Advancing Land Change Modeling Opportunities and Research Requirements (2014) / Chapter Skim
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2 Land Change Modeling Approaches
2 Land Change Modeling Approaches
Pages 29-74
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From page 29... ...
While this approach provided useful distinctions at the time that review was completed, significant progress in developing all modeling approaches has blurred even some of those distinctions. The committee has identified six generally recognized groups of approaches to land change models (LCMs)
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From page 30... ...
Modeling approaches that employ a machine learning or statistical approaches typically receive input in the form of two types of maps: (1) maps of land cover at time points that bound the calibration interval, and (2)
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From page 31... ...
There are many cases where it is possible to obtain land cover maps from more than one time point along with explanatory variables for a study site where the investigator is partially ignorant concerning the detailed processes of land transformation. A machine learning algorithm attempts to learn the mathematical or logical relationships among the patterns of land cover and the patterns of the explanatory variables.
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From page 32... ...
. The algorithm uses an iterative process to fit a relationship between the patterns in the land cover maps and the explanatory variables.
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From page 33... ...
In terms of short-term PP uses of models, machine learning approaches can be used to make useful predictions. There are many cases where it is possible to obtain land cover maps from more than one time point along with explanatory variables for a study site where the investigator is partially ignorant concerning the detailed processes of land transformation.
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From page 34... ...
For these reasons, and because land cover data are more plentiful than land use data, statistical and machine learning approaches are suitable for modeling land cover changes directly, even though these changes may come about through human choices about land use. While machine learning methods have been developed in ways that make them less sensitive to random errors in the input data than statistical methods, systematic biases in data will always affect the resulting models.
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From page 35... ...
For example, a machine learning algorithm might be able to fit a tight relationship between land cover maps and explanatory variables for a given time interval but do a relatively poor job of matching observations when the relationship is extrapolated to time points beyond the calibration interval, for example because the market or policy conditions differ between the two time periods. If the model is overfit during the calibration stage, then the investigator can be lured into a false sense of trust that the model can predict accurately the patterns in data for which the model was not calibrated.
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From page 36... ...
For example, if the machine learning algorithm attempts to maximize the percentage of pixels that agree between the simulated map of land cover types and the reference map for the same time point, then the algorithm might generate output that systematically underestimates the quantity of change. This can occur because a simulation of land change is likely to generate allocation errors when it simulates change; thus, it can reduce the number of those allocation errors by simply predicting very little change.
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From page 37... ...
Cellular models use a variety of input information to simulate the conversions of land cover or land use in these land units based on a rule set or algorithm that is applied synchronously to all spatial units and that represents the modeler's understanding of the land change process. The algorithm represents decision making that is, implicitly, assumed to take place at the level of the spatial units of simulation, with a one-to-one correspondence assumed between the spatial units and decision maker.
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From page 38... ...
In its basic application to land change, spatial data are used to calculate a transition matrix over an historic time period and then used to derive transition probabilities for the different types of conversions. These probabilities are used to calculate land areas of different land types in the future in a nonspatial manner.
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From page 39... ...
While land suitability is often represented only in a relative terms, these suitability models provide a basis for understanding where different land uses or covers are most likely to be found. Whether or not land rents are calculated in absolute or relative terms often depends on data availability, and relative land rents are commonly used for models of land cover, where there may not be a good theoretical link between economic rent and cover type.
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The implementation of suitability maps and their role in allocating land change differs between models. Differences mainly depend on the number of land use and land cover types addressed and the level of competition assumed among the categories.
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From page 41... ...
These land use–change estimates are used as to bound an allocation procedure to identify the locations of land use or land cover changes. Models using a top-down structure are constrained cellular automata models such as Environment Explorer (de Nijs et al., 2004; White and
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From page 42... ...
The cellular data format matches very well the format of land cover data derived from remote sensing and allows a straightforward processing. Also, the underlying concepts of trend projection, location suitability, and neighborhood interactions are intuitive and can be parameterized by empirical analysis of time-series data or more advanced econometric and calibration methods.
