Systemic impacts such as global supply chain failures can spread among urban areas through social and economic linkages. Urban vulnerability to hazards has been studied from the perspective of individual cities, but global vulnerability to systemic impacts at the network scale has not been assessed. Here we analyze the structure of global industrial supply chains as a lens to examine how impacts might spread across the global system of cities. We generate a novel urban risk network that describes industrial flows among 1686 urban areas. In contrast to the prevailing view of the global urban system dominated by the largest, wealthiest cities, we show that the functionality of the network is evenly spread across urban areas. These findings suggest that the network is more vulnerable to multiple simultaneous hazards than to singular impacts to urban areas with the highest nodal strength. We also find that clusters of the most strongly connected urban areas transcend administrative boundaries, increasing the possibility for systemic impacts to spread transnationally. These results illuminate the potential for linkages between city-scale vulnerabilities to climate change impacts and systemic vulnerabilities that emerge at the global network scale.
The long time series of nighttime light (NTL) data collected by DMSP/OLS sensors provides a unique and valuable resource to study changes in human activities. However, its full time series potential has not been fully explored, mainly due to inconsistencies in the temporal signal. Previous studies have tried to resolve this issue in order to generate a consistent NTL time series. However, due to geographic limitations with the algorithms, these approaches cannot generate a coherent NTL timeseries globally. The purpose of this study is to develop a methodology to create a consistent NTLtime series that can be applied globally. Our method is based on a novel sampling strategy to identify pseudoinvariant features. We select data points along a ridgeline—the densest part of a density plot generated between the reference image and the target image—and then use those data points to derive calibration models to minimize inconsistencies in the NTL time series. Results show that the algorithm successfully calibrates DMSP/OLS annual composites and generates a consistent NTL time series. Evaluation of the results shows that the calibrated NTL time series significantly reduces the differences between two images within the same year and increases the correlations between the NTL time series and gross domestic product as well as with energy consumption, and outperforms the Elvidge et al. (2014) method. The methodology is simple, robust, and easy to implement. The quality-enhanced NTL time series can be used in a myriad of applications that require a consistent data set over time.
How were cities distributed globally in the past? How many people lived in these cities? How did cities influence their local and regional environments? In order to understand the current era of urbanization, we must understand long-term historical urbanization trends and patterns. However, to date there is no comprehensive record of spatially explicit, historic, city-level population data at the global scale. Here, we developed the first spatially explicit dataset of urban settlements from 3700 BC to AD 2000, by digitizing, transcribing, and geocoding historical, archaeological, and census-based urban population data previously published in tabular form by Chandler and Modelski. The dataset creation process also required data cleaning and harmonization procedures to make the data internally consistent. Additionally, we created a reliability ranking for each geocoded location to assess the geographic uncertainty of each data point. The dataset provides the first spatially explicit archive of the location and size of urban populations over the last 6,000 years and can contribute to an improved understanding of contemporary and historical urbanization trends.
Global societies are becoming increasingly urban. This shift toward urban living is changing our relationship with food, including how we shop and what we buy, as well as ideas about sanitation and freshness. Achieving food security in an era of rapid urbanization will require considerably more understanding about how urban and food systems are intertwined. Here we discuss some potential understudied linkages that are ripe for further examination.
Land use science has traditionally used case-study approaches for in-depth investigation of land use change processes and impacts. Meta-studies synthesize findings across case-study evidence to identify general patterns. In this paper, we provide a review of meta-studies in land use science. Various meta-studies have been conducted, which synthesize deforestation and agricultural land use change processes, while other important changes, such as urbanization, wetland conversion, and grassland dynamics have hardly been addressed. Meta-studies of land use change impacts focus mostly on biodiversity and biogeochemical cycles, while meta-studies of socioeconomic consequences are rare. Land use change processes and land use change impacts are generally addressed in isolation, while only few studies considered trajectories of drivers through changes to their impacts and their potential feedbacks. We provide a conceptual framework for linking meta-studies of land use change processes and impacts for the analysis of coupled human–environmental systems. Moreover, we provide suggestions for combining meta-studies of different land use change processes to develop a more integrated theory of land use change, and for combining meta-studies of land use change impacts to identify tradeoffs between different impacts. Land use science can benefit from an improved conceptualization of land use change processes and their impacts, and from new methods that combine meta-study findings to advance our understanding of human–environmental systems.
The 2015 United Nations Climate Change Conference in Paris (COP21) highlighted the importance of cities to climate action, as well as the unjust burdens borne by the world’s most disadvantaged peoples in addressing climate impacts. Few studies have documented the barriers to redressing the drivers of social vulnerability as part of urban local climate change adaptation efforts, or evaluated how emerging adaptation plans impact marginalized groups. Here, we present a roadmap to reorient research on the social dimensions of urban climate adaptation around four issues of equity and justice: (1) broadening participation in adaptation planning; (2) expanding adaptation to rapidly growing cities and those with low financial or institutional capacity; (3) adopting a multilevel and multi-scalar approach to adaptation planning; and (4) integrating justice into infrastructure and urban design processes. Responding to these empirical and theoretical research needs is the first step towards identifying pathways to more transformative adaptation policies.
Urban areas are key sources of greenhouse gas (GHG) emissions and also are vulnerable to climate change. The recent IPCC Fifth Assessment Report illustrates a clear need for more research on urban strategies for climate change adaptation and mitigation. However, missing from the current literature on climate change and urban areas is a conceptual framework that integrates mitigation and adaptation perspectives and strategies. Because cities vary with respect to development histories, economic structure, urban form, institutional and financial capacities among other factors, it is critical to develop a framework that permits cross-city comparisons beyond simple single measures like population size.The primary purpose of this paper is to propose a conceptual framework for a multi-dimensional urbanization climate change typology that considers the underlying and proximate causes of GHG emissions and climate change vulnerabilities. The paper reviews some of the basic steps required to build such a typology and associated challenges that must be overcome via a demonstration of a pilot typology with nine case study cities. The paper shows how the proposed framework can be used to evaluate and compare the conditions of GHG emissions and climate change vulnerability across cities at different phases in the urbanization process.
China has high biodiversity and is rapidly urbanizing. However, there is limited understanding of how urban expansion in the country is likely to affect its habitats and biodiversity. In this study, we examine urban expansion patterns and their likely impacts on biodiversity in China by 2030. Our analysis shows that most provinces are expected to experience urban expansion either near their protected areas or in biodiversity hotspots. In a few provinces such as Guangdong in the south, urban expansion is likely to impinge on both protected areas and biodiversity hotspots. We show that policies that could facilitate the integration of natural resource protection into urban planning exist on paper, but the prevailing incentives and institutional arrangements between the central and local governments prevent this kind of integration. Removing these obstacles will be necessary in order to safeguard the country’s rich biodiversity in light of the scale of urbanization underway.
