Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China
At the end of the last century, the expansion of agricultural land in the arid and semi-arid regions of northern China intensified the conflict between agricultural development and ecological protection. Accurately mapping abandoned cropland is crucial for balancing these competing interests. This r...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000469 |
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| author | Deji Wuyun Liang Sun Zhongxin Chen Luís Guilherme Teixeira Crusiol Jinwei Dong Nitu Wu Junwei Bao Ruiqing Chen Zheng Sun Hasituya Hongwei Zhao |
| author_facet | Deji Wuyun Liang Sun Zhongxin Chen Luís Guilherme Teixeira Crusiol Jinwei Dong Nitu Wu Junwei Bao Ruiqing Chen Zheng Sun Hasituya Hongwei Zhao |
| author_sort | Deji Wuyun |
| collection | DOAJ |
| description | At the end of the last century, the expansion of agricultural land in the arid and semi-arid regions of northern China intensified the conflict between agricultural development and ecological protection. Accurately mapping abandoned cropland is crucial for balancing these competing interests. This research evaluates the effectiveness of an innovative remote sensing method for producing 30-meter-resolution long-term maps of abandoned and reclaimed croplands in Inner Mongolia, China, using a temporal segmentation approach developed with Google Earth Engine. The method integrates ground sample collection of major crops and inactive cropland with Normalized Difference Vegetation Index (NDVI) analysis during key growth stages, enabling precise classification of cultivation status. By employing a binary classification strategy and adaptive optimization, the efficiency of sample generation improved, providing more effective samples for the Random Forest algorithm. Cropland status maps were successfully generated for Inner Mongolia from 2000 to 2022 with annual accuracy between 97% and 99%. The Temporal Segmentation of Abandoned and Reclaimed Cropland (TSARC) method created time series maps of abandoned and reclaimed cropland at a 30-meter resolution, achieving an overall accuracy of 87.61%. The proposed remote sensing methodology reveals spatiotemporal trends in abandonment rates across arid and semi-humid regions, offering valuable insights for agricultural and environmental management in Inner Mongolia. Considering regional climatic, hydrological, and phenological conditions improves sample collection efficiency and cropland status monitoring. While designed for northern China, this method is also applicable to other single-season agricultural regions for varied agricultural land use monitoring. |
| format | Article |
| id | doaj-art-8abab0475b7f47d2b279e21039866a0c |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-8abab0475b7f47d2b279e21039866a0c2025-02-07T04:47:20ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104399Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, ChinaDeji Wuyun0Liang Sun1Zhongxin Chen2Luís Guilherme Teixeira Crusiol3Jinwei Dong4Nitu Wu5Junwei Bao6Ruiqing Chen7Zheng Sun8 Hasituya9Hongwei Zhao10State Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaState Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China; Corresponding author.Digitization and Informatics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyEmbrapa Soja (National Soybean Research Center – Brazilian Agricultural Research Corporation), Londrina, BrazilKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaCollege of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot, ChinaInstitute of Rural Economic and Information, Inner Mongolia Academy of Agricultural & Animal Husbandry Science, Hohhot, ChinaState Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaState Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaDigitization and Informatics Division, Food and Agriculture Organization of the United Nations, Rome, Italy; College of Grassland, Resources and Environment, Inner Mongolia Agricultural University, Hohhot, ChinaState Key Laboratory of Efficient Utilization of Arable Land in China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, ChinaAt the end of the last century, the expansion of agricultural land in the arid and semi-arid regions of northern China intensified the conflict between agricultural development and ecological protection. Accurately mapping abandoned cropland is crucial for balancing these competing interests. This research evaluates the effectiveness of an innovative remote sensing method for producing 30-meter-resolution long-term maps of abandoned and reclaimed croplands in Inner Mongolia, China, using a temporal segmentation approach developed with Google Earth Engine. The method integrates ground sample collection of major crops and inactive cropland with Normalized Difference Vegetation Index (NDVI) analysis during key growth stages, enabling precise classification of cultivation status. By employing a binary classification strategy and adaptive optimization, the efficiency of sample generation improved, providing more effective samples for the Random Forest algorithm. Cropland status maps were successfully generated for Inner Mongolia from 2000 to 2022 with annual accuracy between 97% and 99%. The Temporal Segmentation of Abandoned and Reclaimed Cropland (TSARC) method created time series maps of abandoned and reclaimed cropland at a 30-meter resolution, achieving an overall accuracy of 87.61%. The proposed remote sensing methodology reveals spatiotemporal trends in abandonment rates across arid and semi-humid regions, offering valuable insights for agricultural and environmental management in Inner Mongolia. Considering regional climatic, hydrological, and phenological conditions improves sample collection efficiency and cropland status monitoring. While designed for northern China, this method is also applicable to other single-season agricultural regions for varied agricultural land use monitoring.http://www.sciencedirect.com/science/article/pii/S1569843225000469Abandoned croplandAgricultural natural zoneGoogle Earth EngineSample adaptive optimizationAgricultural sustainability |
| spellingShingle | Deji Wuyun Liang Sun Zhongxin Chen Luís Guilherme Teixeira Crusiol Jinwei Dong Nitu Wu Junwei Bao Ruiqing Chen Zheng Sun Hasituya Hongwei Zhao Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China International Journal of Applied Earth Observations and Geoinformation Abandoned cropland Agricultural natural zone Google Earth Engine Sample adaptive optimization Agricultural sustainability |
| title | Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China |
| title_full | Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China |
| title_fullStr | Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China |
| title_full_unstemmed | Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China |
| title_short | Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China |
| title_sort | temporal segmentation method for 30 meter long term mapping of abandoned and reclaimed croplands in inner mongolia china |
| topic | Abandoned cropland Agricultural natural zone Google Earth Engine Sample adaptive optimization Agricultural sustainability |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000469 |
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