A modified model for estimating tree height from PolInSAR with compensation for temporal decorrelation

This chapter is based on the published paper: Ghasemi, N., Tolpekin, V. and Stein, A., (2018). A modified model for estimating tree height from PolInSAR with compensation for temporal decorrelation. International Journal of Applied Earth Observation and Geoinformation, 73, pp.313–322. https://www.sciencedirect.com/science/article/pii/S0303243418302563?via%3Dihub The dataset has been acquired by European Space Agency in the context of BioSAR2010 campaign conducted on Southwest Sweden by airborne SAR sensor and extensive field research. The RMoG (Random-Motion-over-Ground) model is commonly used to ob- tain tree height values from PolInSAR images. The RMoG model borrows its structure function from conventional RVoG (Random-Volume-over-Ground) model which is limited for modelling structural variety in canopy layer. This chapter extends the RMoG model to improve tree height estimation accur- acy by using a Fourier-Legendre polynomial as the structure function. The new model is denoted by the RMoGL model. The proposed modification makes height estimation less prone to errors by enabling more flexibility in representing the vertical structure of the vegetation layer. We applied the RMoGL model on airborne P- and L-band PolInSAR images from the Remingstorp test site in southern Sweden. We compared it with the RMoG and the conventional RVoG models using Lidar height map and field data for validation. For P-band, the relative error was equal to 37.5% for the RVoG model, to 23.7% for the RMoG model, and to 18.5% for the RMoGL model. For L-band it was equal to 30.54% for the RVoG model, to 20.02% for the RMoG model, and to 21.63% for the RMoGL. We concluded that the RMoGL model estimates tree height more accurately in P-band, while in L-band the RMoG model was equally good. The RMoGL model is of a great value for future SAR sensors that are more focused than before on tree height and biomass estimation.

Remote sensing, SAR, PolInSAR, Forest Structure, Temporal decorrelation