Joseph P. Messina

Land use change: complexity and comparisons

Research on the determinants of land use change and its relationship to vulnerability (broadly defined), biotic diversity and ecosystem services (e.g. Gullison et al. 2007), health (e.g. Patz et al. 2004) and climate change (e.g. van der Werf et al. 2004) has accelerated. Evidence of this increased interest is demonstrated by several examples. Funding agencies in the US (National Institutes of Health, National Science Foundation, National Aeronautics and Space Administration and National Oceanic and Atmospheric Administration) and around the world have increased their support of land use science. In addition to research papers in disciplinary journals, there have been numerous edited volumes and special issues of journals recently (e.g. Gutman et al. 2004; Environment & Planning B 2005; Environment & Planning A 2006; Lambin and Geist 2006; Kok, Verburg and Veldkamp 2007). And in 2006, the Journal of Land Use Science was launched. Land use science is now at a crucial juncture in its maturation process. Much has been learned, but the array of factors influencing land use change, the diversity of sites chosen for case studies, and the variety of modeling approaches used by the various case study teams have all combined to make two of the hallmarks of science, generalization and validation, difficult within land use science. This introduction and the four papers in this themed issue grew out of two workshops which were part of a US National Institutes of Health (NIH) ‘Roadmap’ project. The general idea behind the NIH Roadmap initiative was to stimulate scientific advances by bringing together diverse disciplines to tackle a common, multi-disciplinary scientific problem. The specific idea behind our Roadmap project was to bring together seven multi-disciplinary case study teams, working in areas that could be broadly classified as inland frontiers, incorporating social, spatial and biophysical sciences, having temporal depth on both the social and biophysical sides, and having had long-term funding. Early in our Roadmap project, the crucial importance of modeling, particularly agent-based modeling, for the next phase of land-use science became apparent and additional modelers not affiliated with any of the seven case studies were brought into the project. Since agent-based simulations attempt to explicitly capture human behavior and interaction, they were of special interest. At the risk of oversimplification, it is worth briefly reviewing selected key insights in land use science in the past two decades to set the stage for the papers in this themed issue. One of the earliest realizations, and perhaps most fundamental, was accepting the crucial role that humans play in transforming the landscape, and concomitantly the distinction drawn between land cover (which can be seen remotely) and land use (which, in most circumstances, requires in situ observation; e.g. Turner, Meyer and Skole 1994). The complexity of factors influencing land use change became apparent and led to a variety of ‘box and arrow’ diagrams as conceptual frameworks, frequently put together by committees rarely agreeing with one another on all details, but agreeing among themselves that there were many components (social and biophysical) whose role needed to be measured and understood. A series of case studies emerged, recognizing the wide array of variables that needed to be incorporated, and typically doing so by assembling a multidisciplinary team (Liverman, Moran, Rindfuss and Stern 1998; Entwisle and Stern 2005). The disciplinary make-up of the team strongly influenced what was measured and how it was measured (see Rindfuss, Walsh, Turner, Fox and Mishra 2004; Overmars and Verburg 2005), with limited, if any, coordination across case studies (see Moran and Ostrom 2005 for an exception). In large part, the focus on case studies reflected the infancy of theory in land use science. Teams combined their own theoretical knowledge of social, spatial and ecological change with an inductive approach to understanding land use change – starting from a kitchen sink of variables and an in-depth knowledge of the site to generate theory on the interrelationships between variables and the importance of contextual effects. This lack of coordination in methods, documentation and theory made it very difficult to conduct meta-analyses of the driving factors of land use change across all the case studies to identify common patterns and processes (Geist and Lambin 2002; Keys and McConnell 2005). Recognizing that important causative factors were affecting the entire site of a case study (such as a new road which opens an entire area) and that experimentation was not feasible, computational, statistical and spatially explicit modeling emerged as powerful tools to understand the forces of land use change at a host of space–time scales (Veldkamp and Lambin 2001; Parker, Manson, Janssen, Hoffmann, and Deadman 2003; Verburg, Schot, Dijst and Veldkamp 2004). Increasingly, in recognition of the crucial role of humans in land use change, modeling approaches that represent those actors as agents have emerged as an important, and perhaps the dominant, modeling approach at local levels (Matthews, Gilbert, Roach, Polhil and Gotts 2007). In this introductory paper we briefly discuss some of the major themes that emerged in the workshops that brought together scientists from anthropology, botany, demography, developmental studies, ecology, economics, environmental science, geography, history, hydrology, meteorology, remote sensing, geographic information science, resource management, and sociology. A central theme was the need to measure and model behavior and interactions among actors, as well as between actors and the environment. Many early agent-based models focused on representing individuals and households (e.g. Deadman 1999), but the importance of other types of actors (e.g. governmental units at various levels, businesses, and NGOs) was a persistent theme. ‘Complexity’ was a term that peppered the conversation, and it was used with multiple meanings. But the dominant topic to emerge was comparison and generalization: with multiple case studies and agent-based models blooming, how do we compare across them and move towards generalization? We return to the generalization issue at the end of this introductory paper after a brief discussion of the other themes.

