According to UN estimates, approximately nearly half of the world's population now lives in cities and that figure is expected to rise to almost 70% by 2050. Cities now account for around 70% of worldwide greenhouse gas emissions, and this percentage is predicted to rise in the near future as a result of projected increases in global urbanization patterns. It is widely acknowledged that irrational urban planning and design can increase emissions while also exacerbating threats and risks, resulting in a slew of environmental issues such as urban heat islands, air pollution, flooding, amongst other issues, as well as environmental, social, and economic losses. Therefore, these concerns must be addressed promptly in order to cope up with these rising difficulties and make urban environments safer for residents.
With the advancement of remote sensing technology and the use of current remote observation systems, urban data science, remote sensing, and artificial intelligence (AI), modeling and quantifying emergent difficulties in urban regions and urban systems have become easy. They aid in the quantitative analysis of urban shape, functions, and human behavior in cities. Harvesting data, developing models, and suggesting new methodologies will be aided by combining urban ecology with new breakthroughs in data science. This book is of great value to a diverse group of academicians, scientists, students, environmentalists, meteorologists, urban planners, remote sensing and GIS experts with a common interest in geospatial sciences within the earth environmental sciences, as well as human and social sciences.
Author(s): Atiqur Rahman, Shouraseni Sen Roy, Swapan Talukdar, Shahfahad
Series: GIScience and Geo-environmental Modelling
Publisher: Springer
Year: 2023
Language: English
Pages: 481
City: Cham
Foreword
Preface
Contents
Editors and Contributors
1 Remote Sensing and Artificial Intelligence for Urban Environmental Studies
Abstract
1.1 Introduction
1.2 Urban Growth and Expansion Modeling Through Remote Sensing
1.3 Application of Machine Learning for Urban Mapping and Monitoring
1.4 Application of Machine Learning and Earth Observation Data for Urban Environmental Studies
1.5 Organization of the Book
References
Land Use Land Cover, Urban Growth and Sprawl
2 A Systematic Review on the Application of Geospatial Technology and Artificial Intelligence in Urban Growth Modeling
Abstract
2.1 Introduction
2.2 The Concept of Urban Growth and Urban Sprawl
2.3 Methodology
2.3.1 Bibliometric Analysis of Urban Growth Modeling Using Scopus Database
2.4 Results and Discussion
2.4.1 Results of the Bibliometric Analysis
2.4.2 Urban Sprawl and Growth Study
2.4.3 Forms, Patterns, and Temporal Dynamics of Urban Growth
2.4.4 Geospatial Application in Urban Growth Studies
2.4.5 Preprocessing of SRS Data
2.4.6 Satellite Image Classification
2.4.7 Quantifying Urban Growth
2.4.7.1 Change Detection Analysis
2.4.7.2 Landscape Metrics
2.4.7.3 Shannon’s Entropy Approach
2.4.7.4 Pearson’s Chi-Square Statistic and Degree of Goodness
2.4.8 Artificial Intelligence in Urban Growth Modeling
2.4.9 Model Validation and Accuracy Assessment
2.4.9.1 Urban Grown Modeling in Indian Cities
2.5 Findings and Conclusions
References
3 Urban Expansion Monitoring Using Machine Learning Algorithms on Google Earth Engine Platform and Cellular Automata Model: A Case Study of Raiganj Municipality, West Bengal, India
Abstract
3.1 Introduction
3.2 Database and Methods
3.2.1 Study Area
3.2.2 Data Source
3.2.3 Methods
