1. Oregon Department of Forestry Forest Inventory Systems and Lidar
Operationalizing Lidar in Forest Inventory
Tod Haren
1/25/2016 – Olympia, WA

2. Overview of ODF (State Forests) Inventory Tool Chain
Introductions
Overview of ODF (State Forests)
Inventory Tool Chain
Stand Level Inventory
Data Management
Stand Level Imputation
Lidar Processing
Landsat Processing
RandomForests Imputation
What is being inventoried
Who is being served
What outputs are produced
How are species handled
How are outputs integrated with existing systems
Highest priorities for improvement

3. ODF Staff Present Jeff Firman – Forest Inventory Specialist
Mike Wilson – GIS and Information Specialist
Sephe Fox – GIS and Information Specialist
Josh Clark – Modeling Lead, Forest Planning
Tod Haren – Forest Resource Analyst

4. ODF – State Forests 800,000+ acres under management
3% of Oregon’s forested land
Operations funded by timber sale revenue
Nine management districts
Six state forests

5. Common School Land Every 16th and 36th section granted at statehood
Forested parcels managed by ODF under agreement with DSL
Net timber sale revenue contributed to the Common School Fund
ODF is reimbursed for management costs
Elliott State Forests
~84,000 acres resulting from a series of land trades

6. Board of Forestry Land
Acquired from counties following tax foreclosure
Tillamook burn
1933, 1939, 1945, 1951
350,000+ acres
Planting and rehab continued into the 1970’s
Managed for “Greatest Permanent Value”
63.75% of timber sale revenue distributed to the county in which harvest occurs

7. Stand Level Inventory FBRI - Forest Projection System (v6.4+)
Designed for traditional double sampling
Stands delineated on dominant vegetation
Stratification using the FPS veg_lbl method
Dominant species; size class; stocking level
Initiated in ~2000 to support reporting needs anticipated for the then new forest management plans.
Additional fields and tables added
MS Access front-end developed with significant VBA code
In-house developed field data collection software using DataPlus

8. Stand Level Inventory
Originally promoted decentralized inventory to give more local control
Local control quickly became burdensome
Attrition, burnout, inadequate training, etc.
Standards were not being enforced
Reporting and analysis was inconsistent and cumbersome
Salem is now taking a more active roll
Most annual updates funneled through the inventory specialist
More communication and coordination
Migration to a central database - SQL Server and ArcGIS/SDE
Improved access to current and historic data
Ready access to pertinent information for all staff
Better systems integration
Annual updates and “ROOTS” still require individual Access databases

9. Stand Level Inventory
Initial goal was to maintain 50% of stands as recently cruised
In 2008 Tillamook undertook a significant re-typing
Many cruised stands were split, invalidating plot design
Retained plots are questionable due to reconfiguration of stands
Sampling curtailed due to budget cuts in 2009
Reinitiated in 2015
NW Planning area inventory status (2014)

10. Stand Level Inventory Proportional sample allocation by strata
Some input from field based on operational priorities
Plots located along lines to represent typical cover, crossing topographic features and elevation gradient
Typically plots per stand

11. Stand Level Inventory Nested plot design Large tree variable radius
Small trees, snags, understory fixed radius
Down wood line transect
Species, DBH, Damage for all trees
Heights subsampled by species
Site trees selected from dominant & codominant canopy
No radial increment
No upper stem measurement (form)
Plots not geo-located, mapped points are preliminary

12. Stand Level Inventory
Converted from strata expansion to imputation in 2008
Better representation of variability across the landscape
Forester “best guess” assignment of cruised to non-cruised stands
No formal validation process
Tillamook – stands, only 20% cruised (fewer in 2008)
Less confidence in imputation as inventory aged
Maintenance became burdensome

13. Tillamook Imputation Roughly 70% lidar coverage by 2011
Central swath covered in 2012
NE corner partially flown in 2015

