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Frontiers of Earth Science

Front. Earth Sci.    2019, Vol. 13 Issue (2) : 327-335     https://doi.org/10.1007/s11707-018-0734-8
RESEARCH ARTICLE |
Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain
Kun JIA1,2, Jingcan LIU1,2, Yixuan TU1,2, Qiangzi LI3(), Zhiwei SUN4, Xiangqin WEI3, Yunjun YAO1,2, Xiaotong ZHANG1,2
1. State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences, Beijing 100875, China
2. Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
4. Beijing Geoway Times Software Technology Co., Ltd., Beijing 100043, China
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Abstract

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.

Keywords land use and land cover      classification      GF-2      North China Plain      multispectral data     
Corresponding Authors: Qiangzi LI   
Just Accepted Date: 29 November 2018   Online First Date: 25 December 2018    Issue Date: 16 May 2019
 Cite this article:   
Kun JIA,Jingcan LIU,Yixuan TU, et al. Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain[J]. Front. Earth Sci., 2019, 13(2): 327-335.
 URL:  
http://journal.hep.com.cn/fesci/EN/10.1007/s11707-018-0734-8
http://journal.hep.com.cn/fesci/EN/Y2019/V13/I2/327
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Kun JIA
Jingcan LIU
Yixuan TU
Qiangzi LI
Zhiwei SUN
Xiangqin WEI
Yunjun YAO
Xiaotong ZHANG
Payloads Bands No. Spectral range/mm Spatial resolution/m Swath width/km Repetition cycle/day Local time of descending node
Multispectral 1 0.45–0.52 4 45 (two cameras combined) 5 (when sideway) 10:30 AM
2 0.52–0.59
3 0.63–0.69
4 0.77–0.89
Tab.1  The main technical specifications of GF-2 multispectral cameras
Fig.1  The geographical location of the study area in the North China Plain. The image is the standard false color composited image (R: NIR, G: red, B: green) of GF-2 multispectral data acquired on April 5, 2016.
Fig.2  Pixel-based and object-based LULC classification results of the GF-2 multispectral data using MLC and SVM classifiers, respectively (P-MLC: pixel-based classification using the MLC classifier, P-SVM: pixel-based classification using the SVM classifier, O-MLC: object-based classification using the MLC classifier, O-SVM: object-based classification using the SVM classifier).
Fig.3  A typical enlarged region (centered at 37°8′46″N, 117°24′6″E) to show the differences in the LULC classification results from different classification strategies (P-MLC: pixel-based classification using the MLC classifier, P-SVM: pixel-based classification using the SVM classifier, O-MLC: object-based classification using the MLC classifier, O-SVM: object-based classification using the SVM classifier).
Mapped class Ground truth/pixels User accuracy
Winter wheat Woodland Water Artificial surface Vegetables Total
Winter wheat 2372 46 0 1 439 2858 83.00%
Woodland 48 4003 29 171 60 4311 92.86%
Water 0 0 23,681 557 0 24,238 97.70%
Artificial surface 1 17 3452 10320 41 13,831 74.61%
Vegetables 320 714 0 767 2932 4733 61.95%
Total 2741 4780 27,162 11,816 3472 49,971
Producer accuracy 86.54% 83.74% 87.18% 87.34% 84.45%
Tab.2  Confusion matrix for LULC classification of the GF-2 multispectral data using the pixel-based classification strategy and the MLC classifier. (OA: 86.67%, kappa coefficient: 0.796)
Mapped class Ground truth/pixels User accuracy
Winter wheat Woodland Water Artificial surface Vegetables Total
Winter wheat 2444 57 0 26 445 2972 82.23%
Woodland 32 4646 494 300 35 5507 84.37%
Water 0 0 23,989 303 0 24,292 98.75%
Artificial surface 0 5 2679 10,606 51 13,341 79.50%
Vegetables 265 72 0 581 2941 3859 76.21%
Total 2741 4780 27,162 11,816 3472 49,971
Producer accuracy 89.16% 97.20% 88.32% 89.76% 84.71%
Tab.3  Confusion matrix for LULC classification of the GF-2 multispectral data using the pixel-based classification strategy and the SVM classifier (OA: 89.30%, kappa coefficient: 0.836)
Mapped class Ground truth/pixels User accuracy
Winter wheat Woodland Water Artificial surface Vegetables Total
Winter wheat 2357 0 0 0 488 2845 82.85%
Woodland 0 4352 157 26 0 4535 95.96%
Water 0 34 24,922 137 0 25,093 99.32%
Artificial surface 57 0 2083 11,163 20 13,323 83.79%
Vegetables 327 394 0 490 2964 4175 70.99%
Total 2741 4780 27,162 11,816 3472 49,971
Producer accuracy 85.99% 91.05% 91.75% 94.47% 85.37%
Tab.4  Confusion matrix for LULC classification of the GF-2 multispectral data using the object-based classification strategy and the MLC classifier (OA: 91.57%, kappa coefficient: 0.870)
Mapped class Ground truth/pixels User accuracy
Winter wheat Woodland Water Artificial surface Vegetables Total
Winter wheat 2561 0 0 0 435 2996 85.48%
Woodland 6 4748 222 34 0 5010 94.77%
Water 0 0 25,689 330 0 6706 98.73%
Artificial surface 0 0 1251 11,122 20 12,393 89.74%
Vegetables 174 32 0 330 3017 3553 84.91%
Total 2741 4780 27,162 11,816 3472 49,971
Producer accuracy 93.43% 99.33% 94.58% 94.13% 86.90%
Tab.5  Confusion matrix for LULC classification of the GF-2 multispectral data using the object-based classification strategy and the SVM classifier (OA: 94.33%, kappa coefficient: 0.911)
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