scholarly journals CONTINUOUS CHANGE DETECTION OF URBAN LAKES IN WUHAN, CHINA USING MULTI-TEMPORAL REMOTE SENSING IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 2315-2320
Author(s):  
W. Zhang ◽  
X. Kong ◽  
G. Tan ◽  
S. Zheng
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Central Area ◽  
Strong Impact ◽  
Continuous Change ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
Urban Lakes ◽  
Multi Temporal ◽  
Lake Change

Urban lakes are important natural, scenic and pattern attractions of the city, and they are potential development resources as well. However, lots of urban lakes in China have been shrunk significantly or disappeared due to rapid urbanization. In this study, four Landsat images were used to perform a case study for lake change detection in downtown Wuhan, China, which were acquired on 1991, 2002, 2011 and 2017, respectively. Modified NDWI (MNDWI) was adopted to extract water bodies of urban areas from all these images, and OTSU was used to optimize the threshold selection. Furthermore, the variation of lake shrinkage was analysed in detail according to SVM classification and post-classification comparison, and the coverage of urban lakes in central area of Wuhan has decreased by 47.37&amp;thinsp;km<sup>2</sup> between 1991 and 2017. The experimental results revealed that there were significant changes in the surface area of urban lakes over the 27 years, and it also indicated that rapid urbanization has a strong impact on the losses of urban water resources.

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.

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.

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.

scholarly journals Assessing the Effect of Land Use Change on Surface Runoff in a Rapidly Urbanized City: A Case Study of the Central Area of Beijing

Land ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 17 ◽  
Author(s):  
Shanshan Hu ◽  
Yunyun Fan ◽  
Tao Zhang
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Correlation Analysis ◽  
Surface Runoff ◽  
Flood Control ◽  
Central Area ◽  
Dominant Factor ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
The Impact

The change in land use during the process of urbanization affects surface runoff and increases flood risk in big cities. This study investigated the impact of land use change on surface runoff in Beijing’s central area during the period of rapid urbanization from 1984 to 2019. Land use maps of 1984, 1999, 2009, and 2019 were generated by image classification of Landsat images. Surface runoffs were calculated with the Soil Conservation Service curve number (SCS-CN) model. Correlation analysis was used to identify the dominant factor of land use change affecting surface runoff. The result showed that the variation trend of surface runoff was consistent with the trend of impervious land in Beijing’s central area, which increased during 1984~2009 and decreased during 2009~2019. Correlation analysis showed that changes in surface runoff were most strongly correlated with changes in impervious surfaces when compared with the correlation of runoff with other types of land use. The results of this study may provide a reference for city flood control and urban planning in fast growing cities worldwide, especially in developing countries.

scholarly journals Past and Future Trajectories of Farmland Loss Due to Rapid Urbanization Using Landsat Imagery and the Markov-CA Model: A Case Study of Delhi, India

2019 ◽  
Vol 11 (2) ◽  
pp. 180 ◽  
Author(s):  
Junmei Tang ◽  
Liping Di
Keyword(s):  
Transition Probabilities ◽  
Urban Expansion ◽  
Sustainable Urban Development ◽  
Landsat Images ◽  
Rapid Urbanization ◽  
The Past ◽  
Farmland Loss ◽  
Rate Of Increase ◽  
Multi Temporal ◽  
Ca Model

This study integrated multi-temporal Landsat images, the Markov-Cellular Automation (CA) model, and socioeconomic factors to analyze the historical and future farmland loss in the Delhi metropolitan area, one of the most rapidly urbanized areas in the world. Accordingly, the major objectives of this study were: (1) to classify the land use and land cover (LULC) map using multi-temporal Landsat images from 1994 to 2014; (2) to develop and calibrate the Markov-CA model based on the Markov transition probabilities of LULC classes, the CA diffusion factor, and other ancillary factors; and (3) to analyze and compare the past loss of farmland and predict the future loss of farmland in relation to rapid urban expansion from the year 1995 to 2030. The predicted results indicated the high accuracy of the Markov-CA model, with an overall accuracy of 0.75 and Kappa value of 0.59. The predicted results showed that urban expansion is likely to continue to the year of 2030, though the rate of increase will slow down from the year 2020. The area of farmland has decreased and will continue to decrease at a relatively stable rate. The Markov-CA model provided a better understanding of the past, current, and future trends of LULC change, with farmland loss being a typical change in this region. The predicted result will help planners to develop suitable government policies to guide sustainable urban development in Delhi, India.

