scholarly journals Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan

2019 ◽  
Vol 11 (8) ◽  
pp. 2260 ◽  
Author(s):  
Hussam Al-Bilbisi
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Growth ◽  
Urban Areas ◽  
Urban Expansion ◽  
Capital City ◽  
Annual Rate ◽  
Average Annual Rate ◽  
Rate Of Increase ◽  
Landsat Satellite

Amman, the capital city of Jordan, faces urbanization challenges and lacks reliable data for urban planning. This study is aimed at assessing, monitoring, and mapping urban land cover using multitemporal Landsat satellite images. Four different land use/cover maps were produced; periods of over ten years between 1987 and 2017 (i.e., in 1987, 1997, 2007, and 2017) were used to evaluate and analyze urban expansion visually and quantitatively. Supervised classification technique followed by the post classification comparison change detection approach was used to analyze images. Over the past three decades, the urban area has increased rapidly in Amman. It increased by 90.78 km2, from 149.08 km2 in 1987 to 237.86 km2 in 2017, with an average annual rate of increase of 2.03%. Urban area increases were significantly higher in the first 10 years of the study period (i.e., from 1987 to 1997), during which the average annual rate of increase reached 3.33%, while it was 2.04% for the last two decades of the study period (i.e., from 1997 to 2017). Urban growth in Amman generally occurred along transport routes away from the core of Amman, and as a result, this growth led to the expansion of urban areas into other types of land use/cover classes, particularly vegetation areas. The spatial analysis of urban expansion and trends of urban growth in Amman could provide the required input data for the urban modeling of the city.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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.

scholarly journals Patterns of Historical and Future Urban Expansion in Nepal

2020 ◽  
Vol 12 (4) ◽  
pp. 628 ◽  
Author(s):  
Bhagawat Rimal ◽  
Sean Sloan ◽  
Hamidreza Keshtkar ◽  
Roshan Sharma ◽  
Sushila Rijal ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Use Planning ◽  
Environmental Sustainability ◽  
Urban Areas ◽  
Urban Expansion ◽  
Spatial Models ◽  
Agricultural Conversion ◽  
Historical Land Use ◽  
Landsat Satellite

Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal’s Tarai region—a populous area experiencing land-use change due to urbanization and other factors—we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989–2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal’s fertile Tarai region is of serious concern for food-insecure countries like Nepal.

scholarly journals Quantifying the Speed, Landscape Pattern Changes and Its Driving Factors of Shenzhen China

2021 ◽  
Vol 308 ◽  
pp. 02004
Author(s):  
Qinxue He ◽  
Yuhong Chen ◽  
Yunlei Su
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Growth ◽  
Urban Expansion ◽  
Urban Land ◽  
Driving Factors ◽  
Cultivated Land ◽  
Land Use Types ◽  
University Enrolment ◽  
Main Factors

Urban expansion has always been a topic of great concern. The purpose of this study is to explore land use change and the types of urban expansion in Shenzhen from 1995 to 2015, and to indicate the driving factors of this change, so as to provide a paradigm for other similar studies. By analysing the landscape expansion index and the correlation coefficient between urban area and various factors in Shenzhen, the following conclusions are obtained: 1) The main changes of land use types are the decrease of cultivated land and the increase of urban land. The land cover type changed most dramatically from 2000 to 2005, and the urban land transformed from cultivated land and grassland occupied most of the area. 2) Analysis shows that during the 20 years from 1995 to 2015, the main expansion type is edge-expansion. In detail, during the period from 1995 to 2010, the proportion of infilling has been increasing, while that of the outlying has been decreasing. After 2010, the urban area of Shenzhen increased slightly. Besides, according to the landscape expansion index, Shenzhen experienced dramatic urban expansion from 2000 to 2005. 3) Education and population growth are the main factors of urban growth in Shenzhen, which is reflected in the strongest correlation between university enrolment rate and urban area.

scholarly journals SPATIOTEMPORAL LAND USE CHANGE ANALYSIS AND FUTURE URBAN GROWTH SIMULATION USING REMOTE SENSING: A CASE STUDY OF ANTALYA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 657-662
Author(s):  
N. Aslan ◽  
D. Koc-San
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Urban Growth ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Urban Expansion ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Land Use Pattern ◽  
Use Pattern