Cities need to understand and manage their carbon footprint at the level of streets, buildings and communities, urge Kevin Robert Gurney and colleagues.
Global sustainability challenges, from maintaining biodiversity to providing clean air and water, are closely interconnected yet often separately studied and managed. Systems integration—holistic approaches to integrating various components of coupled human and natural systems—is critical to understand socioeconomic and environmental interconnections and to create sustainability solutions. Recent advances include the development and quantification of integrated frameworks that incorporate ecosystem services, environmental footprints, planetary boundaries, human-nature nexuses, and telecoupling. Although systems integration has led to fundamental discoveries and practical applications, further efforts are needed to incorporate more human and natural components simultaneously, quantify spillover systems and feedbacks, integrate multiple spatial and temporal scales, develop new tools, and translate findings into policy and practice. Such efforts can help address important knowledge gaps, link seemingly unconnected challenges, and inform policy and management decisions.
The aggregate potential for urban mitigation of global climate change is insufficiently understood. Our analysis, using a dataset of 274 cities representing all city sizes and regions worldwide, demonstrates that economic activity, transport costs, geographic factors, and urban form explain 37% of urban direct energy use and 88% of urban transport energy use. If current trends in urban expansion continue, urban energy use will increase more than threefold, from 240 EJ in 2005 to 730 EJ in 2050. Our model shows that urban planning and transport policies can limit the future increase in urban energy use to 540 EJ in 2050 and contribute to mitigating climate change. However, effective policies for reducing urban greenhouse gas emissions differ with city type. The results show that, for affluent and mature cities, higher gasoline prices combined with compact urban form can result in savings in both residential and transport energy use. In contrast, for developing-country cities with emerging or nascent infrastructures, compact urban form, and transport planning can encourage higher population densities and subsequently avoid lock-in of high carbon emission patterns for travel. The results underscore a significant potential urbanization wedge for reducing energy use in rapidly urbanizing Asia, Africa, and the Middle East.
We examine the impacts of urbanization on agricultural land loss in India from 2001 to 2010. We combined a hierarchical classification approach with econometric time series analysis to reconstruct land-cover change histories using time series MODIS 250 m VI images composited at 16-day intervals and night time lights (NTL) data. We compared estimates of agricultural land loss using satellite data with agricultural census data. Our analysis highlights six key results. First, agricultural land loss is occurring around smaller cities more than around bigger cities. Second, from 2001 to 2010, each state lost less than 1% of its total geographical area due to agriculture to urban expansion. Third, the northeastern states experienced the least amount of agricultural land loss. Fourth, agricultural land loss is largely in states and districts which have a larger number of operational or approved SEZs. Fifth, urban conversion of agricultural land is concentrated in a few districts and states with high rates of economic growth. Sixth, agricultural land loss is predominantly in states with higher agricultural land suitability compared to other states. Although the total area of agricultural land lost to urban expansion has been relatively low, our results show that since 2006, the amount of agricultural land converted has been increasing steadily. Given that the preponderance of India’s urban population growth has yet to occur, the results suggest an increase in the conversion of agricultural land going into the future.
This edited volume presents material from a forum convened “to reinvent land-change science by exploring new theoretical concepts which reflect contemporary trends in land use, urbanization, and integration of economies.” The book focuses on five themes: competition over and access to productive lands; new forms of distal land connections; the effects of global land connections on local land-use decisions; new agents and practices in global land use; and the normative judgments and evaluations that underlie land-use frameworks. The chapters consider such topics as food production and land use; case studies of urbanization and agriculture in Brazil and China; telecoupling and connections to distant places; emerging institutions of land-use governance; public and private regulation of land use; uniquely urban issues of land use; and future steps to sustainability.
This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities’ increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.
To date, many geography studies have identified GDP, population, FDI, and transportation factors as key drivers of urban growth in China. The political science literature has demonstrated that China’s urban growth is also driven by powerful economic and fiscal incentives for local governments, as well as by the political incentives of local leaders who control land use in their jurisdictions. These parallel but distinct research traditions limit a comprehensive understanding that can result in partial and potentially misleading conclusions of urbanization in China. This paper presents a spatially explicit study that incorporates both political science and geographic perspectives to understand the relative importance of hierarchal administrative governments in affecting urban growth. We use multi-level modeling approach to examine how socio-economic and policy factors – represented here by fiscal transfers – at different administrative levels affect growth in “urban hotspot counties” across three time periods (1995–2000, 2000–2005, and 2005–2008). Our results show that counties that are more dependent on fiscal transfers from the central government convert less cultivated land to urban use, controlling for other factors. We also find that local governments are becoming more powerful in shaping urban land development as a result of local economic, fiscal, and political incentives, as well as through the practical management and control of capital, land, and human resources. By incorporating fiscal transfers in our analysis, our study examines a factor in the urban development of China that had previously been neglected and provides an improved understanding of the underlying processes and pathways involved in urban growth in China.
The world is rapidly urbanizing, but there is no single urbanization process. Rather, urban areas in different regions of the world are undergoing myriad types of transformation processes. The purpose of this paper is to examine how well data from DMSP/OLS nighttime lights (NTL) can identify different types of urbanization processes. Although data from DMSP/OLS NTL are increasingly used for the study of urban areas, to date there is no systematic assessment of how well these data identify different types of urban change. Here, we randomly select 240 sample locations distributed across all world regions to generate urbanization typologies with the DMSP/OLS NTL data and use Google Earth imagery to assess the validity of the NTL results. Our results indicate that where urbanization occurred, NTL have a high accuracy (93%) of characterizing these changes. There is also a relatively high error of commission (42%), where NTL identified urban change when no change occurred. This leads to an overestimation of urbanization by NTL. Our analysis shows that time series NTL data more accurately identifies urbanization in developed countries, but is less accurate in developing countries, suggesting the need to exert caution when using or interpreting NTL in developing countries.
That urban and rural places are connected through trade, people, and policies has long been recognized. The urban land teleconnections (ULT) framework aims advancing conventional conceptualizations of urbanization and land. The conceptual framework thus opens way to identify and examine the processes that link urbanization dynamics and associated land changes that are not necessarily colocated. In this paper, we review recent literature on four manifestations of urbanization that, along the lines of the ULT framework, highlight the importance of process-based conceptualizations of urbanization and land along a continuum of land systems. We then discuss potential approaches to improve the knowledge base on how and where urbanization is driving land change.
India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.