scholarly journals CALIBRATED MULTI-TEMPORAL EDGE IMAGES FOR CITY INFRASTRUCTURE GROWTH ASSESSMENT AND PREDICTION

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 37-44 ◽  
Author(s):  
R. Al-Ruzouq ◽  
A. Shanableh ◽  
Z. Boharoon ◽  
M. Khalil
Keyword(s):  
Change Detection ◽  
Urban Growth ◽  
Traffic Congestion ◽  
Ground Truth ◽  
Living Environment ◽  
Infrastructure Development ◽  
Landsat Images ◽  
Accurate Identification ◽  
Gradual Process ◽  
Multi Temporal

<p><strong>Abstract.</strong> Urban Growth or urbanization can be defined as the gradual process of city’s population growth and infrastructure development. It is typically demonstrated by the expansion of a city’s infrastructure, mainly development of its roads and buildings. Uncontrolled urban Growth in cities has been responsible for several problems that include living environment, drinking water, noise and air pollution, waste management, traffic congestion and hydraulic processes. Accurate identification of urban growth is of great importance for urban planning and water/land management. Recent advances in satellite imagery, in terms of improved spatial and temporal resolutions, allows for efficient identification of change patterns and the prediction of built-up areas. In this study, two approaches were adapted to quantify and assess the pattern of urbanization, in Ajman City at UAE, during the last three decades. The first approach relies on image processing techniques and multi-temporal Landsat satellite images with ground resolution varying between 15 to 60&amp;thinsp;meters. In this approach, the derived edge images (roads and buildings) were used as the basis of change detection. The second approach relies on digitizing features from high-resolution images captured at different years. The latest approach was adopted, as a reference and ground truth, to calibrate extracted edges from Landsat images. It has been found that urbanized area almost increased by 12 folds during the period 1975&amp;ndash;2015 where the growth of buildings and roads were almost parallel until 2005 when the roads spatial expansion witnessed a steep increase due to the vertical expansion of the City. Extracted Edges features, were successfully used for change detection and quantification in term of buildings and roads.</p>

scholarly journals ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 489-492
Author(s):  
S. A. R. Hosseini ◽  
H. Gholami ◽  
Y. Esmaeilpoor
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Ground Truth ◽  
Landsat Images ◽  
Ground Truth Data ◽  
Sensing Technology ◽  
Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

scholarly journals Recent land subsidence caused by the rapid urban development in the Hanoi region (Vietnam) using ALOS InSAR data

2014 ◽  
Vol 14 (3) ◽  
pp. 657-674 ◽  
Author(s):  
V. K. Dang ◽  
C. Doubre ◽  
C. Weber ◽  
N. Gourmelen ◽  
F. Masson
Keyword(s):  
Spatial Distribution ◽  
Urban Development ◽  
Land Subsidence ◽  
Urban Areas ◽  
Surface Deformation ◽  
Seasonal Fluctuation ◽  
Urban Region ◽  
Rapid Urbanization ◽  
Groundwater Levels ◽  
Multi Temporal

Abstract. Since the 1990s the land subsidence due to the rapid urbanization has been considered a severely destructive hazard in the center of Hanoi City. Although previous studies and measurements have quantified the subsiding deformation in Hanoi center, no data exist for the newly established districts in the south and the west, where construction development has been most significant and where groundwater pumping has been very intensive over the last decade. With a multi-temporal InSAR approach, we quantify the spatial distribution of the land subsidence in the entire Hanoi urban region using ALOS images over the 2007–2011 period. The map of the mean subsidence velocity reveals that the northern bank of the Red River appears stable, whereas some areas in southern bank are subsiding with a mean vertical rate up to 68.0 mm yr−1, especially within the three new urban districts of Hoang Mai, Ha Dong – Thanh Xuan and Hoai Duc – Tu Liem. We interpret the spatial distribution of the surface deformation as the combination of the nature of the unsaturated layer, the lowering of groundwater in the aquifers due to pumping withdrawal capacity, the increase of built-up surfaces and the type of building foundation. The piezometric level in Qp aquifer lowers particularly after 2008, whereas the groundwater level in Qh aquifer remains steady, even if it loses its seasonal fluctuation in urban areas and drawdowns in neighboring water production plants. The time evolution deduced from the InSAR time series is consistent with previous leveling data and shows that the lowering rate of the surface slightly decreases till 2008. The analysis of groundwater levels in instrumented wells shows a correlation between the behavior of groundwater with the urban development and the acceleration of groundwater withdrawal. Also, the time variations suggest that the deformation became non-stationary, with upward and downward transient displacements related to the charge and discharge of the aquifers.

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