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

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 (Nmass) 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 Nmass) 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 (R2) and minimum root-mean-squared error (RMSECV) values between observed and predicted herbage BM (R2 = 0.56, RMSECV = 51.54), Nmass (R2 = 0.65, RMSECV = 0.93), and N concentration (R2 = 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 fine-scale 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.

[1]  K. Nielsen,et al.  The Effects of Soil Temperature and Form and Level of Nitrogen on Growth and Chemical Composition of Italian Ryegrass , 1964 .

[2]  D. Wilman.  THE EFFECT OF NITROGENOUS FERTILIZER ON THE RATE OF GROWTH OF ITALIAN RYEGRASS , 1965 .

[3]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[4]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[5]  D. Wilman.  Nitrogen and Italian ryegrass. 1. Growth up to 14 weeks: dry-matter yield and digestibility , 1975 .

[6]  J. Keatinge,et al.  Response of perennial and Italian ryegrass cultivars to temperature and soil water potential , 1980, The Journal of Agricultural Science.

[7]  Douglas E Meisner,et al.  Fundamentals of airborne video remote sensing , 1986 .

[8]  R. Graetz,et al.  Satellite remote sensing of Australian rangelands , 1987 .

[9]  P. Curran.  Remote sensing of foliar chemistry , 1989 .

[10]  Y. Inoue,et al.  Estimating Spatial Distribution of Plant Growth in a Soybean Field Based on Remotely-Sensed Spectral Imagery Measured with a Balloon System , 1995 .

[11]  Jamie Goode,et al.  Precision agriculture: spatial and temporal variability of environmental quality. Symposium proceedings. Wageningen, The Netherlands, 21-23 January 1997. , 1997, Ciba Foundation symposium.

[12]  J. Bouma,et al.  Precision agriculture: introduction to the spatial and temporal variability of environmental quality. , 1997, Ciba Foundation symposium.

[13]  Y. Inoue,et al.  A blimp-based remote sensing system for low-altitude monitoring of plant variables: A preliminary experiment for agricultural and ecological applications , 2000 .

[14]  David Lamb,et al.  PA—Precision Agriculture: Remote-Sensing and Mapping of Weeds in Crops , 2001 .

[15]  J. Dungan,et al.  Estimating the foliar biochemical concentration of leaves with reflectance spectrometry: Testing the Kokaly and Clark methodologies , 2001 .

[16]  K. Kawamura,et al.  Study of Cloud Cover Ratio of Landsat-5 for the Application on Agriculture and Forestry , 2003 .

[17]  Qin Zhang,et al.  Creation of Three-dimensional Crop Maps based on Aerial Stereoimages , 2005 .

[18]  Kazunobu Ishii,et al.  Remote-sensing Technology for Vegetation Monitoring using an Unmanned Helicopter , 2005 .

[19]  Byun-Woo Lee,et al.  Assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least square regression , 2006 .

[20]  W. Kühbauch,et al.  A new algorithm for automatic Rumex obtusifolius detection in digital images using colour and texture features and the influence of image resolution , 2007, Precision Agriculture.

[21]  Ewald Schnug,et al.  Will Site Specific Nutrient Management live up to expectation , 2006 .

[22]  Troy Jensen,et al.  Detecting the attributes of a wheat crop using digital imagery acquired from a low-altitude platform , 2007 .

[23]  Clement Atzberger,et al.  LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements , 2008 .

[24]  Tsuyoshi Akiyama,et al.  Wavelength selection for estimating biomass, LAI, and leaf nitrogen concentration in winter wheat of Gifu prefecture using in situ hyperspectral data , 2008 .

[25]  A. Rango,et al.  Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands , 2011 .

[26]  R. Breckenridge,et al.  Evluation of Bare Ground on Rangelands Using Unmanned Aerial Vehicles: A Case Study , 2011 .