Detection of Forest Disturbance With Spaceborne Repeat-Pass SAR Interferometry

Yang Lei, Richard Lucas, Paul Siqueira, Michael Schmidt, Robert Treuhaft

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

12 Dyfyniadau(SciVal)


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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)2424 - 2439
Nifer y tudalennau15
CyfnodolynIEEE Transactions on Geoscience and Remote Sensing
Rhif cyhoeddi4
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 29 Rhag 2017
Cyhoeddwyd yn allanolIe

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