14. Tillamook Imputation
2012 Cooperative agreement with RMRS, Moscow, ID to develop imputation methods using lidar and landsat
Hudak, Andrew T.; Haren, A. Tod; Crookston, Nicholas L.; Liebermann, Robert J.; Ohmann, Janet L Imputing forest structure attributes from stand inventory and remotely sensed data in western Oregon, USA. Forest Science. 60(2):
All stands projected to each of the lidar flight years
Tested multiple imputation methods, MSN, GNN, RandomForests
Tested imputation of stand signatures onto pixels
Selected RandomForests stand level imputation
Dependent variables included: TPA; BAA; SDI; CCF; HT; QMD; Tot VPA; Merch VPA; Tot Carbon
Nine independent variables selected based on scaled importance
Lidar: Top Ht; Return density in vertical strata; 25th pct ht
Landsat: Brightness; Greenness; Wetness; Topographic variables
Evaluated plot level imputation, but results were very poor due to lack of geo-location

15. Tillamook Imputation Lidar imputation: Observed vs. Imputed
Stand level vs. Pixel level
A&B – Stand to stand
C&D – Stand to pixel
Landsat pixel level is even more skewed toward mean

16. Tillamook Imputation Comparison with 402 stands cruised in 2010
Plot compares difference between imputed and cruised VPA
Landsat and lidar compare observed values with nearest neighbor
402 stands cruised in 2009 compared with the previous imputation assignments
Strata Exp compares against strata average

17. Tillamook Imputation – 2014 Update
Additional cruised stands
Central swath lidar data, ~90% total coverage
All stands grown to 2012
Included additional dependent variables
Reduced the precision for any one variable
Better(?) representation of key structure variables
Integrated landsat and lidar, top 40 variables in final model
Lidar variables most important for basic stand attributes
Landsat variable became important as species and structure attributes were added.

18. Tillamook Imputation – 2014 Update
2014 Observed vs Imputed
Total
Cubic VPA; Scribner VPA; TPA >=8”; SDI >=8”; Top Ht; Top QMD; BAA Std Dev
Conifer
Scribner VPA; SDI >=8”; TPA >= 8”
DF; WH; RA
Scribner VPA; Top Ht

19. Imputation Tools Python R
PyODBC - SQL Server and MS Access data management
Liblas - Catalog lidar (laz) datasets
OGR - Tiling lidar catalogs, post processing pixel level data
Parallel Python - Asynchronous execution of Fusion calls
SQLite - Aggregation of tiled Fusion metrics
Need to evaluate the SciKit-Learn package RandomForest implementation as well as many other machine learning algorithms R
YaImpute - RandomForest model development and evaluation
GGPlot2 - Plotting

20. ODF Forest Inventory Who is being served What outputs are produced
Field foresters – Annual operational planning; T&E assessment
DSL & Counties – Annual reporting
BOF, Stakeholders, Managers – Long-range planning; harvest modeling; growth and yield analysis
What outputs are produced
Static reports – Cruise stats; stand tables
GeoPlanner – GIS overlays, SQL stored procedures generate dynamic stand summaries and prescription analysis
Yield tables – SQLite database integrated with Patchworks harvest scheduling software.

21. ODF Forest Inventory How are species handled
Stand level imputation is single nearest neighbor
Target stand assumes all plot attributes of the source stand
Species level stand attributes are included in the imputation model
Landsat multispectral variables, tasseled cap, band rations, veg. indices improved species level response in the imputation model
Integrating with existing systems
Imputation assignments stored in the [ADMIN] database table and used as a foreign key to the cruise tables

22. ODF Forest Inventory Highest priorities for improvement
Evaluation of small area estimation and pixel based methods
What efficiencies can be gained – Fewer field samples, lower cost
What is the potential for increased information (precision and accuracy)
Need more working examples of alternative inventory methods
What are the costs; what is to be gained
How do the results compare with stand based sampling
Examples of integration with growth and yield, eg. tree list generation
If we ultimately stick with stand based inventory
Does our sampling design adequately represent within stand variation
Could we use a multiple neighbor approach
What about separate imputation models for subsets of stand attributes
Data management becomes tricky with anything beyond single neighbor