Search

To purchase or authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1467-8306.2007.00577.x Byline: Robert Gilmore Pontius (*), Robert Walker ([dagger]), Robert Yao-Kumah (*), Eugenio Arima ([double dagger]), Stephen Aldrich ([dagger]), Marcellus Caldas (s.), Dante Vergara ([dagger]) Keywords: behavioral landscape model; change; land; pattern; scale Abstract: This article describes a quantitative assessment of the output from the Behavioral Landscape Model (BLM), which has been developed to simulate the spatial pattern of deforestation (i.e. forest fragmentation) in the Amazon basin in a manner consistent with human behavior. The assessment consists of eighteen runs for a section of the Transamazon Highway in the lower basin, where the BLM's simulated deforestation map for each run is compared to a reference map of 1999. The BLM simulates the transition from forest to non-forest in a spatially explicit manner in 20-m x 20-m pixels. The pixels are nested within a hierarchical stratification structure of household lots within larger development rectangles that emanate from the Transamazon Highway. Each of the eighteen runs derives from a unique combination of three model parameters. We have derived novel methods of assessment to consider (1) the nested stratification structure, (2) multiple resolutions, (3) a simpler model that predicts deforestation near the highway, (4) a null model that predicts forest persistence, and (5) a uniform model that has accuracy equal to the expected accuracy of a random spatial allocation. Results show that the model's specification of the overall quantity of non-forest is the most important factor that constrains and correlates with accuracy. A large source of location agreement is the BLM's assumption that deforestation within household lots occurs near roads. A large source of location disagreement is the BLM's less than perfect ability to simulate the proportion of deforestation by household lot. This article discusses implications of these results in the context of land change science and dynamic simulation modeling. Author Affiliation: (*)Department of International Development, Community, and Environment, Clark University ([dagger])Department of Geography, Michigan State University ([double dagger])Department of Environmental Studies, Hobart and William Smith Colleges, and Instituto do Homem e Meio Ambiente da Amazonia, Brazil (s.)Department of Geography, Kansas State University Article History: Initial submissions, February 2006; revised submissions, September and October 2006; final acceptance, October 2006 Article note: Correspondence: Department of International Development, Community, and Environment, School of Geography, Clark University, 950 Main Street, Worcester, MA 01610-1477, e-mail: rpontius@clarku.edu (Pontius); Department of Geography, Michigan State University, 118 Geography Bldg., East Lansing, MI, 48824, e-mail: rwalker@msu.edu (Walker), aldric30@msu.edu (Aldrich), vergarad@msu.edu (Vergara); Department of International Development, Community, and Environment, Clark University, e-mail: robby60@hotmail.com (Yao-Kumah); Department of Environmental Studies, Hobart and William Smith Colleges, 300 Pulteney Street, Geneva, NY 14456, e-mail: arima@hws.edu (Arima); Department of Geography, Kansas State University, 116 Seton Hall, Manhattan, KS 66506-2940, e-mail: caldasma@msu.edu (Caldas).

This paper presents methods to test whether map error can explain the observed differences between two points in time among categories of land cover in maps. Such differences may be due to two reasons: error in the maps and change on the ground. Our methods use matrix algebra: (1) to determine whether error can explain specific types of observed categorical transitions between two maps, (2) to represent visually the differences between the maps that error cannot explain, and (3) to examine how the results are sensitive to possible variation in map error. The methods complement conventional accuracy assessment because they rely on standard confusion matrices that use either a random or a stratified sampling design. We illustrate the methods with maps from 1971 and 1999, which show seven land-cover categories for central Massachusetts. The methods detect four transitions from agriculture, range, forest, and barren in 1971 to built in 1999, which a 15 percent error cannot explain. Sensitivity analysis reveals that if the accuracy of the maps were less than 77 percent, then error could explain virtually all of the observed differences between the maps. The paper discusses the assumptions behind the methods and articulates priorities for future research. KEYWORDS: Accuracy, hypothesis, change, land, matrix, uncertainty, Introduction Scientists typically attempt to determine whether historic land change has occurred by overlaying maps that have the same categories from two points in time, and assuming that the differences [...]