1. Dan Couch Olympia, WA DNR January, 2016

2. Outline Rogue Valley LiDAR Background Stand Metrics Comparison Results:  LiDAR vs Timber Cruise BLM Forest Inventory Implications

3. Rogue Valley LiDAR Ref: OLC Rogue River – LiDAR Remote Sensing Data Final Report Flown in 2012

4. LiDAR BLM Sample Plot Development Ref: Rogue Valley BLM Stratified LIDAR Sample Plot Methodology

5. Steps to Derive Stand Metrics LiDAR Bins  75 foot pixels Correlation Plot Tree Data Harvest Unit Polygons  Basis of comparison

6. Principle Components Analysis 80 th Percentile Height  80% of LiDAR height returns below this point above 10’  Six fixed height classes (~30 ft)  Highly accurate height predictions Total Cover %  Three equal width density classes  Low, Moderate, High LiDAR

7. 18 Bins (Strata) 6 Height Classes 3 Density Classes Low Med High

8. Correlation Plot Tree Data ~13 Plots per Bin (strata)  ~42 foot radius 240 Plots, measured 2013 Trees counted & measured Trees less than 6.5” DBH not sampled Ref: Rogue Valley LIDAR Inventory Plot Establishment – Inventory Report

9. LiDAR Derived Stand Metrics Raster 75 foot pixel (8 th ac) data coverage by metric  Height (BA-weighted)  Basal area (BA)  Density (TPA)  Avg diameter at breast height (QMD)  Volume (ft 3 per acre)  Canopy cover (%) Ref: Rogue Valley LIDAR-assisted Inventory - 2015 Final Report to BLM

10. Regression Model Predictions Description Forest variable (live trees >=6.5” DBH) LiDAR Raster Labels live hardwood & softwood (hs) trees >= 6.5” DBH (6in) R2R2 BA-weighted heightLLOR (ft) LLOR_hs_6in 0.91 Basal areaLBA (sqft/ac) LBA_hs_6in 0.70 DensityLDEN (TPA) LDEN_hs_6in 0.63 Quadratic mean diameterLQMD (in) LQMD_hs_6in 0.72 VolumeLVOL (cuft/ac) LVOL_hs_6in 0.79 Canopy CoverPC_1 st (% > 6.6’) PC_1st N/A Height related predictors best fit Stem density (TPA) worst fit

11. LiDAR Stand Metrics Compared to Timber Cruise

12. Comparing LiDAR & Timber Cruise White Castle 9 Units – Timber Cruised 2012  High degree of accuracy – BA, TPA, QMD, Vol  Count included retention trees  Good comparison of stand metrics  Spatial unit GPS’d to high accuracy  Canopy cover NOT compared

13. Comparing LiDAR & Timber Cruise In GIS, LiDAR pixelated metrics interesected and averaged for each White Castle unit  BA, TPA, QMD, Vol summarized by unit.  LiDAR Ft 3 volume converted to MBF by factor of 6.

14. LiDAR vs Timber Cruise Results Quadratic Mean Diameter (DBH) Unit #AcresCruise/Retain QMDLidar QMDQMD Difference% Diff 132.612.813.4-0.6-5% 212.513.413.7-0.3-2% 3 & 413.613.913.70.21% 56.513.614.4-0.8-6% 615.814.214.9-0.7-5% 7**27.712.813.1-0.3-2% 87321.617.93.717% 92.917.319.2-1.9-11% Avg -0.1-2%

15. LiDAR vs Timber Cruise Results Basal Area Unit #AcresCruise/Retain BA/AcLidar BA/AcBA Difference% Diff 132.61621451710% 212.51651362918% 3 & 413.61851642111% 56.52011663517% 615.82251844118% 7**27.71591392013% 8732662273915% 92.930129293% Avg 2613%

16. LiDAR vs Timber Cruise Results Volume (MBF/Ac) Unit #Acres Cruise/Retain Short Log Vol/Ac (MBF) Lidar Converted* Vol/Ac (MBF) MBF Vol/Ac Difference % Diff 132.627.229.2-2.0-7% 212.526.9 0.00% 3 & 413.629.833.7-3.9-13% 56.533.929.14.814% 615.839.341.1-1.7-4% 7**27.729.827.22.69% 87363.459.04.37% 92.970.780.1-9.5-13% Avg -0.7-1%

17. LiDAR vs Timber Cruise Results Trees Per Acre Unit #AcresCruise/Retain TPA/AcLidar TPA/AcTPA Difference% Diff 132.618117563% 212.5168156127% 3 & 413.6174178-4-2% 56.52001712915% 615.82061763015% 7**27.7179165148% 873105151-46-44% 92.91841632111% Avg 82%

18. BLM Forest Inventory Implications

19. BLM Micro*Storms Application Western Oregon BLM’s corporate forest data repository and application for: Forest Vegetation (FOI-VEG) Forest Surveys Forest Treatments BURN REVEG - PLANT HARVEST

20. FOI-VEG vs Treatments/Surveys FOI-VEG Describes BLM Forest Vegetation Entire Western Oregon Coverage Polygon Overlap Not Allowed Treatments/Surveys Overlap

21. FOI-VEG – Forest Vegetation Data Structure

22. FOI-VEG Published Version ID, Geographic Ref, Acres (Unit # - Twnshp, Rg, Section) Forest Stand Description (Spp, size class, density, birth yr)  Need stand exams for spp mix Forest Stand Metrics (Stand level regardless of spp)  Independent from stand description  Can use LiDAR for stand by stand metrics Attributes For Each Forest Stand

23. FOI-VEG Published Version ID ReferenceLayers AttributesStand Attributes OI_KEYCLASSIFIERGIS ACRESSTAND_DESC AGECLS BYR AGECLS 10 LYR_SRCLYR_SRC_DTCANOPYCOVTPA7QMD7BA7MBF_ACSTAND_SRCSTAND_SRC_DT 43348 Person Importing LiDAR Stand Metrics 32.6FCO D3H3-=1950195060Stand Exam-EcoSurvey8/25/20109117513.414529.2LiDAR12/31/2013 1032212.5FCO D4-1780/D3H3=1940194070Stand Exam-EcoSurvey8/24/20108615613.713626.9LiDAR12/31/2013 1032613.6FCO D4-1890/D3H2=1950195060Stand Exam-EcoSurvey9/1/20108717813.716433.7LiDAR12/31/2013 100166.5FCO D4-1780/D3D2-=19201920100Stand Exam-EcoSurvey9/2/20109217114.416629.1LiDAR12/31/2013 1001615.8FCO D4-1780/D3D2-=19201920100Stand Exam-EcoSurvey8/25/20109517614.918441.1LiDAR12/31/2013 1030927.7FCO D4-1780/D3D2-=19201920100Stand Exam-EcoSurvey8/25/20109016513.113927.2LiDAR12/31/2013 4370673FCO D4-1780/D3D2-=19201920100Stand Exam-EcoSurvey8/18/20109015117.922759.0LiDAR12/31/2013 100422.9FCO D4H3-=1910/H2=1973/H1-20061910110Stand Exam-EcoSurvey8/9/20109816319.229280.1LiDAR12/31/2013 Resulting changes from importing LiDAR stand metrics.

24. QUESTIONS?