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From page 43... ...
Operation of cellular models is tied to the spatial data layers on which they rely, not only land cover data but also the potential location factors that determine the likelihood of finding a particular land cover type at a specific location. Often, model applications are based on a rich set of physical factors describing terrain, soil, and climate conditions.
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From page 44... ...
that determines demand and supply relationships and generates aggregate outcomes of prices and land use patterns. Focused on the economic behavior of human actors, economic models tend to focus on land use, as opposed to land cover, though they can be included within integrated models that also produce land cover outputs.
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Structural economic models are specified based on a number of maintained assumptions (e.g., of agents' behaviors, market structure, and functional form) and the parameter values are often estimated using econometric methods.
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Because they are derived from an underlying economic model of behavior, reduced-form econometric models are better suited for modeling land use rather than land cover outcomes. However, when land use data are not available, remotely sensed land cover data can be used as a reason
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While econometric techniques may still be used for identification, the model results reveal causality related to land cover not land use and would be misleading if interpreted in terms of land use. In the absence of a clear identification strategy, reduced-form models estimated with data on land cover provide only a pattern-based correlation analysis similar to the statistical models discussed above.
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. Examples of CGE models analyzing the effect of land cover and land use change include FARM (Wong and Alavalapati, 2003)
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Over the past one and a half decades, different attempts have been made to extend CGE models to allow for detailed analyses of the land use sector. Each modeling approach has its own advantages and drawbacks in terms of data requirements, computational practices, and accuracy of representation.
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From page 50... ...
Spatially Disaggregated Models Spatially disaggregated economic models are based on the assumption of an underlying behavior of profits or utility maximization or cost minimization, all of which are continuous variables. However, land use is typically measured as a categorical variable at the individual parcel or decision-maker level, requiring a discrete choice framework to model an individual's optimal land use decision (Bockstael, 1996)
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From page 51... ...
Because data on revenues and costs associated with spatially disaggregated land use choices are often not available, spatially disaggregate models are often reduced form. In many cases, the model may not be fully reduced to only exogenous variables; that is, the model may include one or more endogenous explanatory variables that are determined by the same equilibrium process as the dependent variable.
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From page 52... ...
Because biodiversity is dependent on spatially disaggregated patterns of land use, a random sample of plot-level data is insufficient for predicting the policy impacts on biodiversity outcomes. The authors solve this problem by applying the parameter estimates from the plot-level econometric model to a complete set of cropland and pasture parcels that are contained within the 2.93 land use patterns using a spatial disaggregated economic model, some kind of a spatial simulation approach needs to be used in addition to the statistical estimation.
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From page 53... ...
The authors conclude that an auction mechanism to elicit landowners' willingness-to-accept bids and some means to provide incen tives for enrollment of spatially contiguous lands are likely necessary to achieve real efficiency gains. Examples The early spatially disaggregated models were reduced-form binary or multinomial discrete-choice models of discrete land use or land cover categories (e.g., Bockstael, 1996; Chomitz and Gray, 1995; Nelson and Hellerstein, 1997)
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Wu and Plantinga (2003) simulated a spatial equilibrium model based on the urban bid-rent model to examine the influence of public open space on the spatial structure of urban land rents and land use.
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First, they account for the fundamental role of prices (e.g., costs and revenues) in explaining individual land use decisions in ways that most machine learning and cellular models do not.
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In addition, econometric structural models require a number of identifying assumptions about the error processes. Spatial simulation models, on the other hand, require assumptions about parameter values.
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, which run with some set of starting conditions over some period of time, allowing the programmed agents to carry out their actions until some specified stopping criterion is satisfied, usually indicated by either a certain amount of time or a specified system state. By simulating the individual actions of many diverse actors, and measuring the resulting system behavior and outcomes over time (e.g., the changes in patterns of land cover)
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From page 58... ...
