A Review of

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• A Review of
• Landscape Dynamic
• Simulation Models

NAESI Project Workshop A Decision Support Process
for Habitat Based Biodiversity Standards Nov
9-10, 2005
Built at Spatialworks
2
Terminology

• Landscape Dynamic Simulation Models
• Land Use Change Models
• Integrated Landscape Management Models
• Landscape Fire Succession Models
• etc...

3

• LDSM characterized (from Lee et al. 2003)
• Provide an approach to understanding the
  distribution of vegetation in space and time for
  large expanses of heterogeneous landscapes.
• Attempt to merge simulation models and GIS.
• Portray changes in vegetation characteristics
  over time while tracking the spatial distribution
  of those changes.

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Applications of LDSM

• Determining pre-settlement landscape
  characteristics of an area
• Determining the natural bounds of variation in
  landscape characteristics
• Predicting future landscape conditions
• Predicting the potential effects of natural
  disturbance, land-use management alternatives and
  climate change

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Attributes of LDSM

• Processes that are simulated
• Vegetation classification system
• Spatial resolution extent
• Time step duration
• Spatial representation raster versus polygon
• Scientific analytical rigor complexity
• Stochastic versus deterministic
• Input/output data requirements format
• Computational requirements memory, time
• Built-in limitations
• Extensibility ease of use parameterization
  additional programming graphical user interface

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Comparisons

• Keane et al. (2004) 45 landscape fire succession
  models (fire and vegetation dynamics)
• Verburg et al. (2004) 6 land use change models
• Lee et al. (2003) 3 landscape dynamic simulation
  models
• Barrett (2001) 4 landscape dynamic simulation
  models
• Others

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BFOLDS

• Boreal Forest Landscape Dynamic Simulator (Perera
  et al. 2002)
• spatially explicit, mechanistic and stochastic
• predicts forest fire regimes, post-disturbance
  early recruitment, and forest cover change
• area million ha
• time frame 100-300 yrs
• input data layers
• dominant species, age, soil, slope, aspect, fire
  weather
• two modules
• forest fire regime simulator
• sub-modules fire ignition and fire spread.
• vegetation transition simulator
• sub-modules early recruitment, vegetation
  transition, and spatial bias

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LANDIS

• Forest Landscape Disturbance and Succession
  (Mladenoff et al. 1996)
• spatially explicit and stochastic
• predicts species level succession and dispersal
  with disturbances including fire, windthrow and
  harvesting.
• area 103 - 107 ha
• time frame 101 - 103 yrs
• major modules include competitive succession,
  establishment and growth, wind and fire
  disturbances, fuel and harvesting

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LANDIS
(Mladenoff 2004)
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Model Linkages
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RAMAS Landscape
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ALCES

• FOREM Technologies
• Explore and quantify dynamic landscapes subjected
  to single or multiple human land use practices
  and various natural disturbance regimes (such as
  fire).
• Identify strategic-level environmental and
  industrial problems associated with landscape
  sustainability.
• Identify mitigation strategies for issues related
  to flows of natural resources.
• (http//www.foremtech.com)

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ALCES

• Land-use practices and processes forestry,
  energy sector development, human population
  dynamics, parks and tourism, aboriginal peoples
  and their features, and landscape composition and
  dynamics.
• Natural processes fire regimes, insect
  disturbances, aquatics, carbon pool dynamics, and
  wildlife habitat and population dynamics.
• Users provide data describing initial landscape
  composition, initial land-use footprint,
  projected land-use trajectories, growth and yield
  curves for merchantable forest trajectories, and
  the demographic characteristics and environmental
  responses of wildlife species.

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ALCES
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Comparing LDSM
(from Barrett 2001)

• FETM Fire Emission Tradeoff Model
• purpose to examine tradeoffs between prescribed
  fire and other fires in context of emissions
• processes succession, harvest, fuel treatment,
  fire
• classification fuel condition classes
• stochastic, non-spatial
• LANDSUM Landscape Succession Model
• purpose to investigate landscape fire succession
  modeling and management effects
• classification structural stages and cover types
• processes succession, harvest, disease, fire
• stochastic, spatial, raster cell with polygon
  identifier

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Comparing LDSM

• SIMPPLLE Simulating Vegetative Processes at
  Landscape Scales
• purpose to investigate how processes and
  vegetation interact to affect landscape change
• processes succession, harvest, disease, insects,
  fire
• classification current species, potential
  vegetation, density, structure
• stochastic, spatial, polygon-based
• VDDT Vegetation Dynamic Development Tool
• purpose to facilitate understanding of
  vegetation change
• processes succession, harvest, disease, insects,
  fire
• classification cover type, structural stage
• non-spatial (TELSA is spatial version)

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Process Comparison
(from Barrett 2001)
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Output Comparison
(Barrett 2001)
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Comparing LDSM
(from Lee et al. 2003)

