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LiDAR in operational LiDAR in operational forest management forest management

-- efforts to improve and extend productsefforts to improve and extend products

UMB, 22.08 2006UMB, 22.08 2006

Dr. Tord AaslandDr. Tord Aasland

Outline of the presentation:Outline of the presentation:• Products• Technical solutions• Tools• Results• New LiDAR activities

– Compartment borders– Tree species– Quality parameters– Updating of existing FMP

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PrevistaPrevistaThe largest forest management planning enterprise in Scandinavia with a century-long experience in forest planning and remote sensing

Introduced LiDAR based forest inventory as an operational method in 2002

One of the only worldwide providers

Methods and procedures based on collaboration with UMB (University of Life Sciences)

In total 15 projects in Scandinavia

LIDAR parameters:• First and last pulse reflection

• 33.000-100.000 pulses per sec

• 1.100-2.000 m altitude

• 20-30 cm footprint size

• 0.9-1.5 m spacing

• 500-1000 m swath

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Combination Combination of of existingexisting stand stand mapmap and laser data and laser data –– reflection in DPW to improve the production linereflection in DPW to improve the production line

PhotoPhoto interpretationinterpretation vsvs fieldworkfieldwork in standsin stands• STAND MAPS (constructed by stereo photogrammetry due to large

topographic variability) Orthophoto (DTM).

• STAND CHARACTERISTICS

Mandatory quantitative Operational quantitative Mandatory qualitative-Mean height -Mean diameter -Recommended treatments

-Stand volume -Stem number -Biodiversity (required for

-Tree species distribution -Diameter distribution certification and public

-Age subsidies)

-Site quality

-Young regenerations: Photo interpretationstem number, tree species, Field workheight, site quality, age

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PhotoPhoto interpretationinterpretation vsvs fieldworkfieldwork in standsin stands• STAND MAPS (constructed by stereo photogrammetry due to large

topographic variability) Orthophoto (DTM).

• STAND CHARACTERISTICS

Mandatory quantitative Operational quantitative Mandatory qualitative-Mean height -Mean diameter -Recommended treatments

-Stand volume -Stem number -Biodiversity (required for

-Tree species distribution -Diameter distribution certification and public

-Age subsidies)

-Site quality

-Young regenerations: Photo interpretationstem number, tree species, Field workheight, site quality, age LiDAR

Our method provides:Our method provides:• Digital orthophotos (2-5 band)

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• LIDAR - data:1. Digital Terrain Model (DTM)2. Forest parameters:

Volume per hectareNumber of stemsDominant heightMean height, HL

Basal area, GMean diameter, Dbh

Diameter distribution

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V=2.326*h90l1.912*d3f

3.142

V=82 m3/ha

LIDAR Production LineLIDAR Production Line• Unique functions per stratum (all

variables)

• Grid enables updating ofboundaries while maintainingaccuracy

From x,y,z to From x,y,z to ForestryForestry

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Vexcel digital camera (2-band) or other similar camerasPhoto equipment

Pinnacle ver 1.0GPS-equipmentOptech / Leica / Riegl (sweeping LiDAR)Output data: X,Y,Z, Δ Z – first/last returnLIDAR

SAS 8.02.SAS/STATTMStatistical calculations

Microsoft SQL-server 2003Prevista LIDAR SoftwareDatabase

Arc GIS 9.0GIS-systems

Our toolsOur tools & Software& Software

10-15%20-30%No of trees, N/ha

10-15%25-35 %Mean diameter, Dg

5-15%15-30 %Volume, V/ha

10-15%20-30 %Basal area, G5-10 %15-25 %Dominant height, H100

5-10 %15-25 %Mean height, HL

LIDARTraditionalStand variables

AccuracyAccuracy ((StdvStdv) ) on compartment level on compartment level ((TraditionalTraditional MethodsMethods vs. LIDAR)vs. LIDAR)

3-8%10-20%Total volume management area

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NorwaySweden

NorwaySweden

Norway

Norway

Norway

Project

60.00020.00

200642

90.0006.000

200521

15.00020043

40.00020032

50.0002002 1

Area, haYearNo.

LiDAR LiDAR projectsprojects -- referencesreferences

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• Delivery of data into grid cells instead of compartments?

• Delineation of compartments/units based on LiDAR data?

• Is it possible to find tree species composition based on LiDAR data and images?

• Use of LiDAR for updating of existing FMP?

Future developmentFuture development (1)(1)

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Mean volumeMean volume (V/ha)(V/ha) in grid cells based on LiDAR data

Mean diamterMean diamter (D(Dgg) in grid cells based on LiDAR data

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LIDAR data for LIDAR data for automaticautomatic delineation into compartmentsdelineation into compartments (1)(1)

HeightHeight--percentilespercentiles from first return and DensityDensity ofLiDAR pulses in canopy gives suggestion todeliniation using eCognitionimage analyses

Density h variables

d9=(n>((max-2)/10*9))/N

d5=(n>((max-2)/10*5))/N

d0=(n>2)/N

f

LIDAR data for LIDAR data for automaticautomatic delineation into compartmentsdelineation into compartments (2)(2)

Stand Stand heightsheightsin 2 min 2 m classesclasses

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LIDAR data and digitalLIDAR data and digital photophoto toto find tree speciesfind tree species? ?

IRIR OrthophotoOrthophoto and LiDAR and LiDAR HeightHeight--percentilespercentiles

FieldField sample plots sample plots ++ orthophotoorthophoto+ LiDAR+ LiDAR densitydensity datadata+ image analyses+ image analyses

== Tree speciesTree species in % forin % for different density classesdifferent density classes

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ResultsResults::

CompartmentCompartment borders andborders and tree species compositiontree species composition

Use of Use of LiDAR for LiDAR for automatic change detectionautomatic change detection

case: Gjerdrum case: Gjerdrum common common land (1999land (1999--2006)2006)

•Red : Original compartment borders •Yellow: Updated by Forest Manager manually•Green : Uptated by LiDAR, digital photo & eCognition

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FutureFuture development development (2)(2)

• Quality parameters– Diameter distribution– Tree crown height and size (3-D)

• C/B-analyses of optimal flight altitude – eg. point density on the ground

• Use of LiDAR for other purposes than estimation of volume

ExampleExample ofof standstand--wisewise diameter diameter distributiondistribution

0

50

100

150

200

1 3 5 7 9 11 13 15 17 19

0

50

100

150

200

1 3 5 7 9 11 13 15 17 19

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50

100

150

200

1 3 5 7 9 11 13 15 17 19

Source: Tron Eid, UMB

Photo intepretation

Relascope sample plots

LIDAR

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

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Research projectResearch project: UMB/Prevista (1): UMB/Prevista (1)• Quality parameters

– Tree crown height and size (3-D)

– Diameter distribution• Case: Aurskog Høland

municipality– Collecting data from/with

harvester– Extra fieldwork (sample

plots and qualityparameters)

– Extra flight (low altitude)

Research projectResearch project: UMB/Prevista (2): UMB/Prevista (2)

• C/B-analyses of optimal flight altitude – eg. point density on the ground

• Case: Hole municipality– Four different flights from the

same study area– Extra fieldwork including large

sample plots (1000 m2)