Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

被引:42
|
作者
Hashimoto, Naoyuki [1 ]
Saito, Yuki [1 ]
Maki, Masayasu [2 ]
Homma, Koki [1 ]
机构
[1] Tohoku Univ, Grad Sch Agr Sci, Sendai, Miyagi 9808572, Japan
[2] Fukushima Univ, Fac Food & Agr Sci, Fukushima 9601296, Japan
关键词
leaf area index; paddy field; radiative transfer model; unmanned aerial vehicle; vegetation index; LEAF-AREA INDEX; WHEAT; RICE; YIELD; MODEL; ASSIMILATION; RADIATION; FRACTION;
D O I
10.3390/rs11182119
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Reflectance and vegetation indices obtained from aerial images are often used for monitoring crop fields. In recent years, unmanned aerial vehicles (UAVs) have become popular and aerial images have been collected under various solar radiation conditions. The value of observed reflectance and vegetation indices are considered to be affected by solar radiation conditions, which may lead to inaccurate estimations of crop growth. In this study, in order to evaluate the effect of solar radiation conditions on aerial images, canopy reflectance in paddy fields was simulated by a radiative transfer model, FLiES (Forest Light Environmental Simulator), for various solar radiation conditions and canopy structures. Several parameters including solar zenith angle, proportion of diffuse light for incident sunlight, plant height, coordinates of plants and leaf area density, were tested in FLiES. The simulation results showed that the solar zenith angle did not vary the canopy reflectance under the conditions of the proportion of diffuse light at 1.0, but the variation was greater at lower proportions of diffuse light. The difference in reflectance caused by solar radiation was 0.01 and 0.1 at the maximum for red and near-infrared bands, respectively. The simulation results also showed that the differences in reflectance affect vegetation indices (Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2)). The variation caused by solar radiation conditions was the least for NDVI and the greatest for SR. However, NDVI was saturated at the least leaf area index (LAI), whereas SR was only slightly saturated. EVI2 was intermediate between SR and NDVI, both in terms of variation and saturation. The simulated reflectance and vegetation indices were similar to those obtained from the aerial images collected in the farmers' paddy fields. These results suggest that a large proportion of diffuse light (close to 1.0) or a middle range of solar zenith angle (45 to 65 degrees) may be desirable for UAV monitoring. However, to maintain flexibility of time and occasion for UAV monitoring, EVI2 should be used to evaluate crop growth, although calibration based on solar radiation conditions is recommended.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] Using Digital Cameras on an Unmanned Aerial Vehicle to Derive Optimum Color Vegetation Indices for Leaf Nitrogen Concentration Monitoring in Winter Wheat
    Jiang, Jiale
    Cai, Weidi
    Zheng, Hengbiao
    Cheng, Tao
    Tian, Yongchao
    Zhu, Yan
    Ehsani, Reza
    Hu, Yongqiang
    Niu, Qingsong
    Gui, Lijuan
    Yao, Xia
    REMOTE SENSING, 2019, 11 (22)
  • [42] Internet of Things (IoT) for Monitoring Air Pollutants with an Unmanned Aerial Vehicle (UAV) in a Smart City
    Hernandez-Vega, Jose-Isidro
    Reyes Varela, Elda
    Hernandez Romero, Natividad
    Hernandez-Santos, Carlos
    Sanchez Cuevas, Jonam Leonel
    Palomares Gorham, Dolores Gabriela
    SMART TECHNOLOGY, 2018, 213 : 108 - 120
  • [43] Unmanned Aerial Vehicle (UAV) real-time video registration for forest fire monitoring
    Zhou, GQ
    Li, CK
    Cheng, PG
    IGARSS 2005: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8, PROCEEDINGS, 2005, : 1803 - 1806
  • [44] Wireless sensor network development in unmanned aerial vehicle (uav) for water quality monitoring system
    Etikasari, B.
    Husin
    Kautsar, S.
    Riskiawan, H. Y.
    Setyohadi, D. P. S.
    SECOND INTERNATIONAL CONFERENCE ON FOOD AND AGRICULTURE 2019, 2020, 411
  • [45] SMALL WHISKBROOM IMAGER FOR ATMOSPHERIC COMPOSITION MONITORING (SWING) FROM AN UNMANNED AERIAL VEHICLE (UAV)
    Merlaud, A.
    Constantin, D. -E.
    Mingireanu, F.
    Mocanu, I.
    Fayt, C.
    Maes, J.
    Murariu, G.
    Voiculescu, M.
    Georgescu, L.
    Van Roozendael, M.
    PROCEEDINGS OF THE 21ST ESA SYMPOSIUM ON EUROPEAN ROCKET & BALLOON PROGRAMMES AND RESEARCH, 2013, 721 : 233 - 239
  • [46] Development of Prototype System for Monitoring and Computing Greenhouse Gases with Unmanned Aerial Vehicle (UAV) Deployment
    Sidek, O.
    Abdullah, A.
    Za'bah, U. N.
    Amran, N. A.
    Jafar, Hadi
    Hadi, Munajat Abdul
    Nikmat, F.
    Halim, Z. A.
    Mansor, M.
    2014 1ST INTERNATIONAL SYMPOSIUM ON TECHNOLOGY MANAGEMENT AND EMERGING TECHNOLOGIES (ISTMET 2014), 2014, : 98 - 101
  • [47] Deformation monitoring of open pit mine slopes using an Unmanned Aerial Vehicle (UAV) system
    Gubaydullina, Rushaniya
    Mustafin, Murat
    GEOMECHANICS AND GEODYNAMICS OF ROCK MASSES (EUROCK2018), VOLS 1 AND 2, 2018, : 1639 - 1644
  • [48] The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions
    Tomczyk, Aleksandra M.
    Ewertowski, Marek W.
    Creany, Noah
    Ancin-Murguzur, Francisco Javier
    Monz, Christopher
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 123
  • [49] Unmanned Aerial Vehicle Framework for Algae Monitoring
    De Almeida, Aline Gabriel
    Do Nascimento, Eduardo Vieira
    Alvarez, Isaac Gaetani
    Correa Kim, Pedro Henrique
    Da Rocha, Lidia Gianne Souza
    Teixeira Vivaldini, Kelen Cristiane
    2021 LATIN AMERICAN ROBOTICS SYMPOSIUM / 2021 BRAZILIAN SYMPOSIUM ON ROBOTICS / 2021 WORKSHOP OF ROBOTICS IN EDUCATION (LARS-SBR-WRE 2021), 2021, : 84 - 89
  • [50] Unmanned aerial vehicle for fire surveillance and monitoring
    Madridano, A.
    Campos, S.
    Al-Kaff, A.
    Garcia, A.
    Martin, D.
    Escalera, A.
    REVISTA IBEROAMERICANA DE AUTOMATICA E INFORMATICA INDUSTRIAL, 2020, 17 (03): : 254 - 263