Abstract. The objectives of this study are: to create land-use maps by 5-year interval from 1995 to 2015, to analyse the land use change and urban development, and to estimate future land-use pattern and urban growth for the years: 2030, 2045 and 2060. Antalya, which is the 5th biggest city of Turkey, was selected as study area. In this study, there are basically three stages: (i) preprocessing and preparing additional bands, (ii) spatiotemporal land use detection using image classification and (iii) land use simulation using urban growth models. Firstly, atmospheric correction was applied to the Landsat 5 TM and Landsat 8 OLI images and land-cover indices, ASTER Global Digital Elevation Model (GDEM), and Nighttime data were prepared to use them as additional bands during the classification process. Secondly, Landsat images were classified using Random Forest (RF) machine-learning algorithm. Thirdly, urban simulations were performed for the years 2005, 2010, and 2015 and land-use pattern and urban growth was estimated for the years 2030, 2045 and 2060. The RF classification accuracies range from 84.44% to 92.82%. The urban areas increased from 49.56 km2 to 96.25 km2 from 1995 to 2015. The simulation accuracies were computed above 80%. According to the 2030, 2045 and 2060 simulation results, the urban areas were computed as 133.61 km2, 148.27 km2 and 156.85 km2, respectively. As a result, it was seen that the urban area of Antalya has almost doubled between the years 1995–2015 and the urban expansion is expected to continue increasing up to 1960.

scholarly journals Spatial change detections and inventory of wetlands in Yenagoa Urban Area: Bayelsa State, Nigeria

2021 ◽  
Vol 6 (6) ◽  
pp. 230-240
Author(s):  
Eze Promise I ◽  
Elemuwa IC ◽  
Lawrence Hart
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Urban Area ◽  
Urban Expansion ◽  
Rate Of Change ◽  
Percentage Change ◽  
Annual Rate ◽  
Land Use Land Cover ◽  
Total Percentage

Yenegoa Town has in recent years witnessed rapid City growth and Urban development and much of these developments are unplanned and unregulated. This has seriously impacted on wetlands in several locations of the town as persistent Wetlands reclamations are being witnessed in study area. This prompted the need for the study which is aimed to map wetlands location in Yenagoa’s urban area using GIS and Remote Sensing approach. The study analyzes land use/land cover changes (LULC) using LANDSAT(5) TM, LANDSAT(5) ETM and LANDSAT(7) OLI satellite imageries of 1990, 2000, 2010 and 2020 respectively. Through this study, the pattern of urban expansion for Thirty years were been studied. The satellite imageries covering the area were acquired and analyzed using ArcGIS 10.1 and ENVI 5.0 software. The supervised image classification method was adopted and the classification results were validated using the Kappa Index of Agreement (KIA) yielding an accuracy of 0.69m for year 1990, 0.62m for year 2000, 0.58m for year 2010 and 0.73m for 2020. A total area of 13,741.4 hectares was delineated in the study area which is identified as Yenagoa’s urban area. After processing the imageries, four land use/land cover (LULC) classes where considered, and the results shows that Built-up area continuously increased in land area from 1990 -2020 with total percentage change of 273.31% (4,178.7ha) and total annual rate of change of 25.33. Vegetation have total percentage change of 38.55% (974.34Ha) and total annual rate of change of 3.85, wetland cover loss with total percentage Change of 61.96% (-51,44.99ha) and total annual rate of change of -6.19ha, and the water body have loss of total percentage of -2.16% (-8.05Ha) and total annual rate of change of -0.22ha wetland at Yenegwe loss by Total %change of -29.918% ( -197.95ha), and wetland at Igbogene loss by total percentage change of -36.028% (-358.7ha). The research findings also revealed that the wetlands in Anyama, Swali, Kpansia and Opolo Towns were completely lost from the third Epoch of 2010, this may be as a result of persistence reclamation of wetland in this parts of the study area. The Markov Chain predicted model were utilized for predicting the likely changes in land use land cover for a period of thirty years. The predicted results also indicates that wetland size of 32.47,%, 30.68% and 28.99% may likely be lost by the year 2030, 2040 and 2050 respectively in study area if no action is taking by concerned authorities to forestall the factors responsible for the lost in wetland. The study justified the dynamics of remote sensing and GIS techniques in modeling wetlands changees over these periods, wise use of wetland resources and improvement of institutional arrangement were recommended so that wetland policies can be fully integrated into the planning process across all disciplines.

