whining from the applications side of the fence
DESCRIPTION
MODELLING TECHNIQUES FOR MAPPING IN FOREST INVENTORIES Gretchen Moisen, Tracey Frescino US Forest Service, FIA. Whining from the applications side of the fence. Outline. Need for new info Data Models 4. Maps and applications 5. Now what. Need for new information: Traditional reports. - PowerPoint PPT PresentationTRANSCRIPT
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MODELLING TECHNIQUES FOR MAPPING IN FOREST INVENTORIESGretchen Moisen, Tracey Frescino
US Forest Service, FIA
Whining from the applications side of the fence
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...and not a thoughtto think.
Data, dataeverywhere...
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Outline
1. Need for new info2. Data3. Models 4. Maps and applications5. Now what
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Need for new information:Traditional reports
• Inventory status and trends in forested ecosystems nationwide
1928 McSweeney-McNary Act
1978 Renewable Resources Act
1998 Farm Bill
• Regional estimates of forest area, tree volume, growth and mortality
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Research to develop new products………In addition to estimates of
population totals…….• Make maps! Show how
forest resources are distributed throughout the landscape
• Use those maps: wildlife, fire, harvest….• Automate data retrieval,
visualization, and analysis tools• Build web-based delivery systems• Just do it
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Need for new information:Development of an interdisciplinary system
• Dialogue with users, define problems
• Build data base, prepare data
• Build and test models
• Test products in real applications
• Get it out and get feedback
QUESTIONS
FIELD DATA
DIGITAL DATA
MODELS
EVALUATION
DELIVERY
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Outline
1. Need for new info
2. Data
3. Models
4. Maps and applications
5. Now what?
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DataSix Ecoregions
• Regional diversity• Forested ecoregions• Within state bounds• Sample across all
owners
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Data:Plot-level Response Variables
Continuous:• Basal area• Biomass• Crown cover• Growth• QMD• Stand age• TPA• Volume
Catagorical:
• Forest/nonforest class
• Select forest type
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Data:
Sample plots
UT1 F: 821 NF: 533
UT2 F: 829 NF: 491
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Data: Sample plots MT1 (F: 1277 NF: 294)MT2 (F: 1612 NF: 2108)
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Data:
Sample plots
AZ1 F: 712 NF: 135
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Process:Many RS-based Predictor Variables
• Raw imagery: TM, MODIS, AVHRR
• NLCD 30 m resolution 19 classes, 8 broad groups
• DEMs: elevation, aspect, slope, hillshade, topographic class
• Spatial coordinates• Other: Soils, TEUs, Precip
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Outline
1. Need for new info2. Data3. Models 4. Maps and applications5. Now what?
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• Extract data from each layer at each FIA location
• Build a model for each FIA variable
Example: Tree cover ~ f(Cover-type, Elev, Aspect, Slope)
Models:Establishing relationships with predictors
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………cover type
……….elevaton
……….aspect
……….slope
to predict
……….crown cover
over unsampled areas
Through the final model, use
Models:Predicting over large areas
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ModelsResponse discrete x continuous x interactions
Forest type
Basal area
Biomass
Crown cover
Growth
QMD
Age
TPA
Volume
NLCD
Soils
Elevation
Aspect
Slope
Hillshade
X
Y
X,Y
Elev, Asp, Slope
NLCD(others)
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Models:Simple Benchmarks
• Discrete variables
Yhat=NLCD class• Continuous variables
Yhat=mean(Y) w/i
NLCD classes• SIMPLE..is it enough?
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Numerous model building tools…..
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GAM
MARS
CART
ANN
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Model Test Using Simulated DataCART LM
GAM MARS ANN
X1,…, X10 ~ Unif(0,1)
Y = 2sin(π*X1*X2) +
.4(X3-.5)2 +
.2(X4) + .1(X5)
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Residual Plots: BIOTOT in UT2CARTNLCD
GAM MARS ANN
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Overview of Analyses
Responses Continuous: BIOTOT,
CRCOV, QMDALL,
STAGE
Discrete: F/NF, F1/F2
Predictors NLCD, AVHRR, topography, UTMs
Technique NLCD, GAM, CART,
MARS, ANN
Evaluation
Criteria
Continuous: RMSE, PWI,
RHO, Runtime
Discrete: PCC, Kappa,
Runtime
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Evaluation criteriaModeling Continuous: RMSE, PWI,
RHO, Runtime
Discrete: PCC, Kappa,
Runtime
System Data preparation requirements?
Nest modelling and prediction within a GIS?
User Do the maps help solve real problems?
Can users drive?
Models fuel estimation and EDA as well?
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Outline
1. Need for new info
2. Data
3. Models
4. Maps and applications
5. Now what?
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Building maps:F/NF, BA, CRCOV, VOL, STAGE, QMD
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Fishlake Applications
Build and test large-scale models predicting…- Presense of cavity
nesting birds- Elk calving sites
…using FIA-generated maps of habitat predictor variables
Tom Edwards, Randy Schultz
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Applications:Web Delivery
• JPEG preview• PDF map• Build a map (Generate a map based on user-defined criteria)
Tracey Frescino, Frank Spirek
http://www.fs.fed.us/rm/ogden/index.html ► Techniques Research
Warning: These maps are prototypes under development. They are NOT final products
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Applications: Interactive Display Environment
• Interactive tool for visualize, summarize, and query resource information
Tracey Frescino
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Outline
1. Need for new info
2. Data
3. Models
4. Maps and applications
5. Now what?
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Future Work:Refining Interdisciplinary System
• Continue dialogue
• Refined retrieval system
• New predictor variables
• Streamlined modeling box
• NFS test applications
• Refined web-based delivery
QUESTIONS
FIELD DATA
DIGITAL DATA
MODELS
EVALUATION
DELIVERY
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Future Work:New Applications
• Prediction for new applications: assessment of resources lost to wildfire or I&D, extension to other
wildlife species
• Improved precision on population estimates
• Improved analyses