[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Contact us::
Site Facilities::
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
:: Volume 7, Issue 4 (3-2020) ::
jgit 2020, 7(4): 1-20 Back to browse issues page
Improvement of Biomass Estimation in Forest Areas based on Polarimetric Parameters Optimization of SETHI airborne Data using Particle Swarm Optimization Method
Samira Hosseini * , Hamid Ebadi , Yaser Maghsoudi
K.N. Toosi University of Technology
Abstract:   (2513 Views)
Estimation of forest biomass has received much attention in recent decades. Airborne and spaceborne (SAR) have a great potential to quantify biomass and structural diversity because of its penetration capability. Polarizations are important elements in SAR systems due to sensitivity of them to backscattering mechanisms and can be useful to estimate biomass. Full Polarimetric Synthetic Aperture Radar (SAR) data used in this research was acquired by SETHI over Remningstorp, a boreal forest in south of Sweden. A new method based on Polarimetric indicators from covariance and coherency matrixes by changing the polarization basis using transformation matrix in the boreal forests at L and P-band is presented. The presented method showed its capability to improve forest biomass estimation. The correlation between biomass and extracted Polarimetric indicators is investigated before and after changing polarization basis. Particle swarm optimization in binary version is used to select optimum Polarimetric indicators and afterward biomass is estimated based on these optimum parameters. Results indicated that maximum correlation between biomass and Polarimetric indicators was in HV and HH-VV polarizations before changing polarization basis. After changing the polarization bases, the results show significantly higher correlation of biomass with the extracted polarization variables. The results have been improved approximately about 6% and 2% in L and P band respectively, after extraction of optimum parameters by particle swarm optimization and using linear regression model for estimation of forest biomass.
Keywords: Biomass Estimation, Backscatter, Particle Swarm Optimization, Polarimetry, Transformation Matrix.
Full-Text [PDF 1824 kb]   (643 Downloads)    
Type of Study: Research | Subject: RS
Received: 2017/02/25 | Accepted: 2017/09/26 | Published: 2020/03/19
Send email to the article author

XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Hosseini S, Ebadi H, Maghsoudi Y. Improvement of Biomass Estimation in Forest Areas based on Polarimetric Parameters Optimization of SETHI airborne Data using Particle Swarm Optimization Method. jgit 2020; 7 (4) :1-20
URL: http://jgit.kntu.ac.ir/article-1-760-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 7, Issue 4 (3-2020) Back to browse issues page
نشریه علمی-پژوهشی مهندسی فناوری اطلاعات مکانی Engineering Journal of Geospatial Information Technology
Persian site map - English site map - Created in 0.05 seconds with 36 queries by YEKTAWEB 4645