High Resolution Inventory Services

DNR Workshop Operationalizing LiDAR based Forest Inventory

January 25, 2016 | tesera.com

The most detailed, accurate and reliable forest inventory data and maps available in the marketplace

HRIS Management & Production TeamTESERA

Bruce MacArthurbruce .m acarthur@tesera .com

President and CEO

Ian Mossian .m oss@tese ra .com

Chief Analytics Officer

Dwight Crousedwigh t.crouse @tese ra .com

Senior Data Analyst

Alex Josephale x.jose ph@te sera .com

Director, Susta inable Solu tions

Dwight Scott Wolfedwigh t.wolfe@te sera .com

Chief Com pliance Officer

Shannon Pattersonshannon .patte rson@te sera .com

Director, User Experienceand Com m unica tions

Inventory … Tool Chain? … What Attributes? … Serving Whom? … With What Kinds of Outputs? … What About Species? … How Do They Use This Stuff? … Highest Priority Improvements?

DNR Workshop AgendaTESERA

17staff

2Post Doctorate (PhD)Analytics, Statistics,

Climate Risk

Experienced entrepreneurs, integrators designers and collaborators

7Software, GIS, Data, Web Design Leaders

5+Project

Management Support

Overview of the Team & the ExperienceTESERA

500+Projects 5

Experienced foresters

HRIS OverviewTESERA

January-19-16

Tesera HRIS Data Compilation

ColourInfrared

Photography

ALS/LidarData*

CIR/LidarData

Fusion

MicrostandGridcell

Delineation

LiDAR/CIRIndice

Compilation

Cutblock / Linear Feature/ Disturbance

GroundPlotData

PhotoPlotData

ReferenceDatasets

Forest LayerLand Cover Class

TargetDataset

GridcellsMicrostands

Young StandCutblockInventoryAttributes

GroundPlot

Data CompilerGrowth Projections

GenerateTerrainIndices*

GenerateClimateWNA

Indices

Roads / PipelinesHydrographySeismic Lines

SampleDesign

January-19-16

Tesera HRIS Modeling & Production

ReferenceDatasets

Forest LayerLand Cover Class

TargetDataset

Gridcell Inventory

Young StandCutblockInventoryAttributes

CustomizedIndicator Variable

Selection **Discrimant Analysis

R Subselect Improve

Tree Cover I

Species CompositionLorey’s Mean Tree Height

Dominant Tree HeightSite Index

AgeCrown Competition Factor

Crown ClosureBasal Area

Trees Per HectareVolume

...Stand Structure Class

Cumulative Distribution Index

Tree Cover II

Tree ListsStand and Stock Tables

Regresssion**R glmnet

kNNCustomized

SciPy KDTree

Fuzzy C-MeansClassification

Model TestingImprovement

Take-One-Leave-Onek-Fold Validation

RMSPESRB

KHAT

Land Cover**

Vegetated-Treed

Vegetated-Non TreedShrubs Open

Shrubs ClosedHerbs

Grasses

Non VegetatedRockWater

River Bank

Blowdown

Regresssion**R glmnet

QCQuantilesMoments

CodesViewing

Microstand Inventory

Final HRIS

Aggregate SimilarMicrostands intoLarger Polygons

HRIS Tool Chain – Ground Plot Data /Inven tory Attribu te s (Python)TESERA

• Plot compiler o Stems Per Hectare o Basal Area o QMD o Lorey’s Mean Tree Height o Merch Volume o Gross Volume o Region o Crown Area o Cumulative Distribution Index (Moss) o GINI Coefficient (Lorenz Curve Area Difference) o Stand Variance Index (STVI; Staudhammer and LeMay 2005) o Species Composition (Basal Area; Crown Area)

• Stand & Stock Tables o TabSpDclLd o TabSpDcl o TabSpLd o TabDclLd o TabSp o TabLd o TabAll

• Tree Compiler (Python) o Species

Update codes o Dbh o [Status (Live or Dead)] o Height o Stems Per Hectare o Basal Area o Merch Volume o Gross Volume o Crown Area o Log number, volume, length, large & small end diameter

• Stand Structure Classification o Version 1 custom algorithm o Version 2 fuzzy c-Means (Bezdek 1981; Python) o Pre- and post classification classifiers

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HRIS Photo Plot Data /Land Cove r Classifica tionTESERA

Grid Cell Land Cover Classification

• Vegetated o Forested (≥ 6% CC) o Non Forested

Shrub Open (≤ 50% cover) Shrub Closed Herb-Forb Grassland

• Non Vegetated

o Rock Bare Rock Sand & Gravel

o Water Flooding – Beaver pond Flooding - Other Plus River Bank

• Severe Windthrow

TESERA

HRIS Tool Chain

● Organize Data :- Postgre s- Quality Control Tools (Python)

● Users Update Config File - In te ractive in in te rpre te r

● Data Dictionary- Variab le Types- Crea ted Autom atica lly- Manual Updating- Option To Change Variab le Nam es

drive:E interActiveConfig:YES backupOldConfig:YES backupConfigFileName: backupFilePath:/Rwd/Python/Config/Backup/ dataInPath:/Rwd/ dataOutPath:/Rwd/ dataInFileName:IDFREGTREE.csv dataOutFileName:IDFREGPLOT.csv errorDiagnosticPath:/Rwd/Python/PyReadError/ loreysHeightVarName:HL cdiVarName:CDI giniVarName:GINI stviVarName:STVI qmdVarName:QMD spPropBphExt:_B spPropCareaExt:_CA spRankPrefix:SP spRankPctPrefix:PCT defaultRankSpName:NA maximumTreeDbh:140 dbhInterval:1 ccVarName:CC printDataInVarTypes:YES defaultFileExtension:.csv useForestTreeConfig:YES dataInUniquePlotIdVarName:MPLOTID dataInUniqueTreeIdVarName:TREEID treeSpeciesVarName:SPECIES dbhVarName:DBH sphVarName:TPH basalAreaPerHectareVarName:BPH regionVarName:REGION heightVarName:HT crownAreaVarName:CAREA merchVolumeVarName:MVPH grossVolumeVarName:GVPH