Because agents can adapt their behavior to changing conditions, the stationarity assumption can be somewhat relaxed in ABMs. To the degree that ABMs rely on input data from some period of time to establish parameter values or fix decision processes that do not adapt in other ways (e.g., through learning mechanisms)
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From page 59... ...
If land cover, the decisions that affect land cover and land management need to be represented explicitly, and the theoretical basis for modeling these land cover or management decisions is not as fully developed across different systems as is land use theory. When they represent land cover patterns, ABMs benefit from Earth observation data, but data to inform agents attributes, their social interactions and decision processes is more commonly required for these models.
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. Parameters on household-level land use preference and risk tolerance in the model were fit so that the model reproduced historical land cover patterns (see map below)
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. FIGURE 2 Simulated land cover areas (ha)
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An intuitive understanding of even a moderately complex model can be challenging, and often requires both an understanding of the underlying code and experience with sensitivity analysis through running the model interactively by changing parameter values. Agent-based models are being coupled with models' natural processes.
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Comparisons are needed between spatial equilibrium models that incorporate agent heterogeneity, but rely on a static spatial equilibrium assumption to derive market prices, and agent-based models that incorporate heterogeneity, alternative types of behaviors, and dynamics over time, but that use ad hoc approaches to derive market prices. • There is need to develop methods to standardize and improve efficiency of parameterizing agent decision models.
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From page 64... ...
• Agent-based modeling efforts benefit from the availability of a wide range of data types. These include survey data that are spatially referenced to parameterize decision functions; data on land management, use, value, and ownership to complement land cover data; and longitudinal versions of all of these data types.
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From page 65... ...
Other allocation mechanisms are possible, building on the statistical, machine learning, and cellular approaches described above. As a final example of hybrid models, coupled representations of land use and land cover dynamics, as a means of representing the dynamics of both the natural and the human processes involved in land change, have been developed by combining the statistical, cellular and agent-based approaches described above.
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From page 66... ...
Because land change modeling often involves representation of cross-scale interactions, interactions among different land types or sectors, and determination of both the amount and spatial pattern of land cover types, there are multiple procedural opportunities for including different modeling approaches. While some opportunities for hybridization (e.g., for representing different land sectors)
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From page 67... ...
model for urban land use with an econometric approach for other land cover types (Verburg and Overmars, 2009)
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From page 68... ...
The ability to evaluate the impacts of environmental and social changes on the land system, especially through the use of scenarios, and to provide input to other models is also an important use to which models have been put. We have arranged the five main modeling approaches in our assessment roughly in order from those most focused on modeling pattern (beginning to Machine Learning and Statistical Approaches)
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From page 69... ...
. Within this mapping, we identify the suitability of machine learning and cellular models in the problem identification step, because of their assumptions of stationarity and lack the richer structural detail about process needed to evaluate the effects of changes in policy structure.
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From page 70... ...
Cellular Land cover, Stationarity A land cover map at some Forecast land cover patterns land use Strong spatial control point in time Evaluate changes in spatial and/or interaction Some number of maps of controls without market No market interactions predictor variable(s) feedbacks Spatially Land use Utility or profit Data on land use or land Reduced-form models: Disaggregated maximization cover at one or more Identify the causal effect of Economic Models Price and/or spatial point(s)
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From page 71... ...
process. Column 3 addresses the types of outcomes (land use, land cover, or both)
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From page 72... ...
. Once policies or decisions have been implemented, the need for evaluating the effects of these implementations, ex post, is often quite effectively met through use of reduced-form economic models that estimate the magnitude of the effect of the intervention, usually by comparing observable outcomes either before and after the intervention or in an intervention area and some comparable
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From page 73... ...
Understanding the underlying structures, assumptions, and data requirements of different modeling approaches is critical to understanding their applicability for various scientific and decision-making purposes. This review provides a framework for comparison of multiple modeling approaches in relation to specific objectives.
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