The Global Forest/Non-Forest Map from TanDEM-X Interferometric SAR Data. Martone, M., Rizzoli, P., Wecklich, C., González, C., Bueso-Bello, J., Valdo, P., Schulze, D., Zink, M., Krieger, G., & Moreira, A. 205:352–373. ![The Global Forest/Non-Forest Map from TanDEM-X Interferometric SAR Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex [Highlights] [::] A method to identify forested areas from TanDEM-X interferometric data is proposed. [::] A global forest/non-forest map at a resolution of 50 m is derived. [::] Potentials for high-resolution (12 m) forest mapping and monitoring are presented. [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 interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50 m × 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 digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur 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, 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, which led to an adjustment of the algorithm settings 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 mountainous 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 high-resolution (at 12 m × 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/non-forest map presented in this paper will be made available to the scientific community for free download and usage.
@article{martoneGlobalForestNonforest2018,
title = {The Global Forest/Non-Forest Map from {{TanDEM}}-{{X}} Interferometric {{SAR}} Data},
author = {Martone, Michele and Rizzoli, Paola and Wecklich, Christopher and González, Carolina and Bueso-Bello, José-Luis and Valdo, Paolo and Schulze, Daniel and Zink, Manfred and Krieger, Gerhard and Moreira, Alberto},
date = {2018-02-01},
journaltitle = {Remote Sensing of Environment},
shortjournal = {Remote Sensing of Environment},
volume = {205},
pages = {352--373},
issn = {0034-4257},
doi = {10.1016/j.rse.2017.12.002},
url = {https://doi.org/10.1016/j.rse.2017.12.002},
urldate = {2019-05-13},
abstract = {[Highlights]
[::] A method to identify forested areas from TanDEM-X interferometric data is proposed.
[::] A global forest/non-forest map at a resolution of 50 m is derived.
[::] Potentials for high-resolution (12 m) forest mapping and monitoring are presented.
[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 interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50 m × 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 digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur 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, 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, which led to an adjustment of the algorithm settings 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 mountainous 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 high-resolution (at 12 m × 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/non-forest map presented in this paper will be made available to the scientific community for free download and usage.},
keywords = {~INRMM-MiD:z-7NQ6GJ3I,classification,forest-resources,global-scale,open-data,radar,remote-sensing,tandem-x}
}
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[::] A global forest/non-forest map at a resolution of 50 m is derived. [::] Potentials for high-resolution (12 m) forest mapping and monitoring are presented. [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 interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50 m × 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 digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur 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, 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, which led to an adjustment of the algorithm settings 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 mountainous 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 high-resolution (at 12 m × 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. The global TanDEM-X forest/non-forest map presented in this paper will be made available to the scientific community for free download and usage.","keywords":"~INRMM-MiD:z-7NQ6GJ3I,classification,forest-resources,global-scale,open-data,radar,remote-sensing,tandem-x","bibtex":"@article{martoneGlobalForestNonforest2018,\n title = {The Global Forest/Non-Forest Map from {{TanDEM}}-{{X}} Interferometric {{SAR}} Data},\n author = {Martone, Michele and Rizzoli, Paola and Wecklich, Christopher and González, Carolina and Bueso-Bello, José-Luis and Valdo, Paolo and Schulze, Daniel and Zink, Manfred and Krieger, Gerhard and Moreira, Alberto},\n date = {2018-02-01},\n journaltitle = {Remote Sensing of Environment},\n shortjournal = {Remote Sensing of Environment},\n volume = {205},\n pages = {352--373},\n issn = {0034-4257},\n doi = {10.1016/j.rse.2017.12.002},\n url = {https://doi.org/10.1016/j.rse.2017.12.002},\n urldate = {2019-05-13},\n abstract = {[Highlights]\n[::] A method to identify forested areas from TanDEM-X interferometric data is proposed.\n[::] A global forest/non-forest map at a resolution of 50 m is derived.\n[::] Potentials for high-resolution (12 m) forest mapping and monitoring are presented.\n\n[Abstract]\nIn 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 interferometric synthetic aperture radar (InSAR) data acquired by the TanDEM-X mission. The data have been collected between 2011 and 2016 in bistatic stripmap single polarization (HH) mode, with the main goal of generating a consistent, timely, and highly accurate 3D representation of the global terrain’s surface (digital elevation model, DEM). The global data set of quicklook images, which represent a spatially averaged version of the original full resolution data at a ground independent pixel spacing of 50 m × 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 digital elevation model (DEM), and the interferometric coherence. Among the several factors contributing to a coherence degradation in InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering phenomena, which typically occur 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, 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, which led to an adjustment of the algorithm settings 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 mountainous 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 high-resolution (at 12 m × 12 m) forest maps and potentials for deforestation monitoring over selected regions are presented as well, demonstrating the unique capabilities offered by the TanDEM-X bistatic system for a broad range of geoinformation services and scientific applications. 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