Mapping herbage biomass and nitrogen status in an Italian ryegrass (Lolium multiflorum L.) field using a digital video camera with balloon system

被引:13
作者
Kawamura, Kensuke [1 ]
Sakuno, Yuji [2 ]
Tanaka, Yoshikazu [2 ]
Lee, Hyo-Jin [1 ]
Lim, Jihyun [1 ]
Kurokawa, Yuzo [3 ]
Watanabe, Nariyasu [4 ]
机构
[1] Hiroshima Univ, Grad Sch Int Dev & Cooperat, Hiroshima 7398529, Japan
[2] Hiroshima Univ, Grad Sch Engn, Hiroshima 7398527, Japan
[3] Hiroshima Univ, Grad Sch Biosphere Sci, Hiroshima 7390046, Japan
[4] NARO Hokkaido Agr Res Ctr, Toyohira Ku, Sapporo, Hokkaido 0628555, Japan
关键词
balloon; digital video camera; herbage biomass; nitrogen status; spatial distribution; precision agriculture; REFLECTANCE; GROWTH;
D O I
10.1117/1.3659893
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Improving current precision nutrient management requires practical tools to aid the collection of site specific data. Recent technological developments in commercial digital video cameras and the miniaturization of systems on board low-altitude platforms offer cost effective, real time applications for efficient nutrient management. We tested the potential use of commercial digital video camera imagery acquired by a balloon system for mapping herbage biomass (BM), nitrogen (N) concentration, and herbage mass of N (N-mass) in an Italian ryegrass (Lolium multiflorum L.) meadow. The field measurements were made at the Setouchi Field Science Center, Hiroshima University, Japan on June 5 and 6, 2009. The field consists of two 1.0 ha Italian ryegrass meadows, which are located in an east-facing slope area (230 to 240 m above sea level). Plant samples were obtained at 20 sites in the field. A captive balloon was used for obtaining digital video data from a height of approximately 50 m (approximately 15 cm spatial resolution). We tested several statistical methods, including simple and multivariate regressions, using forage parameters (BM, N, and N-mass) and three visible color bands or color indices based on ratio vegetation index and normalized difference vegetation index. Of the various investigations, a multiple linear regression (MLR) model showed the best cross validated coefficients of determination (R-2) and minimum root-mean-squared error (RMSECV) values between observed and predicted herbage BM (R-2 = 0.56, RMSECV = 51.54), N-mass (R-2 = 0.65, RMSECV = 0.93), and N concentration (R-2 = 0.33, RMSECV = 0.24). Applying these MLR models on mosaic images, the spatial distributions of the herbage BM and N status within the Italian ryegrass field were successfully displayed at a high resolution. Such finescale maps showed higher values of BM and N status at the bottom area of the slope, with lower values at the top of the slope. C (C) 2011 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.3659893]
引用
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页数:10
相关论文
共 29 条
[1]  
AKIYAMA T, 2003, J JPN SOC PHOTOGRAMM, V42, P29
[2]  
Akiyama Tsuyoshi, 1994, Nippon Sochi Gakkai-Shi, V39, P505
[3]  
Betteridge K, 2008, LANDBAUFORSCH VOLK, V58, P283
[4]  
Bouma J, 1997, Ciba Found Symp, V210, P5
[5]   Evaluation of Bare Ground on Rangelands Using Unmanned Aerial Vehicles: A Case Study [J].
Breckenridge, Robert P. ;
Dakins, Maxine E. .
GISCIENCE & REMOTE SENSING, 2011, 48 (01) :74-85
[6]   REMOTE-SENSING OF FOLIAR CHEMISTRY [J].
CURRAN, PJ .
REMOTE SENSING OF ENVIRONMENT, 1989, 30 (03) :271-278
[7]   Estimating the foliar biochemical concentration of leaves with reflectance spectrometry testing the Kokaly and Clark methodologies [J].
Curran, PJ ;
Dungan, JL ;
Peterson, DL .
REMOTE SENSING OF ENVIRONMENT, 2001, 76 (03) :349-359
[8]   LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements [J].
Darvishzadeh, Roshanak ;
Skidmore, Andrew ;
Schlerf, Martin ;
Atzberger, Clement ;
Corsi, Fabio ;
Cho, Moses .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2008, 63 (04) :409-426
[9]   A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution [J].
Gebhardt, Steffen ;
Kuehbauch, Walter .
PRECISION AGRICULTURE, 2007, 8 (1-2) :1-13
[10]   SATELLITE REMOTE-SENSING OF AUSTRALIAN RANGELANDS [J].
GRAETZ, RD .
REMOTE SENSING OF ENVIRONMENT, 1987, 23 (02) :313-331