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Mid Scale Data
Data to Model Ecosystem Service Outputs for Agency
Planning
Jimmy Kagan
OSU | PSU | UO
Presentation Goals & Outline
1. What is Mid‐Scale Data and how does it compare to National and Local scale info?
2. How is it generated?
3. What does it get you and why is it needed for ecosystem service outputs?
4. How is it used in agency planning?
What is Mid‐Scale Data?
• Defined by– Spatial Extent– Spatial Resolution– Attribute Resolution
Spatial Extent
• Local (e.g., map of birds at PDX)• Regional (e.g., GNN maps of the PNW)• Nationwide (e.g., LANDFIRE EVT, USGS GAPvegetation)
Spatial Resolution
• Fine‐scale data = 1m or finer spatial resolution (NAIP or LIDAR)
• Mid‐scale resolution = 30m spatial resolution (Landsat)
• Broad‐scale resolution >= 250m spatial resolution (Modis)
Attribute resolution
• Single‐species maps• Community types (e.g., Ecological Systems)• Multivariate information on vegetation community composition and structure– NN Imputation = a multivariate prediction– Maintenance of covariance structure (Henderson et al. 2009)
• Confidence of predictions
Uses for estimating ecosystem services
• When data depth is needed• Flexible vegetation summaries.• When accurate areal representation over large areas is of primary importance. (e.g., ‘how much of watershed ‘x’ is covered with closed‐canopy forests?)
2008 USGS Gap Landcover in Oregon
Example: Estimating habitat
What’s missing in GAP?
• Vegetation Structure– useful for estimating habitat value for wildlife or carbon or wood product availability
• Site attributes (down wood, snags, litter)– Useful for evaluating fire risk, wildlife habitat or biomass
• Species Composition of the Vegetation (system)– Useful for summarizing ecological condition
• (e.g., invasive species)
(2) calculate
axis scores of pixel from
mapped data layersstudyarea
(3) find nearest-
neighbor plot in
gradient space
(4) impute nearest
neighbor’s value to
pixel
Methods: GNNgradient space geographic space
CCAAxis 2
(e.g., Climate)
CCAAxis 1
(e.g., elevation, Y)
(1)conductgradient
analysis ofplot data
Using nationally available FIA & AIM data to generate midscale data
Methods: Random Forest (RFNN)• One Classification Tree:
|MTM100 < 21.1095
DEM < 679.825
MTM300 < 13.874
SLPPCT < 29.3858
YFIRE < 3.15519
CANOPY < 42.085930725365 3415
4323
8793 48475767
Methods: Random Forest (RFNN)
• A whole forest of classification trees!
• Each tree model is built from a random subset of explanatory variables and input data.
• When the model is applied to mapped data, each tree ‘votes’ on which Plot best represents a pixel should be.
|TM100 < 22.9069
TM100 < 19.0223 FOG < 0.5
TM100 < 33.82934108 5120
5977 86398622
|ANNHDD < 2766.21
SLPPCT < 10.3216 STDTM100 < 16.1739
ANNHDD < 3469.43STDTM100 < 46.7235
9148 5675 3517 4192 4607 5832
|TM200 < 22.9549
STRATUS < 201.108
TM200 < 35.1356
4156
5559 82698694
|MTM300 < 28.9494
MTM300 < 13.8683 IDSURVEY < 0.5
MTM300 < 43.80133922 4672
6770 89475136
|DECMINT < 48.1696
MTM700 < 27.8564
DECMINT < -214.708DECMINT < -276.567R5700 < 145.622
R5700 < 185.313
4280 36687896 4737
6104
8506 5086
|TM100 < 22.9069
TM100 < 19.0223 DISTNF:b
4108 5120
5833 8480
Mid‐Scale Data vs National Data for Species Habitat Mapping
Northern Spotted Owl Habitat on Mt. Hood.
Greater Sage Grouse Habitat in Southeast Oregon.
Forest Canopy CoverAlso available in national scale data, but more accurate with mid scales information
Tree DiameterAvailable only in mid (or fine) scale data and needed to model ecosystem service outputs
Why Is Mid Scale Data Needed to Model Ecosystem Service Outputs
for Land Management Agency Planning?
Because it is the data needed to make State and Transition Models work
State‐and‐Transition Models (STMs)• Simulate changes in vegetation over time due to succession,
disturbance, and management activities• Are a modeling form of Box and Arrow Diagrams• Agency models historically were developed in the Vegetation Dynamics
Development Tool (VDDT) which are non‐spatial simulations.• These simulations run in the Path Landscape Model• A spatial version (ST‐Sim) is available from APEX (apexrms.com)
Herbaceous Open shrub Closed shrub Woodland
Perennial bunchgrasses
Mixed grass
Exotic annual grasses
SUCCESSIONDEG
RADA
TION
DISTURBANCE
State‐and‐Transition Models
Western Juniper / mountain big sagebrush
Herbaceous layer:
Thinning
Low‐severity fire
High‐severity fire
State and Transitions describe changes over time, which can be converted to ecosystem outputs
Planting
Regeneration
Use Mid Scale Data to Run Models and Hook to Outputs
Fuels
Wildlife habitat
Treatment finances
Output Intrepretations
Watershed
Evaluate management scenario
State and Transition Models
Wildfire‐fuel hazards
Terrestrial habitat
Aquatic habitat
Economic potential
Ecosystem Services
Merchantable Volume, Sawtimber, Biomass
Deschutes National Forest Output Example
Wallowa‐Whitman project area –Current Vegetation
VegetationNo Treatments30 Years
VegetationNo Management 150 years
Alternative 2 Treatments first decade
Vegetation, Alt. 2, 30 Years
Vegetation, Alt 2, 150 years
MC2 Dynamic Global Vegetation Model
Climate‐informedState‐and‐Transition Models
Integrating Climate Change & Land Management Models
Climate Information
Oak woodlands
Early Mid
Late Conifer
Chaparral
Ponderosa Pine
Early Mid
LateOld‐
growth
Douglas‐fir
Early Mid
LateOld‐
growth
Midscale data is not all TerrestrialNWI is relatively high spatial resolution with uncertain accuracy….yet can
Channel Type Classification
Based on Montgomery and Buffington 1997
And Can Include Species
Mid‐scale data can improve species mapsCalifornia mountain kingsnake Lampropeltis zonata
Predicted Habitat quality within occupied watersheds
Conclusions• Mid Scale data can provide information to improve modeling of ecosystem service outputs
• Mid Scale data provides critical inputs into state and transition models which predict many ecosystem service outputs from management decisions
• Mid Scale data is created from nationally available datasets with proven methods
• Mid Scale data is available in some areas of the US, and cab be developed across the country if federal agencies worked together and decided to make it happen
Jimmy Kagan, INR‐Portland, [email protected]@oregonstate.edu, 503‐725‐9955