Detection of Forest Change and Robust Estimation of Forest Height from Two-Level Model Inversion of Multi-Temporal Single-Pass InSAR Data
Paper in proceedings, 2015
In this paper, forest change detection and forest height estimation are studied using two-level model (TLM) inversion of multi-temporal TanDEM-X (TDM) data. Parameter Delta h, describing the distance between ground and vegetation levels, is kept constant for all acquisitions, whereas parameter mu, the area-weighted backscatter ratio, changes with acquisition. Two multi-temporal sets of TDM data, acquired over the hemi-boreal test site Remningstorp, situated in southern Sweden, are studied: one consisting of 12 acquisitions made in the summers of 2011, 2012, 2013, and 2014 with heights-of-ambiguity (HOAs) between 32 m and 63 m, and one consisting of 33 acquisitions made between August 2013 and August 2014 with HOAs between 38 m and 195 m. The first dataset is used to show that commercial thinnings and clear-cuts can be detected by studying the canopy density estimate eta(0) = 1 = (1 + mu). The second dataset is used to show that seasonal change can be observed in eta(0) for deciduous plots, but not for coniferous plots. Moreover, it is shown that 1.3 Delta h is a good estimate of the basal area-weighted (Lorey's) height, with a correlation coefficient equal to 0.98 and a root-mean-square error of 0.9 m.
two-level model (TLM)