FOREST INVENTORY BASED FOREST INVENTORY BASED ON INDIVIDUAL TREE ON INDIVIDUAL TREE

CROWNSCROWNS

Jim FlewellingJim Flewelling

Western Mensurationist Western Mensurationist MeetingMeeting

June 18-20, 2006June 18-20, 2006

OUTLINEOUTLINE

PerspectivePerspective Crown SegmentationCrown Segmentation Tree predictionsTree predictions Sample FrameSample Frame Estimation Estimation SummarySummary

PerspectivePerspective

Aerial Surveys date from 1920’s and Aerial Surveys date from 1920’s and 30’s30’s

Images for stand boundaries and attribution.Images for stand boundaries and attribution. Individual crown locations and delineation Individual crown locations and delineation

since late 1980’s.since late 1980’s. Attribution – training process.Attribution – training process. Research process – match trees and Images.Research process – match trees and Images. Lidar – huge improvement.Lidar – huge improvement. Limited use of sampling theory at tree level.Limited use of sampling theory at tree level.

Crown Segmentation, Crown Segmentation, Delineation & AttributionDelineation & Attribution

Identify individual crowns.Identify individual crowns. Locate center points.Locate center points. Delineate crown boundaries.Delineate crown boundaries.

(non-overlapping)(non-overlapping) Attribute species.Attribute species. Attribute height.Attribute height.

Individual Tree Crown (ITC) Individual Tree Crown (ITC) DelineationDelineation

Valleyfollowing

Deep shade

threshold

Rule-basedsystem

1995

Courtesy of Canadian Forest Service

Delineated Individual Tree CrownsDelineated Individual Tree Crowns

At ~30 cm/pixel,

81% of the ITCs are the

same as interpreted

crownsCourtesy of Canadian Forest Service

Delineated and Classified ITCsDelineated and Classified ITCs

Courtesy of Canadian Forest Service

PredictionsPredictions

Much attribution without specific data.Much attribution without specific data. Goal:Goal:

DBH’s, total heights, correct species, counts. DBH’s, total heights, correct species, counts. Per-acre statistics: BA, volume, biomass. Per-acre statistics: BA, volume, biomass.

Empirical predictions:Empirical predictions: per-acre level is common. per-acre level is common. tree level (matched data) as resolutions and tree level (matched data) as resolutions and

technology improve. technology improve.

Tree Predictions - DataTree Predictions - Data

Ground-measured tree crowns. Ground-measured tree crowns. Rough plot alignmentRough plot alignment

correlated distributions.correlated distributions. Crown Images and Actual Trees aligned.Crown Images and Actual Trees aligned.

Research: 100% mapped, special locators.Research: 100% mapped, special locators. Fixed area plots for inventory.Fixed area plots for inventory.

Sample plan.Sample plan.

Matched Trees & CrownsMatched Trees & CrownsThe tree points

are then matched up with

the tree polygons to create

regressions used for the inventory

calculation.

©ImageTree Corp 2006

Matched Trees and CrownsMatched Trees and Crowns

Matched Trees and CrownsMatched Trees and Crowns

Errors in SegmentationErrors in Segmentation One delineated crown = 2 neighboring trees.One delineated crown = 2 neighboring trees. One real tree wrongly divided into 2 crowns.One real tree wrongly divided into 2 crowns.

Trees entirely missed.Trees entirely missed. Ground vegetation seen as a tree.Ground vegetation seen as a tree. Understory trees don’t contribute.Understory trees don’t contribute. Technical improvements, but Technical improvements, but no no

absolute solution.absolute solution.

Sample Frame - Ground or Sample Frame - Ground or Map?Map?

Individual stand on

LiDAR image after tree polygon

creation. A polygon now

surrounds every visible tree crown.

©ImageTree Corp 2006

Sample Frame - GroundSample Frame - Ground

Traditional forest sampling.Traditional forest sampling. Plots are installed on the ground.Plots are installed on the ground. Stand boundaries recognized in field.Stand boundaries recognized in field. Hope the stand area is correct.Hope the stand area is correct. Awkward to use crown information.Awkward to use crown information.

Sample Frame - Crown MapSample Frame - Crown Map

Data-rich environment.Data-rich environment. Fixed-area plots. Fixed-area plots. New or different challenges:New or different challenges:

sample locationssample locations tree & crown matchingtree & crown matching stand boundariesstand boundaries edge biasedge bias

CROWN BASED SAMPLE FRAMECROWN BASED SAMPLE FRAME

REQUIREMENTREQUIREMENT Trees linked to segmented crowns.Trees linked to segmented crowns. Linkage must be Linkage must be independent of samplingindependent of sampling.. BUTBUT Linkages need not be physically correct.Linkages need not be physically correct. Suppressed trees need not be linked if sampled another way.Suppressed trees need not be linked if sampled another way.

TREE MATCHING SCHEMESTREE MATCHING SCHEMES

SubjectiveSubjective potential for significant biaspotential for significant bias

Crown Captures ALL in tessellated Crown Captures ALL in tessellated area.area. Expand crown area.Expand crown area.

