Remote Sensing Technology and Application, Volume. 39, Issue 1, 248(2024)
Identification of Typical Grassland Degradation Indicator Species based on UAV Hyperspectral Remote Sensing
[1] WANG Yiqun. Research on ecological protection and construction of grassland in Inner Mongolia. Modern Agriculture, 85-86(2019).
[2] ZHANG Xipeng, BI Yuge, YANG Hongyan et al. Research on the classification model of desertification grassland grass species based on hyperspetctral remote sensing. Optical Technology, 46, 483-488(2020).
[3] YANG Hongyan, DU Jianmin, WANG Yuan et al. Grassland species classification method based on UAV remote sensing and convolutional neuralnetwork. Journal of Agricultural Machinery, 50, 188-195(2019).
[4] GUO Lizhu, ZHAO Huan, Jinying LÜ et al. Population structure and quantitative dynamics of degraded typical steppe wolf venom. Chinese Journal of Applied Ecology, 31, 2977-2984(2020).
[5] HU Yina, AN Ru, AI Zetian et al. Research on fine identification of grass species in the Three Rivers Sources based on UAV hyperspetctral images. Remote Sensing Technology and Application, 36, 926-935(2021).
[6] PI W Q, DU J M, BI Y G et al. 3D-CNN based UAV hyperspetctral imagery for grassland degradation indicator ground objet al. classification research. Ecological Informatics, 62, 101278(2021).
[7] Li X B, DANG D L et al. Unmanned Aerial Vehicle (UAV) remote sensing in grassland ecosystem monitoring: A systematic review. Remote Sensing, 14, 1096(2022).
[8] YANG H Y, DU J M. Classification of desert steppe species based on unmanned aerial vehicle hyperspetctral remote sensing and continuum removal vegetation indices. Optik, 4026(2021).
[9] ZHANG X L, ZHANG F, QI Y X et al. New research methods for vegetation information extraction based on visible light remote sensing images from an Unmanned Aerial Vehicle (UAV). International Journal of Applied Earth Observation and Geoinformation, 78, 215-226(2019).
[10] YANG Hongyan. Species classification of desert steppe based on UAV hyperspectral remote sensing(2019).
[11] YANG Hongyan, DU Jianmin, Ruan Peiying et al. Classification method of desert grassland vegetation based on drone remote sensing and random forest. Transactions of the Chinese Society of Agricultural Machinery, 52, 186-194(2021).
[12] ISHIDAA T, KURIHARAA J, VIRAYB F A et al. A novel approach for vegetation classification using UAV-based hyperspectral imaging. Computers and Electronics in Agriculture, 144, 80-85(2018).
[14] NA Mula, LI Yuan, WANG Wuyun et al. Identification of typical species in desert steppe based on UAV multispectral image. China Agricultural Information, 34, 37-48(2022).
[15] ZHANG Fuhua, HUANG Mingxiang, ZHANG Jing et al. Research on the identification of grassland species by hyperspectra:A case study of Xilin Gol grassland. Bulletin of Surveying and Mapping, 66-69(2014).
[16] ZHANG Guanhong, WANG Xinjun, XU Xiaolong et al. Desert vegetation classification based on object-oriented UAV remote sensing imagery. China Agricultural Science and Technology Review, 23, 69-77(2021).
[17] LIU Bin, SHI Yun, WU Wenbin et al. Crop classification based on UAV remote sensing visible light image. China Agricultural Resources and Regional Planning, 40, 55-63(2019).
[18] DING Yong, NIU Jianming, LIU Pengtao et al. Spatial-temporal dynamic analysis of the distribution pattern of herders: A case study of Huanghuashu special of Baiyin Xile Ranch in Inner Mongolia. Journal of Grassland, 22, 1205-1211(2014).
[19] FENG Shuangshuang, TIAN Bing, HU Yincui et al. Characteristics of indicator species of grassland degradation on dams. Resources and Environment in Arid Areas, 30, 133-139(2016).
