LiDAR enhanced Inventory

Transcription

1 To keep that good wood, you need to know what you have and where it is! LiDAR enhanced Inventory ABCFP AGM, Vancouver 24 January, 2011 Roger Whitehead Canadian Wood Fibre Centre Victoria, BC

2 Outline Introduction (3 min) Air Photo interpretation & LiDAR (16 min) basic principles example from E. Canada conventional & LiDAR estimates vs. scaled volumes Obvious applications Other work & further possibilities (1 min) 2

3 CWFC is part of the Canadian Forest Service Cornerbrook 3

4 Pulp & Paper CWFC & FPInnovations Fibre Centre Operations Solid Wood Linking the Forest to the Value Chain 4

5 CWFC research framework 5

6 Inventory - Tools Advanced Forest Resource Inventory Project 4 major components, partnered with Industry Prov. Governments Universities CFS specialists Leveraged funding LiDAR & Digital Imagery 6

7 Traditional inventory techniques Interpreters visual impressions, experience & limited ground sample yield coarse inventory polygon-level low stand-level accuracy essentially aspatial high cost; low refresh rate 7

8 Airborne Light Detection And Ranging Airborne scanner emits LASER pulses, senses returns records intensity of returns and distances & angles of each return relative to on-board Global Positioning System ( point cloud ) Above-ground returns describe the vegetation canopy Last returns describe terrain & yield Digital Elevation Model 8

9 Data is processed to develop predictive models LiDAR Plots Plot-level Canopy Metrics Regression Analyses Ground Plots Plot-level Inventory Metrics Top ht. DBHq BA Density T. Vol. M.Vol. Fuel Biomass 9 Predictive Models

10 Converting LiDAR data to inventory layers Compute plot-level LiDAR canopy 20m 50m resolution Develop correlation models using ground data from plots of same size Apply models to every cell Map inventory attributes at plot-level resolution in GIS volume, piece-size, biomass, etc. 10

11 LiDAR samples 100% of area scanned Statistically-sound, sample-based estimates for every grid-cell Volume = 22,690 m3 +/- 940 m3 Mean DBH = 28 cm +/- 0.8 cm Data is spatial mean & confidence interval for any chosen polygon Vol. = 33,550 m3 More information on within block variability +/- 875 m DBH = 30 cm +/- 1.1 cm 11 3 e.g. piece-size distributions

12 Scalable from stand to drainage to landscape Attributes are very high resolution (20 50m pixels) across entire operating area can address BOTH tactical & strategic objectives 12

13 Advanced Forestorest Resourceesource Inventory Technologies Operational Implementation of Advanced Forest Inventory Research adding value to the forest resource Murray Woods - Doug Pitt Dave Nesbitt - Don Leckie François Gougeon Paul Treitz 13

14 Study Site & Field Data Romeo Malette Forest ~630,000 ha Boreal forest 4 main types Existing standards plot network (400m 2 ) opportune for LiDAR 136 model development plots 138 model validation plots Intolerant Hwds Mixedwoods Slide courtesy AFRIT partnership 14 Jack Pine Black Spruce

15 Study Site LiDAR Data LiDAR acquired in 2004/5... Leica ALS40 laser scanner 9,000 ft. 20 o FOV Scan rate 30 Hz Max pulse rate 32,000 Hz CTS 2.87m ATS 2.4m Sampling density ~0.5 hits/m 2 LiDAR Processing all returns normalized against DEM 400m2 gridcells Predictor variables derived for each cell including height percentiles proportion of total returns by percentile hit density by percentile etc 15 Slide courtesy AFRIT partnership

16 Study Site LiDAR Data Modeling Plot-level predictive models: Height QMDBH Volume (GTV, GMV) Basal area Biomass Density* * Derived from DBHq & BA Polygon-level summaries: Diameter Distributions Sawlog % Slide courtesy AFRIT partnership 16

17 Study Site LiDAR Data Modeling Validation * Derived from DBHq & BA * LiDAR validation results: C.V. similar to model calibration values RMSE results of similar magnitude some forest type models were slightly better than others no substantive bias except in stand density estimates 17 Slide courtesy AFRIT partnership

