LiDAR and EFI Data within BCTS

Presentation Outline Current State and LiDAR/EFI coverage Decision matrix Derivatives or end products Current uses of the data Benefits

Current State 11 out of 12 Business Areas have LiDAR data 3 of those BA’s currently have EFI data First LiDAR acquisition in 2010 in Port McNeill (TST), they have the most experience using the data Approximately 25% LiDAR coverage of Business Areas to date, approx 4.1 million ha (2016 hectares not all delivered) 334,000 ha of EFI planned by end of 2017 (approx)

Current LiDAR coverage

Current EFI Coverage

Acquisition Summary – All Data BCTS LiDAR Acquisition 2010 – Present Business Area 2010 2012 2013 2014 2015 2016 2017 Grand Total TBA 73301 TCC 516226 99778 616004 TCH   2344 3387 14787 360938 50000 431456 TKA 64312 69726 267347 401385 TKO 37353 284877 688124 1010354 TOC 72139 95217 296596 463951 TPG 445892 147046  592941 TSG 2290 5755 44635 130843 76193 258045 TSK 45985 TSN 27312 TST 4318 61483 23095 20999 71564 181459 580671 28850 225503 1026701 2109957 126193 4102194

LiDAR Summary by Operating Area (as of March 6, 2017)

Percentage of LiDAR Coverage within Operating Areas to end of 2017

Percentage of EFI Coverage within Operating Areas to end of 2017

Why did BCTS acquire LiDAR/EFI? To aid in meeting BCTS’s Mandate of collecting data from Auctioned Timber Sales to set the stumpage rate for all Timber harvested from Crown lands by other Licensees. More accurate information for remote, high cost areas LiDAR provides more accurate canopy level information than VRI polygons LiDAR provides more accurate terrain information than TRIM LiDAR and EFI allow for land based information to be operationalized for planning and engineering ie better Total Chance Planning = isolating less timber VRI is more suited for higher level planning and Timber Supply Modelling

Why did BCTS acquire LiDAR/EFI? (con’t) LiDAR/EFI is increasing efficiency in all phases of planning More time is now allocated to office engineering to maximize field work productivity, less rework/pulling out ribbons, less field work in general Better decisions are being made earlier in the planning process, more efficient, cost savings and evaluation of road options using RoadEng Optimal EFI has allowed easier identification of potential Timber Sale quality blocks

Phase 1 Recce Example

Crown height data

RoadEng Software

Creating the Optimum Grade

Deflection Lines

Deflection Line Map

Deflection Line in 3D

Lidar/EFI Data Uses The EFI was used for the Total Chance Plan of the Holberg area Merchantable/saleable stand identification Quantifying the impacts of proposed WHAs using the volume impacted converted to $ RoadEng, Terrain and Location road alignment and block layout by field engineers, regional engineering staff and contractors Mule Deer Winter Range Modelling from the CHM, canopy closure and canopy complexity Many other modelling potential ie Marbled Murrelet, Goshawk

EFI Derived Volumes

Crown Height Model Canopy Closure/Structure

Net Merch Volume

LiDAR vs EFI Tree Heights

Hydrologic Streams and Basins

Stream gradients

Tree Top Analysis

Harvest Block Cut into DSM

What are the Benefits of LiDAR/EFI LiDAR/EFI data identifies canopy level attributes better than VRI, optics are better for prioritizing/planning Timber Sale level blocks LiDAR data easily integrates with existing GIS and Forest Engineering software, helps to maximize Return on Investment Better crown height and ground information available in the planning phase allows better decisions and less rework Potentially less road building as multiple deflection line options can be compared easily beforehand, lower engineering costs per TSL Less field work overall should result in improved safety for workers and a reduction in engineering costs

Benefits (con’t) More accurate Visual Impact Assessments, using better ground and tree heights, potential to harvest more in visually constrained areas Better utilisation of the timber resource and land base with less chance of isolating timber Ability to generate hydrologic streams, reach breaks and barriers to fish passage to identify optimum bridge and culvert crossing locations, better road reactivation data Better able to manage for non timber resources ie crown closure for Mule Deer winter range habitat. Potential to solve the aging workforce issue

Questions?