Airborne LiDAR for EFI : what s operational ; what s R&D today

Transcription

1 Airborne LiDAR for EFI : what s operational ; what s R&D today For. Chron.: 87: SL SL SL Doug Pitt Woodlands Forum, Moncton, NB Apr. 2-4, 213

2 height LiDAR 11 Using the point clouds for area-based estimates D(%) LiDAR Data by elevation LiDAR Point Cloud P Cloud Statistics 1

3 Predicted stand volume - total (m 3 /ha) Predicted LiDAR 11 Using the point clouds for area-based estimates 35 3 Sb GTV (m 3 /ha) = (mean p9) tvol 1: Actual Actual stand volume - total (m 3 /ha)

4 LiDAR predictive models Top and Dom/codominant Height QMDBH Volume (GTV, GMV) Basal area Biomass Density Mean tree volume Sawlog volume Diameter/volume Distributions LiDAR 11 Murray Woods Proper calibration is essential!

5 Predicted Predicted mean Tree tree Diameter diameter (cm) (cm) 2 m LiDAR 11 Using the point clouds for area-based estimates 45 2 m Actual Actual mean Tree tree Diameter diameter (cm) (cm) ˆ y Height Basal area Density Volume (GTV, GMV) Biomass Quadratic mean DBH Mean tree volume Size distribution Now, we have spatial data!

6 Both tactical AND strategic! LiDAR 11

7 Advanced Forest Resource Inventory Technologies Murray Woods - Doug Pitt Dave Nesbitt Dave Etheridge Kevin Lim Margaret Penner - Don Leckie François Gougeon Paul Treitz Jeff Dech

8 Team AFRIT Murray Woods Doug Pitt Dave Nesbitt Margaret Penner Forest Analysis Ltd. Kevin Lim Lim Geomatics Paul Treitz Jeff Dech Don Leckie Dave Etheridge François Gougeon Great bunch to work with!

9 Two Forests ~ 2M ha Northeastern Ontario HEARST Remote Sens.: 4: LiDAR data; relatively low-resolution ROMEO MALETTE Parameter Romeo Malette Hearst Sensor Leica ALS4 Leica ALS5 Platform King Air 9 Cessna 31 Pulse Rate 32,3 Hz 119, Hz Scan Rate 3 Hz 32 Hz FOV 2 deg. 3 deg. H 2,74 2,4 m Line spacing Vert. Accuracy Pulse density 9 m < 5 cm ~.5 /m 2 1, m < 3 cm ~1. /m 2 Relatively low pulse densities!

10 Stratification RMF: 4 strata x 6 plots. Hearst: 9 strata x 5 plots. Intolerant Hwds Mixedwoods LC1 PJ2 MWC PO1 MWH SB1 SB3 SF1 SP1 Jack Pine Black Spruce Through the range of development stages!

11 LiDAR predictive rasters

12 Some results Feedback from the woods OBM 2m Old Lidar 5m New Old New Basic products = huge advantages!

13 Some results Feedback from the woods

14 Some results Feedback from the woods LiDAR Information can add more value to our basic image and inventory interpretation Virtually walk every square inch!

15 GMV m 3 /ha Some results Feedback from the woods PJ9 Sb1 17m 6.91 Site Class 2 CC=6% PJ9 Sb1 18m 6 1. Site Class 2 CC=6% Plonski's Normal Yield Table Jack Pine - Site Class m 3 /ha 15.8 cm PJ9 Sb1 18.6m 6 1. Site Class 2 CC=6% Stand Age

16 GMV m3/ha Stems/ha GMV m3/ha Stems/ha GMV m3/ha Stems/ha 5 Some results Feedback from the woods PJ9 Sb1 17m 6.91 Site Class 2 CC=6% PJ9 Sb1 18m 6 1. Site Class 2 CC=6% LiDAR Derived /-14.6 m 3 /ha /-.7cm / m 3 /ha 2.7 +/-.8 cm Mean Volume and Density by Size Class Stand: Size Class VOLUME DENSITY Mean Volume and Density by Size Class Stand Size Class VOLUME DENSITY PJ9 Sb1 18.6m 6 1. Site Class 2 CC=6% /-12.2 m 3 /ha /-.5 cm Mean Volume and Density by Size Class Stand: Efficiencies in planning! Size Class VOLUME DENSITY

17 Some results Feedback from the woods Clearcuts: e.g., Jack Pine, 35 ha FMP planned: 4,669 m 3 LiDAR predicted: 7,543 m 3 Scaled volume: 7,733 m 3 Image and data courtesy Tembec Inc. The ultimate validation!

18 Some results Advanced Forest Resource Inventory Decision Support System 12 1 Density (stems per ha) GMV (m 3 per ha) 3 Forest-Type strata present: Intolerant Hwd 3 2x2m cells Mixedwood 228 2x2m cells Jack Pine 54 2x2m cells 771 Prediction Units for 3.8 ha IH MW PJ Diameter class (cm)

19 Operational Feedback Some results Cost-benefit the Romeo Malette example e.g., LiDAR Cost Savings 1) Inventory acquisition and processing (6 items) -$.1/m 3 2) Forest operations (2 items) $1.4/m 3 3) Mill operations (4 items) $.3/m 3 Total savings: $1.6/m 3 X 5, m 3 /year: $8,/year Payback 1.3 years Informed decision making pays off!

20 The future OK, so where is the research taking us? LiDAR 25 LiDAR 212 SGM 29 Advances in technology ~ HI-resolution!

21 Actual (%) Semi-auto Species ID The future n = 346 plots; 86 used for validation: Predicted (%) H HC CH C H: HC: 1 CH: C: 4 96 Fraction correct Actual Bf Bw Ce Pb Pj Po Sb Sw Bf Bw 1. Ce Pb Pj Po Sb Species directly? Sw

22 LiDAR Predicted GMV Raster The future Aiding photo interpretation Use LiDAR rasters to create polygons?

23 Wet Areas Mapping The future First step in mapping productivity!

24 driving towards ecosite, The future Add slope, aspect, species preference

25 and soil productivity The future Participants: Clement Akumu, John Johnson, Peter Uhlig, Sean McMurray, David Etheridge (OMNR); Murray Woods (SSIS); Doug Pitt (CWFC); Doug Aspinall (OMAFRA); Paul Arp (UNB); < linked with work in AB, NL, and NB, Time to get excited?

26 GROWING the inventory The future EFI+productivity = view of the future (what mill infrastructure is needed; what impact will silvicultural investment have on the outcome?) Explore successive LiDAR/SGM to quantify change Spatially predict the future?

27 Adding FIBRE Attributes The future Participants: Jeff Dech (Nipissing U); Bharat Pokharel (Nipissing U); FPInnovations; Art Groot, Doug Pitt (CWFC); Murray Woods (SSIS) > linked with work in AB, QC, and NL. Predict wood density, MOE, MOR?

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