Modeling and Detection of Deforestation and Forest Growth in Multitemporal TanDEM-X Data
Journal article, 2018

This paper compares three approaches to forest change modeling in multitemporal (MT) InSAR data acquired with the X-band system TanDEM-X over a forest with known topography. Volume decorrelation is modeled with the two-level model (TLM), which describes forest scattering using two parameters: forest height h and vegetation scattering fraction ζ, accounting for both canopy cover and electromagnetic scattering properties. The single-temporal (ST) approach allows both h and ζ to change between acquisitions. The MT approach keeps h constant and models all change by varying ζ. The MT growth (MTG) approach is based on MT, but it accounts for height growth by letting h have a constant annual increase. Monte Carlo simulations show that MT is more robust than ST with respect to coherence and phase calibration errors and height estimation ambiguities. All three inversion approaches are also applied to 12 VV-polarized TanDEM-X acquisitions made during the summers of 2011-2014 over Remningstorp, a hemiboreal forest in southern Sweden. MT and MTG show better height estimation performance than ST, and MTG provides more consistent canopy cover estimates than MT. For MTG, the root-mean-square difference is 1.1 m (6.6%; r=0.92) for forest height and 0.16 (22%; r=0.48) for canopy cover, compared with similar metrics from airborne lidar scanning (ALS). The annual height increase estimated with MTG is found correlated with a related ALS metric, although a bias is observed. A deforestation detection method is proposed, correctly detecting 15 out of 19 areas with canopy cover loss above 50%.

forest height

Canopy cover

TanDEM-X

interferometric model

interferometric synthetic-aperture radar (InSAR)

deforestation detection

growth model

Author

Maciej Soja

Horizon Geoscience Consulting

University of Tasmania

H. Persson

Swedish University of Agricultural Sciences (SLU)

Lars Ulander

Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

1939-1404 (ISSN)

Vol. 11 10 3548-3563 8493484

Infrastructure

C3SE (Chalmers Centre for Computational Science and Engineering)

Subject Categories

Forest Science

Physical Geography

Probability Theory and Statistics

DOI

10.1109/JSTARS.2018.2851030

More information

Latest update

3/19/2019