[20] DENG Xiaoling, ZENG Guoliang, ZHU Zihao et al. Classification and characteristic band extraction of citrus diseased plants based on UAV hyperspet al.ral remote sensing. Journal of South China Agricultural University, 41, 100-108(2020).
[21] CHEN Shuren, ZOU Huadong, WU Ruimei et al. Identification of weeds and rice at seedling stage of paddy field based on hyperspetctral image technology. Transactions of the Chinese Society for Agricultural Machinery, 44, 253-163(2013).
[22] TONG Qingxi, ZHANG Bing, ZHENG Lanfen et al. The principle, technology and application of hyperspetctral remote sensing. 童庆禧, 等(2006).
[23] Belgiu M, Drăguţ L. Random forest in remote sensing: A review of applications and future diretctions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31(2016).
[24] GAO Xia, SHEN Xin, DAI Jinsong et al. Urban forest tree species classification combined with LiDAR single tree segmentation and hyperspetctral feature extraction. Remote Sensing Technology and Application, 33, 1073-1083(2018).
[25] LI Fuxiang, WANG Xue, ZHANG Chi et al. Support vector machine classification algorithm based on boundary points. Journal of Shaanxi University of Technology(Natural Science Edition), 38, 30-38(2022).
[26] LAN Yubin, ZHU Zihao, DENG Xiaoling et al. Monitoring and classification of citrus Huanglongbing plants based on UAV hyperspetctral remote sensing. Chinese Journal of Agricultural Engineering, 35, 92-100(2019).
[27] [27] SUDaxue, ZHANGZihe, CHENZuozhong,et al. GB 19377-2003, Classification index of natural grassland degradation, desertion and salinization[S].
[28] WANG Zhenhua, XU Lizhi, JI Qing et al. An accuracy evaluation method for remote sensing classfication results using spatial heterogeneity. Remote Sensing Information, 35, 25-31(2020).
[29] WEI Ningning, LIN Yiran, ZHOU Yihu. Study on characteristics and driving forces of urban land expansion in Nanjing city[J]. Jiangsu Agricultural Sciences, 49, 204-209(2021).
[30] ABD-ELRAHMAN A, BRITT K, LIU T. Deep Learning Classification of high-resolution drone images using the ArcGIS Pro Software FOR374/FR444,10/2021. EDIS, 2021(2021).
[31] FENG Q L, LIU J T, GONG J H. UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote sensing, 7, 1074-1094(2015).
[32] ZHAO Y F, ZHU W W, WEI P P et al. Classification of Zambian grasslands using random forest feature importance seletction during the optimal phenological period. Ecological Indicators, 135, 108529(2022).
[33] ZHANG Nina, ZHANG Ke, LI Yunping et al. Research on UAV multispetalral remote sensing machine learning classification of vegetation types in typical humid mountainous areas in Southern China. Remote Sensing Technology and Application, 38, 163-172(2023).
[34] HAO Fangfang, CHEN Yanmei, GAO Jixi et al. Hyperspectral characteristic band identification of grassland degradation indicator species in Bashang area, Hebei Province. Journal of Ecology and Rural Environment, 32, 1024-1029(2016).
[35] MA Jian, LIU Wenhao, Jin Guili et al. UAV multispectral remote sensing grassland plant identification based on CNN and SVM. Journal of Grassland, 30, 3165-3174(2022).
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Nile WU, Yulong BAO, Rentuya BU, Buxinbayaer TU, Saixiyalatu TAO, Yuhai BAO, Eerdemutu JIN. Identification of Typical Grassland Degradation Indicator Species based on UAV Hyperspectral Remote Sensing[J]. Remote Sensing Technology and Application, 2024, 39(1): 248
Category: Research Articles
Received: Aug. 29, 2022
Accepted: --
Published Online: Jul. 22, 2024
The Author Email: Nile WU (Bwunile@163.com)