3.2.3.1 LULC Classification
3.2.3.2 LULC Simulation for 2025 Using Logistic Regression Transition Potential Modelling-Cellular Automata Approach
3.2.3.3 Estimation of the Rate of Urban Expansion
The Urban Expansion Rate
3.3 Results and Discussion
3.3.1 LULC Change Analysis
3.3.2 Prediction of LULC for 2025
3.3.3 Urban Expansion Rate (1990–2025)
3.3.4 Accuracy Analysis of the Outputs
3.4 Conclusion
References
4 Multi-temporal Dynamics of Land Use Land Cover Change and Urban Expansion in the Tropical Coastal District of Kozhikode
Abstract
4.1 Introduction
4.2 Study Area
4.3 Data Sources and Methodology
4.4 Results and Discussion
4.4.1 Land Use Land Cover Classification
4.4.2 Urban Expansion
4.5 Conclusion
References
5 Land Use Land Cover Change Modeling and Future Simulation in Mumbai City by Integrating Cellular Automata and Artificial Neural Network
Abstract
5.1 Introduction
5.2 Materials and Methods
5.2.1 Study Area
5.2.2 Materials Used
5.2.3 Methods
5.2.3.1 Satellite Data Pre-processing
5.2.3.2 LULC Classification and Accuracy Assessment
5.2.3.3 Preparation of the Conditioning Parameters for Future LULC Simulation
5.2.3.4 Simulation of Future LULC Pattern
5.3 Results and Discussion
5.3.1 LULC Pattern During 1991–2018
5.3.2 LU/LC Change During 1991–2018
5.3.3 Sub-district-Wise Change in LU/LC Pattern
5.3.4 Analysis of Classification Accuracy
5.3.5 Analysis of Simulated LU/LC for 2020 and 2030
5.4 Conclusion
References
6 Monitoring Urban Sprawl Using Geo-Spatial Technology: A Case Study of Kanpur City, India
Abstract
6.1 Introduction
6.2 Study Area
6.3 Data Sources and Methods
6.3.1 Data sources
6.3.2 Methodology
6.3.2.1 Urban Compactness and Dispersion Analysis
Shannon’s Entropy
Density Index (DI)
6.4 Results and Discussion
6.4.1 Urban Structure and Complexity Analysis
6.4.1.1 Land Use and Land Cover (2004)
6.4.1.2 Land Use and Land Cover (2011)
6.4.1.3 Land Use and Land Cover (2021)
6.4.2 Shannon’s Entropy Analysis
6.4.3 Density Index Analysis
6.4.4 Urban Sprawl Multiple Ring Buffer (MRB) Analysis
6.5 Conclusion
References
7 Studying Urban Growth Dynamics in Indo-Gangetic Plain
Abstract
7.1 Introduction
7.2 Study Area
7.3 Data Sources and Methodology
7.4 Simulation of Urban Growth
7.5 Discussion
7.6 Conclusion
References
8 Monitoring and Prediction of Spatiotemporal Land-Use/Land-Cover Change Using Markov Chain Cellular Automata Model in Barisal, Bangladesh
Abstract
8.1 Introduction
8.2 Materials and Methods
8.2.1 Study Area
8.2.2 Data Acquisition and Preparation
8.2.3 Image Analysis
8.2.4 Markov Chain Cellular Automata Model
8.3 Results
8.3.1 Accuracy Assessment
8.3.2 Pattern of LULC Change
8.3.3 Analysis of Future LULC Simulation
8.4 Conclusion
References
9 Urban Change Detection Analysis Using Big Data and Machine Learning: A Review
Abstract
9.1 Introduction
9.2 Land Use and Land Cover (LULC) Change Detection Analysis
9.3 Urban Change Detection Using Conventional Techniques
9.4 Urban Change Detection Using Machine Learning
9.5 Conclusion
References
Urban Green and Blue Spaces
10 Urban Green and Blue Spaces Dynamics—A Geospatial Analysis Using Remote Sensing, Machine Learning and Landscape Metrics in Rajshahi Metropolitan City, Bangladesh
Abstract
10.1 Introduction
10.2 Materials and Methods
10.2.1 Study Area
10.2.2 Data Used
10.2.3 Methods
10.3 Results and Discussion
10.3.1 General Land Use/Land Cover
10.3.2 Dynamics of Urban Green and Blue Spaces
10.3.2.1 Urban Green Space Index (UGSI)
10.3.2.2 Urban Blue Surface Index (UBSI)
