TomoSense: A unique 3D dataset over temperate forest combining multi-frequency mono- and bi-static tomographic SAR with terrestrial, UAV and airborne lidar, and in-situ forest census
Journal article, 2023

The TomoSense experiment was funded by the European Space Agency (ESA) to support research on remote sensing of forested areas by means of Synthetic Aperture Radar (SAR) data, with a special focus on the use of tomographic SAR (TomoSAR) to retrieve information about the vertical structure of the vegetation at different frequency bands. The illuminated scene is the temperate forest at the Eifel National Park, North-West Germany. Dominant species are beech and spruce trees. Forest height ranges roughly from 10 to 30 m, with peaks up to over 40 m. Forest Above Ground Biomass (AGB) ranges from 20 to 300 Mg/ha, with peaks up to over 400 Mg/ha. SAR data include P-, L-, and C-band surveys acquired by flying up to 30 trajectories in two headings to provide tomographic imaging capabilities. L- and C-band data were acquired by simultaneously flying two aircraft to gather bistatic data along different trajectories. The SAR dataset is complemented by 3D structural canopy measurements made via terrestrial laser scanning (TLS), Unoccupied Aerial Vehicle lidar (UAV-L) and airborne laser scanning (ALS), and in-situ forest census. This unique combination of SAR tomographic and multi-scale lidar data allows for direct comparison of canopy structural metrics across wavelength and scale, including vertical profiles of canopy wood and foliage density, and per-tree and plot-level above ground biomass (AGB). The resulting TomoSense data-set is free and openly available at ESA for any research purpose. The data-set includes ALS-derived maps of forest height and AGB, forest parameters at the level of single trees, TLS raw data, and plot-average TLS vertical profiles. The provided SAR data are coregistered, phase calibrated, and ground steered, to enable a direct implementation of any kind of interferometric or tomographic processing without having to deal with the subtleties of airborne SAR processing. Moreover, the data-base comprises SAR tomographic cubes representing forest scattering in 3D both in Radar and geographical coordinates, intended for use by non-Radar experts. For its unique features and completeness, the TomoSense data-set is intended to serve as an important basis for future research on microwave scattering from forested areas in the context of future Earth Observation missions.

SAR tomography

Synthetic aperture radar (SAR)

Forest vertical structure

Terrestrial laser scanning (ALS)

Forest census

L-band

Forest above ground biomass

Bistatic radar

P-band

Author

S. Tebaldini

Polytechnic University of Milan

Mauro Mariotti d'Alessandro

Polytechnic University of Milan

Lars Ulander

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Swedish Defence Research Agency (FOI)

Patrik Bennet

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

A. Gustavsson

Swedish Defence Research Agency (FOI)

Alex Coccia

MetaSensing BV

Karlus Macedo

MetaSensing BV

Mathias Disney

University College London (UCL)

Phil Wilkes

University College London (UCL)

Hans Joachim Spors

Landesbetrieb Wald und Holz Nordrhein-Westfalen

Nico Schumacher

Landesbetrieb Wald und Holz Nordrhein-Westfalen

Jan Hanuš

Czech Academy of Sciences

Jan Novotný

Czech Academy of Sciences

Benjamin Brede

German Research Centre for Geosciences (GFZ)

Wageningen University and Research

Harm Bartholomeus

Wageningen University and Research

Alvaro Lau

Wageningen University and Research

Jens van der Zee

Wageningen University and Research

Martin Herold

Wageningen University and Research

German Research Centre for Geosciences (GFZ)

Dirk Schuettemeyer

European Space Research and Technology Centre (ESA ESTEC)

Klaus Scipal

ESRIN - ESA Centre for Earth Observation

Remote Sensing of Environment

0034-4257 (ISSN)

Vol. 290 113532

Subject Categories

Remote Sensing

Forest Science

Geophysics

DOI

10.1016/j.rse.2023.113532

More information

Latest update

4/13/2023