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.



canopy density

forest height

two-level model (TLM)


Maciej Soja

Chalmers, Earth and Space Sciences, Radar Remote Sensing

H. Persson

Swedish University of Agricultural Sciences (SLU)

Lars Ulander

Chalmers, Earth and Space Sciences, Radar Remote Sensing

International Geoscience and Remote Sensing Symposium (IGARSS)

3886-3889 7326673

Driving Forces

Sustainable development

Subject Categories

Remote Sensing


Basic sciences





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