10.3.2.3 Landscape Structure Analysis by Landscape Matrices (LM)
10.3.3 Zonal and Directional Analysis
10.3.4 Spatial Trend and Hotspot Analysis
10.4 Conclusions
Acknowledgements
References
11 Quantifying the Impact of Urban Green Space Pattern to Land Surface Temperature: Evidence from an Urban Agglomeration of Eastern India
Abstract
11.1 Introduction
11.2 Data and Methodology
11.2.1 Study Area
11.2.2 Data Sources
11.2.3 Extraction of Green Space
11.2.4 Estimation of Green Space Changes
11.2.5 Computation of Green Space Indicators
11.2.6 Retrieval of Land Surface Temperature
11.2.7 Statistical Analysis
11.3 Results and Discussion
11.3.1 Spatiotemporal Distribution of Green Space and LST
11.3.1.1 Spatiotemporal Changes of Green Space
11.3.1.2 Spatiotemporal Distribution of LST Changes
11.3.2 Neighborhood Level Pattern of Green Space Indices
11.3.3 Impacts of Green Spaces on LST
11.3.3.1 Relationship Between Green Space and LST
11.3.3.2 Significant Green Space Landscape Metrics Affecting LST Variations
11.4 Conclusion
References
12 Urban Effects on Hydrological Status and Trophic State in Peri-Urban Wetland
Abstract
12.1 Introduction
12.2 Study Area
12.3 Materials and Methods
12.3.1 Materials
12.3.2 Methods
12.3.2.1 Wetland Mapping Using Water Indices and Accuracy Assessment
12.3.2.2 Method for Exploring Hydrological Status of Wetland
12.3.2.3 Methods for Developing Trophic State Index (TSI)
12.3.2.4 Measuring Control of Hydrological State on Trophic State
12.4 Results
12.4.1 Delineated Wetland Area
12.4.2 Accuracy Assessment of Wetland Mapping
12.4.3 Hydrological Status of Wetland
12.4.3.1 State of the Hydrological Components
12.4.3.2 Integrated Hydrological Status and Validation
12.4.4 Trophic State
12.4.5 Control of Hydrological State on Trophic State
12.4.6 Causes of Transformation
12.4.6.1 Encroachment of Agriculture and Built-Up Area
12.4.6.2 Influx of Urban Sewage
12.4.6.3 Partial Closure of Wetland Outlet
12.5 Conclusion
References
13 Integrated Urban Decarbonization Planning Tool for Global Cities
Abstract
13.1 Introduction
13.2 Study Area
13.3 Data Types and Methods
13.3.1 Vetting the Data for Accuracy
13.3.2 Data Processing and Layer Creation Workflow
13.3.3 Suitability Analysis Model
13.3.3.1 Race and Income Supplementary Layers
13.3.3.2 A Geospatial Design for Decarbonization
13.4 Results and Discussion
13.4.1 Race and Income Results
13.4.2 Supplemental Analysis of Existing Grant Funding Initiatives
13.4.2.1 The Bronx Income Breakdown and Potential Homes Eligible for Grant Funding
13.4.2.2 Manhattan Income Breakdown and Potential Homes Eligible for Grant Funding
13.4.2.3 Queens Income Breakdown and Potential Homes Eligible for Grant Funding
13.4.2.4 Brooklyn Income Breakdown and Potential Homes Eligible for Grant Funding
13.4.2.5 Staten Island Income Breakdown and Potential Homes Eligible for Grant Funding
13.5 Conclusion
Additional Studies
Location of Low-Income Groups and Graphical User Interface (GUI) Showing Low-Income Groups
Location of Rental Apartments
Location of Schools, Infrastructures, Historical Buildings
Evacuation Plan
Rehousing During Decarbonization
References
14 Perception of Ecosystem Services from Urban Green Space: A Case from an Urban and a Peri-urban Green Space in English Bazar Urban Agglomeration, Eastern India
Abstract
14.1 Introduction
14.2 Material and Methods
14.2.1 About Sites
14.2.2 Rationale of Selection of UGS Sites
14.2.3 Selection of ES from GS
14.2.4 Data Collection and Questionnaire Survey
14.3 Result
