The scientific community has and will witness a significant increase in the availability of different global satellite derived biophysical data sets. However, the use of such data is currently not supported by accurate in-situ biophysical measurements in both a research and operational context for the monitoring of forest and land dynamics. Terrestrial LiDAR (LIght Detection and Ranging) is a ground-based remote sensing technique that can retrieve the 3D vegetation structure in high detail.
Research within the Laboratory of Geo-Information Science and Remote Sensing is focused on:
- Deriving biophysical parameters to support forest inventory
- Quantifying biomass and changes in biomass
- Combining LiDAR and radiative transfer models to validate air/spaceborne data
GreenValley 3D LiBACKPACK DGC50 (Data Sheet)
- Double Velodyne VLP-16 (600 000 points/s) with integrated camera (360° RGB) and GNSS antenna
- LiBackpack Base Station : Base GNSS
- Scanner mount for manual tilt (adjustable in steps of 15° up to 90°)
- Integrated digital compass and GPS antenna
- Full waveform (FWF) readout
- NIKON D700 digital camera on high precision camera mount (NIKKOR 14/2.8 lens and NIKKOR 85/1.8 lens)
Available terrestrial LiDAR datasets
The listed datasets were collected by the Laboratory of Geo-Information Science and Remote Sensing. Reference data (e.g. forest inventory, hemispherical photography) and additional airborne/spaceborne data are available for most datasets. Please contact us if you are interested in one of the datasets.
- Laegern (Switzerland, 2010, 6 plots with 20m radius)
- Hallerbos (Belgium, 2011-2012 time series, 2 plots with 18m radius)
- Loobos (The Netherlands, 2011, 4 plots of 100m x 100m)
- Antwerp (Belgium, 2012, scan of 7 individual trees in De Villegasstraat)
- Australian projects:
- Biomass harvesting (VIC, 2012-2013, 18 plots of 40m radius)
- Rushworth RF5 (VIC, 2012)
- Temperate rainforest (VIC, 2012, 6 plots)
- EucFACE experiment (NSW, 2012)
- Selection of TERN sites in QLD (2012)
- Gabon (Mondah forest & Lope National Park)
- 1ha GEM plots (2 plots in Mondah forest + 1 plot in Lope National Park, 100x100m)
- Savannah-to-forest biomass gradient at Lope National Park (2 plots for 5 different forest types, 20x40m).
Peru (permanent plots in Tambopata Reserve (Madre de Dios Dept.) and buffer zone of Manu National Park (Cuzco Dept.), Biomass harvest plots in Madre de Dios, biomass plots in peat forest in Iquitos (Maynas Dept.) )
- 1ha GEM plots (100m x 100m) in the biomass gradient Andes to Amazon Transect.
- 2 plots in Tambopata Biosphere Reserve (200 MAMSL)
- 1 plot in San Pedro (1,750 MAMSL),
- 2 plots in Wayquecha Cloud Forest Biological Station (ACCA) (3,000 MAMSL)
- Selective logging biomass harvest plots in Madre de Dios (9 plots, 50m x 30m). TLS before and after logging.
- 1 Transect in peat swamp forest in Iquitos (6 plots of 10 m radius)
- Indonesia - August 2014 (Sampit, Borneo)
- Selective logging biomass harvest plots in peat forest (10 plots, 30m x 40m). TLS before and after logging.
- Guyana – November 2014 (Vaitarna, Guyana)
- Selective logging biomass harvest plots in amazon forest (10 plots, 30m x 40m). TLS before and after logging.
- Chronosequence stumps of 0, 2-3 years and 3 years old (36 scans)
- Ethiopia – November 2015 (Kafa, Ethiopia)
- Plots of 20m radius along forest degradation gradient in Kafa. TLS scans were made from 5 positions
- Ghana – March 2016 (Kumasi and Elubo, Ghana)
- 01 GEM plot in Bobiri
- 02 GEM plots in Kogyae (Transition Forest savanna and savanna
- 01 Afriscat plot (70x100m) next to fluxtower, 10m grid and 2 TLS
- 01 GEM plot in Ankasa National Reserve, Elubo
- Guyana - February 2017 (Rong An Inc. - Berbice)
- 108 individual trees scanned in 37 plots.
- 26 selective logging biomass harvest trees.
- Suriname – February 2019 (Tijgerkreek-West, Commewijne, Mapane, Kabo/Tibiti)
- 118 individual trees scanned in 17 plots.
- 31 selective logging biomass harvest trees.
- French Guiana – October 2019 (Paracou Research centre)
- 5.0 ha intact forest plot (in development)
- CSIRO Land and Water (Vegetation Sensing & Information), VIC, Australia
- University College London (Department of Geography), UK
- Department of Science, Information Technology, Innovation and the Arts (Remote Sensing Centre), QLD, Australia
- University of British Columbia (Integrated Remote Sensing Studio), Canada
- University of Melbourne (Department of Forest and Ecosystem Science), VIC, Australia
- Boston University (Department of Geography and Environment), USA
- Tampere University of Technology (Dept. of Mathematics), Finland
- Oxford University (Environmental Change Institute, School of Geography and the Environment)
- CIFOR (Forests and Environment Programme)
- Guyana Forestry Commission
- CELOS (Center for Agricultural Research in Suriname)
- UMR AMAP
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Other animations can be found on http://www.youtube.com/wurLiDAR
Quantification of Tropical Forest Biomass with Terrestrial LiDAR and 3D Tree Quantitative Structure Modelling
Terrestrial LiDAR and 3D Reconstruction Models for Estimation of Large Tree Biomass in the Tropics
Terrestrial LiDAR and 3D Reconstruction Models for Large Individual Tree Biomass Estimation in Tropics
Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning dataAgricultural and Forest Meteorology 216 (2016). - ISSN 0168-1923 - p. 105 - 114.
Application of terrestrial LiDAR and modelling of tree branching structure for plant-scaling models in tropical forest treesIn: Proceedings of the SilviLaser 2015 conference. - - p. 3 - 3.
Nondestructive estimates of above-ground biomass using terrestrial laser scanningMethods in Ecology and Evolution 6 (2015)2. - ISSN 2041-210X - p. 198 - 208.
Terrestrial Laser Scanning for measuring forest biomass change
Acquisition of terrestrial LIDAR in tropical forest to support ecological research
Evaluation of different scan configurations for an effective field procedure on a terrestrial LiDAR scanner in tropical forest
Implications of sensor configuration and topography on vertical plant profiles derived from terrestrial LiDARAgricultural and Forest Meteorology 194 (2014). - ISSN 0168-1923 - p. 104 - 117.