Enhanced Forest Inventory A case study in the Alberta foothills

Transcription

1 SISCO Southern Interior Silviculture Committee 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 Fraser 1 CFS, Pacific Forestry Centre 1 Current affiliation University of Victoria 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 multiple discrete return (max 4 returns) small footprint (30 cm) 0.75 points/m 2 collected pt cloud, CHM, DEM 4

5 Data Cloud Canopy Metrics Used USDA FS freeware package FUSION/LDV to tile, grid & calculate >50 canopy metrics 25m X 25m grid 13,665,234 grid-cells forest type assigned from AVI stand-level inventory 5

6 Ground calibration WF-HWP maintains >3200 PSP empirical yield curves we used 735 of those plots to train prediction models timing of last msmt & quality of GPS used HWP mensuration data & calcns of top ht, volumes, BA & trees/m 3 biomass from regr. equations of Lambert (2005) or Ung (2008) separate models for each of 3 forest types 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 Yeah, but 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 all cell-level predictions What about the bias? It s the oper planner s fault! 14

15 Existing PSPs calibrate LiDAR Model? PGS plots used were not welldistributed across variation in LIdar metrics For many situations on FMA, models are in extrapoln mode Validation needed for area outside PSP space customized sample design should still better models Frazer et. al, 2011 Partners: WFM - Hinton Wood Prod.; Alberta SRD; CFS PFC; UBC 15

16 Structurally-guided sample design Existing PGS plots 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, results & cost Hinton PSPs maintained since 1950s 3,202 plots est. systematically across FMA designed empirical yield curves Better results from LiDAR models but only 735 plots were used A LiDAR-specific sample design still better results, with even fewer plots allow prediction of other attributes 18

19 What we re working on now Best Practices Guideline support dev. of standards for LiDAR use Strategic, tactical and operational planning support acceptance of LiDAR in FMPs, etc, Linkage to FPInnovations Value Maximization & Decision Support Program FPInterface net value predictions Document cost-benefit across value chain 19

20 What we re working on now Evaluating model improvement (?) with structurally-balanced sample design High Resolution LiDAR & Digital Imagery with Semi Global Matching CHM proposal to re-fly the Hinton FMA explore potential to grow the inventory Going Coastal??? explore object-based predictions with high-res? predicting species & product profiles? 20