Measuring geographic access to health care: raster and network-based methods

BackgroundInequalities in geographic access to health care result from the configuration of facilities, population distribution, and the transportation infrastructure. In recent accessibility studies, the traditional distance measure (Euclidean) has been replaced with more plausible measures such as travel distance or time. Both network and raster-based methods are often utilized for estimating travel time in a Geographic Information System. Therefore, exploring the differences in the underlying data models and associated methods and their impact on geographic accessibility estimates is warranted.MethodsWe examine the assumptions present in population-based travel time models. Conceptual and practical differences between raster and network data models are reviewed, along with methodological implications for service area estimates. Our case study investigates Limited Access Areas defined by Michigan’s Certificate of Need (CON) Program. Geographic accessibility is calculated by identifying the number of people residing more than 30 minutes from an acute care hospital. Both network and raster-based methods are implemented and their results are compared. We also examine sensitivity to changes in travel speed settings and population assignment.ResultsIn both methods, the areas identified as having limited accessibility were similar in their location, configuration, and shape. However, the number of people identified as having limited accessibility varied substantially between methods. Over all permutations, the raster-based method identified more area and people with limited accessibility. The raster-based method was more sensitive to travel speed settings, while the network-based method was more sensitive to the specific population assignment method employed in Michigan.ConclusionsDifferences between the underlying data models help to explain the variation in results between raster and network-based methods. Considering that the choice of data model/method may substantially alter the outcomes of a geographic accessibility analysis, we advise researchers to use caution in model selection. For policy, we recommend that Michigan adopt the network-based method or reevaluate the travel speed assignment rule in the raster-based method. Additionally, we recommend that the state revisit the population assignment method.

Complex systems models and the management of error and uncertainty

For the complex systems modeller, uncertainty is ever-present. While uncertainty cannot be eliminated, we suggest that formally incorporating an assessment of uncertainty into our models can provide great benefits. Sources of uncertainty arise from the model itself, theoretical flaws, design flaws, and logical errors. Management of uncertainty and error in complex systems models calls for a structure for uncertainty identification and a clarification of terminology. In this paper, we define complex systems and place complex systems models into a common typology leading to the introduction of complex systems specific issues of error and uncertainty. We provide examples of complex system models of land use change with foci on errors and uncertainty and finally discuss the role of data in building complex systems models.