China’s urbanization has resulted in significant changes in both agricultural land and agricultural land use. However, there is limited understanding about the relationship between the two primary changes occurring to China’s agricultural land – the urban expansion on agricultural land and agricultural land use intensity. The goal of this paper is to understand this relationship in China using panel econometric methods. Our results show that urban expansion is associated with a decline in agricultural land use intensity. The area of cultivated land per capita, a measurement about land scarcity, is negatively correlated with agricultural land use intensity. We also find that GDP in the industrial sector negatively affects agricultural land use intensity. GDP per capita and agricultural investments both positively contribute to the intensification of agricultural land use. Our results, together with the links between urbanization, agricultural land, and agricultural production imply that agricultural land expansion is highly likely with continued urban expansion and that pressures on the country’s natural land resources will remain high in the future.
Urban areas consume more than 66% of the world’s energy and generate more than 70% of global greenhouse gas emissions. With the world’s population expected to reach 10 billion by 2100, nearly 90% of whom will live in urban areas, a critical question for planetary sustainability is how the size of cities affects energy use and carbon dioxide (CO2) emissions. Are larger cities more energy and emissions efficient than smaller ones? Do larger cities exhibit gains from economies of scale with regard to emissions? Here we examine the relationship between city size and CO2 emissions for U.S. metropolitan areas using a production accounting allocation of emissions. We find that for the time period of 1999–2008, CO2 emissions scale proportionally with urban population size. Contrary to theoretical expectations, larger cities are not more emissions efficient than smaller ones.
Remote sensing offers unique perspectives to study the relationship between urban systems and climate change because it provides spatially explicit and synoptic views of the landscape that are available at multiple grains, extents, and over time. While remote sensing has made significant advances in the study of urban areas, especially urban heat island and urban land change, there are myriad unanswered science and policy questions to which remote sensing science could contribute. Here we identify several key opportunities for remote sensing science to increase our understanding of the relationships between urban systems and climate change.
The science and policy communities increasingly require information about inter-urban variability in form, infrastructure, and energy use for cities globally and in a timely manner. Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) are able to provide information on nighttime luminosity, a correlate of the built environment and energy consumption. Although NTL data are used to map aggregate measures of urban areas such as total area extent, their ability to characterize inter-urban variation is limited due to saturation of the data values, especially in urban cores. Here we propose a new spectral index, the Vegetation Adjusted NTL Urban Index (VANUI), which combines MODIS NDVI with NTL, to achieve three key goals. First, the index reduces the effects of NTL saturation. Second, the index increases variation of the NTL signal, especially within urban areas. Third, the index corresponds to biophysical and urban characteristics. Additionally, the index is intuitive, simple to implement, and enables rapid characterization of inter-urban variability in nighttime luminosity. Assessments of VANUI show that it significantly reduces NTL saturation and increases variation of data values in core urban areas. As such, VANUI can be useful for studies of urban structure, energy use, and carbon emissions.
Urbanization will place significant pressures on biodiversity across the world. However, there are large uncertainties in the amount and location of future urbanization, particularly urban land expansion. Here, we present a global analysis of urban extent circa 2000 and probabilistic forecasts of urban expansion for 2030 near protected areas and in biodiversity hotspots. We estimate that the amount of urban land within 50 km of all protected area boundaries will increase from 450 000 km2 circa2000 to 1440 000 ± 65 000 km2 in 2030. Our analysis shows that protected areas around the world will experience significant increases in urban land within 50 km of their boundaries. China will experience the largest increase in urban land near protected areas with 304 000 ± 33 000 km2 of new urban land to be developed within 50 km of protected area boundaries. The largest urban expansion in biodiversity hotspots, over 100 000 ± 25 000 km2, is forecasted to occur in South America. Uncertainties in the forecasts of the amount and location of urban land expansion reflect uncertainties in their underlying drivers including urban population and economic growth. The forecasts point to the need to reconcile urban development and biodiversity conservation strategies.
Today, urban areas generate more than 90% of the global economy, are home to more than 50% of the world population, consume more than 65% of the world’s energy; and emit 70% of global greenhouse gas emissions. The science and policy communities increasingly recognize that cities, urban areas, and the underlying urbanization process are at the center of global climate change and sustainability challenges. Policymakers need facts, empirical evidence, and theories on how to plan and manage cities and urbanization during the contemporary era of rapid change and environmental uncertainty.
Urban population now exceeds rural population globally, and 60–80% of global energy consumption by households, businesses, transportation, and industry occurs in urban areas. There is growing evidence that built-up infrastructure contributes to carbon emissions inertia, and that investments in infrastructure today have delayed climate cost in the future. Although the United Nations statistics include data on urban population by country and select urban agglomerations, there are no empirical data on built-up infrastructure for a large sample of cities. Here we present the first study to examine changes in the structure of the world’s largest cities from 1999 to 2009. Combining data from two space-borne sensors—backscatter power (PR) from NASA’s SeaWinds microwave scatterometer, and nighttime lights (NL) from NOAA’s defense meteorological satellite program/operational linescan system (DMSP/OLS)—we report large increases in built-up infrastructure stock worldwide and show that cities are expanding both outward and upward. Our results reveal previously undocumented recent and rapid changes in urban areas worldwide that reflect pronounced shifts in the form and structure of cities. Increases in built-up infrastructure are highest in East Asian cities, with Chinese cities rapidly expanding their material infrastructure stock in both height and extent. In contrast, Indian cities are primarily building out and not increasing in verticality. This new dataset will help characterize the structure and form of cities, and ultimately improve our understanding of how cities affect regional-to-global energy use and greenhouse gas emissions.
Urbanization is a demographic, economic, and land transformation process. Building construction and operation are integral aspects of urban land use change and contribute to material and energy resources consumption and the resulting carbon dioxide (CO2) emissions in urban areas. In this paper, we ask two questions regarding the urbanization process: 1) Do the land, material, and energy use efficiencies associated with the construction and operation of buildings increase over time? 2) Do the gains in resource use efficiencies offset the increases in resource demands due to the magnitude of urbanization? To answer these questions, we use a systematic approach similar to a material flow analysis and apply it to the Pearl River Delta, a rapidly urbanizing region in China. We use a combination of satellite data and official statistics to evaluate changes in urban population density and building density from 1988 to 2008. Both density measures decrease from 1988 to 2003; after 2003, building density increases while population density continues to decline. We also track the indirect impacts of urban land expansion on material and energy demands and associated CO2 emissions using concrete and heating/cooling as proxies for building construction and operation, respectively. Throughout the study period, structural changes and efficiency gains decrease the demand per unit floor area for both building materials and energy. However, the efficiency gains are outstripped by the magnitude of urban expansion, therefore leading to an increase in the demand for resources and CO2 emissions per capita. Our results show that focusing only on gains in efficiency for individual buildings without considering the scale of urban expansion results in underestimate of the cumulative energy, material, and greenhouse gas emissions impacts of urbanization. We emphasize the distinction between the rates versus the accumulations of these impacts over spatial and temporal scales. We discuss the relevance of the Environmental Kuznets approaches to tackling environmental impacts that are cumulative in nature and may lead to irreversible changes in the environment. We conclude that tracking the energy, materials, and emissions impacts of urbanization requires a multi-scale approach that ranges from the individual building to the urban region.