14.3.1 Socio-demographic Profile of the Respondents
14.3.2 Perceived ES from GS
14.3.3 Perception Towards Management and Threats to GS
14.4 Discussion
14.4.1 Policy Implication
14.5 Conclusions
References
15 Monitoring Spatiotemporal Reduction of an Urban Wetland Using Landsat Time Series Analysis: A Case Study of Deepor Beel, Assam, India
Abstract
15.1 Introduction
15.2 Study Area
15.3 Materials and Methods
15.3.1 Data
15.3.2 Identification of Wetland
15.3.3 Water Presence Frequency Analysis
15.3.4 Validation
15.3.5 Inconsistency in Wetland Area
15.3.6 Landscape Metrics
15.4 Result and Discussion
15.4.1 Periodic Inventory of Wetland in Pre-Railway (Phase-I, 1988–2000) and Post-Railway Network (Phase-II, 2001–2018) Construction
15.4.2 Change Matrix of Different Classes of Wetlands
15.4.3 Wetland Fragmentation
15.5 Conclusion
References
Urban Climate, Heat Island and Hazard
16 GIS-Based Methodology and World Urban Database and Access Portal Tools (WUDAPT) for Mapping Local Climatic Zones: A Study of Kolkata
Abstract
16.1 Introduction
16.2 Geographical Profile of the Study Area
16.3 Objectives of the Study
16.4 Materials and Method
16.4.1 GIS-Based Methodology
16.4.1.1 GIS Data
16.4.2 World Urban Database and Access Portal Tools (WUDAPT)
16.4.2.1 Data Source
16.4.2.2 Study Plan
16.4.3 Accuracy Assessment
16.5 Results
16.6 Discussion
16.7 Conclusion
References
17 Air Pollutants-Induced Environmental Critical Zones in Capital City of India
Abstract
17.1 Introduction
17.2 Material and Methods
17.2.1 Study Site
17.2.2 Data Sources and Pre-Processing
17.2.3 Estimation of NDVI
17.2.4 Estimation of LST
17.2.5 Air Quality Mapping and Statistical Analysis
17.2.6 Environmental Critical Zones
17.3 Results
17.3.1 Season Specific Spatial Concentration of Criteria Pollutants
17.3.2 Relationship Among Criteria Pollutant, NDVI, and LST
17.3.3 Seasonal and Annual Status of Environmental Critical Zones (ECZ)
17.3.4 Proximate Drivers of Environmental Critical Zones
17.4 Discussion
17.5 Conclusion
References
18 Nexus Between Anthropogenic Heat Flux and Urban Heat Island
Abstract
18.1 Introduction
18.2 Study Area
18.3 Data and Methods
18.3.1 Methods of LST Computation from Landsat Images
18.3.2 Accuracy Assessment of the LST Maps
18.3.3 Methods of Anthropogenic Heat Estimation from Landsat Images
18.3.4 Spatial Linkages Between LST and AHF
18.3.5 Assessing the Connection Between LULC and LST and AHF Maps
18.4 Result and Analysis
18.4.1 Land Surface Temperature and Heat Island
18.4.2 Accuracy Assessment of the LST Maps
18.4.3 Spatio-Temporal Distribution of Anthropogenic Heat Flux
18.4.4 Accuracy of the Anthropogenic Heat Flux with the Help of Previous Literature
18.4.5 Spatial Linkages Between LST and AHF
18.4.6 Association of Different LULCs with LST and AHF
18.4.7 Minimizing Heat Island Effect
18.4.7.1 Effect of Blue and Green Space Proximity on Temperature
18.5 Discussion
18.6 Conclusion
Acknowledgements
References
19 Impact of Urbanization on Land Use and Land Cover Change and Land Surface Temperature in a Satellite Town
Abstract
19.1 Introduction
19.2 Materials and Methods
19.2.1 Study Area: Gurugram City, India
19.2.2 Material Used
19.2.3 Methodology
19.2.3.1 Image Pre-Processing
19.2.3.2 LULC Classification
19.2.3.3 Retrieval of LST from Landsat Images
19.2.3.4 Retrieval of LST from Landsat 5(TM) and Landsat 7(ETM+)
19.2.3.5 Retrieval of LST from Landsat 8 OLI/TIRS
19.2.3.6 Calculation of NDVI and NDBI
19.3 Results and Discussion
19.3.1 Analysis of Variation of LULC Pattern
