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• Innovation:Whatisit?• Inventory:
• Whatisit?• For?• Using?• Construction,Updates,Accessibility?• Why?
• Conclusion• Manypossibleroutes,innovationisthekey.
Innovation: What is it?
• Theadjacentpossible:incubator• Liquidnetworks• Theslowhunch• Serendipity• Error:• Exaptation:Printingpress• Platforms:Atoll(coralreef)
Individual Networked
Market
Non-Market
MasonJar
SuspensionBridge
Airplane
Ecosystem
1 23 4
11 35
35 56
Guier andWeiffenbach –Dopler trackingsputnik
The Fourth Quadrant
Inventory is a
System
Phase II AdjustedInventory
Vegetation Resources Inventory (VRI LRDW)• Vegetation Resource Information Management System (VRIMS)• Geo-referenced polygon vector data (TRIM 1 base)• Feature Manipulation Engine (FME)• Land and Resource Data Warehouse (LRDW)
Forest Cover Inventory (FC)• Polygon delineation / Attributing• Productive / Non-productive• Tree Attributes• Environmentally Sensitive Areas• Administration (TSA,PSYU, FIZ…)• BEC Zone, Subzone, Variant• History (Disturbance Type, Year)
Inventory Updates
Harvesting / Silviculture Activities: RESULTS• Results Based Code Silviculture and Land Status Tracking System
Disturbance Reference Layers (DRL)Natural Disturbance UpdatesFree To Grow Updates
Inventory Projection• Update selected tree attributes• VDYP: Variable Density Yield Projection System
Growth & Yield• PSP Establishment / Measurement• PSP Compilation / Data Management• Top height growth curves (Site Index)• Site Intercept• Site Index Adjustment (SIBEC, SIA)• VDYP Development / Calibration• TASS / TYPSY / OAF’s• PROGNOSIS• Taper / Decay Sampling• Taper Equations• Software SupportStrategic Planning
• Land Use Planning• Forest Resource Analyses/Reporting • Timber Supply (TSR,AAC)• SFM Planning (Certification)• Silviculture Investment Strategies
Licensee Plans• Forest Stewardship Plans
Figure 1. A diagram illustrating the Vegetation Resource Inventory (VRI) and related activities.
National Forest Inventory• Photo Plots• Ground Plots
Phase I VRIPolygon delineation / attributing1. Land Cover Class2. General Attributes3. Treed – Site Index4. Tree Attributes5. Shrub Attributes6. Herb Attributes7. Bryoid Attributes8. Non-vegetation Attributes
Phase II VRI• Sample Design• Plot Establishment / Compilation• Tree Volume Sampling / Adjustment (NVAF: Decay, taper)
Phase II Adjustment• Computer Processing
e.g., Fraser vs. Boston Bar Procedures
Terrain Resource Information Mapping (TRIM)
• Contours (TCTR)• Cultural Features (TCUL)• Land Cover (TCVR)• Neatlines (TNTL)• Surface Features (TSRF)• Transportation (TTRN)• Water Features (TWTR)• Other Features (TMISC
Harvest Reconciliation• Cruise Data• Scale Data• Growth & Depletion Accounting
11.1 1.2
1.3
1.4
1.5
23
4
4.1
4.2
5
6
7
88.1
8.2
Enhanced Inventory• Polygon Tree Lists• Polygon Stand & Stock Tables, etc.
Ecosystem Mapping• Terrestrial Ecosystem Mapping (TEM)• Predictive Ecosystem Mapping (PEM)
9
10
11
Unadjusted Inventory Alternative
Other inputs into / outputs from VRI
Forest Tenures Atlas
Inventory Localization• Local Corrections To Polygon Data
4.3
12
VegetationResourceInventory
Inventory (SFM)Applications
Note — connected with State of the Forest Report in box 8.1
Used to do what?
