Identification of cropland in Tibetan Plateau based on time series remote sensing features
Cropland is crucial for regional food security, especially in vulnerable areas like the Tibetan Plateau. Accurate monitoring was hindered of cropland distribution due to complex topography and diverse crop phenology, making it challenging to assess its agricultural sustainability. To address this, t...
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| Format: | Article |
| Language: | English |
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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/10.1080/10106049.2024.2375583 |
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| author | Xin Du Qiangzi Li Longcai Zhao Yunqi Shen Sichen Zhang Yuan Zhang Hongyan Wang Jingyuan Xu |
| author_facet | Xin Du Qiangzi Li Longcai Zhao Yunqi Shen Sichen Zhang Yuan Zhang Hongyan Wang Jingyuan Xu |
| author_sort | Xin Du |
| collection | DOAJ |
| description | Cropland is crucial for regional food security, especially in vulnerable areas like the Tibetan Plateau. Accurate monitoring was hindered of cropland distribution due to complex topography and diverse crop phenology, making it challenging to assess its agricultural sustainability. To address this, this study aimed to develop a cropland identification approach based on an optimal identification feature knowledge graph (OIFKG) derived from time series remote sensing data. Cropland OIFKG (C_OIFKG) enhanced cropland identification accuracy by 96.6%, with producer’s accuracy and user’s accuracy of 98.1% and 89.9% respectively for cropland. The total cropland area in the Tibetan Plateau for 2022 was estimated at 1,800,160 hectares, representing about 1% of the total land area, with a significant concentration in the northeastern Qinghai province and the Yarlung Zangbo River Valley of Tibet Autonomous Region. The total cropland area estimated in this study for the Tibetan Plateau lied within the range provided by two published land cover datasets, being 3.56% lower than one dataset and 16.4% higher than the other. The cropland identification approach proposed by this study reduced reliance on known samples, improving spatiotemporal generalization capability. In the Tibetan Plateau, where cropland distribution was exceedingly rare, the method still achieved promising performance in cropland identification, demonstrating its effectiveness on the assessment of agriculture sustainability in high-altitude regions with intricate landscapes. Moreover, further assessment of C_OIFKG's applicability in different regions and compatibility with multi-source remote sensing data is needed. |
| format | Article |
| id | doaj-art-65f8a8cc08b248fc8455e1b7b971cee1 |
| 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-65f8a8cc08b248fc8455e1b7b971cee12025-08-20T02:22:09ZengTaylor & Francis GroupGeocarto International1010-60491752-07622024-01-0139110.1080/10106049.2024.2375583Identification of cropland in Tibetan Plateau based on time series remote sensing featuresXin Du0Qiangzi Li1Longcai Zhao2Yunqi Shen3Sichen Zhang4Yuan Zhang5Hongyan Wang6Jingyuan Xu7Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCollege of Resources and Environment, Northwest A&F University, Yangling, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing, ChinaCropland is crucial for regional food security, especially in vulnerable areas like the Tibetan Plateau. Accurate monitoring was hindered of cropland distribution due to complex topography and diverse crop phenology, making it challenging to assess its agricultural sustainability. To address this, this study aimed to develop a cropland identification approach based on an optimal identification feature knowledge graph (OIFKG) derived from time series remote sensing data. Cropland OIFKG (C_OIFKG) enhanced cropland identification accuracy by 96.6%, with producer’s accuracy and user’s accuracy of 98.1% and 89.9% respectively for cropland. The total cropland area in the Tibetan Plateau for 2022 was estimated at 1,800,160 hectares, representing about 1% of the total land area, with a significant concentration in the northeastern Qinghai province and the Yarlung Zangbo River Valley of Tibet Autonomous Region. The total cropland area estimated in this study for the Tibetan Plateau lied within the range provided by two published land cover datasets, being 3.56% lower than one dataset and 16.4% higher than the other. The cropland identification approach proposed by this study reduced reliance on known samples, improving spatiotemporal generalization capability. In the Tibetan Plateau, where cropland distribution was exceedingly rare, the method still achieved promising performance in cropland identification, demonstrating its effectiveness on the assessment of agriculture sustainability in high-altitude regions with intricate landscapes. Moreover, further assessment of C_OIFKG's applicability in different regions and compatibility with multi-source remote sensing data is needed.https://www.tandfonline.com/doi/10.1080/10106049.2024.2375583Automated cropland mappingoptimal identification featureknowledge graphremote sensingTibetan Plateau |
| spellingShingle | Xin Du Qiangzi Li Longcai Zhao Yunqi Shen Sichen Zhang Yuan Zhang Hongyan Wang Jingyuan Xu Identification of cropland in Tibetan Plateau based on time series remote sensing features Geocarto International Automated cropland mapping optimal identification feature knowledge graph remote sensing Tibetan Plateau |
| title | Identification of cropland in Tibetan Plateau based on time series remote sensing features |
| title_full | Identification of cropland in Tibetan Plateau based on time series remote sensing features |
| title_fullStr | Identification of cropland in Tibetan Plateau based on time series remote sensing features |
| title_full_unstemmed | Identification of cropland in Tibetan Plateau based on time series remote sensing features |
| title_short | Identification of cropland in Tibetan Plateau based on time series remote sensing features |
| title_sort | identification of cropland in tibetan plateau based on time series remote sensing features |
| topic | Automated cropland mapping optimal identification feature knowledge graph remote sensing Tibetan Plateau |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2024.2375583 |
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