• VDDT/TELSA
• SIMPPLLE
• RMLANDS Rocky Mountain Landscape Simulator
• purpose to model natural disturbances, human
  activities, vegetation succession
• stochastic, spatial
• Considered
• state space set of possible attributes
  (structure, composition)
• memory ability to apply historical information,
  past events
• modelling approach to landscape dynamics e.g.
  stochastic versus deterministic
• modelling approach to spatial characteristics
  means of managing simulation units and spatial
  relationships

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Comparing LDSM

• VDDT
• pro flexibility in landscape states and process
  open structure direct portability to other
  landscapes good documentation
• con less scientifically and analytically
  rigorous in depicting landscape relationships
• SIMPPLE
• pro relatively sophisticated state space and
  ecological resolution high level of biological
  detail
• con lack of user-friendly GUI developer support
  may be required developer must write
  initialization code
• RMLANDS
• pro high spatial resolution and elaborate
  spatial processes direct linkages to FRAGSTATS
  and wildlife habitat models can project a large
  amount of spatial information
• con lack of user-friendly GUI requires more
  support than SIMPPLE computationally intensive
  many disturbance and management activities not
  represented

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• Berger and Bolte (2004) developed a model to
  assess alternative futures to the year 2050 in
  the agricultural region of the Willamette River
  Basin, Oregon.
• uses a spatially explicit, multi-attribute,
  decision-making process
• simulates land cover change, integrating crop
  rotation, water allocation and land conversion
  policies.
• - agronomic and environmental effects of
  scenarios were determined by assessing resultant
  landscapes for farmland conversion, crop
  distribution, soil erosion, groundwater
  vulnerability, riparian cover and wildlife
  habitat quality.

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Some Considerations
(adapted from Barrett 2001)

• Purpose of Model
• Development objective
• Species, BP, stressors of interest
• Landscapes, ecosystems of interest
• Linkage with other models and tools
• Data Issues
• Matching input data requirements with available
  data
• Matching output with input requirements of other
  models and tools
• Model Attributes
• Processes to be modeled
• Vegetation classification system
• Spatial versus non-spatial
• Spatial and temporal scale and extent
• Treatment of uncertainty
• Types of scenarios
• Complexity

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Some More Considerations

• Model Use
• Ease of formulating different scenarios
• Adaptability of vegetation classification system
• Adaptability of pathways, probabilities and
  transition matrices
• Are there built-in limitations?
• E.g., maximum number of simulation units
• Compatibility with other modeling systems
• Run times for scenarios
• Documentation and training
• User interface

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Still More Considerations

• Validation of Results
• Validation against independent data
• Comparison against other models, projections,
  results
• Do the results make sense to stakeholders?
• Hardware and Software
• Operating system
• Programming language
• GIS software requirements
• Other software requirements
• Memory, disk space requirements
• Development
• Options
• Start from scratch
• Off the shelf
• Something in between
• Cost/licensing fees

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References

• Barrett, T.M. 2001. Models of vegetative change
  for landscape planning a comparison of FETM,
  LANDSUM, SIMPPLLE, and VDDT. Gen. Tech. Rep.
  RMRS-GTR-76-WWW. Ogden, UT U.S. Department of
  Agriculture, Forest Service, Rocky Mountain
  Research Station. 14 p.
• Berger, P.A. and Bolte, J.P. 2004. Evaluating the
  impact of policy options on agricultural
  landscapes an alternative futures approach.
  Ecological Applications 142, 342-354.
• Keane, R.E., Cary, G.J., Davies, I.D., Flannigan,
  M.D., Gardner, R.H., Lavorel, S., Lenihan, J.M.,
  Li, C., and Rupp, T.S. 2004. A classification of
  landscape fire succession models spatial
  simulations of fire and vegetation dynamics.
  Ecological Modelling 1793-27.
• Lee, B. Meneghin, B. Turner, M. Hoekstra, T.
  2003. An evaluation of landscape dynamic
  simulation models. USDA Forest Service. 19 p.

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References

• Mladenoff, D.J., Host, G.E., Boeder, J., and
  Crow, T.R. 1996. LANDIS a spatial model of
  forest landscape disturbance, succession and
  management. In M.F. Goodchild, L.T. Steyaert, B.
  O. Parks, C. Johnston, D. Maidment, M. Crane, S.
  Glendining, eds. GIS and environmental modeling.
  GIS World Books, Fort Collins, CO, USA.
• Mladenoff, D.J. 2004. LANDIS and forest landscape
  models. Ecological Modelling 1807-19.
• Perera, A.H., Yemshanov, D., Weaver, K.,
  Schnekenburger, F., Baldwin, D., and Boychuk, D.
  2002. BFOLDS - A spatially explicit stochastic
  model to simulate boreal forest landscape
  dynamics. In Proceedings of the 87th Annual
  Meeting of the Ecological Society of America 2002
  Annual Meeting, Tucson, Arizona.
• Verburg, P.H. Schot, P.P. Dijst, M.J.
  Veldkamp, A. 2004. Land use change modelling
  current practice and research priorities.
  GeoJournal 61309-324.