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Geo-parcel-based geographical thematic mapping using C5.0 decision tree: a case study of evaluating sugarcane planting suitability

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Abstract

Geographical thematic mapping based on spatial information can effectively support scientific decision-making in Geosciences. To obtain finer spatial decision information, this paper proposes a geo-parcel-based thematic mapping methodology for evaluating cash crop planting suitability using C5.0 decision tree (DT). In this study, geo-parcels are utilized as basic mapping units. Multi-source data are firstly employed to increase geo-parcel units’ attributes and a decision table then is constructed under a multi-attribute index system. Next, rules are mined using a C5.0 DT algorithm according to local geo-parcels in this decision table. Finally, rules are referred as thematic-distinguishing knowledge for inferential mapping in global geo-parcels. A case study of sugarcane planting suitability evaluation is conduct based on the proposed methodology. The experimental results showed that the cross-validation accuracy of the rules is 81.34% and the sum of the very suitable area and suitable area in the generated evaluation map is close to that of historical selected high-yield and high-sugar-content sugarcane bases, which indicated that the mapping result is in good agreement with the actual selection situation. These also demonstrate the effectiveness of our method and thus may be extended to other domains requiring fine geographical thematic mapping of cash crop planting suitability.

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Acknowledgements

This work was partially funded by the National Natural Science Foundation of China (Grant No: 41631179, 41601437); National Key Research and Development Program (Grant No: 2017YFB0503600); Natural Science Basic Research Plan in Shaanxi Province of China (Grant No: 2017JQ4002); Open Projects of Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University (Grant No: 2018LSDMIS03), and State Key Laboratory of Geo-information Engineering (No. SKLGIE2017-Z-4-3); Special Fund for Basic Scientific Research of Central Colleges in Chang’an University (Grant No: 310812163504).

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Authors and Affiliations

  1. Department of Mathematics and Information Science, College of Science, Chang’an University, Xi’an, 710064, People’s Republic of China

    Tianjun Wu

  2. Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350002, People’s Republic of China

    Tianjun Wu

  3. State Key Laboratory of Geo-information Engineering, Xi’an, 710054, People’s Republic of China

    Tianjun Wu

  4. Key Laboratory of Oceanographic Big Data Mining & Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan, 316022, People’s Republic of China

    Tianjun Wu & Weizhi Wu

  5. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Wen Dong, Jiancheng Luo, Yingwei Sun & Xiaodong Hu

  6. University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Wen Dong, Jiancheng Luo, Yingwei Sun & Xiaodong Hu

  7. Agricultural Science and Technology Information Research Institute, Guangxi Academy of Agricultural Sciences, Nanning, 530007, People’s Republic of China

    Qiting Huang

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  1. Tianjun Wu
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  3. Jiancheng Luo
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Correspondence to Jiancheng Luo.

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Communicated by: Hassan Babaie

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Wu, T., Dong, W., Luo, J. et al. Geo-parcel-based geographical thematic mapping using C5.0 decision tree: a case study of evaluating sugarcane planting suitability. Earth Sci Inform 12, 57–70 (2019). https://doi.org/10.1007/s12145-018-0360-8

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