Urban land-cover change threatens biodiversity and affects ecosystem productivity through loss of habitat, biomass, and carbon storage. However, despite projections that world urban populations will increase to nearly 5 billion by 2030, little is known about future locations, magnitudes, and rates of urban expansion. Here we develop spatially explicit probabilistic forecasts of global urban land-cover change and explore the direct impacts on biodiversity hotspots and tropical carbon biomass. If current trends in population density continue and all areas with high probabilities of urban expansion undergo change, then by 2030, urban land cover will increase by 1.2 million km2, nearly tripling the global urban land area circa 2000. This increase would result in considerable loss of habitats in key biodiversity hotspots, with the highest rates of forecasted urban growth to take place in regions that were relatively undisturbed by urban development in 2000: the Eastern Afromontane, the Guinean Forests of West Africa, and the Western Ghats and Sri Lanka hotspots. Within the pan-tropics, loss in vegetation biomass from areas with high probability of urban expansion is estimated to be 1.38 PgC (0.05 PgC yr−1), equal to ∼5% of emissions from tropical deforestation and land-use change. Although urbanization is often considered a local issue, the aggregate global impacts of projected urban expansion will require significant policy changes to affect future growth trajectories to minimize global biodiversity and vegetation carbon losses.
China has undergone large-scale urban expansion and rapid loss of cultivated land for more than two decades. The goal of this paper is to examine the relative importance of socioeconomic and policy factors across different administrative levels on urban expansion and associated cultivated land conversion. We conduct the analysis for urban hotspot counties across the entire country. We use multi-level modeling techniques to examine how socioeconomic and policy factors at different administrative levels affect cultivated land conversion across three time periods, 1989–1995, 1995–2000, and 2000–2005. Our results show that at the county level, both urban land rent and urban wages contribute to total cultivated land conversion. Contrary to expectations, agricultural investment drives farmland conversion, suggesting a policy failure with unintended consequences. At the provincial level, urban wages and foreign direct investment both positively contribute to cultivated land conversion. We also find that higher GDP correlates with more urban expansion but the relationship is nonlinear.
In this paper we ask two questions: Does a multiscalar urban land-change model that couples a region-scale system dynamics model with a local-scale spatial logit model better predict the amount of urban land change than either model alone? Does a multiscalar urban land-change model that couples regional and local-scale factors better predict the spatial patterns of urban land change than a standalone local-scale spatial logit model? To examine these questions, we develop a coupled system dynamics spatial logit (CSDSL) model for the Pearl River Delta, China, that incorporates region-scale population and economic factors with local-scale biophysical and accessibility factors. In terms of predicting the amounts of urban land change, the CSDSL model is 15% and 18% more accurate than the standalone spatial logit and system dynamics models, respectively. In terms of predicting the spatial pattern of urban land change, the CSDSL model slightly outperforms the spatial logit model as measured by four spatial pattern metrics: number of urban patches, urban edge density, average urban patch size, and spatial irregularity of the urban area. Both the CSDSL and spatial logit models underpredict the number of discrete urban patches (by 64% and 80%, respectively) and the urban edge density (by 42% and 62%, respectively). While both models overpredict the average urban patch size, the spatial logit model overpredicts by over 316%, while the CSDSL overpredicts by 192%. Finally, the models perform equally well in predicting the spatial irregularity of urban areas and the location of urban change. Taken together, these results demonstrate that the CSDSL model outperforms a standalone spatial logit or system dynamics model in predicting the amount and spatial complexity of urban land change. The results also show that predicting urban land-change patterns remains more difficult than predicting total amounts of change.
We utilize the operational MODIS BRDF products to create spatially and temporally complete bases. Although a nine-year record of global BRDFs from NASA’s Terra and Aqua satellites now exists, its inclusion in regional and global models has been limited by the extensive data-gaps caused by persistent clouds and ephemeral snow cover. This research focuses on bridging the gaps in the MODIS BRDF products, which is achieved by applying rigorous temporal interpolation techniques based on vegetation development curves. Comparison of the resulting MODIS BRDF products with the direct BRDF retrievals from the POLDER-3 sensor shows a very good linear relationship between these two remotely sensed products. This resulting consistent, high-quality, long-term reflectance anisotropy databases will benefit the regional and global modeling and monitoring communities.
This paper introduces urban land teleconnections as a conceptual framework that explicitly links land changes to underlying urbanization dynamics. We illustrate how three key themes that are currently addressed separately in the urban sustainability and land change literatures can lead to incorrect conclusions and misleading results when they are not examined jointly: the traditional system of land classification that is based on discrete categories and reinforces the false idea of a rural–urban dichotomy; the spatial quantification of land change that is based on place-based relationships, ignoring the connections between distant places, especially between urban functions and rural land uses; and the implicit assumptions about path dependency and sequential land changes that underlie current conceptualizations of land transitions. We then examine several environmental “grand challenges” and discuss how urban land teleconnections could help research communities frame scientific inquiries. Finally, we point to existing analytical approaches that can be used to advance development and application of the concept.
This paper uses a content analysis of the published literature to take stock of current understanding of key social and policy drivers of migration to cities in 11 Asian and African mega-deltas and identifies commonalities and differences among them. The analysis shows that migration to urban centers in mega-deltas is an outcome of many forces: economic policies and incentives, local and destination institutions, government policies to develop small towns, and the geographic concentration of investments. Massive influx of capital to many deltas has transformed the local economic base from a primarily agricultural one to a manufacturing and processing economy. This has created uneven spatial economic development which is the underlying driver of migration to cities in the mega-deltas regardless of geographic context or size. Going forward to 2060, one critical challenge for all the deltas is to increase the labor skill of their workforce and foster technology innovation. Continued economic growth in these regions will require substantial investments in education and capacity building and the ability of urban centers to absorb the migrant labor pool.
Aedes aegypti is implicated in dengue transmission in tropical and subtropical urban areas around the world. Ae. aegypti populations are controlled through integrative vector management. However, the efficacy of vector control may be undermined by the presence of alternative, competent species. In Puerto Rico, a native mosquito, Ae. mediovittatus, is a competent dengue vector in laboratory settings and spatially overlaps with Ae. aegypti. It has been proposed that Ae. mediovittatus may act as a dengue reservoir during inter-epidemic periods, perpetuating endemic dengue transmission in rural Puerto Rico. Dengue transmission dynamics may therefore be influenced by the spatial overlap of Ae. mediovittatus, Ae. aegypti, dengue viruses, and humans. We take a landscape epidemiology approach to examine the association between landscape composition and configuration and the distribution of each of these Aedes species and their co-occurrence. We used remotely sensed imagery from a newly launched satellite to map landscape features at very high spatial resolution. We found that the distribution of Ae. aegypti is positively predicted by urban density and by the number of tree patches, Ae. mediovittatus is positively predicted by the number of tree patches, but negatively predicted by large contiguous urban areas, and both species are predicted by urban density and the number of tree patches. This analysis provides evidence that landscape composition and configuration is a surrogate for mosquito community composition, and suggests that mapping landscape structure can be used to inform vector control efforts as well as to inform urban planning.