scholarly journals Análise da taxa de impermeabilização e tempo de concentração nas sub-bacias da área urbana de Barra do Garças – MT, Pontal do Araguaia – MT e Aragarças – GO

2015 ◽  
Vol 10 (1) ◽  
pp. 27
Author(s):  
G. B. M. Rezende ◽  
S. M. S. Araujo
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Urban Area ◽  
Urban Areas ◽  
Urban Expansion ◽  
Urban Drainage ◽  
Time Of Concentration ◽  
Relationship Of ◽  
The Relationship ◽  
Alternative Solutions

<p>A presente pesquisa objetivou verificar as taxas de impermeabilização e tempo de concentração das sub-bacias presentes na área urbana de Barra do Garças – MT, Pontal do Araguaia – MT e Aragarças – GO. Tais variáveis podem auxiliar no ordenamento territorial da expansão urbana, bem como no planejamento urbano dessas cidades. Os resultados demonstraram que nas áreas já urbanizadas, o grau de impermeabilização e tempo de concentração das sub-bacias apresentaram  níveis considerados “médios e altos”, o que é preocupante, devido a relação dessas variáreis ao crescimento das vazões e volume escoado, e, consequentemente, aumento da frequência de inundações. Medidas não-estruturais, como legislação de uso do solo, com regras e incentivo para aumento de áreas permeáveis em lotes, bem como implantação de soluções alternativas de drenagem urbana que promovam o retardamento das águas pluviais, são soluções que podem ser implementadas na área em estudo.</p><p align="center"><strong><em>Analysis of the waterproofing rate and time of concentration in urban sub-basins of Barra do Garças – MT, Pontal do Araguaia – MT e Aragarças – GO</em></strong></p><p><strong>Abstract</strong><strong>: </strong>This study aimed to verify the waterproofing rates and time of concentration of these sub-basins present in the urban area of Barra do Garças – MT, Pontal do Araguaia – MT e Aragarças – GO. Such variables can assist in land use of urban expansion and the urban planning of these cities. The results demonstrated that in urban areas already, the degree of waterproofing and time of concentration of the sub-basins presented levels considered "medium and high", which is worrying, because the relationship of these variables to the growth of flows and runoff, and, consequently, increased frequency of floods. Non-structural measures such as land use legislation, with rules and incentive to increase permeable areas on lots, and implementation of alternative solutions to urban drainage that promote the slowing of rainwater, are solutions that can be implemented in the study area.</p>

scholarly journals URBAN FOOTPRINT OF MUMBAI - THE COMMERCIAL CAPITAL OF INDIA

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
T. V. RAMACHANDRA ◽  
H. A. BHARATH ◽  
M. V. SOWMYASHREE
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Areas ◽  
Urban Expansion ◽  
Geographical Area ◽  
Remote Sensing Data ◽  
City Centre ◽  
Natural Ecosystems ◽  
The City ◽  
Urban Footprint

Urban footprint refers to the proportion of paved surface (built up, roads, etc.) with the reduction of other land use types in a region. Rapid increase in the urban areas is the major driver in landscape dynamics with the significant erosion in the quality and quantity of the natural ecosystems. The urban expansion process hence needs to be monitored, quantified and understood for effective planning and the sustainable management of natural resources. Cities and towns have been experiencing considerable growth in urban area, population size, social aspects, negative environmental and geographical in?uence, and complexity. Mumbai, the commercial capital of India, has experienced a spurt in infrastructural and industrial activities with globalization and opening up of Indian markets. Unplanned urbanization has resulted in dispersed growth inperi-urban pockets due to socio-economic aspects with the burgeoning population of the city. Consequent to this, there has been an uneven growth pattern apart from the increase in slums in and around the city. This has necessitated the understanding of the urbanization pattern and process focusing especially on the expanding geographical area, its geometry and the spatial pattern of its development. This communication discusses the urban footprint dynamics of Mumbai using multi-temporal remote sensing data with spatial metrics. Land use analysis indicated a decrease of vegetation by 20% with an increase in urban extent by 155% during the last three decades. Landscape metrics aided in assessing the spatial structure and composition of the urban footprints through the zonal analysis by dividing the region into four zones with concentric circles of 1 km incrementing radius from the city centre. The study reveals a significant variation in the composition of the urban patch dynamics with increasing complexity and aggregation of urban area at the centre and sprawl at the outskirts. Shannon’s entropy further confirms of sprawl with time. Further zoning with the circular gradients aided in understanding the transition process of land use categories into urban patch.