Mapping, modeling, and visualization of the influences of geomorphic processes on the alpine treeline ecotone, Glacier National Park, MT, USA

Spatially explicit digital technologies are integrated within a geographic information science (GISc) context to map, model, and visualize selected direct and indirect geomorphic processes that influence the spatial organization of the alpine treeline ecotone (ATE) in Glacier National Park (GNP), MT. GISc is used to examine alpine treeline and its biotic and abiotic controls through the application of multi-resolution remote sensing systems, geospatial information and product derivatives, and simulations of treeline spatial organization. Three geomorphic features are examined: relict solifluction terraces, evidence of nonlinearity in the development of a catena, and the locations of isolated boulders. The significance of these features is in constraining subsequent geomorphic and biogeographic processes, thus leading to disequilibrium. Exploration of these features though GISc indicates that visualizations for characterizing the relations of geomorphic patterns and processes within a three-dimensional context show promise for improved alpine slope models in the future by defining landscape attributes within a spatially and temporally explicit context.

Assessing alternatives for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales

We introduce and evaluate three methods for modeling the spatial distribution of multiple land-cover classes at sub-pixel scales: (a) sequential categorical swapping, (b) simultaneous categorical swapping, and (c) simulated annealing. Method 1, a modification of a binary pixel-swapping algorithm, allocates each class in turn to maximize internal spatial autocorrelation. Method 2 simultaneously examines all pairs of cell-class combinations within a pixel to determine the most appropriate pairs of sub-pixels to swap. Method 3 employs simulated annealing to swap cells. While convergence is relatively slow, Method 3 offers increased flexibility. Each method is applied to a classified Landsat-7 ETM+ dataset that has been resampled to a spatial resolution of 210 m, and evaluated for accuracy performance and computational efficiency.

Characterizing and Modeling Patterns of Deforestation and Agricultural Extensification in the Ecuadorian Amazon

We examine human-environment interactions in the Oriente region of the Ecuadorian Amazon through a Geographic Information Science (GISc) perspective. A remote sensing time-series is used to represent LULC (land use/land cover) dynamics, a GIS to assess resource endowments at local and regional settings, a longitudinal household survey to measure socioeconomic conditions and changes over time at the farm or household finca level, and a community-level survey administered to community leaders, farmers, teachers, women, and health workers in places ranging from tiny communities to the largest city, Lago Agrio to measure infrastructural linkages between households and their communities. Here, we (a) describe the multi-thematic, spatially explicit database assembled to address deforestation and agricultural extensification; (b) indicate changes in finca demographic and land use characteristics reported in the 1990 and 1999 household surveys; (c) use a hybrid digital classification approach for repeatable LULC characterizations for selected Landsat Thematic Mapper (TM) and Multispectral Scanner (MSS) images; (d) apply pattern metrics to classified satellite data to assess the composition and spatial organization of the landscape through trajectories of landscape structure; and (e) use a cellular automata (CA) approach to simulate LULC patterns for antecedent and future periods, given historical and current LULC patterns, as defined through the satellite time-series and hypotheses about the importance of geographic access to and spatial diffusion from the region’s central city.

Dynamic Spatial Simulation Modeling of the Population — Environment Matrix in the Ecuadorian Amazon

This research uses multithematic and spatially explicit data combined from a longitudinal socioeconomic and demographic survey conducted in 1990 and 1999, GIS coverages of resource endowments and geographic accessibility, and a classified Landsat Thematic Mapper (TM) satellite time series. The goal was to combine such data with expert knowledge, a set of analytic results, and dynamic modeling approaches to describe, explain, and explore the causes and consequences of land use and cover change (LUCC) in the northern Oriente region of the Ecuadorian Amazon. First, a cellular automaton (CA) model representing LUCC was developed using a time series of remotely sensed Landsat TM images for a 90 000-ha intensive study area within the region and calibrated using alternative images from the time series. The classified images were linked to spatially referenced biophysical and socioeconomic coverages used as input data, and then combined with ‘rules’ derived from empirical analyses. Second, the CA model was used in dynamic simulations to explore LUCC as both causes and consequences of (a) road development, (b) agricultural extensification and land abandonment, (c) major shifts in world markets and crop prices, and (d) urban expansion of the central city within the region. Finally, complexity theory was explored within the spatial and temporal dynamics associated with population – environment interactions, particularly, deforestation, urbanization, and subsistence and commercial cultivation of agricultural crops on lands made accessible by petroleum-company-built roads and the corresponding in-migration of spontaneous colonists beginning in the late 1960s. This research contributes to the study of population – environment interactions in a frontier environment, and examines how dynamic and complex systems can be modeled using CA-based spatial simulations.

