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Improving DWM Data Quality 3rd of 3 Part Training Series Christopher Woodall DWM National Indicator Advisor.

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Presentation on theme: "Improving DWM Data Quality 3rd of 3 Part Training Series Christopher Woodall DWM National Indicator Advisor."— Presentation transcript:

1 Improving DWM Data Quality 3rd of 3 Part Training Series Christopher Woodall DWM National Indicator Advisor

2 Outline QA/QC Analysis What Customers Want Measurement Errors Hot and Cold Checks Top Six List of Errors Training

3 QA/QC Analysis The analysis of 2001-2004 DWM QA plots is currently ongoing. Matching algorithms are being developed for numerous measurement variables. Expect results for the 2006 P3 Training Sessions. For more information contact: Chris Woodall @ NCFIA and Jim Westfall @ NEFIA

4 What Customers Want A uniform DWM sample design applied across the entire United States producing per acre estimates of fuels, carbon, and wildlife habitat

5 Measurement Errors Establishing Transects FWD Counts Slope versus Horizontal Distances CWD Diameters Correct Units for Duff, Litter, and Fuelbed Depths Microplot coverage and heights Hot checks

6 Measurement Errors Number 1 priority is matching data and determining adherence to MQO’s Number 2 priority is determining cause for errors…then correcting cause Cold/Blind Checks

7 Measurement Error Propagation Database Processing Algorithms Core Table Measurement errors have varying magnitudes of effect

8 Measurement Error Simulation

9 Simulation Conclusions Measurement variables whose errors least affect core table outputs: CWD decay, classes/transect lengths, litter depth, and FWD counts Measurement variables whose errors most affect core table outputs: duff depths, CWD diameters, and slash pile densities

10 FIA’s Top Six Least Wanted DWM Errors 1.CWD Diameters 2.CWD Lengths 3.Duff Depths 4.Litter Depths 5.Slash Pile Density 6.Missing Data

11 CWD Diameters Crews mistakenly record CWD diameters to tenth of inch…used to P2 plots Only measure to nearest inch!! ≠

12 CWD Lengths Some log dimensions recorded in field are impossible 3 inches 15 feet 120 inches =

13 Duff Depth Duff is the heaviest down woody material per unit volume Make sure your measurements (and units) are correct

14 Litter Depth Much lighter than duff…however is usually much deeper Don’t mistakenly enter the litter depth for duff depth

15 Slash Pile Density Only neatly stacked wood can exceed 40- 60% density!

16 Missing/Mismatched Data AKA: Excruciating Headaches for Analysts

17 Missing/Mismatched Data Example: DWM plot sheet indicates CWD transects on a condition class 2…however, only one condition class recorded in P2 record Example: CWD piece is decay class 2, but is missing small and large end diameters Might be your fault, might be data management’s fault, might be computer’s fault…no matter…do what you can to minimize mismatch errors

18 Training

19 Problem Areas Problem: Field crews disturb the CWD too much trying to determine decay class or if segmented Correction: Although field crews must disturb CWD pieces in order to acquire measurements, try to keep disturbance to a minimum

20 Problem Areas Cont’d Problem: Field crews mistakenly enter extra digit for CWD diameter (40 instead of 4 inches) Correction: Unless PDR’s catch them, be sure of very large CWD diameters

21 Problem Areas Cont’d If CWD piece ends in water, treat as if underground, measure piece to water edge For FWD, if transect under water try to enter “0” values and indicate in plot notes

22 Problem Areas Cont’d Problem: Crews dig through litter hunting down pieces of FWD Correction: Crews should only tally obvious FWD pieces, namely those on litter surface Problem: Crews aren’t tallying FWD pieces hung up in slash/saplings Correction: Crews should tally all FWD pieces from forest floor up to 6 feet above ground

23 Problem Areas Cont’d Problem: Crews either include too much of the litter layer or upper soil mineral horizons in estimation of duff depth Correction: Crews should be absolutely sure of what is duff, litter, and mineral horizons. Be absolutely sure of duff measurements!!

24 Problem Areas Cont’d Duff Depths: 1)Identify duff from mineral soil 2)Don’t include moss or litter material 3)What to do with deep duff 4)Anything over 1 foot  be absolutely sure

25 Problem Areas Cont’d Problem: Crews can’t decide on the fuelbed height measurement Correction: Crews should only take 15- seconds to determine height of dead, down woody material, don’t over analyze, use local knowledge and reasonable definition of fuel ladders

26 Problem Areas Cont’d 1)Measure from top of duff to top of fuel complex 2)Fuel complex composed of dead FWD, CWD, shrubs, and litter 3)Gaps allowed in fuel complex where one would reasonably expect flame lengths to connect 4)Plum-bob not required, ocular estimate around sample point 5)15-second rule…Don’t over analyze height of fuelbed…Use your experience and logic Fuelbed Depths

27 Problem Areas Cont’d Problem: Condition class boundary runs through microplot Correction: Use entire forested condition of microplot to estimate coverage and heights = 100% cover of litter for forested conditions (don’t include asphalt or other non forested conditions in cover assessment)

28 Problem Areas Cont’d 1)Train with idea of imaginary 6.8 foot radius cylinder 2)Make sure crews know what herbs and shrubs include 3)Gaps allowed in fuel complex as long as reasonable 4)Branches from shrubs rooted outside microplot allowed 5)Train about vines and canopy herbaceous plants Microplot Heights

29 Problem Areas Cont’d Only include epiphytes or hanging moss up to 6 feet in height Include vines that are within microplot

30 Problem Areas Cont’d Only estimate density of CWD within pile Density should rarely exceed 40% 70% 20%01% Slash Pile Densities

31 Organizing Training Sessions 1)Part 1: Introduction to DWM 2)Part 2: Field Methods 3)Analyst Example (optional) 4)Part 3: Improving DWM Data Quality 5)Certification Test 1)Stations (test optional) 2)Go over one subplot together as group 3)Trainees do at least one subplot on their own – hot audit and/or compare results Classroom Field

32 Bringing it all Together 1)Pick training location where many conditions classes and sampling scenarios exist (see word file) 2)Use powerpoint files to sculpt training session so trainees have understanding of why we need quality DWM data, what we use it for, the theory behind the sampling design, field methods, and problem areas 3)Setting up a quality station course can reduce questions during actual field season – may conduct test

33 Sample Design Changes The DWM Indicator must be responsive to customer needs and improving science/techniques… Don’t assume your ideas are insignificant, you collect the data, assume you know best and pass ideas upwards… Submit your

34 End of Part 3 of 3 onal-programs/indicators/dwm/

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