Improving crop rotation classification using a random forest model incorporating spatial heterogeneity
Accurate and timely classification of crop rotations is essential to confront the issues of agricultural management and food crisis. Crop growth conditions generally exhibit a strong spatial heterogeneity pattern, resulting in crop growth characteristics varying with locations, limiting the classifi...
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Taylor & Francis Group
2024-01-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2024.2384473 |
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| author | Xiaomi Wang Qi Tang Kang Yang |
| author_facet | Xiaomi Wang Qi Tang Kang Yang |
| author_sort | Xiaomi Wang |
| collection | DOAJ |
| description | Accurate and timely classification of crop rotations is essential to confront the issues of agricultural management and food crisis. Crop growth conditions generally exhibit a strong spatial heterogeneity pattern, resulting in crop growth characteristics varying with locations, limiting the classification accuracy of crop rotation. To overcome this limitation, an improved method named random forest based on rotation zoning strategy (RF_RZS) that classifies crop rotations under the consideration of spatial heterogeneity is proposed. In RF_RZS, the regionalization with dynamically constrained agglomerative clustering and partitioning method is used to adaptively mine the spatial homogeneous subzones of soil organic carbon (SOC), which represent the spatial heterogeneity pattern of crop rotation given the highly correlated relationship between crop rotation and SOC. The Boruta algorithm is employed to select the optimal feature subset within each subzone. A random forest classification method is applied to categorize crop rotations within the subzones. Two integrated indexes, OA_OZ and Kc_OZ, are proposed to evaluate the comprehensive performance of RF_RZS. Furthermore, a series of traditional methods is employed for comparison and evaluation. Results demonstrate that the OA_OZ and Kc_OZ of the RF_RZS method are 0.90 and 0.88, respectively, which are increased by 3–23% and 3–27%, respectively, compared with those of traditional classification methods. This research involves mixed cropping pattern of multiple rotational crops, and the proposed methodology can provide an effective guide for the classification of rotational crops with a fragmented distribution and a complex cropping structure. |
| format | Article |
| id | doaj-art-5da02e8a49ba4ebc8ae299c920c972fe |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-5da02e8a49ba4ebc8ae299c920c972fe2025-08-20T01:59:21ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2384473Improving crop rotation classification using a random forest model incorporating spatial heterogeneityXiaomi Wang0Qi Tang1Kang Yang2School of Geographical Sciences, Hunan Normal University, Changsha, ChinaSchool of Geographical Sciences, Hunan Normal University, Changsha, ChinaTechnical Department, Guizhou Zhiyuan Engineering Technology Consulting Co., Ltd, Guiyang, ChinaAccurate and timely classification of crop rotations is essential to confront the issues of agricultural management and food crisis. Crop growth conditions generally exhibit a strong spatial heterogeneity pattern, resulting in crop growth characteristics varying with locations, limiting the classification accuracy of crop rotation. To overcome this limitation, an improved method named random forest based on rotation zoning strategy (RF_RZS) that classifies crop rotations under the consideration of spatial heterogeneity is proposed. In RF_RZS, the regionalization with dynamically constrained agglomerative clustering and partitioning method is used to adaptively mine the spatial homogeneous subzones of soil organic carbon (SOC), which represent the spatial heterogeneity pattern of crop rotation given the highly correlated relationship between crop rotation and SOC. The Boruta algorithm is employed to select the optimal feature subset within each subzone. A random forest classification method is applied to categorize crop rotations within the subzones. Two integrated indexes, OA_OZ and Kc_OZ, are proposed to evaluate the comprehensive performance of RF_RZS. Furthermore, a series of traditional methods is employed for comparison and evaluation. Results demonstrate that the OA_OZ and Kc_OZ of the RF_RZS method are 0.90 and 0.88, respectively, which are increased by 3–23% and 3–27%, respectively, compared with those of traditional classification methods. This research involves mixed cropping pattern of multiple rotational crops, and the proposed methodology can provide an effective guide for the classification of rotational crops with a fragmented distribution and a complex cropping structure.https://www.tandfonline.com/doi/10.1080/10106049.2024.2384473Classification methodcrop rotationspatial heterogeneityfeature selection |
| spellingShingle | Xiaomi Wang Qi Tang Kang Yang Improving crop rotation classification using a random forest model incorporating spatial heterogeneity Geocarto International Classification method crop rotation spatial heterogeneity feature selection |
| title | Improving crop rotation classification using a random forest model incorporating spatial heterogeneity |
| title_full | Improving crop rotation classification using a random forest model incorporating spatial heterogeneity |
| title_fullStr | Improving crop rotation classification using a random forest model incorporating spatial heterogeneity |
| title_full_unstemmed | Improving crop rotation classification using a random forest model incorporating spatial heterogeneity |
| title_short | Improving crop rotation classification using a random forest model incorporating spatial heterogeneity |
| title_sort | improving crop rotation classification using a random forest model incorporating spatial heterogeneity |
| topic | Classification method crop rotation spatial heterogeneity feature selection |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2384473 |
| work_keys_str_mv | AT xiaomiwang improvingcroprotationclassificationusingarandomforestmodelincorporatingspatialheterogeneity AT qitang improvingcroprotationclassificationusingarandomforestmodelincorporatingspatialheterogeneity AT kangyang improvingcroprotationclassificationusingarandomforestmodelincorporatingspatialheterogeneity |