Oregon Department of Forestry
Forest Inventory Systems and Lidar
Operationalizing Lidar in Forest Inventory
Tod Haren
1/25/2016 – Olympia, WA
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Introductions
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Overview of ODF (State Forests)
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Inventory Tool Chain
o Stand Level Inventory
o Data Management
o Stand Level Imputation
 Lidar Processing
 Landsat Processing
 RandomForests Imputation
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What is being inventoried
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Who is being served
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What outputs are produced
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How are species handled
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How are outputs integrated with existing systems
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Highest priorities for improvement
ODF Staff Present
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Jeff Firman – Forest Inventory Specialist
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Mike Wilson – GIS and Information Specialist
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Sephe Fox – GIS and Information Specialist
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Josh Clark – Modeling Lead, Forest Planning
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Tod Haren – Forest Resource Analyst
ODF – State Forests
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800,000+ acres under management
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3% of Oregon’s forested land
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Operations funded by timber sale revenue
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Nine management districts
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Six state forests
Common School Land
 Every
16th and 36th section granted at statehood
o Forested parcels managed by ODF under agreement with DSL
 Net
timber sale revenue contributed to the Common School Fund
o ODF is reimbursed for management costs
 Elliott
State Forests
o ~84,000 acres resulting from a series of land trades
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
 63.75%
occurs
for “Greatest Permanent Value”
of timber sale revenue distributed to the county in which harvest
Stand Level Inventory
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FBRI - Forest Projection System (v6.4+)
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Designed for traditional double sampling
o Stands delineated on dominant vegetation
o Stratification using the FPS veg_lbl method
 Dominant species; size class; stocking level
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Initiated in ~2000 to support reporting needs anticipated for the then new forest
management plans.
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Additional fields and tables added
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MS Access front-end developed with significant VBA code
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In-house developed field data collection software using DataPlus
Stand Level Inventory
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Originally promoted decentralized inventory to give more local control
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Local control quickly became burdensome
o Attrition, burnout, inadequate training, etc.
o Standards were not being enforced
o Reporting and analysis was inconsistent and cumbersome
•
Salem is now taking a more active roll
o Most annual updates funneled through the inventory specialist
o More communication and coordination
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Migration to a central database - SQL Server and ArcGIS/SDE
o Improved access to current and historic data
o Ready access to pertinent information for all staff
o Better systems integration
Annual updates and “ROOTS” still require individual Access databases
Stand Level Inventory
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Initial goal was to maintain 50% of stands as recently cruised
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In 2008 Tillamook undertook a significant re-typing
o Many cruised stands were split, invalidating plot design
o Retained plots are questionable due to reconfiguration of stands
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Sampling curtailed due to budget cuts in 2009
o Reinitiated in 2015
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NW Planning area inventory status (2014)
District
SLI Stands
Astoria
1,813
Forest Grove
1,191
Tillamook
6,010
West Oregon
975
Cascade
778
Western Lane
389
Total
11,156
SLI Acres Stands Cruised
136,993
742
115,003
645
252,345
1,223
36,633
383
47,626
323
25,261
191
613,861
3,507
41%
54%
20%
39%
42%
49%
31%
Acres Cruised
79,907
77,943
89,881
16,578
23,683
16,337
304,328
58%
68%
36%
45%
50%
65%
50%
Stand Level Inventory
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Proportional sample allocation by strata
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Some input from field based on operational priorities
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Plots located along lines to represent typical cover,
crossing topographic features and elevation gradient
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Typically 16-24 plots per stand
Stand Level Inventory
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Nested plot design
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Large tree variable radius
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Small trees, snags, understory fixed radius
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Down wood line transect
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Species, DBH, Damage for all trees
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Heights subsampled by species
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Site trees selected from dominant &
codominant canopy
 No
radial increment
 No
upper stem measurement (form)
 Plots
not geo-located, mapped points are
preliminary
Stand Level Inventory
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Converted from strata expansion to imputation in 2008
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Better representation of variability across the landscape
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Forester “best guess” assignment of cruised to non-cruised stands
 No formal validation process
 Tillamook
– 6000+ stands, only 20% cruised (fewer in 2008)
Less confidence in imputation as inventory aged
Maintenance became burdensome
Tillamook Imputation
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Roughly 70% lidar coverage by 2011
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Central swath covered in 2012
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NE corner partially flown in 2015
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. 2014. Imputing forest structure attributes from stand inventory and remotely sensed
data in western Oregon, USA. Forest Science. 60(2): 253-269.
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All stands projected to each of the lidar flight years
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Tested multiple imputation methods, MSN, GNN, RandomForests
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Tested imputation of stand signatures onto pixels
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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
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Evaluated plot level imputation, but results were very poor due to lack of
geo-location
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
Tillamook Imputation
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Comparison with 402 stands cruised
in 2010
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Plot compares difference between
imputed and cruised VPA
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Landsat and lidar compare observed
values with nearest neighbor
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402 stands cruised in 2009 compared
with the previous imputation
assignments
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Strata Exp compares against strata
average
Tillamook Imputation – 2014 Update
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Additional cruised stands
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Central swath lidar data, ~90% total coverage
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All stands grown to 2012
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Included additional dependent variables
 Reduced the precision for any one variable
 Better(?) representation of key structure variables
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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.
Tillamook Imputation – 2014 Update
2014 Observed vs Imputed
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Total
Cubic VPA; Scribner VPA;
TPA >=8”; SDI >=8”; Top Ht;
Top QMD; BAA Std Dev
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Conifer
Scribner VPA; SDI >=8”; TPA
>= 8”
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DF; WH; RA
Scribner VPA; Top Ht
Imputation Tools
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Python
o PyODBC - SQL Server and MS Access data management
o Liblas - Catalog lidar (laz) datasets
o OGR - Tiling lidar catalogs, post processing pixel level data
o Parallel Python - Asynchronous execution of Fusion calls
o SQLite - Aggregation of tiled Fusion metrics
Need to evaluate the SciKit-Learn package RandomForest
implementation as well as many other machine learning algorithms
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R
o YaImpute - RandomForest model development and evaluation
o GGPlot2 - Plotting
ODF Forest Inventory
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Who is being served
o Field foresters – Annual operational planning; T&E assessment
o DSL & Counties – Annual reporting
o BOF, Stakeholders, Managers – Long-range planning; harvest modeling; growth
and yield analysis
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What outputs are produced
o Static reports – Cruise stats; stand tables
o GeoPlanner – GIS overlays, SQL stored procedures generate dynamic stand
summaries and prescription analysis
o Yield tables – SQLite database integrated with Patchworks harvest scheduling
software.
ODF Forest Inventory
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How are species handled
o
o
o
o
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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
o Imputation assignments stored in the [ADMIN] database table and used as a
foreign key to the cruise tables
ODF Forest Inventory
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Highest priorities for improvement
 Evaluation of small area estimation and pixel based methods
o What efficiencies can be gained – Fewer field samples, lower cost
o What is the potential for increased information (precision and accuracy)
 Need more working examples of alternative inventory methods
o What are the costs; what is to be gained
o How do the results compare with stand based sampling
o Examples of integration with growth and yield, eg. tree list generation
 If we ultimately stick with stand based inventory
o Does our sampling design adequately represent within stand variation
o Could we use a multiple neighbor approach
o What about separate imputation models for subsets of stand attributes
o Data management becomes tricky with anything beyond single neighbor