Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain
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The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data. This study investigated the capability and strategy of GF-2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain. The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF-2 multispectral data. The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance, and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911. Therefore, considering the LULC classification performance and data characteristics, GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring. Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.
Keywordsland use and land cover classification GF-2 North China Plain multispectral data
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The authors would like to thank the anonymous reviewers and the editor for the constructive comments and suggestions, all of which have led to great improvements in the presentation of this article. This study was financially supported by the National Natural Science Foundation of China (Grant No. 41571422) and the National Key Research and Development Program of China (No. 2016YFA0600103). Kun Jia and Qiangzi Li conceived and designed the experiments; Kun Jia and Jingcan Liu performed the experiments and drafted the manuscript; XiangqinWei, Yunjun Yao, and Xiaotong Zhang supplied valuable suggestions on improving the method; Jingcan Liu, Yixuan Tu and Zhiwei Sun were responsible for field survey and classification accuracy assessment; All authors read and revised the manuscript. The authors declare no conflict of interest.
- Baatz M, Schäpe M (2000). Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Strobl J, Blaschke T, Griesebner G, eds. Angewandte Geographische Informations-Verarbeitung XII. Karlsruhe: Wichmann Verlag, 12–23Google Scholar
- Congalton R G, Green K (1999). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Florida: Lewis PublishersGoogle Scholar
- Cooley T, Anderson G P, Felde G W, Hoke M L, Ratkowski A J, Chetwynd J H, Gardner J A, Adler-Golden SM, Matthew MW, Berk A, Bernstein L S, Acharya P K, Miller D, Lewis P (2002). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. In: 2002 IEEE International Geoscience and Remote Sensing Symposium & 24th Canadian Symposium on Remote Sensing. Toronto, Canada: IEEE PressGoogle Scholar
- Duda R O, Hart P E (1973). Pattern Classification and Scene Analysis. New York: WileyGoogle Scholar
- Foody G M, Campbell N A, Trodd N M, Wood T F (1992). Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogramm Eng Remote Sensing, 58: 1335–1341Google Scholar
- Gong P, Wang J, Yu L, Zhao Y C, Zhao Y Y, Liang L, Niu Z G, Huang X M, Fu H H, Liu S, Li C C, Li X Y, Fu W, Liu C X, Xu Y, Wang X Y, Cheng Q, Hu L Y, Yao W B, Zhang H, Zhu P, Zhao Z Y, Zhang H Y, Zheng Y M, Ji L Y, Zhang Y W, Chen H, Yan A, Guo J H, Wang L, Liu X J, Shi T T, Zhu M H, Chen Y L, Yang GW, Tang P, Xu B, Giri C, Clinton N, Zhu Z L, Chen J, Chen J (2013). Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM + data. Int J Remote Sens, 34(7): 2607–2654CrossRefGoogle Scholar