LiDAR Enhanced Forest Inventory FAIB/Tolko/BCTS 2015-2016 Good afternoon First I would like to thank you for inviting us. My colleague Ann Morrison was supposed to give this talk but she could make it. I am glad to be here as I think this group is pretty much linked to our group as we have common interest in geomatics. Xiaoping Yuan For the meeting in Kamloops on May 2, 2016

Outline Team: Xiaoping Yuan (PL), Chris Butson, James Wang, Chris Mulvihill, Geoff Quinn, Rob Kennett/Jamie Skinner Background LiDAR EFI basics Okanagan/Kamloops LiDAR EFI 2015/16 New LiDAR data 2015

Background - LiDAR research and activities across the country and world - TFL 18 LiDAR Pilot 2006 - UBC (Alex Creek) LiDAR EFI 2014 - Northern Vancouver Island LIDAR EFI 2014/15 - Okanagan/Kamloops LiDAR EFI 2015

LiDAR Enhanced Forest Inventory Why LiDAR? A new way of acquiring inventory data Automated, objective and modeling approach vs human, subjective, and interpretation Comparative cost Better and higher resolution inventory Misperceptions

Input Data

LiDAR LiDAR can provide highly accurate 3-dimensional characterization of forest canopy, sub-canopy, vegetation and terrain. Forest stand attributes include: stand height Density/crown closure basal area diameter volume biomass

Input Data Sampling design: representative plots representing all the possible variations of the inventory area Tree/Vegetation cover measured, GPS positions Aerial photos, existing inventory, and other auxiliary data

Output Data Modeling and predictions Analysis Steps Prediction options: traditional multivariate regression and non-parametric estimation Gridded raster predictions Generalization to polygons Validation Attribute Estimation/prediction Area-based approach Individual tree based approach

Okanagan/Kamloops LiDAR EFI Partnership, LiDAR data acquired and owned by BCTS/Tolko FAIB, under the data sharing agreement, for EFI LiDAR area: 367,000 ha, completed 317,000ha (Area 1 and 2) Objectives of the LiDAR EFI: Design, test, and complete an operational inventory Develop standards/specifications for operational inventory Investigate methods for improving the traditional inventory (such as VRI)

Methodology Pre-inventory analysis and stratification Ground sampling design and data collection and compilation LiDAR metrics and prediction modeling LiDAR EFI products and integration with the traditional inventories

Methodology Pre-inventory analysis (VRI and LiDAR canopy) – Stratification (4) Ground sampling (229 plots) Predictions Products: 20m raster, generalize to VRI 2015 Enhanced spatial and new inventory 2016

LiDAR Enhanced Forest Inventory Flowchart Acquisition Processing Acquire LiDAR Pre-process Normalized and classified point cloud Convert to .laz Retile and buffer Remove duplicates Create 1m DEM QA Create 1m CHM Normalized CHM VRI 2014 BEC/TRIM Generate 0.04ha plot metrics Generate 20m metrics Pre-inventory analysis Stratification Sample plot selection Orthophoto Ground data collection Ground data compiled Statistical models 20m raster predicted ATT Metrics indices Ground and modeling Sampling Design Landsat 2014 And 2015 Object segments kNN/RF processing Processing for VRIMS VRI 2014 Processing for VRIMS VRI upgraded & publishing New LEFI for VRIMS Integration Enhancement It is a new inventory tool!

Results Stratification and Ground Sampling Stratum BEC Zone Leading Species Area % Target # Plots* Final # Plots 1 ICH and IDF FD 29 57 61 2 Others 27 55 69 3 ESSF and MS BL 21 41 43 4 23 47 60 Total 100 200 233

2. Variables to be predicted Variable name Units Utilization Description Ba_Ha m2/ha 4.0 cm Basal Area/Ha of live trees DQ cm Quadratic mean Dbh of live trees Calculated as sqrt(BA/TPH/0.00007854). Vht_Wsv Whole stem volume/ha of live trees Vha_nwd Whole stem volume/ha of dead trees Vol_nwb 12.5 cm Net merchantable stem volume. Whole stem volume net of stump and top, Cruiser Decay, Waste and Breakage ht_lor_l M lorey height live trees The basal area-weighted average height 3. Parametric vs Non-parametric Traditional regression modeling kNN and Random Forest imputations

4. LiDAR data processing Original DEM Re-tile Normalized CHM CHM 1m filled

5. Statistical Analysis: parametric Lorey Height by Strata Volume by Strata

Volume against p90 without stratification Circled for >100m3/ha of dead volume

Lorey Height: predicted vs actual by strata and combined Basal Area: predicted vs actual by strata and combined Whole Stem Volume: predicted vs actual by strata and combined

6. Statistical Analysis: non-parametric Basal Area comparison Whole Stem Volume comparison

Summary 2015/2016 Ground sampling, measured, and compiled 223 plots LiDAR CHM, QA, and metrics The 20m raster predicted BA, Quadratic diameter, gross/net volume, dead gross volume.

Summary 2016/2017 Integration with VRIMS Enhanced spatial with new attributes Evaluation and validation Cost and benefit analysis Satellite canopy evaluation Implementation Final summary report