Abstract
Height values of trees are an important indicator of the health and viabilityof forests. At present, it is the main biophysical parameter observable fromremote sensing images, in particular from Polarimetric Interferometric SAR(PolInSAR) data. It is important to have these values as accurately aspossible. The accuracy of estimated tree height obtained by PolInSAR isaffected by temporal decorrelation. Modeling this correlation is the focus ofthe current thesis.The first chapter explores modeling of the structure function. We used theFourierLegendre series and combined it with the Gaussian motion functionfor modeling the vertical displacement of the scatterers. This improved theheight estimation accuracy using a singlebaseline PolInSAR image pair. Theimprovement was higher when applied in Pband than in Lband. The reasonis the different interaction of the ground and vegetation layer and the lowerpenetration of Lband. The penetration depth becomes important if we areinterested in reconstructing the vertical profile of trees at a higher resolution.In this case, Pband should be used; this fortunately will be available insatellite sensors in near future. For Lband, the exponential function asassumed by the RVoG and RMoG model was equally good.The second chapter proposes the use of the Polarimetric Coherence Tomography (PCT) model to estimate height from multibaseline SAR tomostackdata. In the past, temporal decorrelation was considered as a separate sourceof error that is independent of the canopy. It thus causes biased heightestimates. Merging of a FourierLegendre series from the PCT model witha temporal decorrelation function from the Random Motion over Ground(RMoG) model has been explored to solve this problem. Results showed animprovement of height estimation accuracy after applying this modification.The optimal number of terms of the FourierLegendre series varied for eachpixel. This can be used as an indicator of the complexity of the vegetationlayer as for multilayer dense forests, more terms are required. This chaptershows that increasing the number of unknown parameters can be done viasegmenting the area into different height classes and selecting the optimumnumber of unknown parameters for each class.The third chapter focuses on obtaining the most accurate height mapsfrom PolInSAR. This is important by itself, whereas height also serves as themain biophysical parameter contributing to the estimation of biomass. Theeffect of mitigating temporal decorrelation was thus examined on biomassiii retrieval accuracy. This research developed new allometric equations for thispurpose and tested different strategies for regression. This was challenging dueto the lack of sufficient field data. The strategy to develop a new allometricequation based on height only is important. A parameter usually measuredduring fieldwork is H100, defined as the basal area weighted average of the100 highest trees in each plot,. This chapter showed that the relation betweenPolInSAR height and H100 is weak, because PolInSAR height estimates theaverage of heights inside the plots and does not simply coincide with H100.The fourth chapter discusses how to take temporal decorrelation intothe estimation of tree heights. It addresses the sensitivity of the proposedmodified model to the choice of complex coherence estimation method. Thebasic step of estimating height in any of the explained models is the selection of homogeneous pixels. To do so, we distinguished polarimetric frompolarimetricinterferometric information. By addressing the pixel selectionwe could jointly take the phase and the magnitude values of the pixels intoaccount. We employed two adaptive methods to define statistically homogeneous pixels. Height estimation accuracy increased after applying theadaptive methods. Since the proposed adaptive methods are computationallymore intensive, a tradeoff between the desired accuracy and computation isrequired prior to selection of any method.To summarize, this dissertation improved the accuracy of tree heightestimation from airborne fully polarized InSAR data by carefully addressingtemporal decorrelation. This is potentially of use for future SAR satellitemissions.
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution 

Supervisors/Advisors 

Award date  28 Feb 2019 
Place of Publication  Enschede 
Publisher  
Print ISBNs  9789036547307 
DOIs  
Publication status  Published  28 Feb 2019 
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Ghasemi, N. (2019). Estimation of tree height from PolInSAR: The effects of vertical structure and temporal decorrelation. Enschede: University of Twente, Faculty of GeoInformation Science and Earth Observation (ITC). https://doi.org/10.3990/1.9789036547307