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A users wish list for extracting more value from FIA data Steve Prisley Virginia Tech.

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Presentation on theme: "A users wish list for extracting more value from FIA data Steve Prisley Virginia Tech."— Presentation transcript:

1 A users wish list for extracting more value from FIA data Steve Prisley Virginia Tech

2 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)

3 NTFPs and FIA Sponsored by SRS/Jim Chamberlain What can FIA data tell us about the sustainability of NTFP extraction? – Geographic distribution – Numbers of trees and volume (bark surface area) – Trends over time Challenge: combining data across states

4 Nitrogen deposition & FIA Sponsor: EPA/Dr. Quinn Thomas, VT Extract tree increment measurements from undisturbed plots to correlate w/ N dep Challenge: selecting undisturbed plots and matching tree measurements from successive measurements

5 Modeling wood supply Sponsors: CeNRADS (20+ corporations, NGOs) Developing agent-based models to simulate the wood supply chain Using FIA and RS for initial inventory (as sub- county level) Using FIA and TPO for mill demands Challenge: downscaling FIA to smaller units, understanding mill demands

6 Identifying the working forest Sponsor: EPA, CeNRADS partners Use FIA removals (and state harvest notification records) to identify characteristics of forests that have experience harvesting Challenges: – Some potentially important variables masked (landowner classes, distances)

7 EPA and carbon neutrality Sponsor: EPA and ICF/RTI Using regional growth/drain estimates to document replacement for biomass C emissions Challenges: – Identifying the working forest – Lack of G/R data in the west – Combining data across states

8 FIA legacy data Sponsor: FIA/SRS (Smith, Coulston) Build prototype of searchable online database for delivery of standard reports of aggregates of states at any time since surveys were available. Challenge: historical inconsistencies in FIA

9 TPO and consumption proximity Sponsor: CeNRADS partners For classes of mills (e.g., small sawmills, large sawmills, paper mills, etc.), determine average proportion of supply that comes from distance zones around mills Challenge: confidentiality of survey data

10 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 its down? – Use D-I-Y reporting in another DBMS – How about an R package to ingest FIA data and produce standard reports?

11 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?)

12 Data Enhancements Since FIA cant 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

13 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


15 TPO Mill Location Google Maps Location UGA WDRP Location

16 Google Maps Location UGA WDRP Location

17 Analysis enhancements Advanced analysis FAQs? 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

18 Include accuracy information Virginia Forest Acres, 2006

19 Include accuracy information But: Use the error matrix published by Wickham et al. (2013) to correct area estimates based on misclassification rates, then…

20 Virginia Forest Acres, 2006 FIA Accuracy standard: ± 3%? Virginia Forest Acres, 2006

21 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

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