The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-1/W5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 141–146, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-141-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 141–146, 2015
https://doi.org/10.5194/isprsarchives-XL-1-W5-141-2015

  10 Dec 2015

10 Dec 2015

A HYBRID METHOD IN VEGETATION HEIGHT ESTIMATION USING POLINSAR IMAGES OF CAMPAIGN BIOSAR

S. Dehnavi and Y. Maghsoudi S. Dehnavi and Y. Maghsoudi
  • Geodesy and Geomatics Engineering Faculty, K.N.Toosi University of Technology, Tehran, Iran

Keywords: PolInSAR, Forest height estimation, model-based decomposition, Campaign BioSAR

Abstract. Recently, there have been plenty of researches on the retrieval of forest height by PolInSAR data. This paper aims at the evaluation of a hybrid method in vegetation height estimation based on L-band multi-polarized air-borne SAR images. The SAR data used in this paper were collected by the airborne E-SAR system. The objective of this research is firstly to describe each interferometry cross correlation as a sum of contributions corresponding to single bounce, double bounce and volume scattering processes. Then, an ESPIRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) algorithm is implemented, to determine the interferometric phase of each local scatterer (ground and canopy). Secondly, the canopy height is estimated by phase differencing method, according to the RVOG (Random Volume Over Ground) concept. The applied model-based decomposition method is unrivaled, as it is not limited to specific type of vegetation, unlike the previous decomposition techniques. In fact, the usage of generalized probability density function based on the nth power of a cosine-squared function, which is characterized by two parameters, makes this method useful for different vegetation types. Experimental results show the efficiency of the approach for vegetation height estimation in the test site.