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TESERA

HRIS Tool Chain – Basic Inputs (Indices)

● Lidar – LAS Tools● LiDAR /CIR Products

- (Blom ASA; Petteri Packalen)- CIR/LiDAR data fusion (C)- Microstands (eCognition ?)- Grid cells

● ClimateWNA (aka PRISM in OR)● Terrain Indices (ktpi)

- R Raster Package*● Stage (2007) Terrain Indices*

*AWS Cloud Processing*pyRserve*Multiple instances

Microstands &Gridcells

Terrain Indices

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TESERA

HRIS Tool Chain – Reference Data Analysis

● Select XY Variables – Standard csv file for user interaction● Classification – Fuzzy C-Means (Bezdek 1981): Python 2.7● Coarse Variable Selection - Discriminant Analysis: R Subselect● Coarse Variable Importance Assessment : Python● Handling Autocorrelation: R● Linear, Binomial Log Odds, Multinomial Log Odds: R glmnet, Pandas, sciKitLearn● kNN; Multiple Discriminant Analysis: R MASS● Evaluation: Take-One-Leave-One; k-fold validation – R packages● Evaluation: RMSPE, Bias, SRB, KHAT – Python; U -Error - R MASS● QC : Quantile Checker, Code Lists – Python ● QC: Orthogonal Regression: OrthogonalDistanceRegression (Python function)

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TESERA

HRIS Tool Chain – Target Data Processing

● Large datasets – All Python● kNN assignments (Tree Lists + BA adj Tree Lists): KDTREE● Linear equation application● Species transformations: Unpack, Repack● Age as function of height & site index: iterative routine● Quantile + code range checker: referance vs. target● Discrete class generator● Compile unique combinations● Data dictionary compiler● Data transformation manager● Grid cell to microstand summary routine● PrognosisBC batch file production and summary routines● Stand structure classification

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TESERA

Who

● Spray Lake Sawmills, SW Alberta, 330,000 ha – 2 Parcels, 2008 to present

● UBC Alex Fraser Research Forest – Knife Creek, ~ 3500 ha, complex stands, variable radius plots, outliers - 2014, 2015; Negotiations to extend to 1 million Ha in IDF

● WIRE Services (Manitoba Hydro), Costa Rica, biomass & carbon estimation – 2014

● Island Timber (in negotiation) – Vancouver Island – 250,000 ha – site productivity, unstable slopes, standard inventory

● Sechelt Community Forest (in negotiation) – coastal mainland – 10,000 ha standard inventory attributes

● BCMoF: Landscape Vegetation Inventory (LVI; Landsat + Photo Plots)

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TESERA

Highlights

● Species recognition● Deriving unbiased compatible estimates for height, site index, and stand age● Terrain and Stage -Terrain indices● Advancing the system in a cloud and web -enabled environment● Complex stands: Linking the inventory to the growth projection system and using a

forest estate model for harvest scheduling and estimations of sustainable timber supplies (new for BC)

● Identification of outliers where additional samples are needed● Establishment of a simple guideline for use of variable radius plots● Vast improvements in quality control procedures● Extensive documentation to support existing products ● Routines can mostly be used by people familiar with computers but not expert

programmers and with minimal training in use of statistics ● Thorough review by clients and third parties (government agencies)

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TESERA

Challenges & Opportunities for Improvement

● Standardized datasets and methods for reporting on reliability of inventory (best practices)

● Deploying additional analyses pathways as part of the process● Extending the process for use with large datasets ● Height, age, site index compatability and removing bias● Species proportions (vs. Photo Interpretation)● Species proportions with respect to change in diameter● Corresponding LiDAR + CIR metrics● Parametric methods as alternative to kNN (Holy Grail)● Dominant tree & Lorey’s mean tree height ● Crown closure (Gill et al. 2007; Betchold 2004)● Height -to -live crown● Post (grid cell) processing stand delineation● Tree lists: Integrated Inventory, Treatment Unit, Silv presc, GY Foresting, Harvest

Scheduling, Inv. Reconciliiation and Update Process● Operational Applications (Post Production Stand/Treatment Unit Delination)

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TESERA

Thank You

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TESERA

Tree List Discussion Topics (Emphasis on Complex Stands)• Why?

o Species Habitat Assessment o Fire (& other Disturbance)

Hazard/Risk Management o Course Woody Debris (Recruitment) o Log Supply Forecasting o Partial Cutting o Simulating Natural Disturbance o Watershed Dynamic Characteristics o Growth and Yield Forecasting

• What

o Species x Diameter x Status (L/D) o Additional attributes

… o Labeling

Stand structure classification o Site productivity (complex stands)

• How o kNN

Sample design, intensity, and distribution

o Parametric techniques Complex stands?

• Managing

o Silviculture prescriptions o Growth and yield forecasting o Growth and yield monitoring o Inventory update o Forest Estate Analysis

Prescriptions Timing of application (harvest delays) Growth and response curves

o Integration with forest operations Delineating polygons consistent

with prescription guidelines. Locating treatment units in the field Enabling adjustments to the inventory

tree lists / stand and stock tables …

ALS/LiDAR CIR … Other?