Vegetation information extraction in karst area based on UAV remote sensing in visible light band

被引:7
作者
Xu, Anan [1 ]
Wang, Fang [2 ]
Li, Liang [1 ]
机构
[1] Beihang Univ, Sch Aeronaut Sci & Engn, Xueyuan Rd 37, Beijing 100191, Peoples R China
[2] Beihang Univ, Inst Unmanned Syst, Xueyuan Rd 37, Beijing 100191, Peoples R China
来源
OPTIK | 2023年 / 272卷
关键词
Visible light band; UAV remote sensing; Karst; Vegetation information extraction; UNMANNED AERIAL VEHICLES; IMAGES;
D O I
10.1016/j.ijleo.2022.170355
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Karst areas, with their rich species and unique ecological environment, have attracted the attention of many researchers. The unique landscape has brought a wealth of species and great difficulties for research. Karst areas have complex landforms and numerous karst caves, which not only bring a lot of manpower and time investment to the research, but also increase the risk of the research. Therefore, the common field research method is not suitable for karst areas. To solve this problem, this paper adopts the form of UAV remote sensing, uses the visible band, through the calculation of normalized vegetation index, combined with the spectral characteristics of the investigated plants, to extract the vegetation information of the study area. In this paper, the final extracted vegetation information is obtained through practice, and compared with other vege-tation extraction methods. It can be seen from the results that the vegetation information extraction method proposed in this paper to the visible band UAV remote sensing has higher extraction accuracy and better effect than other existing methods. Therefore, it can be used to extract vegetation information in complex terrain areas, reduce labor cost input, save research time, and obtain more targeted regional plants for targeted research, So as to improve the effi-ciency and level of vegetation research in karst areas.
引用
收藏
页数:11
相关论文
共 50 条
[1]   Research on Vegetation Information Extraction from Visible UAV Remote Sensing Images [J].
Yuan, Huijie ;
Liu, Zhengjun ;
Cai, Yulin ;
Zhao, Bing .
2018 FIFTH INTERNATIONAL WORKSHOP ON EARTH OBSERVATION AND REMOTE SENSING APPLICATIONS (EORSA), 2018, :285-289
[2]   New research methods for vegetation information extraction based on visible light remote sensing images from an unmanned aerial vehicle (UAV) [J].
Zhang, Xianlong ;
Zhang, Fei ;
Qi, Yaxiao ;
Deng, Laifei ;
Wang, Xiaolong ;
Yang, Shengtian .
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2019, 78 :215-226
[3]   UAV-based remote sensing using visible and multispectral indices for the estimation of vegetation cover in an oasis of a desert [J].
Wang, Ning ;
Guo, Yuchuan ;
Wei, Xuan ;
Zhou, Mingtong ;
Wang, Huijing ;
Bai, Yunbao .
ECOLOGICAL INDICATORS, 2022, 141
[4]   UAV BASED HYPERSPECTRAL REMOTE SENSING AND CNN FOR VEGETATION CLASSIFICATION [J].
Sankararao, Adduru U. G. ;
Rajalakshmi, P. .
2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, :7737-7740
[5]   Extraction of agricultural plastic film mulching in karst fragmented arable lands based on unmanned aerial vehicle visible light remote sensing [J].
Huang, Denghong ;
Zhou, Zhongfa ;
Zhang, Zhenzhen ;
Zhu, Meng ;
Peng, Ruiwen ;
Zhang, Yang ;
Li, Qianxia ;
Xiao, Dongna ;
Hu, Lingwen .
JOURNAL OF APPLIED REMOTE SENSING, 2022, 16 (03)
[6]   Detecting rice straw burning based on infrared and visible information fusion with UAV remote sensing [J].
Wen, Hao ;
Hu, Xikun ;
Zhong, Ping .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 222
[7]   Classification of coal gangue pile vegetation based on UAV remote sensing [J].
Zhou T. ;
Hu Z. ;
Ruan M. ;
Liu S. ;
Zhang Y. .
Meitan Kexue Jishu/Coal Science and Technology (Peking), 2023, 51 (05) :245-259
[8]   Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing [J].
Wang, Fumin ;
Yao, Xiaoping ;
Xie, Lili ;
Zheng, Jueyi ;
Xu, Tianyue .
REMOTE SENSING, 2021, 13 (17)
[9]   Extraction of Canal Distribution Information Based on UAV Remote Sensing System and Object-Oriented Method [J].
Huo, Xuefei ;
Li, Li ;
Yu, Xingjiao ;
Qian, Long ;
Yin, Qi ;
Fan, Kai ;
Pi, Yingying ;
Wang, Yafei ;
Wang, Wen'e ;
Hu, Xiaotao .
AGRICULTURE-BASEL, 2024, 14 (11)
[10]   Radiation Sensitivity Analysis of Ocean Wake Information Detection System Based on Visible Light Remote Sensing [J].
Ying, Shipeng ;
Qu, Hongsong ;
Tao, Shuping ;
Zheng, Liangliang ;
Wu, Xiaobin .
REMOTE SENSING, 2022, 14 (16)