Forest height estimation using radar remote sensing images
The data will be provided by the supervisor after the approval of the thesis topic.
POLSARPRO software will be used for the processing
With down-looking satellite sensors, our three-dimensional (3D) world is commonly represented with 2D remote sensing images. This intrinsic property of 2D remote sensing data makes forest height estimation from space a challenging issue. Basically, space-based trees’ height estimation requires the remote sensing systems that are able to record electromagnetic scattering from both under-foliage ground and top of canopy. Among different satellite remote sensing systems, the microwave length of synthetic aperture radar (SAR) is able to penetrate within forest vegetation and make it possible to receive backscattering from ground and top-canopy layers. In this context, an efficient methodology called Random Volume Over Ground (RVOG) is a well-adapted model to extract forest parameters (such as forest height, underlying ground topography) from the 2D radar remote sensing images. However, SAR images suffers from speckle noise effects, and thus efficiency of RVOG based parameter extraction can be impaired by the noise effect. This study attempts to assess the impact of images noise in the accuracy of estimated forest parameters from RVOG model using RadarSAT-2 images over Veluwe forest area, in the Netherlands.
The objective of this study is to reveal to what extend noise reduction can improve the accuracy of estimated forest parameters using RVOG model. Several efficient algorithms exist in the literature to suppress the speckle noise in SAR images and this study will find and represent the most robust and effective noise reduction algorithm for forest height mapping using radar remote sensing images.
Note that all processing will be executed using the open-source ESA’s software (POLSARPRO).
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