Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles
Fixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km<sup>2</sup>) environmental monitoring and small-area (<1 km<sup>2</sup>) fine vegetation surveys, respectively, having different characteristics in terms of flight c...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/6/353 |
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| author | Weibo Shi Shaoqiang Wang Huanyin Yue Dongliang Wang Huping Ye Leigang Sun Jia Sun Jianli Liu Zhuoying Deng Yuanyi Rao Zuoran Hu Xiyong Sun |
| author_facet | Weibo Shi Shaoqiang Wang Huanyin Yue Dongliang Wang Huping Ye Leigang Sun Jia Sun Jianli Liu Zhuoying Deng Yuanyi Rao Zuoran Hu Xiyong Sun |
| author_sort | Weibo Shi |
| collection | DOAJ |
| description | Fixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km<sup>2</sup>) environmental monitoring and small-area (<1 km<sup>2</sup>) fine vegetation surveys, respectively, having different characteristics in terms of flight cost, operational efficiency, and landing and take-off methods. However, large-area fine mapping in complex forest environments is still a challenge in UAV remote sensing. Here, we developed a method that combines a multi-rotor UAV and a fixed-wing UAV to solve this challenge at a low cost. Firstly, we acquired small-scale, multi-season ultra-high-resolution red-green-blue (RGB) images and large-area RGB images by a multi-rotor UAV and a fixed-wing UAV, respectively. Secondly, we combined the reference data of visual interpretation with the multi-rotor UAV images to construct a semantic segmentation model and used the model to expand the reference data. Finally, we classified fixed-wing UAV images using the large-area reference data combined with the semantic segmentation model and discuss the effects of different sizes. Our results show that combining multi-rotor and fixed-wing UAV imagery provides an accurate prediction of tree species. The model for fixed-wing images had an average F1 of 92.93%, with 92.00% for <i>Quercus wutaishanica</i> and 93.86% for <i>Juglans mandshurica</i>. The accuracy of the semantic segmentation model that uses a larger size shows a slight improvement, and the model has a greater impact on the accuracy of <i>Quercus liaotungensis</i>. The new method exploits the complementary characteristics of multi-rotor and fixed-wing UAVs to achieve fine mapping of large areas in complex environments. These results also highlight the potential of exploiting this synergy between multi-rotor UAVs and fixed-wing UAVs. |
| first_indexed | 2024-03-11T02:34:07Z |
| format | Article |
| id | doaj.art-da3ff6236a3c4a59be03b74f11028a4a |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T02:34:07Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-da3ff6236a3c4a59be03b74f11028a4a2023-11-18T10:03:54ZengMDPI AGDrones2504-446X2023-05-017635310.3390/drones7060353Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial VehiclesWeibo Shi0Shaoqiang Wang1Huanyin Yue2Dongliang Wang3Huping Ye4Leigang Sun5Jia Sun6Jianli Liu7Zhuoying Deng8Yuanyi Rao9Zuoran Hu10Xiyong Sun11Hubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaHubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaInstitute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050011, ChinaHubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaChina TOPRS Technology Co., Ltd., Beijing 100039, ChinaHubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaHubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaHubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaHubei Key Laboratory of Regional Ecology and Environment Change, School of Geography and Information Engineering, Chinese University of Geosciences, Wuhan 430074, ChinaFixed-wing unmanned aerial vehicles (UAVs) and multi-rotor UAVs are widely utilized in large-area (>1 km<sup>2</sup>) environmental monitoring and small-area (<1 km<sup>2</sup>) fine vegetation surveys, respectively, having different characteristics in terms of flight cost, operational efficiency, and landing and take-off methods. However, large-area fine mapping in complex forest environments is still a challenge in UAV remote sensing. Here, we developed a method that combines a multi-rotor UAV and a fixed-wing UAV to solve this challenge at a low cost. Firstly, we acquired small-scale, multi-season ultra-high-resolution red-green-blue (RGB) images and large-area RGB images by a multi-rotor UAV and a fixed-wing UAV, respectively. Secondly, we combined the reference data of visual interpretation with the multi-rotor UAV images to construct a semantic segmentation model and used the model to expand the reference data. Finally, we classified fixed-wing UAV images using the large-area reference data combined with the semantic segmentation model and discuss the effects of different sizes. Our results show that combining multi-rotor and fixed-wing UAV imagery provides an accurate prediction of tree species. The model for fixed-wing images had an average F1 of 92.93%, with 92.00% for <i>Quercus wutaishanica</i> and 93.86% for <i>Juglans mandshurica</i>. The accuracy of the semantic segmentation model that uses a larger size shows a slight improvement, and the model has a greater impact on the accuracy of <i>Quercus liaotungensis</i>. The new method exploits the complementary characteristics of multi-rotor and fixed-wing UAVs to achieve fine mapping of large areas in complex environments. These results also highlight the potential of exploiting this synergy between multi-rotor UAVs and fixed-wing UAVs.https://www.mdpi.com/2504-446X/7/6/353fixed-wing UAVsmulti-rotor UAVssemantic segmentation methodtree species classificationforest inventory |
| spellingShingle | Weibo Shi Shaoqiang Wang Huanyin Yue Dongliang Wang Huping Ye Leigang Sun Jia Sun Jianli Liu Zhuoying Deng Yuanyi Rao Zuoran Hu Xiyong Sun Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles Drones fixed-wing UAVs multi-rotor UAVs semantic segmentation method tree species classification forest inventory |
| title | Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles |
| title_full | Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles |
| title_fullStr | Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles |
| title_full_unstemmed | Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles |
| title_short | Identifying Tree Species in a Warm-Temperate Deciduous Forest by Combining Multi-Rotor and Fixed-Wing Unmanned Aerial Vehicles |
| title_sort | identifying tree species in a warm temperate deciduous forest by combining multi rotor and fixed wing unmanned aerial vehicles |
| topic | fixed-wing UAVs multi-rotor UAVs semantic segmentation method tree species classification forest inventory |
| url | https://www.mdpi.com/2504-446X/7/6/353 |
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