Abstract

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 zeta, accounting for both canopy cover and electromagnetic scattering properties. The single-temporal (ST) approach allows both h and zeta to change between acquisitions. The MT approach keeps h constant and models all change by varying zeta. 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%.

Keywords

Canopy cover; deforestation detection; forest height; growth model; interferometric model; interferometric synthetic-aperture radar (InSAR); TanDEM-X

Published in

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
2018, Volume: 11, number: 10, pages: 3548-3563
Publisher: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

SLU Authors

• Persson, Henrik

  • Department of Forest Resource Management, Swedish University of Agricultural Sciences
    

Associated SLU-program

Remningstorp


UKÄ Subject classification

Forest Science
Remote Sensing


Publication identifier

DOI: https://doi.org/10.1109/JSTARS.2018.2851030

Permanent link to this page (URI)

https://res.slu.se/id/publ/97011