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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