The conversion of Earth’s land surface to urban uses is one of the most irreversible human impacts on the global biosphere. It drives the loss of farmland, affects local climate, fragments habitats, and threatens biodiversity. Here we present a meta-analysis of 326 studies that have used remotely sensed images to map urban land conversion. We report a worldwide observed increase in urban land area of 58,000 km2 from 1970 to 2000. India, China, and Africa have experienced the highest rates of urban land expansion, and the largest change in total urban extent has occurred in North America. Across all regions and for all three decades, urban land expansion rates are higher than or equal to urban population growth rates, suggesting that urban growth is becoming more expansive than compact. Annual growth in GDP per capita drives approximately half of the observed urban land expansion in China but only moderately affects urban expansion in India and Africa, where urban land expansion is driven more by urban population growth. In high income countries, rates of urban land expansion are slower and increasingly related to GDP growth. However, in North America, population growth contributes more to urban expansion than it does in Europe. Much of the observed variation in urban expansion was not captured by either population, GDP, or other variables in the model. This suggests that contemporary urban expansion is related to a variety of factors difficult to observe comprehensively at the global level, including international capital flows, the informal economy, land use policy, and generalized transport costs. Using the results from the global model, we develop forecasts for new urban land cover using SRES Scenarios. Our results show that by 2030, global urban land cover will increase between 430,000 km2 and 12,568,000 km2, with an estimate of 1,527,000 km2 more likely.
Urban areas concentrate people, economic activity, and the built environment. As such, urbanization is simultaneously a demographic, economic, and land-use change phenomenon. Historically, the remote sensing community has used optical remote sensing data to map urban areas and the expansion of urban land-cover for individual cities, with little research focused on regional and global scale patterns of urban change. However, recent research indicates that urbanization at regional scales is growing in importance for economics, policy, land use planning, and conservation. Therefore, there is an urgent need to understand and monitor urbanization dynamics at regional and global scales. Here, we illustrate the use of multi-temporal nighttime light (NTL) data from the U.S Air Force Defense Meteorological Satellites Program/Operational Linescan System (DMSP/OLS) to monitor urban change at regional and global scales. We use independently derived data on population, land use and land cover to test the ability of multi-temporal NTL data to measure regional and global urban growth over time. We apply an iterative unsupervised classification method on multi-temporal NTL data from 1992 to 2008 to map urbanization dynamics in India, China, Japan, and the United States. For two-year intervals between 1992 and 2000, India consistently experienced higher rates of urban growth than China, and both countries exceeded the urban growth rates of the United States and Japan. This is not surprising given that the populations of India and China were growing faster than those of the U.S. and Japan during those periods. For two-year intervals between 2000 and 2008, China experienced higher rates of urban growth than India. Results show that the multi-temporal NTL provides a regional and potentially global measure of the spatial and temporal changes in urbanization dynamics for countries at certain levels of GDP and population-driven growth.
Editorial overview written for issue of Current Opinion in Environmental Sustainability entitled “Human settlements and industrial systems.” The overview discusses the confluence of urbanization and global environmental change and introduces articles found in the issue.
Contemporary urbanization differs from historical patterns of urban growth in terms of scale, rate, location, form, and function. This review discusses the characteristics of contemporary urbanization and the roles of urban planning, governance, agglomeration, and globalization forces in driving and shaping the relationship between urbanization and the environment. We highlight recent research on urbanization and global change in the context of sustainability as well as opportunities for bundling urban development efforts, climate mitigation, and adaptation strategies to create synergies to transition to sustainability. We conclude with an analysis of global greenhouse gas emissions under different scenarios of future urbanization growth and discuss their implications.
In 2008, the global urban population exceeded the nonrural population for the first time in history, and it is estimated that by 2050, 70% of the world population will live in urban areas, with more than half of them concentrated in Asia. Although there are projections of future urban population growth, there is significantly less information about how these changes in demographics correspond with changes in urban extent. Urban land-use and land-cover changes have considerable impacts on climate. It has been well established that the urban heat island effect is more significant during the night than day and that it is affected by the shape, size, and geometry of buildings as well as the differences in urban and rural gradients. Recent research points to mounting evidence that urbanization also affects cycling of water, carbon, aerosols, and nitrogen in the climate system. This review highlights advances in the understanding of urban land-use trends and associated climate impacts, concentrating on peer-reviewed papers that have been published over the last two years.
In lieu of an abstract, the following is a chapter excerpt:
Cities are the dominant form of human settlements and their interaction with the global environment presents great challenges for sustainability. This paper analyzes the evolution of urban form in three rapidly-growing Chinese metropolitan areas in the Pearl River Delta: Shenzhen, Foshan and Guangzhou. It is the first study to utilize a combination of time-series satellite imagery, GIS, and a time-series of spatial pattern statistics based on rank-size distributions to evaluate the evolving nature of urban clusters in Chinese cities. Defining the urban clusters – contiguous urban built-up areas – as the unit of our analysis, we estimate exponents of rank-size distributions for each city’s clusters for the years between 1988 and 1999. We observe substantial variation in the evolution of urban form across time. For all three metropolitan areas, the rank-size distribution exponents evolve in an oscillatory fashion within the 11-year period as the metropolitan areas grow through a process of cluster birth and coalescence. The analysis sheds light on the evolving nature of urban clusters that can help us better understand urban phenomena, and make inferences on how socioeconomic processes influence urban form which in turn has considerable effects on the ecology of the urban system and the local and regional environment. We show that a time-series analysis of rank-size distributions of urban clusters reveals trends in spatial patterns of urban form that can aid in the design of cities and help achieve more sustainable land-uses.
What determines urban growth and how do these factors vary globally? An understanding of the factors that drive urban spatial form will be critical for urban—and ultimately environmental—sustainability. We hypothesize that easy access to economic or social activity is a primary driver of urban form. From this, a city’s spatial form is largely determined by the time–cost of access to transportation and activities. We use a stochastic pixel-based model to test the hypothesis of accessibility-driven urban growth using two case studies: Silicon Valley, U.S., and Bangalore, India. This study is the first to develop a spatially explicit modeling approach to urban growth in a comparative framework spanning the developed and developing world. Our analysis shows that Silicon Valley’s relatively inexpensive auto-based transport (in time and financial costs), dispersed employment locations, and high labor force participation rates have resulted in intermittent and expansive highway-oriented urban growth patterns. In contrast, Bangalore’s expensive non-auto transport (in time and financial costs), low participation in the formal economy, and emphasis on informal economic activity has produced a tighter clustering of urban development near existing urban locations. Over time, generally decreasing transport costs in both locations have led to increased dispersion of urban development. Economic growth in India and the inflows of IT-related foreign investment in Bangalore may further create urban forms increasingly similar to those found in the Silicon Valley. The results have important implications for the development of policies that may lead to more sustainable forms of urban development.
