1. An overview of Lidar remote sensing of forests C. Véga French Institute of Pondicherry

2. Outline  Principle and History  Systems and Platform  Data processing / Forestry  Airborne discrete Lidar  Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

3. What is Lidar ?  LIght Detection And Ranging or Laser Scanning  Active remote sensing measuring distance to target based on « time of flight » e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore ©Calypso, CNES, 2006 R = range t = time C = light speed

4. History e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Sixties : Airborne laser for measuring flight altitude  Seventies – Eighties : Airborne profiling systems (topography and forestry)  Nineties: Scanning systems with GPS and INS -> Georeferencing  2000 ongoing : Industrial development – costs reduction

5. Systems e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Full-waveform systems  Discrete systems  Scanning > 300 kHz Record the complete range of energy reflected by surfaces Record 1 up to N returns by emitted pulse Precision : 1 m xy; 0.1 m z

6. Platforms SATELLITES (GLAS- 600 km / CALIOP- 705 km) High Altitude Planes (SLICER) Mean Altitude Planes HELICOPTERS Low Altitude (corridor mapping 50-150 m ) 500 - 1000 km 8 - 12 km 1200 - 3500 m 100 - 1000 m ALTITUDE 0 m Ground or Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

7. Data acquisition e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Small Footprint Airborne Lidar

8. Data Visualisation e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Small Footprint Airborne Lidar 833 m 890 m Draix, France)

9. Data Visualisation e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Small Footprint Airborne Lidar

10. Data Visualisation e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Terrestrial Lidar

11. Point cloud Processing  Discrete Airborne Laser Scanning (ALS)  Small Scale parameter estimation -> Plot Level  Large Scale parameter estimation -> Tree Level  Terrestrial Laser Scanning (TLS)  Stem characterization  Tree architecture e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

12. Preprocessing Raw point cloudDTM Normalized point cloud = Raw - DTM e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore 833 m 890 m 21 m 0 m

13.  Estimating Field parameters from Lidar parameters  Multiplicative models  Stepwise approach e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore Forest Parameters Field = Function (Lidar) Calibration Inversion

14. Small Scale Mapping Field Plots Lidar Grid Photo interpretation Terrain + Lidar Volume estimated per grid cell Summed by stand -> mean/ha  Volume estimation (Naesset, 2005) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

15.  Tree-based approaches - Segmentation methods  Local maxima extraction on raster + polygon fitting (Popescu et al., 2003, 2004) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore Large Scale Mapping

16.  Tree-based approaches - Segmentation methods  Direct segmentation of the point cloud Lateral viewTop view e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore Large scale mapping

17. Individual tree approaches  Direct estimation of tree density and tree parameters  Improving equations for volume and biomass (height and crown dimension)  Crown dimension explain better AGB (Popescu 2003)  Stem to stem management -> thinning e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

18. Terrestrial lidar  Limited to small surfaces (Plots)  Very high density (mm)  Utility for allometry, tree architecture and forest modeling e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

19. Terrestrial Lidar  Stem Characterization  Automatic Stem Extraction (PCA- Hough) (Bac et al. 2013)

20. Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore (Bac et al. 2013)

21. Terrestrial Lidar e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore  Tree architecture  L-Architect (Côté et al. 2011)

22. Potential for Indian Forestry  Measuring biomass -> issue in complex tropical forests  Conventional remote sensing -> signal saturation at low AGB  Lidar  Directly related to forest structure  No saturation with AGB  Best current data for plot and landscape estimation of forest parameters  Utility for calibrating texture indices from satellites images for ABG estimations at regional level e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

23. Variety of applications… e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore GeomorphologyHabitat Mapping Angkor ruins under the forest canopy (Chase and al., 2010) Archeology Erosion / Flooding Bird

24. Thank you !

25. Forest Parameter Estimation  Plot-based Approach N Lidar Plots Statistical descriptors N Field Plots Regression analysis Validation Large scale mapping e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

26. Point Classification First Return Vegetation Last Return Ground  Example for an ALS system recording 2 returns  Issue: Point penetration within canopy e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

27. Point Classification Unique return = Ground (First= Last) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

28. Point Classification First Return Vegetation Last Return Vegetation e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

29. Point Classification  Classification algorithms : extracting ground points  Lot of approaches and algorithms  Best one Iterative Tin – Delauney triangulation ©F. Bretar, 2006 3D points Local minima Initial TIN Surface TIN Densification Angle & Distance Axelsson (1999) e Forests - National Workshop on Info Systems for Decision Making in Forestry 9,10 & 11th May 2013 Bangalore

30. The Big Picture Model Dynamic Fonctionning T-Lidar Architecture Allometry Porosity A-Lidar Structure Biomass Dynamics dbh Height Texture Forest tpye Biomass DART Images (AMAP – CESBIO)