Detection of Forest Disturbance With Spaceborne Repeat-Pass SAR Interferometry

Authors Organisations
  • Yang Lei(Author)
    California Institute of Technology
  • Richard Lucas(Author)
    University of New South Wales
  • Paul Siqueira(Author)
    University of Massachusetts
  • Michael Schmidt(Author)
    Remote Sensing Centre, Queensland
  • Robert Treuhaft(Author)
    California Institute of Technology
Type Article
Original languageEnglish
Pages (from-to)2424 - 2439
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number4
Publication statusPublished - 29 Dec 2017
Externally publishedYes
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Focusing on open forests and woodlands within the Injune Landscape Collaborative Project research area in central southeast Queensland, Australia, and using dual-pol (HH and HV) ALOS PALSAR repeat-pass InSAR data (temporal baseline of 92 days), this paper explores the detection of forest disturbance from the spaceborne repeat-pass InSAR correlation magnitude by developing a simple and efficient forest disturbance detection approach. In particular, a generic physical InSAR scattering model is derived by accounting for the forest disturbance infor- mation as well as the normal temporal decorrelation effects that are later compensated for using the modified Random Volume over Ground model. Based on the generic model, a quantitative indicator of forest disturbance is retrieved, namely, disturbance index that varies from 0 (no disturbance) to 1 (complete defor- estation). This index is compared with that identified using a time series of Landsat sensor data over a selective logging area and has a relative root mean square error of 13% at a spatial resolution of 0.8 ha. This paper highlights the use of the co-pol InSAR correlation magnitude for forest disturbance detection, which serves as a complimentary application to using the cross- pol counterpart for forest height inversion in a companion work. Given the global availability of this type of data (e.g., Japanese Aerospace Exploration Agency’s ALOS-1/2 and NASA- ISRO’s NISAR), the method is anticipated to contribute to the range of tools being developed for large-scale forest disturbance assessment and monitoring.


  • forest disturbance, forest height, inversion, selective logging, spaceborne repeat-pass InSAR