Title :
Study on estimating pigment contents in canopy of chinese fir under disease stress based on hyperspectral data
Author :
Li Hongjun ; Tan Yimin ; Zang Zhuo ; Liu Junang ; Liu Qianli ; Zhou Guoying
Author_Institution :
Key Lab. of Forestry Biotechnol. Hunan Province, Central South Univ. of Forestry & Technol., Changsha, China
Abstract :
Using hyperspectral technology to test canopy of the fir which was damaged by the anthracnose, exploring and built the hyper-spectral estimation models of the pigment content in canopy of Chinese fir under the disease stress, the results will promote the application of the hyper-spectral remote sensing-technology in the forest pest and disease monitoring. From May to July in 2012, You Xian, Hunan Province, we investigated the anthracnose of Chinese fir, and measured the canopy pigment content and the spectral reflectance of the Chinese fir which was damaged by the anthracnose. The correlation between pigment contents of fir leaves with spectra reflectance, the first derivative of reflectance and spectral characteristic parameters were analyzed respectively. The result showed that the visible light and near-infrared region were the sensitive region which were reflected and absorbed by the pigment in disease of Chinese fir; pigment content had the highest correlation with the first order differential spectra in red edge (695-754 nm), and the correlation coefficient of single band first order differential spectrum in 741 nm was the largest. And it also showed that the accuracy of Chla+b, Chla and Chlb contents was the highest estimated by power function model which used the difference vegetation index DVI [FD587,FD741] as the variable, the relative errors were less than 15%, the RMS error was in the range of 0.093 to 0.241.
Keywords :
environmental monitoring (geophysics); hyperspectral imaging; organic compounds; remote sensing; spectrochemical analysis; vegetation; AD 2012 05 to 07; China; Chinese fir canopy; Chinese fir disease stress; DVI; Hunan Province; You Xian; anthracnose; chlorophyll a content; chlorophyll b content; difference vegetation index; first order differential spectra; forest pest; hyperspectral data; hyperspectral estimation models; hyperspectral remote sensing technology; near infrared wavelength region; pigment content stimation; plant disease monitoring; spectra reflectance; visible wavelength region; wavelength 695 nm to 754 nm; Correlation; Diseases; Estimation; Hyperspectral sensors; Pigments; Reflectivity; Chinese fir; Hyperspectral; disease stress; estimation; pigment content;
Conference_Titel :
Earth Observation and Remote Sensing Applications (EORSA), 2014 3rd International Workshop on
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-5757-6
DOI :
10.1109/EORSA.2014.6927926