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LiDAR application for forestinventory needs in different

scalesJussi Peuhkurinen

Outline

What is LiDAR?LiDAR application(s) in forest inventory

Individual Tree DelineationArea Based Approach

ExamplesFinlandPlantations, BrazilBiomass inventory, Nepal

Conclusions

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What is LiDAR?

What is Airborne Laser Scanning?Scanning LiDAR from airborne vehicle

What is LiDAR?

LiDAR scanning produces 3D description fromthe object

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5LiDAR applications in forestinventory

LiDAR basedvegetation mappingmethods rely onaccurate 3Ddescription of thevegetation andterrain surface

LiDAR applications in forestinventory

LiDAR methods are based in:LiDAR observed height correlates with mean treeheight/tree sizeVegetation density correlates with number ofstems/basal area/tree sizeLidar measurements are not same as fieldmeasurementsLidar observations must be calibrated with fieldobservations

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LiDAR applications in forestinventory

ModelingField calibration plots/trees are used for estimatingmodel parameters for prediction models

Hest = x1* hperc80

AGBest = x1* hperc70 +x2* vegetation density

LiDAR applications in forestinventory

.Result calculationResults are calculated for agrid cell as mean values (forexample, volume/ha) orFor individual treesStand level results areaggregated from basicinventory unit (cell/tree)Full census data

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9LiDAR applications in forestinventory

2 inventory approaches

Individual Tree Delineation/Detection (ITD)

Canopy Height Distribution Method or AreaBased Approach (ABA)

Automatic stand delineation

10LiDAR applications in forestinventory - ITD

High pulse density

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11LiDAR applications in forestinventory - ITD

Delineation ofindividual trees

12LiDAR applications in forestinventory - ITD

Extractingindividual treevariables: LiDARpoint heights,mean crowndiameter etc.

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13LiDAR applications in forestinventory - ITD

Predicting tree species

Modelling tree height and diameter-> Volume of the tree

Totals as a sum and average of individual trees

14LiDAR applications in forestinventory - ABA

Low or medium pulse density

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15LiDAR applications in forestinventory - ABA

Estimation unit is plot or grid cell of certainarea (e.g. 200 - 500 m2) instead of individualtree

Estimation of the variables of interest is basedon statistical correlation between fieldmeasured variables and LiDAR pulse heightdistribution

16LiDAR applications in forestinventory - ABA

LiDAR variables:

Height percentilesand quantiles

Proportion ofvegetation hits vs.ground hits

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17LiDAR applications in forestinventory - ABA

Local statistical models of the variables ofinterest and LiDAR variables

Modelling using ground sample plot data

Spectral variables from aerial imagery/satelliteimagery for estimation of tree speciesproportions

18LiDAR applications in forestinventory – Comparison of ABAand ITD

ABA ITD

Estimation unit Grid cell (200 - 500 m2) Tree

LiDAR dataLow pulse density (0.5 - 1pulse / m2)

High pulse density (> 4pulse / m2)

Other RS data

Aerial images or VHRsatellite data for speciesregocnition

Aerial images for improvingspecies

Field reference dataGNSS located field plots,100 - 1000 / project

GNSS located trees 100 -1000 / project

Tree species recognition Based on optical dataBased on crown shape andoptical data

Accuracy

Volume better than 10 % atstand level. Unbiased resultsfor project area

Volume better than 10 % atstand level. Unbiased resultsare not quaranteed

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Examples – Finland

BackgroundFinnish government noticed a need for a new forestinventory approachThe traditional method (field inventory bycompartments) had became too expensive and it wasnot possible to reach the annual inventory goalsGoal of the new method: cost savings, more efficientforest policy because of better inventory dataExtensive testing of new methods (aerial images,satellite data, and new technology: LiDAR, based onNorwegian examples)

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Examples – Finland

Based on research and test results, LiDAR andArea Based Approach was selected as the bestcandidate method

LiDAR based method were the only ones whichfulfilled the accuracy requirementsIndividual Tree Delineation was too expensive andresearch had not produced reliable practical method

ABA method was piloted with success andcurrently ~2 million hectares are inventoriedannually using the methodMethod still under constant development

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Examples – Finland

OrganizationsFinnish Forest Centre (governmental organization forcollecting and maintaining data from private ownedforests)Metsähallitus (state forests)Forest industry (and other big forest owners)

All the biggest forest owners use currentlyLiDAR and ABA as their default inventorymethod in Finland

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Examples – Finland

Inventory needs, Finnish Forest Center:Inventory period ~ 10 years (every stand inventoriedafter 10 year period)The inventory requirements are derived from fieldbased inventory by compartments –method(traditional method)

Forest inventory data needed for forest management planning(every stand needs to be inventoried)Volume, mean height, mean diameter, basal area, number ofstems, age, species proportionsExtra variables: silvicultural need, pre-commercial thinning, sitetype, harvest planningStand delineation

