High Resolution Inventory Services The most detailed accurate

High Resolution Inventory Services The most detailed, accurate and reliable forest inventory data and maps available in the marketplace DNR Workshop Operationalizing Li. DAR based Forest Inventory January 25, 2016 | tesera. com

TESERA HRIS Management & Production Team Bruce Mac. Arthur Ian Moss Dwight Crouse bruce. macarthur@tesera. com ian. moss@tesera. com dwight. crouse@tesera. com President and CEO Chief Analytics Officer Senior Data Analyst Alex Joseph Dwight Scott Wolfe alex. joseph@tesera. com dwight. wolfe@tesera. com Director, Sustainable Solutions Chief Compliance Officer Shannon Patterson shannon. patterson@tesera. com Director, User Experience and Communications

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

TESERA Overview of the Team & the Experience 17 Experienced entrepreneurs, integrators designers and collaborators staff 2 Post Doctorate (Ph. D) Analytics, Statistics, Climate Risk 7 5+ Project Management Support Software, GIS, Data, Web Design Leaders 500 + Projects 5 Experienced foresters

TESERA HRIS Overview

TESERA HRIS Tool Chain – Ground Plot Data/Inventory Attributes (Python)

TESERA HRIS Photo Plot Data/Land Cover Classification 7

TESERA HRIS Tool Chain ● Organize Data: - Postgres - Quality Control Tools (Python) ● Users Update Config File - Interactive in interpreter ● Data Dictionary - Variable Types - Created Automatically - Manual Updating - Option To Change Variable Names

TESERA HRIS Tool Chain – Basic Inputs (Indices) ● Lidar – LAS Tools ● Li. DAR /CIR Products - (Blom ASA; Petteri Packalen) - CIR/Li. DAR data fusion (C) - Microstands (e. Cognition ? ) - Grid cells ● Climate. WNA (aka PRISM in OR) ● Terrain Indices (ktpi) - R Raster Package* ● Stage (2007) Terrain Indices* Microstands &Gridcells Terrain Indices *AWS Cloud Processing *py. Rserve *Multiple instances 9

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, sci. Kit. Learn k. NN; 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: Orthogonal. Distance. Regression (Python function) 10

TESERA HRIS Tool Chain – Target Data Processing ● ● ● ● Large datasets – All Python k. NN 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 Prognosis. BC batch file production and summary routines Stand structure classification 11

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 ● BCMo. F: Landscape Vegetation Inventory (LVI; Landsat + Photo Plots) 12

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) 13

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 Li. DAR + CIR metrics ● Parametric methods as alternative to k. NN (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) 14

TESERA Thank You 15

TESERA Tree List Discussion Topics (Emphasis on Complex Stands) ALS/Li. DAR CIR … Other? 16