Sustainable Development Using Geospatial Techniques E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 693

Veröffentlichungsjahr: 2024

Beschreibung

This book is a must-have for anyone interested in leveraging geospatial technology, as it covers a wide range of applications and offers valuable insights into the mapping, visualization, and analysis of natural resource planning using GIS, remote sensing, and GPS.

Geospatial technology (GT) is a combination of geographic information systems (GIS), remote sensing (RS), and the global position system (GPS) for the mapping, visualization, and analysis of natural resource planning. Nowadays, GIS is widely used throughout the globe for a wide range of applications. GIS is a system that combines locations, geography, hardware, software, statistics, planning, and digital mapping. GIS is a system in which one can store, manipulate, analyze, and visualize or display spatial data. The basic components of GIS are hardware, software, data, input, and manpower. One can develop spatial, temporal, and dynamic models using GIS, which may help in effective decision-making tools.

Geospatial information is a computer programme that collects, stores, verifies, and presents information on locations on the surface of the Earth. Geographical information systems play a key role in sustainable development. Geospatial technology combines traditional database operations like query and statistical analysis with the specific graphical and geographic analytical capabilities offered by maps.

Leseprobe

Table of Contents

Cover

Table of Contents

Series Page

Title Page

Copyright Page

Preface

1 Development of a Two-Layer Meta-Classifier–Based Drought Stress Detection System for Wheat Crop Sustainability

1.1 Introduction

1.2 Literature Review

1.3 Background

1.4 Problem Formulation

1.5 Methodology for Drought/Water Stress Detection

1.6 Application of Meta-Classifier–Based Detection for the Development of the Sustainable Model for Drought Detection

1.7 Conclusion

References

2 Comparison of Geo-Statistical Techniques: A Study Based on Mapping Soil Properties for a Lower Himalayan Watershed

2.1 Introduction

2.2 Materials and Methods

2.3 Results and Discussion

2.4 Conclusion and Future Scope

References

3 Monitoring Crop Conditions of Punjab State Using Big Data Analytics

3.1 Introduction

3.2 Objectives

3.3 Study Area and Data

3.4 Methods

3.5 Results and Discussions

3.6 Conclusions

References

4 An Investigation into Use of Ethereum Blockchain Technology to Validate the Reliability and Quality of Stored Satellite Images

4.1 Introduction

4.2 Literature Review

4.3 Methodologies and Tools Used for Simulating the Ethereum Blockchain

4.4 Evaluation Metrics and Criteria for Assessing the Effectiveness of Ethereum Blockchain in Satellite Image Safekeeping

4.5 Case Studies and Experiments Demonstrating the Integrity and Accuracy of Satellite Images Using Ethereum Blockchain Simulation

4.6 Research Method

4.7 Data Analysis and Critical Discussions

4.8 Testing Phase

4.9 Challenges and Future Directions

Conclusion

References

5 Urban Expansion and Traffic Congestion: A Geographical Study of Shimla

5.1 Introduction

5.2 Study Area

5.3 Methodology

5.4 Result and Discussion

5.5 Accuracy Assessment

5.6 Traffic Congestion Analysis

5.7 Conclusion

References

6 Landslide Susceptibility Analysis for Sustainable Development in the Indian Himalayas

6.1 Introduction

6.2 Study Area

6.3 Data

6.4 Methodology

6.5 Results and Discussion

6.6 Conclusions

References

7 Application of Geospatial Tools in Glacial Lake Outburst Floods: Mapping and Monitoring

7.1 Introduction

7.2 Geospatial Techniques

7.3 Discussion and Conclusion

References

8 Dynamic Coastal Flood Risk Assessment of a Coastal Island in West Bengal, India

8.1 Introduction

8.2 Study Area

8.3 Method

8.4 Results and Discussion

8.5 Conclusion

Acknowledgments

References

9 A Review of Methods for Studying Glacier Dynamics Due to Climate Change in the Himalayas

9.1 Introduction

9.2 Monitoring of Himalayan Glaciers by Different Methods

9.3 Discussion and Conclusion

References

10 Geospatial Techniques for Flash Flood Hazard Assessment and Management

10.1 Understanding Flash Flood Hazards

10.2 Geospatial Tools Overview

10.3 Remote Sensing for Flash Flood Assessment

10.4 GIS Applications in Flash Flood Management

10.5 Drone Mapping for Rapid Response

10.6 Collaborative Approaches and Socioeconomic Considerations

10.7 Future Trends and Conclusion

References

11 Prediction of Water Hardness Using Machine Learning and Model Interpretation

11.1 Introduction

11.2 Literature Survey

11.3 Materials and Methods

11.4 Results and Discussions

11.5 Conclusion

Conflict of Interest

References

12 Air Quality Mapping Using GIS for Kanpur City, India

12.1 Introduction

12.2 Methodology

12.3 Data Analysis and Results

12.4 Discussions and Conclusion

References

13 Geospatial Modeling Approach and Characteristics Study of Graphene-Anchored Cu-Nanoferrites and Their Potential in Arsenic Containing Wastewater Treatment

