Detection of Urban Expansion by Using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

2019 ◽  
pp. 1372-1382
Author(s):  
Cihan Uysal ◽  
Derya Maktav
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Dynamic Structure ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Spectral Mixture

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

Detection of Urban Expansion by using DMSP-OLS, Landsat Data and Linear Spectral Unmixing Method

2015 ◽  
Vol 4 (2) ◽  
pp. 58-67
Author(s):  
Cihan Uysal ◽  
Derya Maktav
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Dynamic Structure ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Linear Mixture Model ◽  
Linear Spectral Unmixing ◽  
Spectral Mixture

Urbanization has been increasingly continuing in Turkey and in the world for the last 30 years. Especially for the developing countries, urbanization is a necessary fact for the sustainability of the urban growth. Yet, this growth should be controlled and planned; otherwise, many environmental problems might occur. Therefore, the urban areas having dynamic structure should be monitored periodically. Monitoring the changes in urban environment can be provided with land cover land use (LCLU) maps produced by the pixel based classification methods using ‘maximum likelihood' and ‘isodata' techniques. However, these thematic maps might bring about inaccurate classification results in heterogeneous areas especially where low spatial resolution satellite data is used since, in these approaches, each pixel is represented with only one class value. In this study, considering the spectral mixture analysis (SMA) each pixel is represented by endmember fractions. The earth is represented more accurately using 'substrate (S)', ‘green vegetation (V)' and ‘dark surfaces (D)' spectral endmember reflectances with this analysis based on linear mixture model. Here, the surrounding of Izmit Gulf, one of the most industrialized areas of Turkey, has been chosen as the study area. SMA has been applied to LANDSAT images of the years of 1984, 1999 and 2009. In addition, DMSP-OLS data of 1992, 1999 and 2009 has been used to detect urban areas. According to the results, the changes in LCLU and especially the urban growth areas have been detected accurately using the SMA method.

scholarly journals Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

2020 ◽  
Vol 12 (22) ◽  
pp. 3826 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Driving Forces ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Arid Grasslands ◽  
Spectral Mixture ◽  
The Impact ◽  
Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

scholarly journals Land Use Dynamics and Variations in Sprawl across Municipalities in the Bamenda Urban-scape from 1996-2018

2020 ◽  
Vol 13 (4) ◽  
pp. 224
Author(s):  
Fombe Lawrence F. ◽  
Acha Mildred E.
Keyword(s):  
Land Use ◽  
Urban Growth ◽  
Urban Areas ◽  
Agricultural Land ◽  
Pearson Correlation ◽  
Town Planning ◽  
Land Values ◽  
Landsat Images ◽  
Land Use Dynamics ◽  
Spatiotemporal Analyses

Worldwide urban areas are having increasing influence over the surrounding landscape. Peri-urban regions of the world are facing challenges which results from sprawl with increasing problems of social segregation, wasted land and greater distance to work. This study seeks to examine the trends in land use dynamics, urban sprawl and associated development implications in the Bamenda Municipalities from 1996 to 2018. The study made use of the survey, historical and correlational research designs. The purposive and snowball techniques were used to collect data. Spatiotemporal analyses were carried out on Landsat Images for 1996, 2008, and 2018 obtained from Earth Explorer, Erdas Image 2014 and changes detected from the maps digitized. The SPSS version 21 and MS Excel 2016 were used to analyze quantitative and qualitative data. The former employed the Pearson correlation analysis. Analysis of land use/land cover change detection reveals that built-up area has increased significantly from 1996 to 2018 at the detriment of forest, wetland and agricultural land at different rates within each municipality. These changes have led to invasion of risk zones, high land values, uncoordinated, uncontrolled and unplanned urban growth. The study suggests that proactive planning, use of GIS to monitor land use activities, effective implementation of existing town planning norms and building regulations, are invaluable strategies to sustainably manage urban growth in Bamenda.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Spatiotemporal Pattern Analysis of Rapid Urban Expansion Using GIS and Remote Sensing

2010 ◽  
Vol 1 (2) ◽  
pp. 55-70 ◽  
Author(s):  
Hyun Joong Kim
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Growth ◽  
Urban Areas ◽  
Temporal Dynamics ◽  
Urban Expansion ◽  
Local Scale ◽  
Spatiotemporal Pattern ◽  
Land Use Land Cover ◽  
Modeling Studies

