Detecting Urban Change Over Time

Research Summary

Our lab groups seek to understand the extent and historical patterns of urban growth at local, regional, and global scales.

Our work relies primarily on satellite remote sensing.  Images are processed using computer algorithms to yield information about land cover, surface temperature, and other indicators of the built environment.  Change detection analysis and geographic information science are used to uncover spatial and temporal patterns in urban growth. Case studies, especially in rapidly urbanizing areas of China and India, continue to improve our methodologies, allowing us to relate satellite imagery, land-use maps, and official datasets to ground-level sources of information such as stakeholder interviews, archival material, and in situ measurement and observation.

Recent Findings

Our work has revealed that nearly all urban areas are growing, having added 58,000 square kilometers in built-up land since 1970, an area equivalent to the size of Lake Michigan. While we find some global trends of rapid development near coastal and ecologically sensitive areas, urban areas also vary widely in their spatial patterns, rates, and types of growth (Seto et al., 2011).

Time-Lapse of Pearl River Delta Urbanization

Related Publications

Pandey B, Seto KC.  2014.  Urbanization and agricultural land loss in India: Comparing satellite estimates with census data. Journal of Environmental Management.

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.

 
Zhang Q, Seto KC.  2013.  Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures Remote Sensing. 5(7):3476-3494.

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.

Remote Sensing 5(7): 3476-3494
Pandey B, Joshi P.K., Seto KC.  2013.  Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation. 23:49-61.

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.

Zhang Q, Schaaf C, Seto KC.  2013.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sensing of Environment. 129:32-41.

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.

Frolking S, Milliman T, Seto KC, Friedl MA.  2013.  A global fingerprint of macro-scale changes in urban structure from 1999 to 2009. Environmental Research Letters. 8(2):024004.

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.

Zhang Q.  2012.  A Gap-filled MODIS BRDF Database to Improve Surface Characterization.

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.

Seto KC, Fragkias M, Güneralp B, Reilly MK.  2011.  A meta-analysis of global urban land expansion. PLOS ONE. 6:e23777.

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.

PLOS ONE 6: e23777
Zhang Q, Seto KC.  2011.  Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of the Environment. 115(9):2320-2329.

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.

Seto KC, Sánchez-Rodríguez R, Fragkias M.  2010.  The new geography of contemporary urbanization and the environment. Annual Review of Environment and Resources. 35:167-194.

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.

Boucher A, Seto KC, Gamba P, Harold M.  2009.  Methods and Challenges for Using High-Temporal Resolution Data for Monitoring Urban Growth. Global Mapping of Human Settlement: Experiences, Datasets, and Prospects. :339-350.
In lieu of an abstract, the following is an excerpt from the chapter’s introduction.
 
Earth-observing satellites have collected remote sensing data for more than 30 years, yet most urban mapping studies do not take full advantage of the historical record and the temporal frequency of the observations available. That information is ever more important as remote sensing images are increasingly being used with other types of data such as demographics, economics, and policy to understand the link between human activity and impacts on the landscape. Linking social processes with spatial patterns observed in remote sensing has been the subject of numerous studies. Yet, it is almost without exception that the spatial patterns in these studies are observed in only two or three periods. The underlying assumption is that the relationship between landscape dynamics and social processes can be understood with several observations in time. Although this may hold true for relatively slow land-use and land-cover changes, the assumption is not valid for rapidly urbanizing landscapes.
 
Fragkias M, Seto KC.  2009.  Evolving rank-size distributions of intra-metropolitan urban clusters in South China. Computers, Environment and Urban Systems. 33(3):189-199.

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.

Reilly MK, O'Mara MP, Seto KC.  2009.  From Bangalore to the Bay Area: Comparing transportation and activity accessibility as drivers of urban growth. Landscape & Urban Planning. 92(1):24-33.

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.

Seto KC, Fragkias M, Schnieder A.  2007.  20 Years After Reforms: Challenges to Planning and Development in China’s City-Regions and Opportunities for Remote Sensing. Applied Remote Sensing for Urban Planning, Governance and Sustainability. :249-269.

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.

Boucher A, Seto KC, Journél AG, Weng Q, Quattrochi DA.  2006.  A novel method for mapping land cover changes: Incorporating time and space with geostatistics. IEEE Transactions on Geoscience and Remote Sensing. 44(11):3427.

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.

Seto KC, Fragkias M.  2005.  Quantifying spatiotemporal patterns of urban land-use change in four cities of China with time series landscape metrics. Landscape Ecology. 20(7):871-888.

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.

Landscape Ecology 20(7): 871-888
Seto KC, Entwisle B, Stern PC.  2005.  Economies, societies, and landscapes in transition: Examples from the Pearl River Delta, China and the Red River Delta, Vietnam. Population, Land Use, and Environment: Research Directions. :193-216.

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.

Schnieder A, Seto KC, Webster DR.  2005.  Urban growth in Chengdu, western China: Application of remote sensing to assess planning and policy outcomes. Environment and Planning B. 32(3):323-345.

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.

Kaufman RK, Seto KC.  2005.  Using logit models to classify land cover and land-cover change from Landsat Thematic Mapper. International Journal of Remote Sensing. 26(3):263-577.

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.

Liu W, Seto KC.  2004.  ART-MMAP: A neural network approach to sub-pixel classification. IEEE Transactions on Geoscience and Remote Sensing. 42(9):1976-1983.

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.

Seto KC, Liu W.  2003.  Comparing ARTMAP neural network with Maximum-Likelihood classifier for detecting urban change. Photogrammetric Engineering and Remote Sensing. 69(9):981-990.

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.

Duong NDinh, Thoa LKim, Hoan NThanh, Tuan TAnh, Le Thu H, Seto KC.  2003.  A study on the urban growth of Hanoi using multi-temporal and multi-sensor remote sensing data. Asian Journal of Geoinformatics. 3(3):69-72.

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.

Seto KC, Woodcock CE, Song C, Huang X, Lu J., Kaufman RK.  2002.  Monitoring land-use change in the Pearl River Delta using Landsat TM. International Journal of Remote Sensing. 23(10):1985-2004.

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.

Kaufman RK, Seto KC.  2001.  Change detection, accuracy, and bias in a sequential analysis of Landsat imagery in the Pearl River Delta, China: econometric techniques. Agriculture, Ecosystems & Environment. 85(1-3):95-105.

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.

Song C, Woodcock CE, Seto KC, Lenney MPax, Macomber SA.  2001.  Classification and change detection using Landsat TM data: When and how to correct atmospheric effects? Remote Sensing of the Environment. 75(2):230-244.

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.