Mapping regional cropping patterns by using GF-1 WFV sensor data

doi:10.1016/S2095-3119(16)61392-8

Journal of Integrative Agriculture ›› 2017, Vol. 16 ›› Issue (02): 337-347.DOI: 10.1016/S2095-3119(16)61392-8

收稿日期:2015-10-20 出版日期:2017-02-20 发布日期:2017-01-31

Mapping regional cropping patterns by using GF-1 WFV sensor data

SONG Qian, ZHOU Qing-bo, WU Wen-bin, HU Qiong, LU Miao, LIU Shu-bin

¹Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China ²Remote Sensing Technology Center, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, P.R.China

Received:2015-10-20 Online:2017-02-20 Published:2017-01-31
Contact: ZHOU Qing-bo, Tel: +86-10-82106237, E-mail: zhouqingbo@caas.cn
About author:SONG Qian, Mobile: +86-18504512350, E-mail: songqian01@caas.cn
Supported by:
This research was financially supported by the Opening Foundation of the Key Laboratory of Agricultural Information Technology, Ministry of Agriculture, China (2014009), the Non-Profit Research Grant of the National Administration of Surveying, Mapping and Geoinformation of China (201512028), the National Natural Science Foundation of China (41271112) and the Youth Foundation of Heilongjiang Academy of Agricultural Science, China (QN024).

摘要/Abstract

Abstract: The successful launched Gaofen satellite no. 1 wide field-of-view (GF-1 WFV) camera is characterized by its high spatial resolution and may provide some potential for regional crop mapping. This study, taking the Bei’an City, Northeast China as the study area, aims to investigate the potential of GF-1 WFV images for crop identification and explore how to fully use its spectral, textural and temporal information to improve classification accuracy. In doing so, an object-based and Random Forest (RF) algorithm was used for crop mapping. The results showed that classification based on an optimized single temporal GF-1 image can achieve an overall accuracy of about 83%, and the addition of textural features can improve the accuracy by 8.14%. Moreover, the multi-temporal GF-1 data can produce a classification map of crops with an overall accuracy of 93.08% and the introduction of textural variables into multi-temporal GF-1 data can only increase the accuracy by about 1%, which suggests the importance of temporal information of GF-1 for crop mapping in comparison with single temporal data. By comparing classification results of GF-1 data with different feature inputs, it is concluded that GF-1 WFV data in general can meet the mapping efficiency and accuracy requirements of regional crop. But given the unique spectral characteristics of the GF-1 WFV imagery, the use of textual and temporal information is needed to yield a satisfactory accuracy.

Key words: crop mapping, GF-1, object-based, Random Forest

SONG Qian, ZHOU Qing-bo, WU Wen-bin, HU Qiong, LU Miao, LIU Shu-bin. [J]. Journal of Integrative Agriculture, 2017, 16(02): 337-347.

SONG Qian, ZHOU Qing-bo, WU Wen-bin, HU Qiong, LU Miao, LIU Shu-bin. Mapping regional cropping patterns by using GF-1 WFV sensor data[J]. Journal of Integrative Agriculture, 2017, 16(02): 337-347.

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Mapping regional cropping patterns by using GF-1 WFV sensor data

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