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Despite growing recognition of the important role of cities in economic, political and environmental systems across the world, comparative, global-scale research on cities is severely limited. This paper examines the similarities and differences in urban form and growth that have occurred across 25 mid-sized cities from different geographical settings and levels of economic development. The results reveal four city types: low-growth cities with modest rates of infilling; high-growth cities with rapid, fragmented development; expansive-growth cities with extensive dispersion at low population densities; and frantic-growth cities with extraordinary land conversion rates at high population densities. Although all 25 cities are expanding, the results suggest that cities outside the US do not exhibit the dispersed spatial forms characteristic of American urban sprawl.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long-term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

We performed a multi-date composite change detection technique using a dense-time stack of Landsat data to map land-use and land-cover change (LCLUC) in Mainland Southeast Asia (MSEA) with a focus on the expansion of boom crops, primarily tree crops. The supervised classification was performed using Support Vector Machines (SVM), which are supervised non-parametric statistical learning techniques. To select the most suitable SMV classifier and the related parameter settings, we used the training data and performed a two-dimensional grid search with a three-fold internal cross-validation. We worked in seven Landsat footprints and found the linear kernel to be the most suitable for all footprints, but the most suitable regularization parameter C varied across the footprints. We distinguished a total of 41 LCLUCs (13 to 31 classes per footprint) in very dynamic and heterogeneous landscapes. The approach proved useful for distinguishing subtle changes over time and to map a variety of land covers, tree crops, and transformations as long as sufficient training points could be collected for each class. While to date, this approach has only been applied to mapping urban extent and expansion, this study shows that it is also useful for mapping change in rural settings, especially when images from phenologically relevant acquisition dates are included.

The authors establish the effect of urbanization on precipitation in the Pearl River Delta of China with data from an annual land use map (1988–96) derived from Landsat images and monthly climate data from 16 local meteorological stations. A statistical analysis of the relationship between climate and urban land use in concentric buffers around the stations indicates that there is a causal relationship from temporal and spatial patterns of urbanization to temporal and spatial patterns of precipitation during the dry season. Results suggest an urban precipitation deficit in which urbanization reduces local precipitation. This reduction may be caused by changes in surface hydrology that extend beyond the urban heat island effect and energy-related aerosol emissions.

Significant advances have been made to understand the interrelationship between humans and the environment in recent years, yet research has not produced useful localized estimates that link population forecasts to environmental change. Coarse, static population estimates that have little information on projected growth or spatial variability mask substantial impacts of environmental change on especially vulnerable populations. We estimate that 20 million people in the United States will be affected by sea-level rise by 2030 in selected regions that represent a range of soci- ociciodemographic characteristics and corresponding risks of vulnerability. Our results show that the impact of sea-level rise extends beyond the directly impacted counties due to migration networks that link inland and coastal areas and their populations. Substantial rates of population growth and migration are serious considerations for developing mitigation, adaptation, and planning strategies, and for future research on the social, demographic, and political dimensions of climate change.

According to the UN, the number of urban dwellers is expected to increase from roughly 3.2 billion today to more than 4.9 billion by 2030. An accurate and regularly updated estimate of the extent and spatial distribution of urban land is an important first step in our search for realistic responses to the ecological and social consequences of what promises to be the most rapid urbanization in world history. By employing circa-2000 satellite remote sensing imagery, geographic information systems, and census data, six groups from government and academia in both the EU and the US have created global maps that can be used to describe urban land. We compare these maps from global to sub-national scales, for the first time applying Discrete Global Grids to the problem of global-scale map comparison. Although most of these maps share common data inputs, they differ by as much as an order of magnitude in their estimates of the total areal extent of the Earth's urban land (from 0.27 to 3.52 million km²). A sub-national analysis of the spatial distribution of urban land reveals that inter-map correlations are highest in North America (­­r̄= 0.90), intermediate in Europe, South and Central America, and Sub-Saharan Africa (r̄ = 0.78), and lowest in Asia (r̄ = 0.63). Across most regions, our analysis uncovers a degree of variance that is high enough to call into question the consistency of each group's approach to urban land, pointing to the need for both a common urban taxonomy and a global urban assessment effort.

In this study, three cities located in the U. S. Corn Belt are evaluated for impacts of past (1992–2001) and projected (2001–2030) urban expansion on ecosystem goods and services, with a specific focus on changes in energy balance, hydrology, and productivity. Scenarios for high-, medium- and low-density urban areas are simulated using a dynamic agro-ecosystem model (Agro-IBIS), by incorporating new parameterizations for impervious surfaces and turf grass. Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m albedo data and remote sensing-derived 30-m resolution maps from the U.S. National Land Cover Database are used as model input to simulate biogeochemical, thermodynamic, and hydrological cycles for a range of land-cover types in each region. The results show that the expanding urban areas have a significant impact on each city's capacity to regulate climate and flooding. High-density urban areas, for instance, have soil surface temperatures up to 6°C higher than soils within natural and managed ecosystems. Expansion of turf grass in residential areas could require an additional 8–105 million m³ of water use annually, which increases runoff by 15–48% and reduces the capacity to respond/adapt to flooding. Finally, the analysis shows that net primary productivity (NPP) decreases as expected due to the removal of cropland, forests, and grasslands in favor of development, but increased urban turf grass provides an annual offset of 40–210 g C m⁻². Urban expansion through 2030 is estimated to lower total annual crop production by 8.1, 8.6, and 16.7% for the Madison, Peoria, and Indianapolis regions, respectively. Given current projections for city growth to exceed 2–3% per year in the north-central U.S., urban expansion across a nine-state region in the Corn Belt could potentially take an additional 210,000-310,000 ha of farmland out of production annually at a time when demand for food, fuel, and fiber is increasing. Because conversion of cropland to urban uses is nearly always unidirectional, any changes to ecosystem goods and services due to urbanization are likely to be permanent and irreversible.

This study examines whether locationally preferential tax incentives for firms encourage differential urban expansion in China. Using satellite-based data from 1978 to 2009, we find that zones established early tend to have large and persistent effects, and that provincially determined zones have little impact relative to those designated by national authorities. Zones established later tend only to have effects where counties already had an export-advantageous position. We conclude that such policies can encourage differential growth in the presence of significant distortions in the rest of the economy, but become less effective as these distortions are removed.

When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote‐sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser‐scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, long‐term trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.