By Topic

Study on estimation model of vegetation cover in the upstream regions of Shule River Basin based on hyperspectral

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

5 Author(s)
Min Xu ; Cold & Arid Regions Environ. & Enginerring Res. Inst., Lanzhou, China ; Shuhua Yi ; Shilong Ren ; Baisheng Ye
more authors

There are lots researchs about estimation of biological parameters such as vegetation cover (PVC), aboveground biomass and leaf area index by remote sensing satellite data. In this paper, we set nineteen remote sensing plots in the upstream regions of Shule River Basin, they are all 30meters*30meters. We used ASD to collect reflectance of vegetation at Transit time when Landsat TM is passing, there are 168 plots which 50cm*50cm in remote sensing plots in all. Then We use reflectance data to simulated Landsat TM red and near infrared bands. The normalized difference vegetation index (NDVI), renormalized difference vegetation index (RDVI), soil adjusted vegetation index (SAVIL = 0.5), modified soil adjusted vegetation index (MSAVI), difference vegetation index (DVI), ratio vegetation Index (RVI) are caculated through red and near infrared bands. Red edge area (SDre), red edge slope (Dre) and red edge position (λre) are caculated through reflectance of 680nm-780nm. We compared results of estation. The percentage of vegetation cover was estimated using mult-spectral camera. Relationships between percentage of vegetation cover and various vegetation indices and red-edge parameters were compared using a linear and second-order polynomial regression. Our analysis indicated that NDVI and RVI yielded more accurate estimations for a wide range of vegetation cover than other vegetation indices and red-edge parameters for the linear and second-order polynomial regression. We estimate the PVC using remote sensing image and evaluate the result by second-order polynomial regression model.

Published in:

Remote Sensing, Environment and Transportation Engineering (RSETE), 2011 International Conference on

Date of Conference:

24-26 June 2011