Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery
文献类型：期刊论文
| 作者 | Shi, Weibo1; Liao, Xiaohan1,4; Sun, Jia; Zhang, Zhengjian6,7; Wang, Dongliang1,4; Wang, Shaoqiang5,9; Qu, Wenqiu1,4; He, Hongbo1,4; Ye, Huping1,4; Yue, Huanyin1,4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 15期号:8页码:2205 |
| 关键词 | unmanned aerial vehicles convolutional neural networks tree species classification vegetation indices Faxon fir forest inventory |
| DOI | 10.3390/rs15082205 |
| 文献子类 | Article |
| 英文摘要 | Faxon fir (Abies fargesii var. faxoniana), as a dominant tree species in the subalpine coniferous forest of Southwest China, has strict requirements regarding the temperature and humidity of the growing environment. Therefore, the dynamic and continuous monitoring of Faxon fir distribution is very important to protect this highly sensitive ecological environment. Here, we combined unmanned aerial vehicle (UAV) imagery and convolutional neural networks (CNNs) to identify Faxon fir and explored the identification capabilities of multispectral (five bands) and red-green-blue (RGB) imagery under different months. For a case study area in Wanglang Nature Reserve, Southwest China, we acquired monthly RGB and multispectral images on six occasions over the growing season. We found that the accuracy of RGB imagery varied considerably (the highest intersection over union (IoU), 83.72%, was in April and the lowest, 76.81%, was in June), while the accuracy of multispectral imagery was consistently high (IoU > 81%). In April and October, the accuracy of the RGB imagery was slightly higher than that of multispectral imagery, but for the other months, multispectral imagery was more accurate (IoU was nearly 6% higher than those of the RGB imagery for June). Adding vegetation indices (VIs) improved the accuracy of the RGB models during summer, but there was still a gap to the multispectral model. Hence, our results indicate that the optimized time of the year for identifying Faxon fir using UAV imagery is during the peak of the growing season when using a multispectral imagery. During the non-growing season, RGB imagery was no worse or even slightly better than multispectral imagery for Faxon fir identification. Our study can provide guidance for optimizing observation plans regarding data collection time and UAV loads and could further help enhance the utility of UAVs in forestry and ecological research. |
| 学科主题 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS关键词 | SPECIES CLASSIFICATION ; VEGETATION ; UAV ; FOREST ; NDVI ; RED ; PHOTOGRAMMETRY ; PERSPECTIVES ; DISEASES ; BIOMASS |
| 语种 | 英语 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193492]  |
| 专题 | 区域可持续发展分析与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Univ Geosci, Sch Geog & Informat Engn, Hubei Key Lab Reg Ecol & Environm Change, Wuhan 430074, Peoples R China 2.Univ Copenhagen, Dept Geosci & Nat Resource Management, DK-1172 Copenhagen, Denmark 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.Wanglang Mt Remote Sensing Observat & Res Stn Sich, Mianyang 621000, Peoples R China 6.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China 7.Civil Aviat Adm China, Key Lab Low Altitude Geog Informat & Air Route, Beijing 100101, Peoples R China 8.Lund Univ, Dept Phys Geog & Ecosyst Sci, POB 117, SE-22100 Lund, Sweden 9.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shi, Weibo,Liao, Xiaohan,Sun, Jia,et al. Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery[J]. REMOTE SENSING,2023,15(8):2205. |
| APA | Shi, Weibo.,Liao, Xiaohan.,Sun, Jia.,Zhang, Zhengjian.,Wang, Dongliang.,...&Tagesson, Torbern.(2023).Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery.REMOTE SENSING,15(8),2205. |
| MLA | Shi, Weibo,et al."Optimizing Observation Plans for Identifying Faxon Fir (Abies fargesii var. Faxoniana) Using Monthly Unmanned Aerial Vehicle Imagery".REMOTE SENSING 15.8(2023):2205. |
入库方式： OAI收割
来源：地理科学与资源研究所
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