Rapidly growing urban areas tend to reveal distinctive spatial and temporal variations of land use/land cover in a locally urbanized environment. In this article, the author analyzes urban growth phenomena at a local scale by employing Geographic Information Systems, remotely sensed image data from 1984, 1994, and 2004, and landscape shape index. Since spatial patterns of land use/land cover changes in small urban areas are not fully examined by the current GIS-based modeling studies or simulation applications, the major objective of this research is to identify and examine the spatial and temporal dynamics of land use changes of urban growth at a local scale. Analytical results demonstrate that sizes, locations, and shapes of new developments are spatio-temporally associated with their landscape variations and major transportation arteries. The key findings from this study contribute to GIS-based urban growth modeling studies and urban planning practices for local communities.

Quantifying Urban Expansion Using Landsat Images and Landscape Metrics: A Case Study of the Halton Region, Ontario

GEOMATICA ◽  
2020 ◽  
Author(s):  
Liyuan Qing ◽  
Hasti A. Petrosian ◽  
Sarah N. Fatholahi ◽  
Michael A. Chapman ◽  
Jonathan Li
Keyword(s):  
Landscape Metrics ◽  
Urban Areas ◽  
Census Data ◽  
Urban Expansion ◽  
Landsat Images ◽  
Factors Affecting ◽  
Supervised Learning Algorithms ◽  
Dramatic Rise ◽  
Multi Temporal ◽  
Gis Software

Urbanization is considered as one of the main factors affecting global change. The Halton Region as part of the Great Toronto Area (GTA), is regarded as one of the fastest growing regions in Canada, generating 20% of national GDP. It is also one of the most desirable places for living and thriving business. This research attempts to assess the urban expansion in the Halton Region, Ontario, Canada from 1989 to 2019 using satellite images, analysis approaches and landscape metrics. Multi-temporal Landsat images, and the supervised learning algorithms in GIS software were used to explore the dynamic changes, and to classify the urban and non-urban areas. The temporal urban expansion in the Halton Region experienced a dramatic rise, and mainly occurred from the centre of the area. The analysis of landscape metrics based on different methods, including Land Use in Central Indiana (LUCI) model, Vegetation-Impervious Surface-soil (V-I-S) model, and the census data of Canada was carried out to understand the transition mode of the urbanization in the Halton Region. Also, the population growth in the centre of the Halton Region was considered as one of driven forces affecting urban expansion. The results showed that most of the landscape metrics rose between 1989 and 2019, indicating leapfrog pattern of urbanization occurred over the entire period. The contribution of this research is to evaluate the urbanization in the Halton Region, and give the city managers a clear mind to make appropriate decisions in further urban planning.

scholarly journals Using building types and demographic data to improve our understanding and use of urban sprawl simulation

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Mojtaba Eslahi ◽  
Rani El Meouche ◽  
Anne Ruas
Keyword(s):  
Cellular Automata ◽  
Urban Growth ◽  
Urban Areas ◽  
Geographical Information Systems ◽  
Dynamic Models ◽  
Urban Expansion ◽  
Demographic Data ◽  
Population Data ◽  
Geographical Information ◽  
Sleuth Model

<p><strong>Abstract.</strong> Many studies, using various modeling approaches and simulation tools have been made in the field of urban growth. A multitude of models, with common or specific features, has been developed to reconstruct the spatial occupation and changes in land use. However, today most of urban growth techniques just use the historical geographic data such as urban, road and excluded maps to simulate the prospective urban maps. In this paper, adding buildings and population data as urban fabric factors, we define different urban growth simulation scenarios. Each simulation corresponds to policies that are more or less restrictive of space considering what these territories can accommodate as a type of building and as a global population.</p><p>Among the urban growth modeling techniques, dynamic models, those based on Cellular Automata (CA) are the most common for their applications in urban areas. CA can be integrated with Geographical Information Systems (GIS) to have a high spatial resolution model with computational efficiency. The SLEUTH model is one of the cellular automata models, which match the dynamic simulation of urban expansion and could be adapted to morphological model of the urban configuration and fabric.</p><p>Using the SLEUTH model, this paper provides different simulations that correspond to different land priorities and constraints. We used common data (such as topographic, buildings and demography data) to improve the realism of each simulation and their adequacy with the real world. The findings allow having different images of the city of tomorrow to choose and reflect on urban policies.</p>

scholarly journals THE STUDY OF HEAT ISLAND AND ITS RELATION WITH URBANIZATION IN GURUGRAM, DELHI NCR FOR THE PERIOD OF 1990 TO 2018