scholarly journals REMOTE SENSING AND MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE ESTIMATION OF URBAN EXPANSION (CASE OF DARKHAN CITY, MONGOLIA)

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 721-726
Author(s):  
P. Myagmartseren ◽  
D. Ganpurev ◽  
I. Myagmarjav ◽  
G. Byambakhuu ◽  
G. Dabuxile
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Regression Model ◽  
Urban Area ◽  
Urban Growth ◽  
Logistic Regression Model ◽  
Urban Expansion ◽  
Landsat Tm ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

Abstract. Urban expansion and land use and land cover change (LUCC) studies are a key knowledge of efficient local governance and urban planning and a lot contributing to the future sustainable development of the city. The main goal of the paper is to model a future urban spatial expansion by 2029 and 2039 of Darkhan city using Landsat TM satellite imagery (land use and cover change map of 1999, 2009, and 2019) and multivariate logistic regression model. Clark Lab’s (Clark University) IDRISI &amp; TerrSet software applied for the urban expansion prediction and the correlation between expansion and driving factors. On account of multivariate logistics regression modelling, eight physical factors influencing urban expansion identified to predict urban expansion based on USGS Landsat TM imageries (Landsat Multispectral Scanner with 60 m resolution). The regression statistic accounted for the probability of future urban expansion was positive. Overall, the LUCC has estimated the transition of natural cover to the impervious surface in Darkhan city. Our result estimates an increase in the built-up area and slum area during the period 1999–2009 and 2009–2019, represents LUCC was characterized by an external transformation from natural to urban area. According to the future urban growth prediction, the urban area would be significantly spread into the open space and natural vegetation area. The main findings stated here are that Darkhan city is expanding in an unsystematic way, even though the urban growth has not been analysed in detail and has a bad case of urban unregulated sprawl.

scholarly journals URBAN FOOTPRINT OF MUMBAI - THE COMMERCIAL CAPITAL OF INDIA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
T. V. RAMACHANDRA ◽  
H. A. BHARATH ◽  
M. V. SOWMYASHREE
Keyword(s):  
Land Use ◽  
Urban Area ◽  
Urban Areas ◽  
Urban Expansion ◽  
Geographical Area ◽  
Remote Sensing Data ◽  
City Centre ◽  
Natural Ecosystems ◽  
The City ◽  
Urban Footprint

Urban footprint refers to the proportion of paved surface (built up, roads, etc.) with the reduction of other land use types in a region. Rapid increase in the urban areas is the major driver in landscape dynamics with the significant erosion in the quality and quantity of the natural ecosystems. The urban expansion process hence needs to be monitored, quantified and understood for effective planning and the sustainable management of natural resources. Cities and towns have been experiencing considerable growth in urban area, population size, social aspects, negative environmental and geographical in?uence, and complexity. Mumbai, the commercial capital of India, has experienced a spurt in infrastructural and industrial activities with globalization and opening up of Indian markets. Unplanned urbanization has resulted in dispersed growth inperi-urban pockets due to socio-economic aspects with the burgeoning population of the city. Consequent to this, there has been an uneven growth pattern apart from the increase in slums in and around the city. This has necessitated the understanding of the urbanization pattern and process focusing especially on the expanding geographical area, its geometry and the spatial pattern of its development. This communication discusses the urban footprint dynamics of Mumbai using multi-temporal remote sensing data with spatial metrics. Land use analysis indicated a decrease of vegetation by 20% with an increase in urban extent by 155% during the last three decades. Landscape metrics aided in assessing the spatial structure and composition of the urban footprints through the zonal analysis by dividing the region into four zones with concentric circles of 1 km incrementing radius from the city centre. The study reveals a significant variation in the composition of the urban patch dynamics with increasing complexity and aggregation of urban area at the centre and sprawl at the outskirts. Shannon’s entropy further confirms of sprawl with time. Further zoning with the circular gradients aided in understanding the transition process of land use categories into urban patch.

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