A dynamic species distribution model of Glossina subgenus Morsitans: The identification of tsetse reservoirs and refugia

Tsetse flies are the primary vector for African trypanosomiasis, a neglected tropical disease that affects both humans and livestock across the continent of Africa. In 1973 tsetse were estimated to inhabit 22% of Kenya; by 1996 that number had risen to roughly 34%. Efforts to control the disease are hampered by a lack of information and costs associated with the identification of infested areas. To aid control efforts we have constructed the Tsetse Ecological Distribution Model (TED Model). The TED Model is a raster based dynamic species distribution model that predicts tsetse distributions at 250 m spatial resolution, based on habitat suitability and fly movement rates, at 16-day intervals. Although the TED Model can be parameterized to any tsetse subgenus/species requirements, for the purpose of this study the TED Model was parameterized to identify suitable habitat for Glossina subgenus Morsitans. Using the TED Model we have identified where and when Glossina subgenus Morsitans populations should be co...

Defoliation and the war on drugs in Putumayo, Colombia

Analysis of three Landsat Enhanced Thematic Mapper Plus (ETM+) images of the Putumayo region of Colombia, one of the primary regions of coca production in Colombia, demonstrated that aerial spraying of defoliants under the US ‘Plan Colombia’ programme impacted broad swaths of the landscape and had the unintended consequence of defoliating contiguous and interspersed native plant and food crop parcels. Using fractional coverage, field data collections and a hybrid classification, 106 178 ha of impacted land were found, compared with the United Nations Drug Control Program reported reduction in coca of 71 891 ha, an unexplained difference of 34 287 ha. The complex spatial organization of the Colombian coca‐producing landscape appeared to confound the spraying of defoliants, and as demonstrated here, many non‐coca land cover classes have been affected adversely.

Modeling spatial decisions with graph theory: logging roads and forest fragmentation in the Brazilian Amazon

This article addresses the spatial decision-making of loggers and implications for forest fragmentation in the Amazon basin. It provides a behavioral explanation for fragmentation by modeling how loggers build road networks, typically abandoned upon removal of hardwoods. Logging road networks provide access to land, and the settlers who take advantage of them clear fields and pastures that accentuate their spatial signatures. In shaping agricultural activities, these networks organize emergent patterns of forest fragmentation, even though the loggers move elsewhere. The goal of the article is to explicate how loggers shape their road networks, in order to theoretically explain an important type of forest fragmentation found in the Amazon basin, particularly in Brazil. This is accomplished by adapting graph theory to represent the spatial decision-making of loggers, and by implementing computational algorithms that build graphs interpretable as logging road networks. The economic behavior of loggers is conceptualized as a profit maximization problem, and translated into spatial decision-making by establishing a formal correspondence between mathematical graphs and road networks. New computational approaches, adapted from operations research, are used to construct graphs and simulate spatial decision-making as a function of discount rates, land tenure, and topographic constraints. The algorithms employed bracket a range of behavioral settings appropriate for areas of terras de volutas, public lands that have not been set aside for environmental protection, indigenous peoples, or colonization. The simulation target sites are located in or near so-called Terra do Meio, once a major logging frontier in the lower Amazon Basin. Simulation networks are compared to empirical ones identified by remote sensing and then used to draw inferences about factors influencing the spatial behavior of loggers. Results overall suggest that Amazonias logging road networks induce more fragmentation than necessary to access fixed quantities of wood. The paper concludes by considering implications of the approach and findings for Brazils move to a system of concession logging.