DocumentCode
2191754
Title
Relating envisat ASAR and ALOS PALSAR backscattering coefficient to spot NDVI for monitoring seasonal change of pasture biomass in Western Australia
Author
Wang, Xin ; Li, Xiaojing ; Ge, Linlin
Author_Institution
Sch. of Surveying & Spatial Inf. Syst., Univ. of New South Wales, Sydney, NSW, Australia
fYear
2012
fDate
22-27 July 2012
Firstpage
3744
Lastpage
3747
Abstract
Regular estimates of pasture biomass are invaluable for managing the supply of annual pasture in Western Australia (WA). NDVI has been used for estimation of biomass in Australia, with some shortcomings which could be overcome by SAR. The regression model between pasture biomass and SAR backscatter could be obtained if sufficient ground measurements of biomass are available. However, biomass measurements over a large area are not easy to be obtained. Therefore, we are trying to relate ENVISAT ASAR/ALOS PALSAR backscatter to NDVI, and then to biomass. It was found that time series of NDVI is significantly correlated to ASAR HH, HV+HH, VV+VH (dB) with R2 of 0.71, 0.67, and 0.63, respectively. NDVI is also significantly related to ALOS PALSAR HH (R2=0.82). In conclusion, the investigation confirmed the potential of ASAR and PALSAR data monitoring biomass and its seasonal change in the temperate Southwestern Australia, and provided a solid foundation for further research concerning quantitative estimation of biomass.
Keywords
remote sensing by radar; vegetation; ASAR data biomass monitoring; ENVISAT ALOS PALSAR backscattering coefficient; ENVISAT ASAR backscattering coefficient; NDVI; PALSAR data biomass monitoring; Southwestern Australia; Western Australia; biomass estimation; biomass measurements; biomass quantitative estimation; pasture biomass seasonal change monitoring; regression model; Australia; Backscatter; Biomass; Mathematical model; Monitoring; Synthetic aperture radar; Vegetation mapping; Biomass; Correlation; Fourier Fitting; NDVI; SAR Backscatter;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
Conference_Location
Munich
ISSN
2153-6996
Print_ISBN
978-1-4673-1160-1
Electronic_ISBN
2153-6996
Type
conf
DOI
10.1109/IGARSS.2012.6350503
Filename
6350503
Link To Document