Next Article in Journal
Improved Detection of Tiny Macroalgae Patches in Korea Bay and Gyeonggi Bay by Modification of Floating Algae Index
Next Article in Special Issue
Enhanced Measurements of Leaf Area Density with T-LiDAR: Evaluating and Calibrating the Effects of Vegetation Heterogeneity and Scanner Properties
Previous Article in Journal
Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures
Previous Article in Special Issue
Forest Variable Estimation Using a High Altitude Single Photon Lidar System
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Remote Sens. 2018, 10(9), 1477; https://doi.org/10.3390/rs10091477

High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering

Microwaves and Radar Institute, German Aerospace Center, Münchener Straße 20, 82234 Weßling, Germany
*
Author to whom correspondence should be addressed.
Received: 22 August 2018 / Revised: 5 September 2018 / Accepted: 13 September 2018 / Published: 16 September 2018
(This article belongs to the Special Issue Remote Sensing Techniques for Precision Forestry)
Full-Text   |   PDF [5998 KB, uploaded 16 September 2018]   |  

Abstract

In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to 6 m. The investigated data set was acquired between 2011 in bistatic stripmap single polarization (HH) mode. Among the different factors affecting the quality of InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering, which typically occurs in the presence of vegetation, and is a very sensitive indicator for the discrimination of forested from non-forested areas. For this reason, it has been chosen as input observable for performing the classification. In this framework, both standard boxcar and nonlocal filtering methods have been considered for the estimation of the volume correlation factor. The resulting forest/non-forest mosaics have been validated using an accurate vegetation map of the region derived from Lidar-Optic data as external independent reference. Thanks to their outstanding performance in terms of noise reduction, together with spatial features preservation, nonlocal filters show a level of agreement of about 80.5% and we observed a systematic improvement in terms of accuracy with respect to the boxcar filtering at the same resolution of about 4.5 percent points. This approach is therefore of primary importance to achieve a reliable classification at such fine resolution. Finally, the high-resolution forest/non-forest classification product of the State of Pennsylvania presented in this paper demonstrates once again the outstanding capabilities of the TanDEM-X system for a wide spectrum of commercial services and scientific applications in the field of the biosphere. View Full-Text
Keywords: TanDEM-X mission; forest classification; SAR interferometry (InSAR); nonlocal filtering TanDEM-X mission; forest classification; SAR interferometry (InSAR); nonlocal filtering
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

Martone, M.; Sica, F.; González, C.; Bueso-Bello, J.-L.; Valdo, P.; Rizzoli, P. High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering. Remote Sens. 2018, 10, 1477.

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