Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1080/01431161.2020.1800123 |
A methodology to determine the optimal quadrat size for desert vegetation surveying based on unmanned aerial vehicle (UAV) RGB photography | |
Hao, Mengyu; Zhao, Wenli; Qin, Longjun; Mao, Peng; Qiu, Xu; Xu, Lijie; Xiong, Yu Jiu; Ran, Yili; Qiu, Guo Yu | |
通讯作者 | Qiu, GY |
来源期刊 | INTERNATIONAL JOURNAL OF REMOTE SENSING |
ISSN | 0143-1161 |
EISSN | 1366-5901 |
出版年 | 2021 |
卷号 | 42期号:1页码:84-105 |
英文摘要 | Quadrat sampling is one of the most widely accepted methods for conducting vegetation surveys over the world for centuries. However, it is difficult to determine an optimal size to adequately represent the community compositions in quadrat samplings. Traditional labour-intensive census-based quadrat sampling is also very time consuming and insufficient to represent spatial community characters outside of the quadrat extent. In order to improve the above deficiencies, an unmanned aerial vehicle (UAV)/red-green-blue (RGB) photography based vegetation survey methodology was proposed in this study. The essential steps in the proposed method include: 1) obtaining high spatial resolution optical images from the UAV; 2) extracting species based on the orthographic image after mosaic; 3) performing statistics on a given species within the image through different quadrat sizes; 4) determining an optimal quadrat size according to the changing trend of the statistical data. In addition, a case study applied this proposed methodology was conducted in a desert area in Northwest China, whereAmmopiptanthus mongolicusandZygophyllum xanthoxylondominated. The results show that the remote sensing UAV method could obtain RGB images and orthoimage with flexible control. The statistical data of species density decreased with the increase of quadrat size, but the values changed slightly after 20 m x 20 m, which was larger than the typical quadrat size (10 m x 10 m) used to investigate shrubs. An analysis based on the relationship among species density, plants distribution, and quadrat size further indicated the reasonability of determining the optimal size. Based on the results, it is concluded that a minimum quadrat size of 20 m x 20 m should be adopted for investigating the density and spatial pattern characteristics ofA. mongolicusandZ. xanthoxylon, and the proposed UAV-based method provides an alternative for vegetation survey with high efficiency and accuracy. |
类型 | Article |
语种 | 英语 |
收录类别 | SCI-E |
WOS记录号 | WOS:000580527500001 |
WOS关键词 | SPATIAL-PATTERNS ; POINT ; CLASSIFICATION ; INFORMATION ; IMAGERY ; PLANT ; AREA ; SEGMENTATION ; SYSTEMS ; FUSION |
WOS类目 | Remote Sensing ; Imaging Science & Photographic Technology |
WOS研究方向 | Remote Sensing ; Imaging Science & Photographic Technology |
来源机构 | 北京大学 |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/327938 |
作者单位 | [Hao, Mengyu; Zhao, Wenli; Qin, Longjun; Mao, Peng; Qiu, Guo Yu] Peking Univ, Shenzhen Grad Sch, Sch Environm & Energy, Shenzhen 518055, Peoples R China; [Qiu, Xu] Univ Virginia, Coll Arts & Sci, Charlottesville, VA USA; [Xu, Lijie] Inner Mongolia Agr Univ, Coll Desert Control Sci & Engn, Hohhot, Peoples R China; [Xiong, Yu Jiu] Sun Yat Sen Univ, Sch Civil Engn, Guangzhou, Peoples R China; [Ran, Yili] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou, Peoples R China |
推荐引用方式 GB/T 7714 | Hao, Mengyu,Zhao, Wenli,Qin, Longjun,et al. A methodology to determine the optimal quadrat size for desert vegetation surveying based on unmanned aerial vehicle (UAV) RGB photography[J]. 北京大学,2021,42(1):84-105. |
APA | Hao, Mengyu.,Zhao, Wenli.,Qin, Longjun.,Mao, Peng.,Qiu, Xu.,...&Qiu, Guo Yu.(2021).A methodology to determine the optimal quadrat size for desert vegetation surveying based on unmanned aerial vehicle (UAV) RGB photography.INTERNATIONAL JOURNAL OF REMOTE SENSING,42(1),84-105. |
MLA | Hao, Mengyu,et al."A methodology to determine the optimal quadrat size for desert vegetation surveying based on unmanned aerial vehicle (UAV) RGB photography".INTERNATIONAL JOURNAL OF REMOTE SENSING 42.1(2021):84-105. |
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