high-resolution forest mapping from tandem-x interferometric data exploiting nonlocal filtering

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ID: 156712
2018
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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.
Reference Key
martone2018remotehigh-resolution Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Michele Martone;Francescopaolo Sica;Carolina González;José-Luis Bueso-Bello;Paolo Valdo;Paola Rizzoli
Journal Journal of pharmacological sciences
Year 2018
DOI
10.3390/rs10091477
URL
http://www.mdpi.com/2072-4292/10/9/1477
Keywords Keywords not found

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