Sarwat Butt Chairperson WPC - FIA Additional Director FIA HQs
A user’s wish list for extracting more value from FIA data
description
Transcript of A user’s wish list for extracting more value from FIA data
A user’s wish list for extracting more value from FIA data
Steve PrisleyVirginia Tech
Context: Current projects• Resource Assessment Center:
– Modeling wood supply with FIA/RS – Identifying the “working forest”– TPO and consumption proximity zones
• EPA Carbon neutrality of biomass– Identifying the “working forest”– G:R by region for working forest
• NTFPs and FIA (Chamberlain, USFS)• Nitrogen deposition and FIA plot productivity (Thomas,
EPA$)• FIA legacy data Q&R (Smith, USFS)
Challenge: Multi-state analyses
• FIADB in Access:– Excellent tool, good reporting capability– Limited in size (by Access)
• For analyses of large areas:– Use Evalidator• What about when it’s down?
– Use D-I-Y reporting in another DBMS– How about an R package to ingest FIA data and
produce standard reports?
Web tool enhancements
• Evalidator- powerful tool, tremendous flexibility• But tedious for repetitive tasks involving detailed lists
of states, complex filters• Save queries or criteria? (E.g., list of states, screening
criteria- show the entire select statement to copy/paste?)
Data Enhancements
• Since FIA can’t deliver detailed ownership at the plot level, they need to do more analysis relating inventory, growth, and removals by ownership class
• Develop summaries by detailed owner class over FIA units– E.g., acres, harvest, growth, mortality, GS volume,
etc., over detailed private ownership classes
Data Enhancements
• TPO mill locations- need more accuracy• Large disparities between TPO and proprietary
products• Should USFS/FIA be the “go to” place for wood
utilization data?• Example: Morgan Lumber Company
TPO Mill Location
Google Maps LocationUGA WDRP Location
Google Maps Location
UGA WDRP Location
Analysis enhancements
• Advanced analysis FAQ’s? Analysis Wiki?– Tricks and tips for connecting plots over time– Tricks and tips for connecting trees over time– Finding plot disturbances– Explaining the head-scratchers
Include accuracy information
K Acres K AcresNLCD Raw FIA Diff %
Coniferous 2,599 3,088 -15.8%Decid/Wet 12,488 11,010 13.4%Mixed 905 1,561 -42.0%Tot Forest 15,992 15,659 2.1%
Virginia Forest Acres, 2006
Include accuracy informationBut: Use the error matrix published by Wickham et al. (2013) to correct area estimates based on misclassification rates, then…
Virginia Forest Acres, 2006
K Acres K Acres K AcresNLCD Raw FIA Diff % NLCD Corr Diff %
Coniferous 2,599 3,088 -15.8% 3,124 1.2%Decid/Wet 12,488 11,010 13.4% 10,614 -3.6%Mixed 905 1,561 -42.0% 1,467 -6.0%Tot Forest 15,992 15,659 2.1% 15,205 -2.9%
FIA Accuracy standard: ± 3%?
Virginia Forest Acres, 2006
Program enhancements
• Ability to conduct rapid update of likely disturbed plots– Many RS products focused on rapid identification
of change/disturbance (e.g., VCT)• Develop approach for estimation for annual
updates:– Disturbed plots/lost volume; prob(disturbance)?– Grow plots?– Sounds like AFIS