POTENTIAL APPLICATION OF NOVEL HYPERSPECTRAL LIDAR FOR MONITORING CROPS NITROGEN STRESS

被引:2
作者
Shi, Shuo [1 ,2 ,3 ]
Gong, Wei [1 ,2 ]
Du, Lin [2 ,4 ]
Sun, Jia [2 ]
Yang, Jian [2 ]
机构
[1] Collaborat Innovat Ctr Geospatial Technol, Wuhan, Peoples R China
[2] Wuhan Univ, Key Lab Informat Engn Surveying Mapping & Remote, Wuhan, Peoples R China
[3] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China
[4] Wuhan Univ, Sch Phys & Technol, Wuhan, Peoples R China
来源
XXIII ISPRS CONGRESS, COMMISSION VIII | 2016年 / 41卷 / B8期
基金
中国国家自然科学基金;
关键词
LiDAR; Hyperspectral; Nitrogen stress; Crops; Remote sensing; LEAF NITROGEN; WINTER-WHEAT; CANOPY; DESIGN; SYSTEM; CORN; EFFICIENCY; WATER;
D O I
10.5194/isprsarchives-XLI-B8-1043-2016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.
引用
收藏
页码:1043 / 1047
页数:5
相关论文
共 50 条
  • [1] Monitoring of nitrogen accumulation in wheat plants based on hyperspectral data
    Song, Xiao
    Xu, Duanyang
    Huang, Chenchen
    Zhang, Keke
    Huang, Shaomin
    Guo, Doudou
    Zhang, Shuiqing
    Yue, Ke
    Guo, Tengfei
    Wang, Shasha
    Zang, Hecang
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2021, 23
  • [2] Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer
    Sun, Jia
    Shi, Shuo
    Gong, Wei
    Yang, Jian
    Du, Lin
    Song, Shalei
    Chen, Biwu
    Zhang, Zhenbing
    SCIENTIFIC REPORTS, 2017, 7
  • [3] Hyperspectral UAV Images at Different Altitudes for Monitoring the Leaf Nitrogen Content in Cotton Crops
    Yin, Caixia
    Lv, Xin
    Zhang, Lifu
    Ma, Lulu
    Wang, Huihan
    Zhang, Linshan
    Zhang, Ze
    REMOTE SENSING, 2022, 14 (11)
  • [4] SIMULATION OF SPACEBORNE HYPERSPECTRAL REMOTE SENSING TO ASSIST CROP NITROGEN CONTENT MONITORING IN AGRICULTURAL CROPS
    Berger, K.
    Wang, Z.
    Danner, M.
    Wocher, M.
    Mauser, W.
    Hank, T.
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 3801 - 3804
  • [5] Mapping biomass and stress in the Sierra Nevada using lidar and hyperspectral data fusion
    Swatantran, Anu
    Dubayah, Ralph
    Roberts, Dar
    Hofton, Michelle
    Blair, J. Bryan
    REMOTE SENSING OF ENVIRONMENT, 2011, 115 (11) : 2917 - 2930
  • [6] Development and Application of Airborne Hyperspectral LiDAR Imaging Technology
    Gong Wei
    Shi Shuo
    Chen Bowen
    Song Shalei
    Wu Decheng
    Liu Dong
    Liu Zhengjun
    Liao Meisong
    ACTA OPTICA SINICA, 2022, 42 (12)
  • [7] Hyperspectral Fluorescence LIDAR Based on a Liquid Crystal Tunable Filter for Marine Environment Monitoring
    Aruffo, Eleonora
    Chiuri, Andrea
    Angelini, Federico
    Artuso, Florinda
    Cataldi, Dario
    Colao, Francesco
    Fiorani, Luca
    Menicucci, Ivano
    Nuvoli, Marcello
    Pistilli, Marco
    Spizzichino, Valeria
    Palucci, Antonio
    SENSORS, 2020, 20 (02)
  • [8] A NOVEL ENSEMBLE CLASSIFIER OF HYPERSPECTRAL AND LIDAR DATA USING MORPHOLOGICAL FEATURES
    Xia, Junshi
    Yokoya, Naoto
    Iwasaki, Akira
    2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2017, : 6185 - 6189
  • [9] Application of Hyperspectral LiDAR on 3-D Chlorophyll-Nitrogen Mapping of Rohdea Japonica in Laboratory
    Du, Lin
    Jin, Zhili
    Chen, Bowen
    Chen, Biwu
    Gao, Wei
    Yang, Jian
    Shi, Shuo
    Song, Shalei
    Wang, Mengmeng
    Gong, Wei
    Wang, Wei
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2021, 14 : 9667 - 9679
  • [10] Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR
    Du, Lin
    Gong, Wei
    Shi, Shuo
    Yang, Pan
    Sun, Jia
    Zhu, Bo
    Song, Shalei
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2016, 44 : 136 - 143