Paper Info
Title
A Simple Moment Method of Forest Biomass Estimation From Non-Gaussian Texture Information by High-Resolution Polarimetric SAR
Abstract
A simple method is described to estimate forest biomass by high-resolution polarimetric synthetic aperture radar (SAR). The method is based on the regression analysis between the measured biomass from the ground survey and the second intensity moment of the non-Gaussian texture in the cross-polarized L-band SAR images. The SAR data used in the analysis were acquired by the airborne polarimetric interferometric SAR over the coniferous forest in Hokkaido, Japan. The regression analysis was first carried out, and a model function was derived to relate the intensity moment and the measured biomass in 19 forest stands. Using this model function, the biomass values were estimated and compared with those of 21 different stands with known biomass. The average accuracy of the moment model was found to be 85%, which is similar to that of the previous K -distribution model. The advantage of this method over the distribution-based model is that there is no need to search a specific distribution function which fits best to the image texture.
Year
DOI
Venue
2010
10.1109/LGRS.2010.2047839
IEEE Geosci. Remote Sensing Lett.
Keywords
Field
DocType
high resolution polarimetric sar,synthetic aperture radar,nongaussian texture information,ground survey,japan,synthetic aperture radar (sar),regression analysis,forest biomass,non-gaussian texture,intensity moment,polarimetric high-resolution data,remote sensing by radar,hokkaido,geophysical image processing,image texture,method of moments,forest biomass estimation,vegetation,radar polarimetry,moment method,forestry,high resolution,l band,biomass,cross polarization,distribution function,estimation,remote sensing
Biomass,Computer vision,Polarimetry,Synthetic aperture radar,Image texture,Regression analysis,Remote sensing,Gaussian,Artificial intelligence,Distribution function,Mathematics,Method of moments (statistics)
Journal
Volume
Issue
ISSN
7
4
1545-598X
Citations 
PageRank 
References 
2
0.40
13
Authors
2
Name
Order
Citations
PageRank
Haipeng Wang1999.71
Kazuo Ouchi211215.71