Forest parameter retrieval from SAR data using an estimation algorithm applied to regrowing forest stands in Queensland, Australia

Clewley, D., Lucas, R. M., Moghaddam, M., Bunting, Pete, Dwyer, J. and Carreiras, J. (2010). Forest parameter retrieval from SAR data using an estimation algorithm applied to regrowing forest stands in Queensland, Australia. In: 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010). IGARSS 2010: 30th IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, (1238-1241). 25-30 July, 2010. doi:10.1109/IGARSS.2010.5652883


Author Clewley, D.
Lucas, R. M.
Moghaddam, M.
Bunting, Pete
Dwyer, J.
Carreiras, J.
Title of paper Forest parameter retrieval from SAR data using an estimation algorithm applied to regrowing forest stands in Queensland, Australia
Conference name IGARSS 2010: 30th IEEE International Geoscience and Remote Sensing Symposium
Conference location Honolulu, HI, USA
Conference dates 25-30 July, 2010
Proceedings title 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010)
Journal name IEEE International Geoscience and Remote Sensing Symposium Proceedings
Place of Publication Piscataway, NJ, USA
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publication Year 2010
Sub-type Fully published paper
DOI 10.1109/IGARSS.2010.5652883
Open Access Status
ISBN 9781424495658
9781424495641
9781424495665
ISSN 2153-6996
2153-7003
Start page 1238
End page 1241
Total pages 4
Language eng
Formatted Abstract/Summary
The use of a non-linear estimation algorithm for retrieving the biomass and structure of vegetation from polarimetric Synthetic Aperture Radar (SAR) data is demonstrated for woody regrowth in Queensland, Australia dominated by Acacia harpophylla (Brigalow). By varying the size and density of trees and associated woody components (branches and trunks), multiple simulations of the backscattering coefficient (σ0) were performed based on the SAR simulation model of [1]. Functions relating σ0 to these variables were subsequently used to generate spatial estimates from NASA JPL airborne SAR (AIRSAR) data. Above ground biomass was estimated from stem density and size measurements using available allometric relationships. The study demonstrates potential for retrieval of regrowth structure and biomass through nonlinear estimation.
Subjects 1900 Earth and Planetary Sciences
1706 Computer Science Applications
Keyword Synthetic Aperture Radar
Estimation algorithm
Forest structure
Biomass
Injune
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Conference Paper
Collections: School of Biological Sciences Publications
Ecology Centre Publications
 
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