DocumentCode :
1535432
Title :
Forest Modeling For Height Inversion Using Single-Baseline InSAR/Pol-InSAR Data
Author :
Garestier, Franck ; Le Toan, Thuy
Volume :
48
Issue :
3
fYear :
2010
fDate :
3/1/2010 12:00:00 AM
Firstpage :
1528
Lastpage :
1539
Abstract :
The Random Volume over Ground (RVoG) model has been extensively applied to polarimetric synthetic aperture radar interferometry (Pol-InSAR) data for forest height inversion. The model assumes forest as a homogeneous volume of randomly oriented particles characterized by a constant extinction but does not take into account the forest vertical heterogeneity, to which interferometric coherence is sensitive. In order to integrate vertical heterogeneity in forest models, two complementary models, which take into consideration the forest natural structure, are investigated through analysis of volume interferometric coherence. The first model assumes a vertically varying extinction in the volume layer, and the second model considers predominant contributions localized in a finite height interval, modeled as a Gaussian-distributed backscatter. The two forest models are compared with constant extinction RVoG in the coherence and interferometric phase aspects. Finally, the contribution of these new models for forest height inversion using the Pol-InSAR technique is discussed in the context of a two-layer ground + canopy medium.
Keywords :
forestry; geophysical signal processing; height measurement; inverse problems; radar interferometry; radar polarimetry; radar signal processing; remote sensing by radar; synthetic aperture radar; Gaussian distributed backscatter; Pol-InSAR data; RVoG model; canopy medium; forest height inversion; forest modeling; forest natural structure; forest vertical heterogeneity; height interval; interferometric coherence volume analysis; polarimetric synthetic aperture radar interferometry; random volume over ground model; single baseline InSAR data; two layer ground medium; Forestry; interferometry; polarimetry; synthetic aperture radar (SAR);
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2009.2032538
Filename :
5308279
Link To Document :
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