Detecting Urban Land-Use and Land-Cover Changes in Mississauga Using Landsat TM Images

Li, J.; Zhao, He

doi:10.3808/jei.200300016

Cited by 14 publications

(8 citation statements)

References 14 publications

(15 reference statements)

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“…Landsat images have been extensively utilized for UHI study (Buyantuyev, Wu, and Gries 2007;Kato and Yamaguchi 2005;Li and Zhao 2003;Lu and Weng 2005;Sobrino, Jimenez-Munoz, and Paolini 2004;Srivastava, Majumdar, and Bhattacharya 2009). Landsat 5 provides TM images that have six bands in the visible and near infrared regions with 30 m spatial resolution, and one thermal infrared band with 120 m spatial resolution.…”

Section: Study Area and Datamentioning

confidence: 99%

“…Dewan and Yamaguchi (2009) found that substantial growth of built-up areas in Greater Dhaka resulted in a significant decrease in the area of water bodies, cultivated land, vegetation, and wetlands. In addition, rapid urban expansion induced a wide range of environmental impacts, including habitat quality (Bayarsaikhan et al 2009;Dewan and Yamaguchi 2009;Li and Zhao 2003).…”

Section: Introductionmentioning

confidence: 99%

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Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Ding

Shi

2013

International Journal of Remote Sensing

136

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Rapid global economic development has resulted in a corresponding intensification of urbanization, which has in turn impacted the ecology of vast regions of the world. A series of problems have thus been introduced, such as changes in land-use/land-cover (LULC) and changes in local climate. The process of urbanization predominantly represents changes in land-use, and is deemed by researchers to be the chief cause of climate change and ecological change. One of the principal purposes of the research in this field is to find ways to mitigate the influence of land-use change on local or global environments. In the study presented in this article, satellite images were utilized to extract information regarding land-use in Beijing City, and to develop maps of land surface temperature (LST) during two different periods of time: 2 August 1999 and 8 August 2010. A supervised classification scheme, a support vector machine, was used to derive the land-use change map for the above periods. Maps of surface temperature are derived from the thermal band of Landsat images using the mono-window algorithm. Results from post-classification comparison indicated that an increase in impervious surface areas was found to be dramatic, while the area of farmland decreased rapidly. The changes in LULC were found to have led to a variation in surface temperature, as well as a spatial distribution pattern of the urban heat island phenomenon. This research revealed that the hotspots were mainly located in areas dominated by three kinds of material: bare soil, rooftops, and marble surfaces. Results from the local Moran's I index indicated that the use of lower surface temperature materials will help to mitigate the influence of the urban heat island phenomenon. The results of this research study provide a reference for government departments involved in the process of designing residential regions. Such a reference should enable the development of areas sympathetic to environmental changes and hence mitigate the effects of the growing intensity of urbanization.

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Section: Study Area and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Ding

Shi

2013

International Journal of Remote Sensing

136

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“…For a 17-year land cover change analysis, the City of Mississauga is divided into four different sections according to the administrative map and using existing roads as natural boundaries. The four sections are southeast region, middle part, international airport area and northwest section (Li and Zhao, 2004). In each section, evaluating points are randomly generated using the same method as in accuracy assessment procedure.…”

Section: V-i-s Pattern Recognitionmentioning

confidence: 99%

Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images

Zhao¹

2021

Preprint

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Urban environments belong to the most dynamic system on the earth's surface. Urban areas contain nearly half of the world's population. Understanding the growth and change brought on by urbanization is critical for urban planning, environmental studies, and resource management. This study is an attempt to present a satellite-based approach to modelling urban population growth from multitemporal and multispectral Landsat image data. The focus is placed on two aspects: detection of urban land cover changes and population prediction modeling associated with the urban expansion. The study consists of an experimental set-up to generate the land cover maps and to recognize the vegetation-impervious surface-soil (V-I-S) patterns followed by integrating population census data and remote sensing data at the city planning district level. This is done in conjunction with geographic information systems (GIS) in order to model population growth from 1996 to 2001 in the City of Mississauga, Ontario. The main findings of this research show that a total of 81.6 km² of built-up areas have been added with Mississauga's boundaries between 1985 and 2002. This accounts for 25.5% of the total area of Mississauga at the expense of non-built and water covered areas. The results show an increase of 6.5% in built-up areas in the last three years (1999-2002), which results in an average growth rate of 7 km²/year. The previous 14 years (1985-1999) have shown an increase of 19.0% in development, which equals 4.3 km²/year. The investigation also shows that a linear equation adequately describes the relationship between the population counts and the built-up area, or "C-442" area, of V-I-S components.

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“…Examining LULC change in the last few decades has become an increasing concern because of biodiversity decreasing, habitat changing, and altering the regional and global climate patterns and composition (J. Li & Zhao, 2003;Lilly Rose, 2009;Mishra & Rai, 2016). It can be challenging, complicated, and likely to yield contradictory results to detect and tests changes in LULC through direct field visits (Meyer & Turner, 1992;M.…”

Section: Introductionmentioning

confidence: 99%

Use of Cellular Automata-Based Artificial Neural Networks For Detection and Prediction of Land Use Changes in North Eastern Dhaka City

Rahman

2021

Preprint

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The purpose of this study was to examine the pattern of change in Land Use Land Cover (LULC) and Land Surface Temperature (LST) in Mirpur and its surrounding area (north-eastern part of Dhaka) over the last 30 years using Landsat Satellite images and remote sensing indices, and to develop relationships between LULC types and LST, as well as to analyze their impact on local warming. Using this analyzed data, a further projection of LULC and LST change over the next two decades was made. From 1989 to 2019, five-year intervals of Landsat 4-5 TM and Landsat 8 OLI pictures were utilized to track the relationship between LULC changes and LST. Cellular Automata-based Artificial Neural Network (CA-ANN) algorithm was used to model the LULC and LST maps for the year 2039. Two environmental indices were analyzed to determine their link with LST: the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI). The link between LST and LULC types indicates that built-up area raises LST by substituting non-evaporating surfaces for natural vegetation. The average surface temperature has been increasing steadily for the previous 30 years. For the year 2019, it was determined that roughly 86 percent of total land area has been converted to built-up area and that 89 percent of land area has an LST greater than 28°C. According to the study, if the current trend continues, 72 percent of the Mirpur area is predicted to see temperatures near 32°C in 2039. Additionally, LST had a significant positive association with NDBI and a negative correlation with NDVI. The overall accuracy of LULC was greater than 90%, with a Kappa coefficient of 0.83. The study may assist urban planners and environmental engineers in comprehending and recommending effective policy measures and plans to mitigate the consequences of LULC.

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Detecting Urban Land-Use and Land-Cover Changes in Mississauga Using Landsat TM Images

Cited by 14 publications

References 14 publications

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Land-use/land-cover change and its influence on surface temperature: a case study in Beijing City

Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images

Use of Cellular Automata-Based Artificial Neural Networks For Detection and Prediction of Land Use Changes in North Eastern Dhaka City

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