Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models
Drought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water cons...
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| Format: | Article |
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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/S1569843225000354 |
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| author | Li Zhang Xiaodong Gao Shuyi Zhou Zhibo Zhang Tianjie Zhao Yaohui Cai Xining Zhao |
| author_facet | Li Zhang Xiaodong Gao Shuyi Zhou Zhibo Zhang Tianjie Zhao Yaohui Cai Xining Zhao |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | Drought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water conservation despite facing challenges such as limited rainfall and frequent extreme drought events. However, accurate identification of standing dead trees (SDTs) within plantations using remote sensing techniques remains underexplored, and the spatial distribution patterns of SDTs across the CLP are poorly understood. Therefore, this study leveraged unmanned aerial vehicle (UAV) remote sensing to capture high-resolution RGB images of Robinia pseudoacacia plantations. These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. Particularly, the YOLOv9-ECA was developed by incorporating the ECA module into key network layers to enhance channel dependency modeling and improve feature representation for SDTs detection. Its merit lies in adaptively reweighting feature channels, enabling efficient detection in resource-constrained environments. As expected, the YOLOv9-ECA model demonstrated significant advancements, achieving a detection speed of 123.5f/s, a mAP of 97.8%, and an F1 score of 0.97, outperforming other models in both detection efficiency and accuracy. Subsequently, the model was employed to quantify the spatial distribution of SDTs across the CLP by estimating the number of dead trees per unit area. Results revealed an increasing trend in the number of dead trees per unit along decreasing precipitation gradients, emphasizing the vulnerability of Robinia pseudoacacia plantations in drier regions. Additionally, the number of dead trees per unit varied with slope aspect, with sunny slopes exhibiting the highest values and shady slopes the lowest. This study highlights the potential of YOLOv9-ECA as a powerful tool for the efficient detection of SDTs, offering insights for the sustainable management of Robinia pseudoacacia plantations on the CLP and holding potential applicability to similar environments globally. |
| format | Article |
| id | doaj-art-b069d15386fc4dbdaed42544af3daf71 |
| 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-b069d15386fc4dbdaed42544af3daf712025-02-03T04:16:38ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104388Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning modelsLi Zhang0Xiaodong Gao1Shuyi Zhou2Zhibo Zhang3Tianjie Zhao4Yaohui Cai5Xining Zhao6College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100 ChinaInstitute of Soil and Water Conservation, Northwest A & F University, Yangling 712100 China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100 China; Corresponding author at: Institute of Soil and Water Conservation, Northwest A & F University, Yangling 712100, China.College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100 ChinaCollege of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100 ChinaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101 ChinaInstitute of Soil and Water Conservation, Northwest A & F University, Yangling 712100 China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100 ChinaInstitute of Soil and Water Conservation, Northwest A & F University, Yangling 712100 China; Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100 ChinaDrought-induced tree mortality has increasingly expanded worldwide under the influence of climate warming, with China’s Loess Plateau (CLP) emerging as a critical hotspot for such impacts. As one of the most active tree-planting regions globally, the CLP primarily aims to achieve soil and water conservation despite facing challenges such as limited rainfall and frequent extreme drought events. However, accurate identification of standing dead trees (SDTs) within plantations using remote sensing techniques remains underexplored, and the spatial distribution patterns of SDTs across the CLP are poorly understood. Therefore, this study leveraged unmanned aerial vehicle (UAV) remote sensing to capture high-resolution RGB images of Robinia pseudoacacia plantations. These images were then integrated with a comprehensive evaluation of multiple detection algorithms, including Faster R-CNN, EfficientDet, YOLOv4, YOLOv5, YOLOv8, YOLOv9, and a novel model, YOLOv9-ECA. Particularly, the YOLOv9-ECA was developed by incorporating the ECA module into key network layers to enhance channel dependency modeling and improve feature representation for SDTs detection. Its merit lies in adaptively reweighting feature channels, enabling efficient detection in resource-constrained environments. As expected, the YOLOv9-ECA model demonstrated significant advancements, achieving a detection speed of 123.5f/s, a mAP of 97.8%, and an F1 score of 0.97, outperforming other models in both detection efficiency and accuracy. Subsequently, the model was employed to quantify the spatial distribution of SDTs across the CLP by estimating the number of dead trees per unit area. Results revealed an increasing trend in the number of dead trees per unit along decreasing precipitation gradients, emphasizing the vulnerability of Robinia pseudoacacia plantations in drier regions. Additionally, the number of dead trees per unit varied with slope aspect, with sunny slopes exhibiting the highest values and shady slopes the lowest. This study highlights the potential of YOLOv9-ECA as a powerful tool for the efficient detection of SDTs, offering insights for the sustainable management of Robinia pseudoacacia plantations on the CLP and holding potential applicability to similar environments globally.http://www.sciencedirect.com/science/article/pii/S1569843225000354PlantationTree mortalityDroneDeep learningManagement |
| spellingShingle | Li Zhang Xiaodong Gao Shuyi Zhou Zhibo Zhang Tianjie Zhao Yaohui Cai Xining Zhao Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models International Journal of Applied Earth Observations and Geoinformation Plantation Tree mortality Drone Deep learning Management |
| title | Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models |
| title_full | Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models |
| title_fullStr | Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models |
| title_full_unstemmed | Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models |
| title_short | Identification of standing dead trees in Robinia pseudoacacia plantations across China’s Loess Plateau using multiple deep learning models |
| title_sort | identification of standing dead trees in robinia pseudoacacia plantations across china s loess plateau using multiple deep learning models |
| topic | Plantation Tree mortality Drone Deep learning Management |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000354 |
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