover Classification with Classification and Regression Tree Applied to MODIS Imagery

This study attempted to develop a low-cost, high-precision method, to acquire land use/cover data by combining multi-temporal and multi-spectral Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that, Classification and Regression Tree algorithm clearly outperforms the Maxi...

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Published inJournal of applied sciences (Asian Network for Scientific Information) Vol. 13; no. 18; p. 3770
Main Authors Liu, Jianguang, Sun, Danfeng, He, Feng, Zhang, Weiwei, Guan, Xiaoke
Format Journal Article
LanguageEnglish
Published 2013
Subjects
Algorithms
Classification
Land use
MODIS
Regression
Spectroradiometers
Time series
Trees
Pakistan, Faisalabad
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Summary:This study attempted to develop a low-cost, high-precision method, to acquire land use/cover data by combining multi-temporal and multi-spectral Moderate Resolution Imaging Spectroradiometer (MODIS). The results indicate that, Classification and Regression Tree algorithm clearly outperforms the Maximum Likelihood in land use/cover classification using MODIS, and the first principal component with multi-spectral MODIS image that reflected more soil information can efficiently improve the accuracy of classification based on MODIS NDVI time series.
Bibliography:ObjectType-Article-1
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ISSN:1812-5654
1812-5662
DOI:10.3923/jas.2013.3770.3773