Land Cover Change Model

One of the most influential factors in human-induced global change over the last two hundred years has been the transformation of land and resource use associated with urbanization and sprawling development (Pataki et al. 2006). Land cover change is expected to increase significantly in the coming decades as populations continue to expand.

As part of a first the Biocomplexity project, Dr. Alberti (University of Washington) and Jeff Hepinstall (University of Georgia) have developed a coupled human-natural land-cover change model to assess the impact of alternative development patterns in the Central Puget Sound Region that can support land use and infrastructure investment decisions. The model has successfully been applied to assess the impact of land cover change on bird diversity (Hepinstall et al 2008). As part of this project, the model is currently being extended to 2050 and further partitioned to fully represent the complex and heterogeneous land use/land cover patterns and behaviors emerging along the urbanization gradient.

King County Land Cover Change Model

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Land Cover Change Models

Marina Alberti (PI), Jeff Hepinstall

The land cover change model consists of a set of spatially explicit multinomial logit models of site-based land cover transitions. The model is structured to predict the probability of a single cell changing from one discrete land cover class to another class as a function of present land cover class of that cell, a set of attributes of the cell, and the specific development event predicted by the development model within the cell. The model incorporates the spatial context of the 30-m cell by assigning to the cell the landscape composition and configuration of a 150-m window centered around the 30-m cell and determining the distance of the cell from recent and predicted development transitions. The probability of transition of a 30-m pixel from one discrete land cover class i to another cover class j is influenced by the intensity of a development event predicted by the development model, a set of attributes of the pixel, and the land cover composition and configuration of the neighboring pixels. The transition probability equations are estimated empirically as a function of a set of independent variables comparing land cover data interpreted from Landsat Thematic Mapper (TM) imagery for the Puget Sound region taken every two years between 1986 and 2001. We use Monte Carlo simulation to determine whether each pixel of a specified land cover class changes to another cover type or remains in its current state.

Independent variables include those that measure biophysical factors (e.g., landscape composition and configuration) and those that measure human factors (e.g., land development). We will use a set of landscape metrics derived from information theory to model the effect of the complex spatial pattern of land use and land cover on human and ecological processes. These metrics characterize the composition (e.g. diversity, dominance etc.), spatial configuration (e.g. density, size, shape, edge, connectivity, fractal dimension) and spatial neighborhood (e.g. heterogeneity and contagion) of the landscape. We will also improve the realism of the land cover change component by specifying land cover attributes of parcels and their neighborhoods in the development component of UrbanSim. Adding spatial configuration and neighborhood effects of both land use parcels and land cover patches also provides additional realism to the urban and land cover models. Spatial metrics will be used to model the effects of land use and cover patterns on ecosystem change.

Funded by: National Science Foundation