The Global Forest/Non-Forest Map from TanDEM-X Interferometric SAR Data
Martone M, Rizzoli P, Wecklich C, Gonzalez C, Bueso-Bello JL, Valdo P, Schulze D, Zink M, Krieger G, Moreira A (2018)
Publication Type: Journal article
Publication year: 2018
Journal
Publisher: Elsevier
Edited Volumes: Remote Sensing of Environment
Book Volume: 205
Pages Range: 352-373
URI: https://elib.dlr.de/114017/
DOI: 10.1016/j.rse.2017.12.002
Abstract
In this paper we present the activities performed at the Microwaves and Radar Institute of the German Aerospace Center (DLR) to derive global forest/non-forest classification mosaics from TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) data. The data have been acquired for the generation of the global digital elevation model (DEM) between 2011 and 2016 in stripmap single polarization (HH) mode. The global data set of quicklook images, characterized by a ground resolution of 50 m x 50 m was used as input, in order to limit the computational burden. For classification purposes, several observables, systematically provided by the TanDEM-X system, can be exploited, such as the calibrated amplitude, the bistatic coherence, and the digital elevation model (DEM). In particular, the volume correlation factor quantifies the amount of decorrelation due to multiple scattering within a volume, which typically occurs in presence of vegetation. This quantity is directly derived from the interferometric coherence and used as main indicator for the identification of vegetated areas. For this purpose, a fuzzy multi-clustering classification approach, which takes into account the geometry and acquisition configuration (namely the incidence angle and the height of ambiguity) for the definition of the cluster centers, is applied to each acquired scene separately. A certain variability of the interferometric coherence at X band was observed among different forest types, mainly due to changes in forest structure, density, and tree height. This leads to an adjustment of the algorithm settings, and in particular to the derivation of different cluster centers, depending on the considered type of forest. The identification of additional information layers, such as urban settlements or water areas, is also discussed, and the procedure for mosaicking of overlapping acquisitions (two at global scale, up to ten over mountaineous terrain, forests, and desert regions) to improve the classification accuracy is detailed. The resulting global forest/non-forest map was validated using external reference information as well as with other existing classification maps and an overall agreement was observed that often exceeds 90\%. Finally, examples for highresolution (at 12 m x 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEMX bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/nonforest map presented in this paper will be made available to the scientific community for free downloading and usage.
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APA:
Martone, M., Rizzoli, P., Wecklich, C., Gonzalez, C., Bueso-Bello, J.-L., Valdo, P.,... Moreira, A. (2018). The Global Forest/Non-Forest Map from TanDEM-X Interferometric SAR Data. Remote Sensing of Environment, 205, 352-373. https://dx.doi.org/10.1016/j.rse.2017.12.002
MLA:
Martone, Michele, et al. "The Global Forest/Non-Forest Map from TanDEM-X Interferometric SAR Data." Remote Sensing of Environment 205 (2018): 352-373.
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