Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery
Forest Canopy Closure (CC) is vital for assessing forest ecosystems. This study integrates multispectral imagery with enhanced U-Net models (U-Net, U-Net++, U-Net3+) to achieve cost-effective large-scale CC estimation. These models are optimized by reordering the network output layers and enhancing...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2545910 |
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| _version_ | 1849405101352943616 |
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| author | Lei Chen TingTing Yang ZhiQiang Wu XinLong Li YanZhen Lin Yi Lian |
| author_facet | Lei Chen TingTing Yang ZhiQiang Wu XinLong Li YanZhen Lin Yi Lian |
| author_sort | Lei Chen |
| collection | DOAJ |
| description | Forest Canopy Closure (CC) is vital for assessing forest ecosystems. This study integrates multispectral imagery with enhanced U-Net models (U-Net, U-Net++, U-Net3+) to achieve cost-effective large-scale CC estimation. These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. By experimenting with different combinations of multi-parameters with the improved U-Net architectures, we estimate CC and validate the results using airborne Light Detection and Ranging (LiDAR) CC data. The results show that (1) Ratio Vegetation Index (RVI) had the strongest correlation with CC (R2= 0.8135). (2) U-Net exhibits optimal stability under structural adjustments; (3) Adjust-U-Net++ achieves the highest accuracy, with R2=0.8785, RMSE = 0.1256, EA = 77.46% and MAE = 0.0800; (4) Multi-parameter combinations outperform single parameters in CC estimation. By exploring both the selection of input parameters and the structural optimization of U-Net models, this study provides an effective approach for large-scale, low-cost CC estimation. |
| format | Article |
| id | doaj-art-f3c7bf9b62d04290b67ece9013db3c4c |
| institution | Kabale University |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-f3c7bf9b62d04290b67ece9013db3c4c2025-08-20T03:36:45ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2545910Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imageryLei Chen0TingTing Yang1ZhiQiang Wu2XinLong Li3YanZhen Lin4Yi Lian5Geography Department, Tianjin Normal University, Tianjin, ChinaGeography Department, Tianjin Normal University, Tianjin, ChinaSchool of Surveying and Mapping, Information Engineering University, Zhengzhou, ChinaZhongshui Northeast Survey, Design and Research Co., Ltd, Changchun, Jilin, ChinaGeography Department, Tianjin Normal University, Tianjin, ChinaGeography Department, Tianjin Normal University, Tianjin, ChinaForest Canopy Closure (CC) is vital for assessing forest ecosystems. This study integrates multispectral imagery with enhanced U-Net models (U-Net, U-Net++, U-Net3+) to achieve cost-effective large-scale CC estimation. These models are optimized by reordering the network output layers and enhancing feature fusion between convolutional and pooling operations. By experimenting with different combinations of multi-parameters with the improved U-Net architectures, we estimate CC and validate the results using airborne Light Detection and Ranging (LiDAR) CC data. The results show that (1) Ratio Vegetation Index (RVI) had the strongest correlation with CC (R2= 0.8135). (2) U-Net exhibits optimal stability under structural adjustments; (3) Adjust-U-Net++ achieves the highest accuracy, with R2=0.8785, RMSE = 0.1256, EA = 77.46% and MAE = 0.0800; (4) Multi-parameter combinations outperform single parameters in CC estimation. By exploring both the selection of input parameters and the structural optimization of U-Net models, this study provides an effective approach for large-scale, low-cost CC estimation.https://www.tandfonline.com/doi/10.1080/10106049.2025.2545910GF-1 multi-spectral imagerycanopy closureU-Netmodel structure adjustmentinput parameter changes |
| spellingShingle | Lei Chen TingTing Yang ZhiQiang Wu XinLong Li YanZhen Lin Yi Lian Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery Geocarto International GF-1 multi-spectral imagery canopy closure U-Net model structure adjustment input parameter changes |
| title | Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery |
| title_full | Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery |
| title_fullStr | Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery |
| title_full_unstemmed | Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery |
| title_short | Application of U-net models in estimating forest canopy closure based on multi-source remote sensing imagery |
| title_sort | application of u net models in estimating forest canopy closure based on multi source remote sensing imagery |
| topic | GF-1 multi-spectral imagery canopy closure U-Net model structure adjustment input parameter changes |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2545910 |
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