China is home to one-fifth of the world’s population and that population is increasingly urban. The landscape is also urbanizing. Although there are studies that focus on specific elements of urban growth, there is very little empirical work that incorporates feedbacks and linkages to assess the interactions between the dynamics of urban growth and their environmental impacts. In this study, we develop a system dynamics simulation model of the drivers and environmental impacts of urban growth, using Shenzhen, South China, as a case study. We identify three phases of urban growth and develop scenarios to evaluate the impact of urban growth on several environmental indicators: land use, air quality, and demand for water and energy. The results show that all developable land will be urban by 2020 and the increase in the number of vehicles will be a major source of air pollution. Demand for water and electricity will rise, and the city will become increasingly vulnerable to shortages of either. The scenarios also show that there will be improvements in local environmental quality as a result of increasing affluence and economic growth. However, the environmental impacts outside of Shenzhen may increase as demands for natural resources increase and Shenzhen pushes its manufacturing industries out of the municipality. The findings may also portend to changes other cities in China and elsewhere in the developing world may experience as they continue to industrialize.
Predicting patterns of urban growth will be a major challenge for policy makers and environmental scientists in the 21st century. How cities grow—their shape and size—will have enormous implications for environmental sustainability and infrastructure needs. This paper presents a spatiotemporal ART-MMAP neural method to simulate and predict urban growth. Factors that affect urban growth—that is, transportation routes, land use, and topography—were directly used as inputs to the neural network model for model calibration. The calibrated network was then applied to a study site—St Louis, Missouri—to predict future urban growth and to examine future land development scenarios. This paper also introduces an effective and straightforward method for model validation and accuracy assessment, the prediction error matrix, which has been used in the pattern recognition field for several decades. In order to assess the performance of the neural network model, an in-depth accuracy assessment was conducted in which the model results were compared against a null model, an alternative naïve model, and two random models. The neural network model consistently outperformed the naïve model and two random models, and produced similar or better results than the null model. Furthermore, we evaluated the models’ performance at different spatial resolutions. The prediction accuracy increases when spatial resolution becomes coarser. One particularly interesting result is that when the results are aggregated to 1 km spatial resolution, there is 100% accuracy of urban growth predicted by the neural network model versus actual urban growth.
Since economic and agricultural reforms were initiated in the late 1970s, China’s cities have grown at a remarkable pace. Urban population increased from 172 million in 1978 to 517 million in 2003, increasing the urbanization level from 19 percent to 40 percent (2004 State Statistical Bureau data). The number of Chinese cities has increased from 132 in 1949 to 667 in 1999 (Anderson and Ge 2004). It is estimated that urban population will grow to almost 5 billion by 2030, an expected increase of 2 billion people from the estimated level for 2003 (United Nations 2004). However, aggregate growth measures give limited information regarding spatial patterns of urbanization or the underlying processes that shape urban areas.
Although there exist numerous urban growth models, most have significant data input requirements, limiting their utility in a developing-world context. Yet, it is precisely in the developing world where there is an urgent need for urban growth models and scenarios since most expected urban growth in the next two decades will occur in such countries. This paper describes a physical urban growth model that requires few, but widely available, spatially explicit data. Utilizing binary urban/nonurban maps generated by satellite imaging, our model can inform urban planners and policy makers about the most probable locations and periods of future urban land-use change. Using a discrete choice framework, the model employs a spatially explicit logistic regression analysis to evaluate probabilities of urban growth for a baseline period. It calibrates parameters, validates results, predicts urban land-use change and examines future growth scenarios. Future growth scenarios can be generated through the inclusion of land prohibited from development, transportation routes, or new planned urban developments. A novel and important element of the model is the incorporation of an explicit policy-making framework that captures and reduces model uncertainty (theory and specification uncertainties), effectively addressing problems of predictive bias; this framework also allows the user or policy maker to associate predictions with a loss function. The model is applied to three cities in southern China that have experienced dramatic urban land growth in the last two decades. From 1988 to 1999, urban land in the region increased by 451.6% or at an annual rate of approximately 16.5%. Results show that the model achieves 73% – 77% accuracy for different cities at 30 m and 60 m resolutions. Aggregating the predictions to the county/administrative district shows that prediction through thresholding underperforms in comparison to the technique of sample enumeration.
Remote sensing data have been proposed as a potential tool for monitoring environmental treaties. However, to date, satellite images have been used primarily for visualization, but not for systematic monitoring of treaty compliance. In this paper, we present a methodology to operationalize the use of satellite imagery to assess the impact of the Ramsar Convention on Wetlands. The approach uses time series analysis of landscape pattern metrics to assess land cover conditions before and after designation of Ramsar status to monitor compliance with the Convention. We apply the methodology to two case studies in Vietnam and evaluate the success of Ramsar using four metrics: (1) total mangrove extent; (2) mangrove fragmentation; (3) mangrove density; and (4) aquaculture extent. Results indicate that the Ramsar Convention did not slow the development of aquaculture in the region, but total mangrove extent has remained relatively constant, primarily due to replanting efforts. Yet despite these restoration efforts, the mangroves have become fragmented and survival rates for replanting efforts are low. The methodology is cost effective and especially useful to evaluate Ramsar sites that rely mainly on self-reporting methods and where third parties are not actively involved in the monitoring process. Finally, the case study presented in this paper demonstrates that with the appropriate satellite record, in situ measurements and field observations, remote sensing is a promising technology that can help monitor compliance with international environmental agreements.
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.
Landsat data are now available for more than 30 years, providing the longest high-resolution record of Earth monitoring. This unprecedented time series of satellite imagery allows for extensive temporal observation of terrestrial processes such as land cover and land use change. However, despite this unique opportunity, most existing change detection techniques do not fully capitalize on this long time series. In this paper, a method that exploits both the temporal and spatial domains of time series satellite data to map land cover changes is presented. The time series of each pixel in the image is modeled with a combination of: 1) pixel-specific remotely sensed data; 2) neighboring pixels derived from ground observation data; and 3) time series transition probabilities. The spatial information is modeled with variograms and integrated using indicator kriging; time series transition probabilities are combined using an information-based cascade approach. This results in a map that is significantly more accurate in identifying when, where, and what land cover changes occurred. For the six images used in this paper, the prediction accuracy of the time series improves significantly, increasing from 31% to 61%, when both space and time are considered with the maximum likelihood. The consideration of spatial continuity also reduced unwanted speckles in the classified images, removing the need for any postprocessing. These results indicate that combining space and time domains significantly improves the accuracy of temporal change detection analyses and can produce high-quality time series land cover maps.