19.3.2 Analysis of LST Pattern
19.3.3 Analysis of NDVI and NDBI
19.3.4 Association Between LULC Changes and LST
19.4 Conclusion
References
20 Identifying the Flood Hazard Zones in Urban Area Using Flood Hazard Index (FHI)—A Case of Capital City of India
Abstract
20.1 Introduction
20.2 Study Area
20.3 Materials and Methods
20.3.1 Flood Extent Mapping
20.3.2 Identification of Flood Causative Parameters
20.3.3 Rating the Values of Different Parameters
20.3.4 Expert Advice for Parameters Weights
20.3.5 Consistency Check
20.3.6 Flood Hazard Zonation/Zoning
20.4 Results
20.4.1 Flood-Causative Parameters
20.4.2 Flood Extent Mapping
20.4.3 Flood Hazard Index
20.4.4 Validation
20.5 Discussion
20.6 Conclusion
References
21 An Assessment of Traffic Noise Level in Agartala Municipal Corporation Using Geo-spatial Technology in Tripura, India
Abstract
21.1 Introduction
21.2 Methods and Materials
21.2.1 Study Area
21.2.2 Data Collection Process
21.2.3 Data Analysis
21.2.4 Noise Mapping Using Geospatial Techniques
21.3 Results and Discussion
21.3.1 Noise Level in Different Roads of AMC
21.3.1.1 Noise Descriptors Used in Assessment of Traffic Noise Level
21.3.1.2 Noise Climate
21.3.1.3 Noise Pollution Level
21.3.1.4 Traffic Noise Index
21.3.2 Noise Level and Traffic Volume
21.3.2.1 Relation Between Noise Level and Traffic Volume
21.3.2.2 Noise Level and Category of Vehicles
21.3.2.3 Traffic Noise and Carriageway Width
21.3.3 Noise Mapping
21.3.4 Traffic Noise at Terminals
21.3.4.1 Noise Level at Nagerjala Bus Stand
21.3.4.2 Noise Level at Agartala Railway Station
21.3.4.3 Noise Level at Maharaja Bir Bikram Airport
21.3.5 Land Use and Traffic Noise Level
21.3.6 Temporal Variation in Traffic Noise Level at Areas with Different Land Use Category
21.3.7 Spatial Variation in Traffic Noise Level at Areas with Different Land Use Category
21.3.7.1 Industrial Zone (A.D Nagar Industry)
21.3.7.2 Commercial Zone (Battala Bazar)
21.3.7.3 Residential Zone (Ramnagar Road no. 4 Area)
21.3.7.4 IGM Hospital (Silent Zone)
21.3.8 Population Affected by Noise Level in AMC
21.4 Conclusion
References
Urban Environmental Planning and Waste Management
22 Solid Waste Management Scenario of Raiganj Municipality, West Bengal, India
Abstract
22.1 Introduction
22.2 Materials and Methods
22.2.1 Study Area
22.2.2 Materials
22.2.3 Methods
22.3 Result and Discussion
22.3.1 MSW Generation of Study Area
22.3.2 Collection and Transportation
22.3.3 Disposal of Waste
22.4 Conclusion
References
23 Integration of Advanced Technologies in Urban Waste Management
Abstract
23.1 Introduction
23.2 Waste Generation and Classification
23.2.1 Municipal Solid Waste
23.2.2 Industrial Solid Waste
23.2.3 Agricultural Waste
23.2.4 Hazardous Waste
23.2.5 Construction and Demolition Waste
23.3 Current Waste Management Practices in India
23.3.1 Collection and Transfer
23.3.2 Disposal
23.3.2.1 Open Dumping
23.3.2.2 Sanitary Landfill
23.3.2.3 Incineration
23.3.3 Recycling and Composting
23.3.3.1 Recycling
23.3.3.2 Composting
23.3.4 Critical Problems and Inadequacies and Limitations of Current Waste Management System
23.4 Need for Advanced Technologies in Urban Waste Management
23.5 Applications of Technologies in Municipal Solid Waste Management
23.5.1 Generation of Solid Waste
23.5.2 RFID Enabled Door-to-Door Waste Collection Monitoring
23.5.3 Smart Bins
23.5.4 Collection
23.5.5 Transportation
23.5.6 Smart Sorting
23.5.7 Smart Recycling
23.5.8 Disposal
23.5.9 Landfill Management
23.5.9.1 Landfill Site Selection with the Integration of GIS and AI