18 Study Site LiDAR Data Modeling Validation Application LiDAR models applied to ea. 20m grid-cell & mapped for full area Slide courtesy AFRIT partnership 18

19 Study Site LiDAR Data Modeling Validation Application Operational Implementation of LiDAR models 19.5 cm cm Inventory Information Slide courtesy M. Woods, OMNR 19

20 Study Site LiDAR Data Modeling Validation Application FMP Harvest Allocation from FRI data Slide courtesy M. Woods, OMNR 20 Actual Harvest Area

21 Study Site LiDAR Data Modeling Validation Application FMP Harvest Allocation Slide courtesy M. Woods, OMNR 21 Correlation between Actual Harvest area and LiDAR-predicted QMDBH

22 Study Site LiDAR Data Modeling Validation Application Planned Allocation Actual Harvest Potential savings/benefits better harvest planning & log allocation strategies match cutblocks to mill requirements More efficient road & landing layout inventory suited to spatial modeling tools (FPInterface, Patchworks, Woodstock, WoodSim, etc) accurate forecasts of future log characteristics better decisions for capital projects Correlation of Actual Harvest and GMV 22.and a host of others Graphics courtesy M. Woods, OMNR

23 Romeo Malette Forest 5-year plan ( ) FMP plans & LiDAR predictions vs. scaled volume Area harvested: 6,349 ha FMP-planned volume*: 725,851 m 3 LiDAR-predicted volume*: 952,454 m 3 Actual scaled volume: 885,234 m 3 * 3% Cull factor applied FRI 15% underestimate LiDAR - 8% overestimate Data courtesytembec Inc. 23

24 Actual scaled volume vs. FRI & LiDAR predictions 35 ha harvested 2008 (jack pine) FMP planned: 4,669m 3 LiDAR predicted: 7,543m 3 Scaled Volume: 7,733m 3 LiDAR predictions of volume on all clearcut harvest blocks examined was within 5% of scaled volume removed Image and data courtesy Tembec Inc. 24

25 Actual scaled volume vs. FRI & LiDAR predictions 91 ha harvested 2007 (poplar) FMP planned: 9,943m 3 LiDAR predicted: 13,258m 3 Scaled Volume: 12,340m 3 LiDAR predictions for all similarly-treated harvest blocks were within 5-15% of scaled volumes Image and data courtesytembec Inc. 25

26 Actual scaled volume vs. FRI & LiDAR predictions 197 ha harvested 2009 (Sb/Larch) FRI planned: 24,152m 3 LiDAR predicted: 16,606m 3 Scaled Volume: 13,238m 3 LiDAR predictions on all similarlytreated harvest blocks were within 25% of scaled volume... even without netdowns Image and data courtesy Tembec Inc. 26

27 Demonstrated Benefit Tembec diameter/cost study 2007/8 study at one sawmill that receives some logs from RMF (high-speed, twin log line; small/medium diameter specific) showed: net negative value for very small diameter logs break-even for small diameter logs net positive value for medium diameter logs net negative value for larger diameter logs With LiDAR-enhanced inventory, planners are now able to more effectively allocate logs from cutblocks in the RMF to mills to generate a positive cashflow Information courtesy Tembec Inc. 27

28 Operational cruise of RMF in early 2000 s, to determine avg. diameter size class across the forest license, found many larger-diam. trees available Timmins sawmill replaced small log line with larger diameter line, but feedstock over following years was predominantly small-medium diameter & mill was closed in LiDAR-enhanced inventory data in 5-year harvest plan, showed average DBHq (all spp.) of 16cm Had this data been available before re-tooling Information courtesy Tembec Inc. 28

29 CWFC Research Partnerships Inventory Tools In addition to parameters discussed today, we are working with partners to develop RS tools to add: Species (ITC algorhithms; Digital Imagery) Wood Quality Indicators (knotsize, wood density. MoE, MFA) Fibre Quality Indicators (Mature Fibre Length; Fibre Coarseness, Cell Dimensions) Many other applications are possible 29