2. How are you using lidar? Peter Gould and Jacob Strunk WA State Department of Natural Resources

3. Participants Tod Haren, Oregon Department of Forestry Leah Rathbun, USFS Region 6 Ian Moss, Tesera George McFadden, BLM Chris Butson, MFLNRO

4. Questions 1)Inventory tool chain 2)What are they inventorying 3)Who are they serving 4)What sorts of outputs are they producing 5)How do they deal with species 6)How do they integrate outputs with an existing system 7)Highest priority for improvements

5. Washington DNR 2.1 Million Acres of Forest Land

6. Who are we serving? Will become the central inventory for DNR Field folks Habitat plan compliance Planners Currently transitioning to RS inventory from conventional stand-based inventory Not trying to replace everything (pre-sale cruises, etc)

7. What are we reporting? The typical Volume, basal area, height, trees per acre, QMD Less typical Large tree densities (> 20”, > 30”) Canopy cover and closure The atypical Snags per acre of different size classes (>20”, >21”, >30”) Volume of down wood Primary, secondary species, proportion hwd age

8. Inventory Tool Chain Area-based prediction (not individual tree) Step Remote sensing acquisition Field data acquisition Data management Modeling and prediction Final products

9. Inventory Tool Chain Remote sensing acquisition Publicly available data collected is past 5 years Contracting ($1 million 2013-15. $500k 2015-17) Puget Sound Lidar Consortium, Quantum Spatial GeoTerra (3Di) Costs around $0.80 per acre Debating specs for reflights Digital surface models from imagery (Phodar) NAIP 2013 (previously purchased) Evaluating contracting for Phodar

10. Inventory Tool Chain NAIP PHODAR LIDAR

11. Field Data Acquisition Four field technicians Panels on a randomized grid Dual purpose: model development, area estimation

12. Field Data Acquisition Validation blocks Test spatial autocorrelation Test larger area performance

13. Field Data Acquisition Live and dead trees 1/10 th Acre for trees >= 5.5” DBH 1/40 th acre for trees down to 2” 1/250 th acre for trees down to 1’ ht Canopy cover and closure Moosehorn Spherical densiometer Down wood transects 2 x 50 ft Javad Triumph 2 GPS Android tablets Zip file with SQLite DB, GPS data, photos

14. Data Management Critical components Storage: lidar library. 10’s of terabytes 105 lidar acquisitions R: foRtools package Fusion PostgreSQL DB plays central role Field data Project status, LAS status Grid metrics (Fusion output) Models and other R objects Spatial lookups (plot x las tile intersections) Powerful workstation 72 cores 384 GB RAM

15. Modeling and prediction select * from modeling.plot_forest_metric A join modeling.plot_rs_metric B on A.plot_id = B.plot_id Linear regression Standard model forms (ht_p90, cover) Best subsets Individual models for large projects, general models for smaller areas (inadequate N).

16. Modeling and prediction select * from modeling.plot_forest_metric A join modeling.plot_rs_metric B on A.plot_id = B.plot_id Linear regression Standard model forms (ht_p90, cover) Best subsets Individual models for large projects, general models for smaller areas (inadequate N).

17. Dealing with species Hardwood vs conifer Best subset regression of lidar did poorly Lidar intensity too variable Other structural variables were poor Regression using NAIP plot spectral metrics did poorly Tried segmenting NAIP bands with poor results Best approach was stand-level data using LandSat products (copied GNN) Used previous inventory for primary, secondary species Evaluating strategies

18. RS-FRIS Version 1.0 PROJECTAcresPercentage NAIP_2013 (PHODAR) 104683466.0% OESF_2014 1232997.8% COLOCKUM_2014 887805.6% GRAYS_HARBOR_2012 792675.0% AHTANUM_2006 668744.2% THURSTON_2011 632164.0% HOH_2012 478223.0% TULALIP_2013 327992.1% NOOKSACK_2013 144140.9% WINSTON_2013 92290.6% QUINAULT_2011 80880.5% QUINAULT_2012 27110.2% JEFFERSON_CLALLAM_2012 26880.2% HOH_2013 950.0% Total1,586,116

19. RS-FRIS Version 1.0 Combined projects based on recent activities.

20. What sort of outputs are they producing? Rasters (1/10 th acre) Polygons Average of raster values (beware on interpretation) Polygons from previous inventory Polygons generated with eCognition

21. What sort of outputs are they producing? Remote-Sensing Polygons FRIS Polygons

22. What sort of outputs are they producing?

23. System Integration Version 1.0 is a demo Working on transition Potential to be disruptive Often operate on the boundary of our habitat constraints Criteria have different meanings

24. Highest priorities for improvement Sustainable RS data collection (reduce acquisition costs) We’d like to cover every acre every two years Current budget allows about $0.25 per DNR acre Phodar is feasible at this price Can we tweak lidar specs? Plot-level imputation Feasible but haven’t done it Don’t have high expectations for results Develop strategies for difficult metrics (e.g., species, down wood). Stratum-level means Environmental – lidar models Imputation and let the chips fall where they may