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

ScanningActive 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

2

tcR

©Calypso, CNES, 2006

R = ranget = timeC = 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 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

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

PreprocessingRaw point cloud DTM

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

Photointerpretation

Terrain + Lidar

Volume estimated per grid cellSummed 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 view Top 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

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

Geomorphology Habitat Mapping

Angkor ruins under the forest canopy (Chase and al., 2010)

Archeology

Erosion / Flooding

Bird

Thank you !

Forest Parameter Estimation

Plot-based ApproachN Lidar PlotsStatistical descriptors

N Field Plots

Regression analysisValidationLarge scale mapping

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

Point Classification

First Return First Return VegetationVegetation

Last Return Last Return GroundGround

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 ReturnVegetation

Last ReturnVegetation

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, 2006

3D points Local minima

Initial TIN Surface

TIN DensificationAngle & Distance

Axelsson (1999)

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

The Big Picture

Mo

del

DynamicFonctionning

T-L

idar

ArchitectureAllometry

Porosity

A-L

idar

StructureBiomass

Dynamics

dbh

Height

Tex

ture

Forest tpyeBiomass

DART Images(AMAP – CESBIO)