23.5.9.2 Using AI Drones for Landfill Management
23.5.9.3 Landfill Monitoring
23.5.9.4 Landfill Mapping
23.5.9.5 Calculating Landfill Capacity
23.5.9.6 Monitoring Methane Emissions
23.6 Application of Technologies in Biomedical Waste Management
23.6.1 Design Requirement for Robot
23.6.2 Design Solution for AI-Enabled Robot
23.7 E-Waste and Its Management
23.7.1 Indian Initiatives to Tackle E-Waste
23.7.1.1 Pyrolysis Technology to Recover Precious Metals by IIT Delhi
23.7.1.2 E-Parisaraa Recycling Facility, Bangalore
23.7.1.3 SDMC Facility for E-Waste Collection
23.7.2 Deployment of Advanced Technology in E-Waste Sector Globally
23.7.2.1 Remote Sensing and Image Analysis for E-Waste Contamination
23.7.2.2 Using Machine Learning and Artificial Intelligence for E-Waste Sorting
23.7.2.3 Using Augmented Reality (AR) in Operations
23.7.2.4 Technologizing the Electronic Market for Long-Term Usage
23.8 Application in Construction and Demolition Waste
23.8.1 IOT in Construction and Demolition Waste Management
23.9 Liquid Waste and Its Management
23.9.1 Reuse Through Recycling Using IoT
23.10 Conclusion
References
24 Rethinking the Urban Form and Quality of Walking Experience Using Geospatial Technology
Abstract
24.1 Introduction
24.2 Background to Walkable Urban Environments
24.3 Defining Walksheds
24.4 Community Characterization
24.5 Tuscany School Walkshed Description
24.6 Walking Distances Within and Outside Community
24.7 Walk Wrap Assessment
24.7.1 Resistance to Urban Form
24.7.2 Quality and Safety of the Walking Experience
24.8 Conclusion
References
25 A Remote Sensing and GIS-Based Approach for Assessment of Drinking Water Quality and Its Association with Land-Use Land-Cover in Azamgarh City, India
Abstract
25.1 Introduction
25.2 Material and Methods
25.2.1 Study Area
25.2.2 Water Samples Collection and Laboratory Analysis
25.2.3 Inverse Distance Weighting (IDW) Interpolation for Spatial Mapping
25.2.4 WQI Calculation
25.2.5 LULC Preparation
25.3 Results and Discussion
25.3.1 Physico-Chemical Characteristics of Water Samples
25.3.2 LULC of Azamgarh City
25.3.3 WQI and LULC
25.4 Conclusion
References
26 Urban Planning in Perspective of UN Sustainable Development Goal-11 Using Geospatial Technology: A Case Study of Kolkata Megapolis (India)
Abstract
26.1 Introduction
26.2 Materials and Methods
26.2.1 The Study Area
26.2.2 Data Used
26.2.3 Image Classification
26.2.4 Modeling of Urban Trajectories
26.2.5 LST Retrieval
26.2.6 Urban Thermal Field Variance Index (UTFVI)
26.3 Results
26.3.1 Built-Up Land Expansion and Its Trends
26.3.2 Modeling of Urban Trajectories
26.3.3 Thermal State Intensification Induced by Urbanization
26.3.4 Ecological Vulnerability Growth
26.4 Discussion
26.4.1 Urbanization: A Perspective of SDG-11
26.4.2 Kolkata Megapolis Planning Vis-A-Vis Environmental Viability
26.5 Conclusions
References
27 An Introduction to Big Data and Its Possible Utility in the Urban Context
Abstract
27.1 Introduction to Big Data
27.2 Complexities in a City
27.3 Application of Big Data in a City
27.3.1 Santa Monica’s Well-Being Project—California, USA
27.3.2 Smart Dubai Happiness Meter—Dubai, UAE
27.3.3 Storms of the East Coast—USA
27.4 Conclusion
References
28 Rethinking Progress in Approaches and Techniques for the Urban Environmental Studies
Abstract
28.1 Introduction
28.2 Development in Remote Sensing Data and Approaches for Urban Studies
28.3 Current Status of Urban Studies in Developed and Developing World
28.4 Future Scope
References
