Development of a geospatial model to quantify, describe and map urban growth

In the United States, there is widespread concern about understanding and curbing urban sprawl, which has been cited for its negative impacts on natural resources, economic health, and community character. There is not, however, a universally accepted definition of urban sprawl. It has been described using quantitative measures, qualitative terms, attitudinal explanations, and landscape patterns. To help local, regional and state land use planners better understand and address the issues attributed to sprawl, researchers at NASA's Northeast Regional Earth Science Applications Center (RESAC) at The University of Connecticut have developed an urban growth model. The model, which is based on land cover derived from remotely sensed satellite imagery, determines the geographic extent, patterns, and classes of urban growth over time.Input data to the urban growth model consist of two dates of satellite-derived land cover data that are converted, based on user-defined reclassification options, to just three classes: developed, non-developed, and water. The model identifies three classes of undeveloped land as well as developed land for both dates based on neighborhood information. These two images are used to create a change map that provides more detail than a traditional change analysis by utilizing the classes of non-developed land and including contextual information. The change map becomes the input for the urban growth analysis where five classes of growth are identified: infill, expansion, isolated, linear branch, and clustered branch.The output urban growth map is a powerful visual and quantitative assessment of the kinds of urban growth that have occurred across a landscape. Urban growth further can be characterized using a temporal sequence of urban growth maps to illustrate urban growth dynamics. Beyond analysis, the ability of remote sensing-based information to show changes to a community's landscape, at different geographic scales and over time, is a new and unique resource for local land use decision makers as they plan the future of their communities.     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Linking satellite images and hand-held infrared thermography to observed neighborhood climate conditions

While satellite images effectively show surface urban heat islands in urbanized areas, linking surface temperatures to actual ambient temperatures remains a research challenge. Microclimates in urbanized settings can vary tremendously in very short distances, making adequate climate interpolations across a large metropolitan area difficult, at best. This study links the coarse scale of satellite (ASTER) images to the fine scale of hand-held thermography as part of an in-depth suburban neighborhood climate study to determine if ASTER imaging can be used to adequately estimate neighborhood climate conditions in an urbanized area. The study utilizes day and night remotely-sensed and ground data from June, 2004 for Phoenix, Arizona. Microclimate conditions of three urban fringe neighborhoods with varying amounts of natural vegetation and development density were studied, along with ASTER remote sensing data, mobile climate transects, and spot infrared thermographic images.These neighborhoods, though variable, showed only minor differences, and the study indicates that daytime images (11:20 am) do not adequately rank observed conditions within these neighborhoods — the highest ASTER surface temperatures were recorded for the least-dense neighborhood with a natural desert landscaping, though lowest ambient temperatures were measured there. Daytime mean surface temperatures versus air temperatures were 50.4 °C (30.8 °C air temp); 53.5 °C (29.7 °C); and 50.6 °C (31.9 °C). It was found that nighttime (10:40 pm LST) differences among neighborhoods of surface and air temperatures were relatively consistent, with the most densely developed neighborhood having the highest ASTER surface temperatures (29.0 °C) and transect-derived air temperatures (30.0 °C). Issues of view angle, shadowing, emissivity, resolution, and wind conditions for daytime results with their relatively small mean differences observed across the neighborhoods may explain why the rank of ASTER thermal conditions versus observed ambient conditions was poor. However, following sunset, these issues of view angle, etc., are much less problematic.     

Calibration of the SLEUTH urban growth model for Lisbon and Porto, Portugal

The SLEUTH model (slope, landuse, exclusion, urban extent, transportation and hillshade), formerly called the Clarke Cellular Automaton Urban Growth Model, was developed for and tested on various cities in North America, including Washington, DC, and San Francisco. In contrast, this research calibrated the SLEUTH model for two European cities, the Portuguese metropolitan areas of Lisbon and Porto. The SLEUTH model is a cellular automaton model, developed with predefined growth rules applied spatially to gridded maps of the cities in a set of nested loops, and was designed to be both scaleable and universally applicable. Urban expansion is modeled in a modified two-dimensional regular grid. Maps of topographic slope, land use, exclusions, urban extents, road transportation, and a graphic hillshade layer form the model input. This paper examines differences in the model's behavior when the obviously different environment of a European city is captured in the data and modeled. Calibration results are included and interpreted in the context of the two cities, and an evaluation of the model's portability and universality of application is made. Questions such as scalability, sequential multistage optimization by automated exploration of model parameter space, the problem of equifinality, and parameter sensitivity to local conditions are explored. The metropolitan areas present very different spatial and developmental characteristics. The Lisbon Metropolitan Area (the capital of Portugal) has a mix of north Atlantic and south Mediterranean influences. Property is organized in large patches of extensive farmland comprised of olive and cork orchards. The urban pattern of Lisbon and its environs is characterized by rapid urban sprawl, focused in the urban centers of Lisbon, Oeiras, Cascais Setúbal, and Almada, and by intense urbanization along the main road and train lines radiating from the major urban centers. The Porto Metropolitan Area is characterized by a coastal Atlantic landscape. The urban pattern is concentrated among the main nuclei (Porto and Vila Nova de Gaia) and scattered among many small rural towns and villages. There are very small isolated patches of intensive agriculture and pine forests in a topography of steep slopes. These endogenous territorial characteristics go back in time to the formation of Portugal — with a “Roman-Visigod North” and an “Arabic South” [Firmino, 1999 (Firmino, A., 1999. Agriculture and landscape in portugal. Landscape and Urban planning, 46, 83–91); Ribeiro, Lautensach, & Daveau, 1991 (Ribeiro, O., Lautensach, H., & Daveau, S., 1991. Geografia de portugal (4 Vols., published between 1986 and 1991). Lisbon, Portugal: João Sá de Costa)]. The SLEUTH model calibration captured these city characteristics, and using the standard documented calibration procedures, seems to have adapted itself well to the European context. Useful predictions of growth to 2025, and investigation of the impact of planning and transportation construction can be investigated as a consequence of the successful calibration. Further application and testing of the SLEUTH model in non-Western environments may prove it to be the elusive universal model of urban growth, the antithesis of the special case urban models of the 1960s and 1970s.