Land Use Change Models in Idrisi

 

INTRODUCTION

            Spatially explicit models of land use change focus on (1) the rate of change between two or more classes, (2) the location of change to one or more classes, or (3) the rate and location of change between two or more classes.

            Models are usually calibrated using past time periods and/or hypothesizes of the driving factors of change.

            Output may be the likelihood of each cell converting to a given class (at an unspecified time) or predicted land use at one or more (specified) dates.

 

 

MODELING APPROACHES

 

1. Markov Chain Models

Definition: Land use at time t+1 is based solely on land use at a previous t

 

Commands in Idrisi:

   Step 1: Markov (output = suitability transition images and change matrix),

   Step 2: CA-Markov (output = predicted land use at specified date)

 

Comments: It is very simple, only needing land use at two time periods. Limitations:  does not consider drivers of change; can not be used over long time periods because of stationary transition values; does not sufficiently differentiate between suitable locations.

 

References:

Turner, M.G. 1987. Spatial simulation of landscape changes in Georgia: a comparison of 3 transition models. Landscape Ecology 1(1): 29-36.

López, E., G. Bocco, M. Mendoza, and E. Duhau. 2001. Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and Urban Planning 55: 271-285.

 

2. Cellular Automata (CA)

Definition: Land use at time t+1 is based on a set of rules that take into account some combination of starting land use, neighboring land use, and other information.

 

Commands in Idrisi:

   Step 1: Cellatom and/or CA-Markov (output = predicted land use at specific date)

 

Comments: Location and rate of change are part of same process (notable exception is work by White and Engelen).  Able to represent (predict) complex patterns associated with urban development. Capable of representing emergent properties. Limitations: difficult to calibrate; does not necessarily explicitly incorporate drivers of change. In Idrisi may take a LONG time to run.

 

References:

Clarke, K.C., S. Hoppen, and L. Gaydos. 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design 24: 247-261.

Yeh, A. G.-O. and X. Li. 2003. Simulation of development alternatives using neural networks, cellular automata, and GIS for urban planning. Photogrammetric Engineering and Remote Sensing 69(9): 1043-1052.

White, R. and G. Engelen. 2000. High-resolution integrated modelling of the spatial dynamics of urban and regional systems. Computers, Environment and Urban Systems 24: 383-400.

White, R., G. Engelen and I. Uljee. The Use of Constrained Cellular Automata for High-resolution Modelling of Urban Land Use Dynamics," Environment and Planning B: Planning and Design 24: 323-343.

 

3. Multi-Criteria Evaluation Model (MCE)

Definition: Also cartographic or overlay modeling.  When suitability factors are added combined to identify sites that have high suitability values for each land use type.  Land use at time t is located on those cells that are most suitable.

 

Command in Idrisi:

   Step 1: Decision Wizard (output = transition suitability images for each class and predicted land use image)

OR

   Step 1: MCE (output = transition suitability images for each class)

   Step 2: Rank (output = suitability’s ranked in order)

   Step 3: MOLA (output = predicted land use based on number of conversions)

 

Comments: Incorporates hypothesized drivers of change. In Idrisi can handle multiple objectives quickly. Can combine factors based on different distributions (ie does not assume all factors have linear relationship to change). Can update input factors. Limitations: unclear process of identifying appropriate factors; unclear how to incorporate categorical factors; unclear how to weight factors.

 

References:

Schneider, L.C. and R. G. Pontius Jr. 2001. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems, and Environment 85: 83-94.

 

 

4. Logistic Regression Based Model

Definition: Uses logistic regression to calculate relationship between land use change and suitability factors between time t and t+1.  Uses that relationship to predict suitability at time t+2.  Land use at time t+2 is located on those cells most suitable.

 

Commands in Idrisi:

   Step 1: Logi (output = transition suitability image)

   Step 2: CA-Markov  (output = predicted land use at specified time) OR   

               Fuzzy, Rank, Reclass (output = predicted land use at specified time) OR

               STchoice (output = predicted land use at specified time).

 

Comments: Easy to incorporate categorical factors. Clear how to test importance of each factor through more extensive statistical diagnostics. Limitations: assumes logistic relationship between factors and change; deterministic compared to CA approaches.

 

References:

Schneider, L.C. and R. G. Pontius Jr. 2001. Modeling land-use change in the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems, and Environment 85: 83-94.

Berry, M.W., B.C. Hazen, R.L. MacIntyre, and R.O. Flamm. 1996. Lucas: a system for modeling land-use change. IEEE Computaional Science and Engineering 3(1): 24-35.

Wear, D.N. and P. Bolstad. 1998. Land-use changes in southern Appalachian landscapes: spatial analysis and forecast evaluation. Ecosystems 1: 575-594.

 

 

5. GEOMOD

Definition: This module is like a dynamic (input variables update after each time step) MCE that runs for multiple time steps

 

Commands: GeoMod

 

Comments: runs multi-time step simulations where amount of change is specified by user and transition suitability values are defined based on change “driver” images. Likelihood of change for a given characteristic is determined based on the percent of cells converting in calibration time period.

 

References:

Pontius, R.G. Jr, J.D. Cornell, and C.A.S. Hall. 2001. Modeling the spatial pattern of land-use change with GEOMOD2: application and validation. Agriculture, Ecosystems, and Environment 85: 191-203.

 

 

VALIDATION

 

1. Validate: calculates various kappa statistics

 

2. ROC (Relative Operating Characteristic): Compares transition suitability image with reference land use image.

Reference: Pontius Jr, R G and L Schneider. 2001. Land-cover change model validation by a ROC method. Agriculture, Ecosystems & Environment.

 

3. Crosstab, Histogram, Area, Perm, etc.