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Examples – Finland

Solution; Finnish Forest Centre:LiDAR data and aerial images collected for forestinventory needs in co-operation with National LandSurvey of Finland (national height model production)Field data collected by Finnish Forest Centre (500 –700 field plots for each project area)Forest stand delineation and inventory calculation byusing Area Based Approach (private companies, likeArbonaut, offer services)Between the inventories the stand data is updatedusing growth models and management reports

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Examples – Finland

Solution; Finnish Forest Centre:~10 projects in year, ~200 000 ha eachTime frame of an inventory project

Remote sensing and field data collection in summerData analysis in autumn/winterDelivery of inventory product in spring/winterQuality check and publishing the data inspring/summer/autumn

Inventory project from data collection to publishingwith quality checks in about a year

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25ArboLiDARinventories

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Examples – Finland

Solution;Finnish ForestCentre:

Both grid andstand levelinventoryresultsSameinformationcontent inboth data sets

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Examples – Finland

Solution;Finnish ForestCentre:

Approach allowsto aggregatestand levelresults from thegrid to anystandboundaries

ArboLiDAR process

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ArboLiDAR process

Process starts with analysing theproject.

This is followed by the project plan:Input dataMethodologyQuality controlScheduleDeliverables

ArboLiDAR process

Field data calculationField campaign

RS campaign- LiDAR- aerial images

RS and fieldcampaign design

RS features - forestcharacteristic

modelling

RS data featureextraction

Inventory calculation

Automaticsegmentation

Delivery

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ArboLiDAR processRemote sensing campaign

Remote sensing datacontains:

Sparse LiDAR(point density 0.5-1 points/m2)CIR images

LiDAR is classified intoground andvegetation points.CIR images are usedto extract speciesinformation and toestimate the healthstatus.

ArboLiDAR processField campaign

Representative sample of field calibration plots aremeasured to represent the whole variation of the inventoryarea.The GPS coordinates of the plots are carefully recorded andcorrected to achieve submeter accuracy.Timber characteristics and biomass are calculated for thefield plots using allometric models

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ArboLiDAR processAutomatic segmentation

Segmentation rasteris based on heightand densityinformation fromLiDAR and optionallytree speciesinformation fromCIR images.Automatic StandDelineationalgotrithm produces”microstands”, whichare homogenousforest units.

ArboLiDAR processInventory modeling andcalculation

Inventory results areestimated using non-parametric estimationmethodsInventory model,combining theindependent anddependent variables, isproduced to achieve bestpossible results.Estimation results can beproduced to

Automatically createdmicrostandsGRID

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ArboLiDAR processDelivery

Inventory results arecarefully checked andquality report isproducedData gaps andpossible unreliableresults are reported

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Examples – Finland

General notificationsThe product specified by the Finnish Forest Center hasbecame almost an industry standardState forests and private sector took the new methodin use almost at the same timeBehind the success: Government’s investments inresearch and piloting

New inventory method has pushed forestorganizations to renew their forest informationsystems and the way the inventory informationis used

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Examples – Plantations

In plantation forestry age and species (clone)are usually exactly knownLiDAR is used to measure height, diameter,basal area, number of stems, volume andgrowthAccuracy requirements extremely high

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Examples – Plantations

Quality in Eucalyptus plantations, Brazil

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Examples – Biomass inventory

Requirements for large area (national) biomassinventory

Data collection costs should be minimizedUnbiased estimatesChange analysis must be possibleAccuracy of local estimates not so importantMust be based in solid theoretical background (therole of research and research organizations big)

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Examples – Biomass inventory

Large area biomass inventory; Solution:LiDAR Assisted Multisource Program, LAMPLiDAR used as a sampling toolWhole area coverage estimates by using satellitedata

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Random or systematic

Forest type mapElevation

Distancefrom aiports

Design of lidar sample (1-10 %)

Blocks or strips

1. Sampling design

Orientationofblocks/strips

Accessibility

WeightsStratification

Examples – Biomass inventory

Design of field plot sample1. Field data

Random orsystematicStratifiedWeightedClustered

Examples – Biomass inventory

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2. Remote sensing data pre-processingAtmospheric correction of satellite imagesMosaickingCloud masking

Examples – Biomass inventory

3. Biomass inventory modelling & calculation

Lidar model

Computation of LiDAR variables(percentiles, vegetation vs.ground hits etc.)

Regression model between fielddata and LiDAR variables

Forest biomassestimates

for LiDAR areas

Examples – Biomass inventory

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Satellite model

Computation of satellite variables(NDVI, texture, band ratios)

Regression model between LiDARestimates of biomass and satellitevariables

Extrapolating from LiDAR areas toentire project area

= Final forest biomass map

3. Biomass inventory modelling & calculationExamples – Biomass inventory

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Conclusions

LiDAR can be used for various forest inventoryproblemsLiDAR can improve the efficiency of theinventory by providing accurate results withoutextensive field campaignHowever,

Field campaign is needed for collecting calibrationdataThe inventory needs should be recognised prior toLiDAR project and the project should be plannedaccordingly

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Thank You!

Jussi PeuhkurinenArbonaut