13.1 Introduction

13.2 Experimentation

13.3 Results and Discussion

13.4 Conclusion

References

14 Managing Construction and Demolition Waste Illegal Dumping through GIS: A Case Study of Urban Metropolitan

14.1 Introduction

14.2 CDWM Using GIS Tools and Multivariate Analysis Techniques

14.3 The Case Study of Gurugram Municipality

14.4 Methodology

14.5 Results and Discussion

14.6 Conclusion

Acknowledgment

References

15 Assessment of Human Health Risk in Baitarani Basin, Odisha Using Water Quality Index (WQI), Cluster Analysis (CA), and Geographic Information Systems (GIS)

15.1 Introduction

15.2 Study Area

15.3 Materials, Sampling, and Analysis

15.4 Methodology

15.5 Results and Discussions

15.6 Conclusions

Acknowledgments

References

16 Drone Mapping for Agricultural Sustainability: Applications and Benefits

16.1 Introduction

16.2 Agricultural Remote Sensing: Satellite and Drones

16.3 Drone Mapping for Precision Agriculture

16.4 Economic Perspectives

16.5 Challenges

16.6 Future Perspectives

16.7 Conclusions

References

17 Advanced Use of Drones in Irrigation and Water Management

17.1 Introduction

17.2 Study Area

17.3 Methodology

17.4 Result and Discussion

17.5 Conclusion

Acknowledgments

References

Index

Also of Interest

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Comparison of six different preprocessing pipelines.

Table 1.2 Performance comparison of six different cascaded pipelines.

Table 1.3 AUC test score of RF algorithm on two different feature combinations...

Table 1.4 AUC ROC performance evaluation of nine base learners/ classifiers.

Table 1.5 Pipeline of the models evaluated to finalize the meta-classifier.

Chapter 2

Table 2.1 Performance statistics of soil moisture based on spatial interpolati...

Table 2.2 Grassland landform error estimates.

Table 2.3 Forest landform error estimates.

Table 2.4 Agriculture landform error estimates.

Chapter 3

Table 3.1 Deviation classes.

Table 3.2 District-wise area statistics.

Table 3.3 District-wise wheat yield (per hectare in kilogram) statistics.

Chapter 4

Table 4.1 The deployed smart contract technology in block 1.

Table 4.2 Automated validation of smart contracts.

Chapter 5

Table 5.1 Satellite imagery and data sources.

Table 5.2 Overview of the classifications for land cover and usage.

Table 5.3 Population and land use distribution in different years.

Table 5.4 Comparison of land use statistics for different years and change ana...

Table 5.5 Results of land use/cover mapping accuracy.

Table 5.6 Confusion matrix for land use/cover classification.

Table 5.7 Areas in Shimla with their travel distance and travel time during pe...

Table 5.8 Spatial distribution of traffic congestion and urban sprawl in diffe...

Table 5.9 Characteristics of land use categories and their contribution to urb...

Chapter 6

Table 6.1 FR table showing the weights assigned to the various classes of the ...

Chapter 7

Table 7.1 The glacial lakes classification system [29].

Chapter 8

Table 8.1 Dataset, source, and description required for the analysis.

Table 8.2 Exposure parameters.

Table 8.3 Vulnerability parameters.

Table 8.4 Exposure indicator classification.

Table 8.5 Sociodemographic indicator classification.

Table 8.6 Economic indicator classification.

Table 8.7 Infrastructure indicator classification.

Table 8.8 Accessibility indicator classification.

Table 8.9 Growth influencing parameters.

Table 8.10 Comparison of land-use features in 2012, 2020, and 2050.

Table 8.11 Indicator score based on AHP.

Chapter 9

Table 9.1 List of surface velocity calculations by remote sensing.

Table 9.2 List of glaciological methods used in Himalayan glaciers.

Chapter 11

Table 11.1 Statistical analysis of all parameters.

Table 11.2 Machine learning models and selected hyperparameters.

Table 11.3 Results (R

2

and MSE) of the machine learning models.

Chapter 12

Table 12.1 Monitoring sites operated by UPPCB (Uttar Pradesh Pollution Control...

Table 12.2 PM

10

(µg/m

3

) descriptive statistics (mean and standard deviation).