Forest Inventory
AACDetermination
AllocationofTimberRights
Harvesting SawmillingProductionProductivityRoad&Bridge
ConstructionMaintenance
UnstableSlopes
WatershedProtection Habitat
ManagementGuidelines
FishHabitatProtection
Cruising
SitePreparationReforestationBrushingandWeeding
LoggingEquipmentPurchase&Maintenance
LogYardDesign&Storage
ForestProductivity
Growth&YieldForecasting
LogSupplyForecasting
TreeImprovementFertilizationNatural
Disturbances
ForestProtection
ForestPolicy
Stumpage&Appraisals
HarvestSchedule
Scaling
ClimateChange
ForestProductivity
SustainabilityPropertyRightsForestHealthResilienceCommunityStabilityWellBeingEcosystemServicesEconomyCompliance&Enforcement
LandCoverClassification
LandUse
WoodProductProductionSales
LumberYardDesign&Storage
SilvicultureSurveys
RecreationCulturalValues
WasteAssessments
Inventory: Desired Characteristics
Stand Structure
Species Composition Trees
Forest SiteCharacteristics
Forest Inventory ClassificationsDoors to a complex world
• Fitforpurpose:hasnecessaryattributesatappropriatescales.
• Connected:grounddata;longtermmonitoring• Highresolution:segments,gridcells
• Detailed:stand&stocktables• Precise:potentialerrorofestimationquantified
• Flexible:scale,selectionofattributes• Linked:polygon-levelgrowth,yield,&mortalityforecasts
• Current:regularupdates,slowamortizationrate
• Accessible:Easytoaccess,viewatmultiplescales
+LandCoverClassification
Inventory: Information
Using what?
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9.0
10.0
11.0
12.0
Bas
al A
rea
Per
Hec
tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
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rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
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17 m2 ha-1
0.0
1.0
2.0
3.0
4.0
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7.0
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12.0
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rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
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12.0
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2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
17 m2 ha-1
0.0
1.0
2.0
3.0
4.0
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7.0
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12.0
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2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
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12.0
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2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
17 m2 ha-12
3
0.0
1.0
2.0
3.0
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6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
16 m2 ha-1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
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rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
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rea
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2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
16 m2 ha-1
0.0
1.0
2.0
3.0
4.0
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6.0
7.0
8.0
9.0
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11.0
12.0
Bas
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rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
Hec
tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
16 m2 ha-1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
16 m2 ha-1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
Hec
tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
16 m2 ha-1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
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al A
rea
Per
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
29 m2 ha-1
0.0
1.0
2.0
3.0
4.0
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7.0
8.0
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11.0
12.0
Bas
al A
rea
Per
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
29 m2 ha-1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
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12.0
Bas
al A
rea
Per
Hec
tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
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12.0
Bas
al A
rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
29 m2 ha-1
0.0
1.0
2.0
3.0
4.0
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7.0
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12.0
Bas
al A
rea
Per
Hec
tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
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12.0
Bas
al A
rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
29 m2 ha-1
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
11.0
12.0
Bas
al A
rea
Per
Hec
tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
10.0
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12.0
Bas
al A
rea
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tare
2.5 4 7.5 12.5 17.5 22.5 27.5 32.5 42.5 52.5 62.5 82.5 102.5 150Diameter Class Lower Limit
Stock Table
29 m2 ha-14
The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again.
Linked with: Growth & yield +
disturbance updates
1 2
3
413
5
14
7
8
6
9
12
10
C11
<
C
16
15C
17
CC
SinglelayeredModerateDensity
Complex
SinglelayeredHighDensity
Made available for application
Why: Sustainability
Risk Management
Why: Overcoming Cognitive Bias
Heuristic(a)AvailabilityRepresentativenessConfirmation(affectheuristic)
OverconfidenceOverprecisionOverestimationOverplacement
Framing
BoundedawarenessUncertainty(risk)
(C)ConfirmationbiasAnchoringandadjustmentHindsightConformityGroupthinkSocialloafing
JudgementDecisionBahviour
Bias
CognitiveBias
(a)Availabilitybias
(b)IgnoranceofbaserateIgnoranceofsamplesizeMisunderstandingofprobabilityRegressiontomeanConjunctionfallacy
IllusionofcontrolFallacyofplanOptimisticbias
LossaversionStatusquobiasEndowmenteffectMentalaccounting
Why: Purposeful Change
Learning to predict • 10Commandments• Triage:chooseyourbattles.• Breakproblemsintosub-problems
• Balance:insidevsoutsideviews• Evidence:avoidunder-vs.overreacting• Evidence:recognizeargumentandcounter-argumentaspossibilities
• Uncertainty:Attachmeasures/degrees• Balance:under/overconfidence,prudencevs.decisiveness.
