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

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

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

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

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

Platforms SATELLITES (GLAS- 600 km / CALIOP- 705 km) High Altitude Planes (SLICER) Mean Altitude Planes HELICOPTERS Low Altitude (corridor mapping m ) km km m 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

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

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)

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

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

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

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

 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

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

 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

 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

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

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

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

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

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)

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

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

Thank you !

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

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

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

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

Point Classification  Classification algorithms : extracting ground points  Lot of approaches and algorithms  Best one Iterative Tin – Delauney triangulation ©F. Bretar, D 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

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)