Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning
In the Hotan region of Xinjiang, where arable land is scarce, an agroforestry system integrating walnut trees with crops has been implemented to maximize land-use efficiency. While this dense planting enhances land use, it also limits the availability of light to the understory crops, potentially im...
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Elsevier
2024-12-01
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| Series: | Smart Agricultural Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375524002466 |
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| author | Wenqi Kou Zhanfeng Shen Yihan Zhang Haoyu Wang Pengfei Ji Lan Huang Chi Zhang Yubo Ma |
| author_facet | Wenqi Kou Zhanfeng Shen Yihan Zhang Haoyu Wang Pengfei Ji Lan Huang Chi Zhang Yubo Ma |
| author_sort | Wenqi Kou |
| collection | DOAJ |
| description | In the Hotan region of Xinjiang, where arable land is scarce, an agroforestry system integrating walnut trees with crops has been implemented to maximize land-use efficiency. While this dense planting enhances land use, it also limits the availability of light to the understory crops, potentially impacting their yield and quality. To address this issue and enhance the system's sustainability and productivity, precise delineation of the planting structure is critical. This study proposes a novel framework that leverages multi-source remote sensing data combined with advanced deep learning techniques to analyze the agroforestry planting structure. The methodological approach consists of three key phases. First, an instance segmentation model was employed to extract farmland parcels from high-resolution imagery, providing a basis for vegetation classification. Next, a time series model using irregular satellite image time series (irSITS) tracked the growth dynamics of the vegetation. Finally, the spatial planting structure of the walnut trees was quantified using the d-LinkNet model, integrated with a template filling algorithm.The results demonstrated a classification accuracy of 97.85 % in extracting parcel-level planting structures, identifying 42,955 farmland parcels, including 21,153 intercropped parcels. The temporal and spatial characteristics of the agroforestry system were then analyzed, leading to a grading of canopy cover and walnut tree density within the intercropped areas. This comprehensive spatiotemporal planting structure offers a valuable foundation for informed local agricultural policy adjustments. In conclusion, this approach advances the understanding of complex agroforestry systems and provides a robust scientific basis for optimizing intercropping practices, contributing to sustainable agricultural development in arid regions. |
| format | Article |
| id | doaj-art-b8d78f7bce4c4d938a833486aef0833b |
| institution | OA Journals |
| issn | 2772-3755 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj-art-b8d78f7bce4c4d938a833486aef0833b2025-08-20T01:59:35ZengElsevierSmart Agricultural Technology2772-37552024-12-01910064110.1016/j.atech.2024.100641Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learningWenqi Kou0Zhanfeng Shen1Yihan Zhang2Haoyu Wang3Pengfei Ji4Lan Huang5Chi Zhang6Yubo Ma7National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Corresponding author.National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaNational Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, ChinaIn the Hotan region of Xinjiang, where arable land is scarce, an agroforestry system integrating walnut trees with crops has been implemented to maximize land-use efficiency. While this dense planting enhances land use, it also limits the availability of light to the understory crops, potentially impacting their yield and quality. To address this issue and enhance the system's sustainability and productivity, precise delineation of the planting structure is critical. This study proposes a novel framework that leverages multi-source remote sensing data combined with advanced deep learning techniques to analyze the agroforestry planting structure. The methodological approach consists of three key phases. First, an instance segmentation model was employed to extract farmland parcels from high-resolution imagery, providing a basis for vegetation classification. Next, a time series model using irregular satellite image time series (irSITS) tracked the growth dynamics of the vegetation. Finally, the spatial planting structure of the walnut trees was quantified using the d-LinkNet model, integrated with a template filling algorithm.The results demonstrated a classification accuracy of 97.85 % in extracting parcel-level planting structures, identifying 42,955 farmland parcels, including 21,153 intercropped parcels. The temporal and spatial characteristics of the agroforestry system were then analyzed, leading to a grading of canopy cover and walnut tree density within the intercropped areas. This comprehensive spatiotemporal planting structure offers a valuable foundation for informed local agricultural policy adjustments. In conclusion, this approach advances the understanding of complex agroforestry systems and provides a robust scientific basis for optimizing intercropping practices, contributing to sustainable agricultural development in arid regions.http://www.sciencedirect.com/science/article/pii/S2772375524002466Agroforestry systemsDeep learningRemote sensingPlanting structureFarmland parcels |
| spellingShingle | Wenqi Kou Zhanfeng Shen Yihan Zhang Haoyu Wang Pengfei Ji Lan Huang Chi Zhang Yubo Ma Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning Smart Agricultural Technology Agroforestry systems Deep learning Remote sensing Planting structure Farmland parcels |
| title | Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning |
| title_full | Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning |
| title_fullStr | Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning |
| title_full_unstemmed | Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning |
| title_short | Spatio-temporal analysis of agroforestry systems in hotan using multi-source remote sensing and deep learning |
| title_sort | spatio temporal analysis of agroforestry systems in hotan using multi source remote sensing and deep learning |
| topic | Agroforestry systems Deep learning Remote sensing Planting structure Farmland parcels |
| url | http://www.sciencedirect.com/science/article/pii/S2772375524002466 |
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