Cart (Loading....) | Create Account
Close category search window

Monitoring biomass of water hyacinth by using hyperspectral remote sensing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jingjing Wang ; Inst. of Agric. Econ. & Inf., Jiangsu Acad. of Agric. Sci., Nanjing, China ; Ling Sun ; Huazhou Liu

Establishment biomass monitoring methods of invasive species has an important instructive meaning for control and treatment of invasion, which was also beneficial to national ecological safety and social stability. This study researched the estimation methods of biomass of water hyacinth by hyperspectral remote sensing. The spectral reflectance characteristics were analyzed based on field experiments consisting of different biomass levels. Spectral reflectance and hyperspectral parameters were constructed to be correlated with biomass of water hyacinth and the sensitive parameters were chosen to build biomass estimation models. The result showed that reflectance of near-infrared had better correlation with water hyacinth biomass compared with other wavelength but the correlation is not significant at the 99% confidence level. Red edge parameters including the amplitude of red edge (Dr) and the area of red edge (Dr) and the vegetation indexes including the ration vegetation index (Rnir/Ro) and normalized difference vegetation index ((Rnir - Ro)/(Rnir + Ro)) had significant correlation with water hyacinth, especially the latter two parameters which had the correlative coefficient value reaching the 99% confidence level. The estimation models built by sensitive hyperspectral parameters had been verified the precision respectively and the linear regression model based by vegetation index (Rnir/Ro) got the best accuracy with the RMSE as 1.94 and MRE as 4.26%. The result of this study provide theoretical basis and technology approach for monitoring biomass of water hyacinth by hyperspectral satellite remote sensing.

Published in:

Agro-Geoinformatics (Agro-Geoinformatics), 2012 First International Conference on

Date of Conference:

2-4 Aug. 2012

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.