Detecting Urban Change Over Time

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 ART_a and overcomes the limitation of class category in ART_b. Results from experiments demonstrate the superiority of ART-MMAP in terms of estimating the fraction of land cover within a single pixel.

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.

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.