Data mining methods have been widely and successfully used in many fields in the last decade. And geographic knowledge discovery and spatial data mining also have attracted more attentions recently. This paper presents an ART-MMAP neural network based spatio-temporal data mining method to simulate and predict urban expansion. The spatial matrices derived from different urban related features, i.e. transportation, land use, topography, were directly used as inputs to the neural network model for learning. The trained network was then applied to research region to predict the land use change to urban. The learning and prediction process are automatic and free of intervention. The method has been successfully validated with the urban growth prediction at St. Louis region at Missouri, USA.
This paper provides a dynamic inter- and intra-city analysis of spatial and temporal patterns of urban land-use change. It is the first comparative analysis of a system of rapidly developing cities with landscape pattern metrics. Using ten classified Landsat Thematic Mapper images acquired from 1988 to 1999, we quantify the annual rate of urban land-use change for four cities in southern China. The classified images were used to generate annual maps of urban extent, and landscape metrics were calculated and analyzed spatiotemporally across three buffer zones for each city for each year. The study shows that for comprehensive understanding of the shapes and trajectories of urban expansion, a spatiotemporal landscape metrics analysis across buffer zones is an improvement over using only urban growth rates. This type of analysis can also be used to infer underlying social, economic, and political processes that drive the observed urban forms. The results indicate that urban form can be quite malleable over relatively short periods of time. Despite different economic development and policy histories, the four cities exhibit common patterns in their shape, size, and growth rates, suggesting a convergence toward a standard urban form.
This chapter describes work that links multiple data sources and research perspectives to advance understanding of the dynamics and human causes of land use change, particularly urban growth, in the Pearl River Delta of southern China and the Red River Delta of northern Vietnam. Thus far, the research effort has concentrated on three interrelated questions:
How has land cover and land use changed over the last 20 to 30 years in both of these regions? What are the spatial dynamics of these changes and over what time scales do they occur?
What are the major human causes of the observed land cover changes? How does the transition from a centrally planned to a market-oriented economy affect land use? What are the broader social, political, and economic factors at the macro level that influence local land use decisions?
What are the environmental consequences of changes in the land system? How will land use change affect biophysical properties and biogeochemical cycles?
The chapter includes discussions of completed and ongoing research in the context of theoretical frameworks, methods used, and lessons learned, including a description of the two study regions; the history of the projects; the assembly and processing of remote sensing, spatial, field, and survey data; and conceptual and methodological challenges in implementing the research.
The majority of studies on Chinese urbanization have been focused on coastal areas, with little attention given to urban centers in the west. Western provinces, however, will unquestionably undergo significant urban change in the future as a result of the ‘Go West’ policy initiated in the 1990s. In this paper the authors examine the relationship between drivers of urban growth and land-use outcomes in Chengdu, capital of the western province of Sichuan, China. In the first part of this research, remotely sensed data are used to map changes in land cover in the greater Chengdu area and to investigate the spatial distribution of development with use of landscape metrics along seven urban-to-rural transects identified as key corridors of growth. Results indicate that the urbanized area increased by more than 350% between 1978 and 2002 in three distinct spatial trends: (a) near the urban fringe in all directions prior to 1990, (b) along transportation corridors, ring roads, and near satellite cities after 1990, and, finally, (c) infilling in southern and western areas (connecting satellite cities to the urban core) in the late 1990s. In the second part of this paper the authors connect patterns of growth with economic, land, and housing market reforms, which are explored in the context of urban planning initiatives. The results reveal that, physically, Chengdu is following trends witnessed in coastal cities of China, although the importance of various land-use drivers differs from that in the east (for example, in the low level of foreign direct investment to date). The information provided by the land-use analysis ultimately helped tailor policies and plans for better land management and reduced fragmentation of new development in the municipality.
In this paper, we use logit models to classify data from Landsat Thematic Mapper (TM) among 23 land-cover change classes. The logit model is a simple statistical technique that is designed to analyse categorical data. Diagnostic statistics indicate that the logit model can classify remotely sensed data in a statistically significant fashion. User accuracies for individual land-cover classes range between 50 and 92%, with an overall accuracy of 79%. To assess these accuracies, we compare them to those generated by a Bayesian maximum likelihood classifier. While the overall accuracies are similar, the accuracies for individual land-cover categories differ. These differences may be associated with the size of the training data for each land-cover class. There is some evidence that the logit models generate higher accuracies for land-cover categories for which relatively few training pixels are available. Finally, a comparison of classification results using a 12-band composite of the six reflective TM bands and their change vectors versus a six-band composite of the three Tasselled Cap bands and their change vectors indicates that the latter reduces classification accuracies.
Global or continental-scale land cover mapping with remote sensing data is limited by the spatial characteristics of satellites. Subpixel-level mapping is essential for the successful description of many land cover patterns with spatial resolution of less than ~1 km and also useful for finer resolution data. This paper presents a novel adaptive resonance theory MAP (ARTMAP) neural network-based mixture analysis model-ART mixture MAP (ART-MMAP). Compared to the ARTMAP model, ART-MMAP has an enhanced interpolation function that decreases the effect of category proliferation in ARTa and overcomes the limitation of class category in ARTb. Results from experiments demonstrate the superiority of ART-MMAP in terms of estimating the fraction of land cover within a single pixel.
One of the most salient impacts of policy reforms in China is the high rates of urban growth and urbanization. Urban growth, the expansion of urban areas into villages, farmland, and natural ecosystems, was made possible through neo-liberal policies, foreign investments, and economic development. Urbanization, the increase in urban population, was the result of an expanding economy and a relaxation of social controls such as the household registration system. This paper describes the patterns of urban growth and land-use change in the Pearl River Delta, South China, the macro-level drivers of these changes, and the winners and losers of policy reforms. Improvements in social and economic indicators of well-being at the national scale suggest that the country as a whole has benefited from the market reforms. A city-level assessment indicates that reforms resulted in winners, losers, and regions that were “catching up” or “falling behind.”