Chapter 13

Table 13.1 Cell volume, lattice constant, and crystallite size of CuFe

2

O

4

and ...

Table 13.2 Saturation magnetization, remanence, and coercivity of CuFe

2

O

4

and ...

Table 13.3 Surface area, pore size, adsorption pore volume, and desorption por...

Chapter 14

Table 14.1 Selected significantly correlated 23 variables.

Table 14.2 Total explained variance.

Table 14.3 Individual commonalities of the selected 27 variables calculated th...

Table 14.4 Synthesis of the methodology developed.

Table 14.5 Table showing the distribution of ILs across the three probability ...

Table 14.6 The findings of the MI/MC model’s sensitivity analysis taking into ...

Table 14.7 CDW generation per demand point.

Table 14.8 Individual facility reception capacity.

Chapter 15

Table 15.1 Mathematical approaches implemented for the study.

Table 15.2 The relative weight of each selected parameter.

Table 15.3 Statistical analysis of analyzed physicochemical properties.

Chapter 16

Table 16.1 Commonly used drone sensors in agricultural monitoring.

Table 16.2 Commonly used vegetation indices for agricultural monitoring.

Chapter 17

Table 17.1 Names of the villages and their nomenclature in the study area.

List of Illustrations

Chapter 1

Figure 1.1 Pearson correlation analysis for 23 GLCM texture variables.

Figure 1.2 Graphical representation of Fo (minimum fluorescence) to Fmax (maxi...

Figure 1.3 Wheat canopy component analysis for segmented control image.

Figure 1.4 Landmark features on segmented wheat canopy.

Figure 1.5 Flow of meta-learning stacking ensemble for drought stress detectio...

Figure 1.6 AUC ROC score comparison of different classification algorithms.

Figure 1.7 Two-layer meta-classifiers 1 to 4 comparison graph.

Figure 1.8 Model for water stress detection for the identification of losses d...

Chapter 2

Figure 2.1 Clockwise representation of the location of the Suketi River catchm...

Figure 2.2 (a) The observed and predicted points in gridded location for measu...

Figure 2.3 (a) LULC map. (b) Elevation map of the Suketi watershed.

Figure 2.4 Spatial maps of soil moisture, organic content, and elevation prepa...

Figure 2.5 Spatial maps of soil moisture, organic content, and elevation prepa...

Chapter 3

Figure 3.1 Study area.

Figure 3.2 Research methodology.

Figure 3.3 Generated datasets: (a) reference mean of 2014–2019 and (b) current...

Figure 3.4 Deviation raster (red-green color ramp shows areas of negative to p...

Figure 3.5 Stressed areas (red color) in Punjab state.

Figure 3.6 Stressed areas (red color) around Dera Baba Nanak in Gurdaspur.

Figure 3.7 Stressed areas (red color) around Bassi Pathanan in Fatehgarh Sahib...

Chapter 4

Figure 4.1 Smart contract: Ensuring and returning RTCM, NMEA, and surveyor: Al...

Figure 4.2 Connecting to NTRIP caster and GPS receiver: Algorithm 2.

Figure 4.3 Connecting to this Ethereum blockchain and deploying this smart con...

Figure 4.4 GPS positioning: Algorithm 4.

Chapter 5

Figure 5.1 Geographic overview: Study area location map.

Figure 5.2 (a) Land use and land cover map of Shimla subdistrict in 1993. (b) ...

Chapter 6

Figure 6.1 Geographical map of the study region showing the locations of lands...

Figure 6.2 Digital maps of the landslide causal factors.

Figure 6.3 ROC curve for the generated LSZ map used for validation.

Figure 6.4 Landslide susceptibility map of the study area.

Chapter 7

Figure 7.1 A rock and ice avalanche produced a 16-foot-high surge wave to floo...

Figure 7.2 This map depicts the Himalayan mountain region, colloquially called...

Figure 7.3 Maps illustrating several examples of glacial lake types, including...

Figure 7.4 Represents some examples of Normalized Difference Water Index (NDWI...

Chapter 8

Figure 8.1 Study area: Sagar Island.

Figure 8.2 Multidimensional parametric flood risk model (MPFR - model) method.

Figure 8.3 FUTURES model conceptual framework [19].

Figure 8.4 Land use for 2012 and 2020.

Figure 8.5 Land use for 2012 and 2020.

Figure 8.6 Proximity analysis of mangroves, rivers, and amenities.

Figure 8.7 Proximity analysis of roads, shoreline, and slope.

Figure 8.8 Development pressure and Suitability map.

Figure 8.9 Land-use change 2020–2050.

