1	Model Analysis: Kellogg Project Stuart Gage: Subproject leader Manuel Colunga: Software and analysis development Bryan Pijanowski: Model Development David Skole: Human Dimensions of LUCC Conduct analysis of land transformation models Develop analytical methods Examine high resolution time series Examine change in land transformation over time Link to economic databases
2	Michigan Diversity
3	17 (now 33) County Training Set
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5	Results: Future Trends
6	Data Acquisition and Processing Worked with each of the multi-county council of governments to ID MiRIS updates Negotiated data transfers with each local government including signing of agreements and agreeing to give presentations back to PCs and COGs Data provided were in different projections, data structures and with different classification systems All data were aggregated and a standard data format developed
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8	Land Conversion
9	Fragmentation
10	Critical variables to assess land fragmentation Composition Proportional abundance of each class Richness Evenness Diversity Spatial configuration Patch size distribution and density Patch shape complexity Core Area Isolation/Proximity Contrast Dispersion Contagion & Interspersion Subdivision Connectivity
11	Landscape Classification
12	Database Development
13	Landscape and Class Variables
14	Region-wide Fragmentation Variables
15	Database Query
16	Landscape Diversity Concept (1980)
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18	Land Use Fragmentation
19	Kellogg Project: Model Analysis and Enhancement Develop an analytical framework for land use information Objectives Implement the capacity to analyze land use by: Developing a methodology to compute spatial statistics for changing land use patterns Enabling an analytical framework to process digital spatial data for any area or set of areas of land use and land cover change in Michigan (Counties, Minor Civil Divisions, Census Districts; Ecological regions)
20	Test Case to Demonstrate Analytical Framework Minor Civil Divisions: Muskegon River Watershed Used enhanced LTM to produce land use change projections for 75 MCD’s in the MRW An analysis of built, agriculture, forest and other vegetation land use classes was conducted for each time step considering: Percent of land in each class at reach time step Number of patches of land in each class at each time step
21	High Resolution Land Transformation Questions: How rapidly is land use changing? Do the trends over time show patterns? Are the patterns consistent with conventional wisdom? Can we conduct an economic assessment of these patterns?
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24	Muskegon River Watershed LTM Projections for MCD’s 1980-2035
25	Muskegon River Watershed LTM MCD Projections 1980-2035 5 Year Increments
26	Watershed-wide trends in land use change 5-year time series showing built, agriculture, forest, other vegetation with respect to: Percent of land Number of patches MCD Diversity
27	Land Change Variables Percent Land in Class The percent of area of the polygon occupied by a specific land use class Number of Patches The number of polygons in a specific land use class Diversity The number of different classes and their distribution in a polygon
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32	Patterns of change in the MRW Examination of change in selected MCD’s: Percent of class Number of patches of a class
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44	(Criterion: > 70% in 1980)
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46	(Criterion: > 75% in 1980)
47	Accomplishments Refined LTM projections by tuning transformation characteristics (i.e. correct issues in urban transformation (golf course; cemetery) Added additional updates to land use database Developed capacity to simulate shorter time steps (2020, 2040 -> 5 year intervals) Developed additional analytical capacity to compute landscape fragmentation statistics Demonstrated capacity of above developments
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