Enhanced Forest Inventory A case study in the Alberta foothills

Transcription

1 Enhanced Forest Inventory A case study in the Alberta foothills Roger Whitehead & Jim Stewart CFS, Canadian Wood Fibre Centre Glenn Buckmaster West Fraser Mills, Hinton Wood Products Mike Wulder, Joanne White, Gordon Frazer 1 & Geordie Hobart CFS, Pacific Forestry Centre 1 Current affiliation GWF LiDAR Services, Victoria, BC 1

2 Outline Site & Data Sources What we did Model predictions Validation & discussion What we re working on now 2

3 Study Area & Data Sources Hinton FMA West Fraser Mills, Hinton WP 988,870 ha; 185,000 AVI polygons LiDAR & AVI data Alberta ESRD /WF- HWP Ground Calibration data HWP s established network of Permanent Growth Sample Plots 3

4 LiDAR data Alberta ESRD provided HWP with full FMA coverage small footprint (30 cm) /- points/m 2 multiple discrete return (max 4 returns) collected pt cloud, CHM, DEM 4

5 Data Cloud Canopy Metrics Used USDA FS freeware package FUSION/LDV to tile, grid & calculate canopy metrics on 25m X 25m grid 13,665,234 grid-cells forest type assigned from AVI polygon level Conifer. Deciduous, Mixed 5

6 Ground calibration/model training WF-HWP maintains >3200 PSP empirical yield curves we used 735 of those plots to train prediction models date of last measure & GPS quality. used HWP mensuration / calculations for top ht, volumes, BA & trees/m 3 biomass from Lambert (2005) & Ung (2008) separate models for each forest type using area-based approach conifer, deciduous, & mixedwood 6

7 LiDAR based Prediction of Attributes Total Above Ground Biomass (tonnes/ha) Used Random Forests ( R ) to create prediction models Top height, Co-dominant & Mean height DBHq & BA Total Volume & Merchantable Volume Above Ground Biomass Mean piece-size (trees/m 3 ) Partners: WFM - Hinton Wood Prod.; Alberta SRD; CFS PFC; UBC 7

8 Mapped as GIS raster layers 25m cell level AVI Polygon level 33 m 3 /ha 384 m 3 /ha 247 m 3 /ha 14 m 3 /ha 331 m 3 /ha Merch. Volume (m3/ha) For ~1 million ha Hinton FMA 525 m 3 /ha 276 m 3 /ha 164 m 3 /ha 0 m 3 /ha 8

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13 So are any predictions correct? Weight-scaled volume from 272 cutblocks harvested since LiDAR acquisition compared to predictions from LiDAR vs. Cover Type Adjusted Volume Tables Block Size (m 3 X1000) Source of Prediction Predicted Volume Scaled Volume Statistically significant? < 5 n = 138 LiDAR CT Vol. Table -6.7% -23.7% No Yes 5 10 n = 76 LiDAR CT Vol. Table +1.8% -17.4% No Yes n = 25 LiDAR CT Vol. Table -1.2% -22.3% No Yes n = 15 LiDAR CT Vol. Table -4.4% -23.5% No Yes >20 n = 18 LiDAR CT Vol. Table +6.6% -17.4% No No Vol.T. underestimated scaled volume by 19.8% LiDAR overestimated scaled volume by 0.6% Information courtesy Hinton Wood Products 13

14 Why are the Volume Tables so far off? Volume Table predictions rely on AVI polygon height don t handle within-polygon variability well Polygon-level LiDAR predictions don t rely on age or SI 50 aggregate cell-level predictions What about the bias? It s the operational planner s fault 14

15 The problems with using existing PSPs The 735 PGS plots we used were not well-distributed across variation in LiDAR metrics biased to young, even-age conifer stands customized sample design should better models Frazer et. al, 2011 Partners: WFM - Hinton Wood Prod.; Alberta SRD; CFS PFC; UBC 15

16 Structurally-guided sample design PGS plots used Structurally-guided sample White et al,

17 Required sample size will depend on acceptable error confidence level required # of forest types modeled ACCEPTABLE ERROR CONFIDENCE LEVEL REQUIRED SAMPLE SIZE (per forest type ) 5% 95% % 95% 96 10% 90% 68 White et al,

18 Sampling Intensity influences cost HWP has maintained 3202 plots since 1950s, specifically empirical yield curves LiDAR prediction models used only 735 of these plots better volume predictions LiDAR-specific sample design should more cost-effectively still better results train models to predict attributes wanted CBD? ht to live crown? understory? etc. 18

19 What we re working on now Evaluating model improvements with structurally-balanced sample design Best Practices Guideline (31 March, 2013) support standards for LiDAR-enhanced inventory Support acceptance of LiDAR in Forest Management Plans & AAC determination Complex yield curves from LiDAR rasters (fireorigin stands) Woodstock TS Analyses 19

20 What we re working on now High resolution LiDAR & digital imagery with SGM HWP re-flight proposal UBC/CFS to explore growing the inventory object-based predictions species & product profiles Images courtesy Steve Platt, Strategic Group, Campbell R, BC 20

21 Strategic tactical operational LiDAR rasters Link to FPInnovations Value Maximization & Decision Support net cell & polygon-level cost-benefit across full value chain Discussion session #3 21

22 Linking Block Planning to Mill Needs 3-5 TPM < 2 TPM 3-8 TPM 22

23 Linking Compartment Planning to Mill Needs Column A Column B Column C Column D Column E Trees Per Metre Potential Range of 68% of the log profile Planning Unit harvest area (ha) MEAN Lower Higher GALL 2 1, CONK 21 3, CONK 20 3, CONK 8 2, GALL 13 1, CONK 4 1, BURL 7 2, CONK 10 2, BURL 8 1, BURL 11 2, BURL 6 1, BURL 21 2, CONK 14 4, BURL 1 2,