estimation of tree height from polinsar: the effects of vertical structure and temporal decorrelation
Nafiseh Ghasemi is a PhD student in the Department of Earth Observation Science. Her supervisor is prof.dr.ir. A. Stein from the Faculty of Geo-information Science and Earth Observation.
Height values of trees are an important indicator of the health and viability of forests. At present, it is the main biophysical parameter observable from remote sensing images, in particular from Polarimetric Interferometric SAR (PolInSAR) data. It is important to have these values as accurately as possible. The accuracy of estimated tree height obtained by PolInSAR is affected by temporal decorrelation. Modeling this correlation is the focus of the current thesis.
The first chapter explores modeling of the structure function. We used the Fourier-Legendre series and combined it with the Gaussian motion function for modeling the vertical displacement of the scatterers. This improved the height estimation accuracy using a single-baseline PolInSAR image pair. The improvement was higher when applied in P-band than in L-band. The reason is the different interaction of the ground and vegetation layer and the lower penetration of L-band. The penetration depth becomes important if we are interested in reconstructing the vertical profile of trees at a higher resolution. In this case, P-band should be used; this fortunately will be available in satellite sensors in near future. For L-band, the exponential function as assumed 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 multi-baseline SAR tomostack data. In the past, temporal decorrelation was considered as a separate source of error that is independent of the canopy. It thus causes biased height estimates. Merging of a Fourier-Legendre series from the PCT model with a temporal decorrelation function from the Random Motion over Ground (RMoG) model has been explored to solve this problem. Results showed an improvement of height estimation accuracy after applying this modification. The optimal number of terms of the Fourier-Legendre series varied for each pixel. This can be used as an indicator of the complexity of the vegetation layer as for multi-layer dense forests, more terms are required. This chapter shows that increasing the number of unknown parameters can be done via segmenting the area into different height classes and selecting the optimum number of unknown parameters for each class.
The third chapter focuses on obtaining the most accurate height maps from PolInSAR. This is important by itself, whereas height also serves as the main biophysical parameter contributing to the estimation of biomass. The effect of mitigating temporal decorrelation was thus examined on biomass retrieval accuracy. This research developed new allometric equations for this purpose and tested different strategies for regression. This was challenging due to the lack of sufficient field data. The strategy to develop a new allometric equation based on height only is important. A parameter usually measured during fieldwork is H100, defined as the basal area weighted average of the 100 highest trees in each plot,. This chapter showed that the relation between PolInSAR height and H100 is weak, because PolInSAR height estimates the average of heights inside the plots and does not simply coincide with H100.
The fourth chapter discusses how to take temporal decorrelation into the estimation of tree heights. It addresses the sensitivity of the proposed modified model to the choice of complex coherence estimation method. The basic step of estimating height in any of the explained models is the selection of homogeneous pixels. To do so, we distinguished polarimetric from polarimetric-interferometric information. By addressing the pixel selection we could jointly take the phase and the magnitude values of the pixels into account. We employed two adaptive methods to define statistically homogeneous pixels. Height estimation accuracy increased after applying the adaptive methods. Since the proposed adaptive methods are computationally more intensive, a trade-off between the desired accuracy and computation is required prior to selection of any method.
To summarize, this dissertation improved the accuracy of tree height estimation from airborne fully polarized InSAR data by carefully addressing temporal decorrelation. This is potentially of use for future SAR satellite missions.