Trees compete to be captured.Trees compete to be captured. Consider DBH, height, species …Consider DBH, height, species … Ground plot size > crown plot size.Ground plot size > crown plot size.

Sample LocationsSample Locations(Crown Map as Sample (Crown Map as Sample

Frame)Frame) Select fixed-area plot centersSelect fixed-area plot centers

GroundGround - usual compromises plus boundary issues.- usual compromises plus boundary issues.

Crown mapCrown map rigorous random selection process.rigorous random selection process. Difficult to find on the ground.Difficult to find on the ground.

Both:Both: Unequivocal tree & crown matching?Unequivocal tree & crown matching?

Crown-based sampling Crown-based sampling schemescheme

Crown delineation, all stands, all Crown delineation, all stands, all crowns.crowns.

Select Sample Stands (in strata)Select Sample Stands (in strata) Randomly locate 2 plot centers on Randomly locate 2 plot centers on

map.map. GPS to those locations.GPS to those locations. Install stem-mapped ground plots.Install stem-mapped ground plots.

Challenges - plot location.Challenges - plot location.

Map error + GPS error of several meters.Map error + GPS error of several meters. Process to find ground plot center on Process to find ground plot center on

crown map.crown map. (x, y) plus angular shift.(x, y) plus angular shift.

Force ground plot to include selected pt?Force ground plot to include selected pt? Accept the random deviation?Accept the random deviation?

Ground plot center outside of stand?Ground plot center outside of stand? Altered probability density. Altered probability density.

Challenges - Edge EffectsChallenges - Edge Effects

Edge bias correction SIMPLEEdge bias correction SIMPLE ““Tree concentric method.”Tree concentric method.” Computer finds area of “tree-center Computer finds area of “tree-center

plot” within stand boundary.plot” within stand boundary. More efficient than field-based methods.More efficient than field-based methods.

Plot location - random error.Plot location - random error. Minor alteration in probability density.Minor alteration in probability density. Computer can correct.Computer can correct.

Estimation Estimation

Research focus is deterministic.Research focus is deterministic. Attempt to remove uncertainty.Attempt to remove uncertainty.

Alternative is stochastic modeling.Alternative is stochastic modeling. Each crown has multiple outcomes: Each crown has multiple outcomes:

trees and species.trees and species. DBH, heights vary with outcome.DBH, heights vary with outcome. Stand prediction = sum of expectations.Stand prediction = sum of expectations.

Estimation (continued)Estimation (continued)

Approximate Unbiasedness (strata).Approximate Unbiasedness (strata). Model-assisted survey estimators (regr.)Model-assisted survey estimators (regr.)

DBH distributions NOT unbiased.DBH distributions NOT unbiased. ““Regression towards the mean”Regression towards the mean” Can correct for unbiased width.Can correct for unbiased width.

Could use data from sampled stands Could use data from sampled stands to improve to improve thosethose stands. stands.

SummarySummary

Attractive technology.Attractive technology. Best for which forest typesBest for which forest types

Irregular spatial tree distributions.Irregular spatial tree distributions. Some multi-species situations.Some multi-species situations. Detailed predictions without sampling all Detailed predictions without sampling all

stands. stands. Useful spatial information.Useful spatial information. Sampling theory has been under-utilized.Sampling theory has been under-utilized.

AcknowledgementsAcknowledgements

Many slides were provided by Francois Many slides were provided by Francois Gougeon and are courtesy of Natural Gougeon and are courtesy of Natural Resources Canada, Canadian Forest Resources Canada, Canadian Forest Service.Service.

Other slides were provided by ImageTree Other slides were provided by ImageTree Corporation.Corporation.

Mike Wulder, Canadian Forest Service.Mike Wulder, Canadian Forest Service. Adam Rousselle, Vesa Leppanen, Olavi Adam Rousselle, Vesa Leppanen, Olavi

Kelle, Bob Pliszka (Falcon Informatics).Kelle, Bob Pliszka (Falcon Informatics).

ResourcesResources

2005 Silviscan 2005 Silviscan http://http://cearscears.fw.vt.edu/silviscan/.fw.vt.edu/silviscan/

2004 ISPRS Laser-Scanner for Forest and - 2004 ISPRS Laser-Scanner for Forest and - http://www.isprs.org/commission8/workshohttp://www.isprs.org/commission8/workshop_laser_forest/p_laser_forest/

ImageTree Corp. ImageTree Corp. www.imagetreecorp.comwww.imagetreecorp.com Pacific Forestry Center Pacific Forestry Center

http://www.pfc.forestry.ca/index_e.htmlhttp://www.pfc.forestry.ca/index_e.html Precision Forestry Coop (U.W.) Precision Forestry Coop (U.W.) http://www.cfr.washington.edu/research.shttp://www.cfr.washington.edu/research.s

mc/mc/

THANK YOU questions?