The ability to predict spatial patterns of species richness using a few easily measured environmental variables would facilitate timely evaluation of potential impacts of anthropogenic and natural disturbances on biodiversity and ecosystem functions. Two common hypotheses maintain that faunal species richness can be explained in part by either local vegetation heterogeneity or primary productivity. Although remote sensing has long been identified as a potentially powerful source of information on the latter, its principal application to biodiversity studies has been to develop classified vegetation maps at relatively coarse resolution, which then have been used to estimate animal diversity. Although classification schemes can be delineated on the basis of species composition of plants, these schemes generally do not provide information on primary productivity. Furthermore, the classification procedure is a time- and labour-intensive process, yielding results with limited accuracy. To meet decision-making needs and to develop land management strategies, more efficient methods of generating information on the spatial distribution of faunal diversity are needed. This article reports on the potential of predicting species richness using single-date Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper (TM). We use NDVI as an indicator of vegetation productivity, and examine the relationship of three measures of NDVI—mean, maximum, and standard deviation—with patterns of bird and butterfly species richness at various spatial scales. Results indicate a positive correlation, but with no definitive functional form, between species richness and productivity. The strongest relationships between species richness of birds and NDVI were observed at larger sampling grains and extent, where each of the three NDVI measures explained more than 50% of the variation in species richness. The relationship between species richness of butterflies and NDVI was strongest over smaller grains. Results suggest that measures of NDVI are an alternative approach for explaining the spatial variability of species richness of birds and butterflies.
Urbanization has profound effects on the environment at local, regional, and global scales. Effective detection of urban change using remote sensing data will be an essential component of global environmental change research, regional planning, and natural resource management. This paper presents results from an ARTMAP neural network to detect urban change with Landsat TM images from two periods. Classification of urban change, and, in particular, conversion of agriculture to urban, was statistically more accurate with ARTMAP than with a more conventional technique, the Bayesian maximum-likelihood classifier (MLC). The effect of different levels of class aggregation on the performance of change detection was also explored with ARTMAP and MLC. Because ARTMAP explicitly allows many-to-one mapping, classification using coarse class resolution and fine class resolution training data generated similar results. Together, these results suggest that ARTMAP can reduce labor and computational costs associated with assembling training data while concurrently generating more accurate urban change-detection results.
This paper estimates econometric models of the socioeconomic drivers of land use change in the Pearl River Delta, China. The panel data used to estimate the models are generated by combining high-resolution remote sensing data with economic and demographic data from annual compendium. The relations between variables are estimated using a random coefficient model. Results indicate that urban expansion is associated with foreign direct investment and relative rates of productivity generated by land associated with agricultural and urban uses. This suggests that large-scale investments in industrial employment, rather than local land users, play the major role in urban land conversion.
Hanoi is the capital of Vietnam with population of about 2.5 millions. Recent development in the economy has obvious impacst on growth of Hanoi City. This change can be monitored using multitemporal remote sensing images. In this study, the authors use multitemporal remote sensing images from 1975 to 2001 to monitor the growth of Hanoi city areas. The remote sensing data set is composed of LANDSAT MSS, TM, SPOT and TERRA ASTER images. These images have been geo-referenced and resampled to 15 m resolution. Both visual interpretation and Maximum Likelihood classification methods have been applied. Finally, a map of urban growth of Hanoi was established. By combination of socio-economic and other local geographical information with results derived from remote sensing data analysis, some discussions on urban growth of Hanoi from 1975 to 2001 were presented. The study also aims to demonstrate the usefulness of mutitemporal remote sensing data usage for monitoring dynamic phenomena such as urban growth.
The Pearl River Delta in the People’s Republic of China is experiencing rapid rates of economic growth. Government directives in the late 1970s and early 1980s spurred economic development that has led to widespread land conversion. In this study, we monitor land-use through a nested hierarchy of land-cover. Change vectors of Tasseled Cap brightness, greenness and wetness of Landsat Thematic Mapper (TM) images are combined with the brightness, greenness, wetness values from the initial date of imagery to map four stable classes and five changes classes. Most of the land-use change is conversion from agricultural land to urban areas. Results indicate that urban areas have increased by more than 300% between 1988 and 1996. Field assessments confirm a high overall accuracy of the land-use change map (93.5%) and support the use of change vectors and multidate Landsat TM imagery to monitor land-use change. Results confirm the importance of field-based accuracy assessment to identify problems in a land-use map and to improve area estimates for each class.
Time series data from high resolution satellite imagery provide researchers with an opportunity to develop sophisticated statistical models of land-cover change. As inputs to statistical models, land-cover change data that are generated from satellite imagery must be both accurate and unbiased. This paper describes a new change detection method to determine the date of land-cover change in a sequential series of Landsat TM images of the Pearl River Delta, China. The method is a three-step change detection procedure that uses time series and panel econometric techniques. In the first step, regression equations are estimated for each of the six DN bands for each of seven stable land-cover classes. In the second step, the regression equations for each class are used to calculate DN values for change land-cover classes for each of the eight possible dates of change (1989-1996). In the third step, the date of land-cover change is identified by comparing a pixel’s DN values against the eight possible dates of change using tests for predictive accuracy. The accuracy and bias of the dates of change identified by the econometric technique compare favorably to a more conventional change detection technique. Furthermore, the econometric technique may reduce efforts required to assemble the training data and to correct the images for atmospheric effects. Together, these results indicate that is possible to generate land-use change estimates from a time series of satellite images that can be used in conjunction with socioeconomic data to estimate statistical models of land-use change.
The electromagnetic radiation (EMR) signals collected by satellites in the solar spectrum are modified by scattering and absorption by gases and aerosols while traveling through the atmosphere from the Earth’s surface to the sensor. When and how to correct the atmospheric effects depend on the remote sensing and atmospheric data available, the information desired, and the analytical methods used to extract the information. In many applications involving classification and change detection, atmospheric correction is unnecessary as long as the training data and the data to be classified are in the same relative scale. In other circumstances, corrections are mandatory to put multitemporal data on the same radiometric scale in order to monitor terrestrial surfaces over time. A multitemporal dataset consisting of seven Landsat 5 Thematic Mapper (TM) images from 1988 to 1996 of the Pearl River Delta, Guangdong Province, China was used to compare seven absolute and one relative atmospheric correction algorithms with uncorrected raw data. Based on classification and change detection results, all corrections improved the data analysis. The best overall results are achieved using a new method which adds the effect of Rayleigh scattering to conventional dark object subtraction. Though this method may not lead to accurate surface reflectance, it best minimizes the difference in reflectances within a land cover class through time as measured with the Jeffries–Matusita distance. Contrary to expectations, the more complicated algorithms do not necessarily lead to improved performance of classification and change detection. Simple dark object subtraction, with or without the Rayleigh atmosphere correction, or relative atmospheric correction are recommended for classification and change detection applications.
We have compared the official estimates of agricultural land and rates of agricultural land conversion with those derived from Landsat thematic mapper satellite images for 10 counties in the Pearl River Delta, which is one of the fastest-developing regions in China. Ground- based field assessments verify the high accuracy of our techniques in estimating the area of agricultural land and its change through time. Our results indicate that there is significantly more agricultural land than reported in official statistics. Although this underreporting is well documented, particularly using coarse resolution (1-km) satellite data sets, our study is the first to use high-resolution satellite imagery to quantify this bias.