A ground-based hyperspectral imaging system for characterizing vegetation spectral features

Authors:
Xujun Ye;Kenshi Sakai;Hiroshi Okamoto;Leroy O. Garciano
Affiliations:
United Graduate School of Agriculture, Tokyo University of Agriculture and Technology, Tokyo 183-8509, Japan and College of Life Sciences, Zhejiang University, Zijingang Campus, Hangzhou 310058, P ...;Institute of Symbiotic Science and Technology, Tokyo University of Agriculture and Technology, Tokyo,183-8509, Japan;Faculty of Agriculture, Hokkaido University, Sapporo 060-0808, Japan;Biological and Agricultural Engineering Department, University of California Davis, CA 95616, USA
Venue:
Computers and Electronics in Agriculture
Year:
2008

Citing 1
Cited 2

Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data

Computers and Electronics in Agriculture

Comparative analysis of three chemometric techniques for the spectroradiometric assessment of canopy chlorophyll content in winter wheat

Computers and Electronics in Agriculture
Using vegetation index and modified derivative for early detection of soybean plant injury from glyphosate

Computers and Electronics in Agriculture

Quantified Score

Hi-index	0.00

Visualization

Abstract

A cropping system is usually characterized by continuous spatio-temporal vegetation variability. Vegetation variability can be detected by changes in several vegetation parameters defined according to purpose. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and sums, or by forming linear combinations of spectral band data. Spectrometers or sensors have been used to acquire visible and infrared spectral properties of vegetation. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. The hyperspectral sensor used was a ground-based line sensor, ImSpector (V10-12-102), with a nominal spectral resolution of 1.5-2nm and a wavelength range of 360-1010nm. A graphical user interface (GUI) was developed in a MATLAB environment to aid in processing and analysis of acquired multidimensional spectral image data. Issues that arise when applying the imaging system to a particular field include acquiring hyperspectral images, selecting appropriate vegetation features or VIs, and quantifying the selected vegetation features or indices with the GUI developed. Vegetation features extracted by the proposed imaging system contribute not only to monitoring vegetation variability in crop systems, but also provide a potential source of relevant variables that can be used to estimate various vegetation parameters. A study that was set up to investigate the alternate bearing phenomenon of citrus trees illustrates the basic elements of the proposed approach.