
Open access
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Date
2017-12Type
- Journal Article
Citations
Cited 37 times in
Web of Science
Cited 44 times in
Scopus
ETH Bibliography
yes
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Abstract
Synthetic Aperture Radar Tomography (TomoSAR) allows the reconstruction of the 3D reflectivity of natural volume scatterers such as forests, thus providing an opportunity to infer structure information in 3D. In this paper, the potential of TomoSAR data at L-band to monitor temporal variations of forest structure is addressed using simulated and experimental datasets. First, 3D reflectivity profiles were extracted by means of TomoSAR reconstruction based on a Compressive Sensing (CS) approach. Next, two complementary indices for the description of horizontal and vertical forest structure were defined and estimated by means of the distribution of local maxima of the reconstructed reflectivity profiles. To assess the sensitivity and consistency of the proposed methodology, variations of these indices for different types of forest changes in simulated as well as in real scenarios were analyzed and assessed against different sources of reference data: airborne Lidar measurements, high resolution optical images, and forest inventory data. The forest structure maps obtained indicated the potential to distinguish between different forest stages and the identification of different types of forest structure changes induced by logging, natural disturbance, or forest management. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000224732Publication status
publishedExternal links
Journal / series
Remote SensingVolume
Pages / Article No.
Publisher
MDPISubject
synthetic aperture radar (SAR); tomography; forest structure; forest dynamics; horizontal forest structure; vertical forest structure; L-band; Compressive sensingMore
Show all metadata
Citations
Cited 37 times in
Web of Science
Cited 44 times in
Scopus
ETH Bibliography
yes
Altmetrics