Next Article in Journal
Using Copernicus Atmosphere Monitoring Service Products to Constrain the Aerosol Type in the Atmospheric Correction Processor MAJA
Previous Article in Journal
Investigation of Water Temperature Variations and Sensitivities in a Large Floodplain Lake System (Poyang Lake, China) Using a Hydrodynamic Model
Previous Article in Special Issue
Nearest-Regularized Subspace Classification for PolSAR Imagery Using Polarimetric Feature Vector and Spatial Information
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2017, 9(12), 1229; doi:10.3390/rs9121229

Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography

1
Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Oberpfaffenhofen, Germany
2
Institute of Environmental Engineering, ETH Zurich, 8093 Zürich, Switzerland
3
Department of Ecological Modelling, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, 04318 Leipzig, Germany
4
Faculty of Forest Science and Resource Management, Technical University of Munich, Hans-Carl-v.-Carlowitz-Platz 2, 85354 Freising, Germany
*
Author to whom correspondence should be addressed.
Received: 14 September 2017 / Revised: 14 September 2017 / Accepted: 2 November 2017 / Published: 28 November 2017
(This article belongs to the Special Issue Recent Advances in Polarimetric SAR Interferometry)
View Full-Text   |   Download PDF [39364 KB, uploaded 28 November 2017]   |  

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. View Full-Text
Keywords: synthetic aperture radar (SAR); tomography; forest structure; forest dynamics; horizontal forest structure; vertical forest structure; L-band; compressive sensing synthetic aperture radar (SAR); tomography; forest structure; forest dynamics; horizontal forest structure; vertical forest structure; L-band; compressive sensing
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Cazcarra-Bes, V.; Tello-Alonso, M.; Fischer, R.; Heym, M.; Papathanassiou, K. Monitoring of Forest Structure Dynamics by Means of L-Band SAR Tomography. Remote Sens. 2017, 9, 1229.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top