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 49-56
Author(s):  
S. Kushwaha ◽  
Y. Nithiyanandam
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Urban Expansion ◽  
Large City ◽  
Capital City ◽  
Spatial Decision Support System ◽  
Heat Island ◽  
Landsat Images ◽  
Infrared Band ◽  
Major Satellite

Abstract. Rapid growth in population and land cover makes urban areas more vulnerable to Urban Heat Island. Due to which, cities experience higher mean temperature than its proximate surrounding rural or non-urban area. The relationship between UHI and urbanization is proven in previous studies. Delhi the capital city of India is well known for its extreme heat condition in summer and air pollution. In this study, an attempt has been made to understand UHI behavior in a satellite town of Delhi. Satellite town or cities are the small independent towns built in the vicinity of a large city or metropolitan city. In this paper 4 major satellite towns of Delhi, i.e. Gurugram (name changed from Gurgaon in April 2016), Noida, Faridabad and Ghaziabad has been studied to understand the changing trends in urbanization and temperature. The parameters used are rate of urban expansion, population density, GDP growth and increasing temperature over the last two decades. Gurugram showed the maximum urbanization and identified as study area. Gurugram has undergone a major growth journey from being a small town to ‘The Millennium city’ of the country in a short span. The Landsat images of past three decades ranging from different time period i.e. 1990, 1996, 2002, 2009, 2014 and 2018 were investigated by applying integrated approach of GIS and Remote sensing. The images represent the condition of UHI and urbanization in different period. The temporal change in LULC was used to study the rate of urban growth in last three decades. The results showed the increase in built-up area out of the total area of Gurugram from 10% (i.e.50.6 sq. km) in 1990 to 17.25% (80.5 sq. km) in 2002 which further increased to 45.1% (210.4 sq. km) in 2018. Thermal Infrared band of Landsat series were used to retrieve land surface temperature (LST) intensity of the study period. The results show a positive correlation (r = 0.46) between impervious surfaces and LST. The results of the study could be helpful in identifying the causative factors and level of impacts in different zones and also enable us to develop a mitigation strategy based on spatial decision support system.

scholarly journals MODELLING URBAN GROWTH FOR BANGKOK AND ASSESSING LINKAGES WITH ROAD DENSITY AND SOCIO-ECONOMIC INDICATORS

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 255-262
Author(s):  
S. A. Kamarajugedda ◽  
E. Y. M. Lo
Keyword(s):  
Urban Growth ◽  
Urban Areas ◽  
Turning Point ◽  
Urban Expansion ◽  
Growth Patterns ◽  
Economic Indicators ◽  
Sustainable Urban Development ◽  
City Development ◽  
Road Density ◽  
The Future

Abstract. The fastest urbanization is occurring in the Global South which includes many developing nations in Asia. However, a rapid and unplanned urban growth could threaten the sustainability of the process. A key step towards a sustainable urban development is to better understand interdependencies amongst urban growth patterns, infrastructure and socio-economic indicators. Here we chose Bangkok, Thailand as a megacity case study to assess the spatio-temporal urban growth dynamics and specifically its dependency with road density at intra-city scales. The SLEUTH urban growth model is further applied for predicting future expansion over the next decade and to assess the future intra-city expansion. Urban expansion patterns for Bangkok were generated for 1987 and 2017 using Landsat derived urban land-cover maps. Open Street Map (OSM) is used to generate a 2017 road density map. The urban expansion (1987–2017) was observed to follow a radially outward expanding pattern inland, with the logarithmic urban expansion rate having an inverted concave trend with road density. The rising/falling limbs then indicated an increase/decrease of urban expansion for which a road density “turning point” is readily identified and further used to develop a road density-based zoning map that highlights the different intra-city urban expansion rates. The SLEUTH predicted urban growth till year 2027 which also showed expansion outward from existing urban areas. The future expansion trend is also consistent with the turning point trend. This study showed that such spatial-temporal analysis of urban expansion coupled with SLEUTH can be useful for investigating likely outcomes of city development plans.

Quantifying Urban Sprawl with Spatial Autocorrelation Techniques using Multi-Temporal Satellite Data

2014 ◽  
Vol 5 (2) ◽  
pp. 19-37 ◽  
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Remote Sensing Data ◽  
Small Towns ◽  
Temporal Data ◽  
Landsat Images ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

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