• Finderror:bewareofhindsightbias,reviewbothfailuresandsuccess.
• Bringoutthebest:inothersandletothersbringitoutinyou.
• Mastertheerror-balancingbicycle.• +1• Don’ttreatcommandmentsascommandments.
Judgement
Input Prediction
Training
Action Outcome
=data
Feed-back
AbrahamWald
Conclusion: Innovation in forest inventory Building & putting the pieces together • Inventorysystem(measures,processing,production,maintenance,storage,access,applications,interactions):opentochangeandimprovement• Innovation:organizedchaosthroughpublic&privateinvestment• Practicing:doingitareasonablescalewiththenecessaryresources• Forecasting:thebusinessofforestinventory-disciplinedopennesstorangeofpossibilities,quantifiedoutcomes• Challenge:upgradingthesystemwhilekeepingitafloat• Demonstratingthebenefits:capitalizingontheinvestmentthroughimproveddecision-makingandincreasedoperationalefficiencies• Withamorefocusedagenda:modelforests,innovativeforestpracticesareas,forestpracticespilotstudyareas,researchforests
Thank You Ian Moss, PhD RPF [email protected]
References
• Atoll:"MaamigiliIslandRaaAtoll"byLoamaresort-Ownwork.LicensedunderCCBY-SA4.0viaCommons-https://wiki2.org/en/File:Maamigili_Island_Raa_Atoll.jpg#/media/File:Maamigili_Island_Raa_Atoll.jpg
• CurrentForestInventoryFigure:Yousefpour,R.,Jacobsen,J.B.,Thorsen,B.J.,Meilby,H.,Hanewinkel,M.,andOehler,K.2012.Areviewofdecision-makingapproachestohandleuncertaintyandriskinadaptiveforestmanagementunderclimatechange.AnnalsofForestScience69:1-15.
• Dopplertracking:https://timeandnavigation.si.edu/multimedia-asset/experts-discussing-doppler-tracking
• Johnson,S.Wheregoodideascomefrom.• Knowledgecoststructure&Privatevs.PublicInvestment:Foray,D.2006.Theeconomicsofknowledge.
Paperbackedition.MITPress,Cambridge,MA.• Public/PrivateFunding:Brown,K.M.1998.Downsizingscience.TheAEIPress,Washington,D.C.Citedin:
Foray,2006.• Purposefulchange:Weichselgartner,J.andPigeon,P.2015.Theroleofknowledgeindisasterrisk
reduction.Int.J.DisasterRiskSci.6:107-116.
References (cont.)
• LearningtoPredict:Agrwal,A.,Gans,J.,Goldfarb,A.2018.Predictionmachines.Thesimpleeconomicsofartificialintelligence.HarvardBusinessReviewPress,Boston,Massachusetts.FigureonP.75.ThestoryofAbrahamWaldwasalsointroducedinthisbook(P.101).
• LearningtoPredict:PhotoofAbrahamWald…http://www-history.mcs.st-andrews.ac.uk/PictDisplay/Wald.html
• Why:OvercomingCognititiveBiases:Murata,A.,Nakamura,T.,andKarwowski,W.2015.Influenceofcognitivebiasindistorteddecisionmakingandleadingtocriticalunfavourableinclidents.Safety,1:44-58.
