Remote Sensing
Tayebe Managhebi; Akram JafarAghaee
Abstract
Forest biomass is one of the most important parameters in the ecosystem changes assessment and global carbon cycle modelling. In the other hand, the forest height is an effective parameter in the allometric equations which are used for biomass estimation. In this research the effect of two physical factors, ...
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Forest biomass is one of the most important parameters in the ecosystem changes assessment and global carbon cycle modelling. In the other hand, the forest height is an effective parameter in the allometric equations which are used for biomass estimation. In this research the effect of two physical factors, forest height and forest density, will be evaluated in the applicability of the four common inversion algorithms for forest height estimation based on the Polarimetric Interferometry SAR (PolInSAR) technique. The applicability of the digital elevation model (DEM) differencing, volume coherence amplitude, hybrid and three-stage methods is studied for different forest height and forest density by using simulated polarimetric interferometric SAR data in L-band. The experimental results of the forest height estimation in simulated data with a density of 100 to 900 trees per hectare and a height of 10 to 18 meters show that the results of the hybrid method show high sensivity to changes in both height and density. The root mean square of error was 5.8, 5.6, 3.2 and 4 m for data with variable height and 11.6, 6.7, 5.8 and 5.3 m for data with different densities, respectively.