Figure 8.10 Flood inundation map (Yaas).

Figure 8.11 Exposure mapping.

Figure 8.12 Vulnerability mapping (sociodemographic, economic, and infrastruct...

Figure 8.13 Vulnerability mapping (accessibility).

Figure 8.14 Cumulative flood vulnerability.

Figure 8.15 Coastal flood risk: (a) 2020 and (b) 2050.

Chapter 9

Figure 9.1 Distributions of glaciers in the Himalaya.

(Source: Linda; 2007).

Chapter 10

Figure 10.1 Applications of remote sensing and geospatial tools for flash floo...

Chapter 11

Figure 11.1 Need for XAI.

Figure 11.2 Sampling locations in Punjab.

Figure 11.3 Correlation matrix (total_alka: total alkalinity, so4: sulfate, cl...

Figure 11.4 Random forest algorithm.

Figure 11.5 Typical MLP neural network.

Figure 11.6 Internal structure and output of a single neuron.

Figure 11.7 Model flow diagram.

Figure 11.8 Prediction error plot of (a) MLP, (b) RF, (c) SVR, (d) kNN, and (e...

Figure 11.9 SHAP variable importance plot (MLP).

Figure 11.10 SHAP variable importance plot (SVR).

Figure 11.11 SHAP variable importance plot (PLSR).

Figure 11.12 Relationship between hardness and prominent features.

Chapter 12

Figure 12.1 Locations of air monitoring stations in Kanpur city, Uttar Pradesh...

Figure 12.2 Winter months (Nov 16–Jan 17) concentrations of PM

10

from eight mo...

Figure 12.3 Predicted surfaces using (a) IDW and (b) kriging for November 2016...

Figure 12.4 Hotspots of air pollution.

Chapter 13

Figure 13.1 Sampling location for GIS modeling in Peshawar. Source: Ahmad

et a

...

Figure 13.2 Study area and sampling location in Sheikhupura. Source: Shaheen

e

...

Figure 13.3 Synthesis procedure of G/CuFe

2

O

4

-NP composites.

Figure 13.4 Pattern of XRD for a series of G/CuFe

2

O

4

-NP composites with varied...

Figure 13.5 FTIR spectra obtained for pure and composite samples.

Figure 13.6 SEM images of graphene to CuFe

2

O

4

: (a) 80% graphene 20% CuFe

2

O

4

, (...

Figure 13.7 EDX graphs of graphene/CuFe

2

O

4

: (a) 80% graphene 20% CuFe

2

O

4

, (b) ...

Figure 13.8 VSM graphs of graphene to CuFe

2

O

4

: (a) pure graphene, (b) 80% grap...

Figure 13.9 BET graphs of CuFe

2

O

4

to graphene: (a) pure ferrite, (b) 80% CuFe

2

Figure 13.10 The influence of the duration of interaction on the behavior of A...

Chapter 14

Figure 14.1 Circular flow of construction materials [6].

Figure 14.2 CDW composition in India [3].

Figure 14.3 Material and cash flow in standardized CDWM [7].

Figure 14.4 (a) Minimize impedance; (b) maximize coverage; (c) maximize attend...

Figure 14.5 The examples of ILs located in the study area: along roadsides, cr...

Figure 14.6 Map depicting the distribution of validation (93) and calibration ...

Figure 14.7 Maps depicting (a) ward-wise population density (inhabitants per s...

Figure 14.8 Maps depicting (e) vegetative and vacant land areas; and (f) eleva...

Figure 14.9 Framework of proposed methodology.

Figure 14.10(A) Spatial join analysis overlay of factors (F1 to F3).

Figure 14.11 Final predictive probability occurrence map.

Figure 14.12 MCG CDW demand points and feasible collection points (candidate f...

Figure 14.13 Results of different location-allocation models with 3-km impedan...

Figure 14.14 Sensitivity analysis of MI/MC model with 3-km impedance.

Figure 14.15 Chosen designated collection points (20) spread across MCG.

Figure 14.16 Map showing the distance of collection points from the recycling ...

Chapter 15

Figure 15.1 Index map of the study area.

Figure 15.2a Distribution of pH for both seasons.

Figure 15.2b Distribution of turbidity for both seasons.

Figure 15.2c Distribution of TDS for both seasons.

Figure 15.2d Distribution of TSS for both seasons.

Figure 15.2e Distribution of EC for both seasons.

Figure 15.2f Distribution of DO for both seasons.

Figure 15.2g Distribution of alkalinity for both seasons.

Figure 15.2h Distribution of BOD for both seasons.

Figure 15.2i Distribution of TH for both seasons.

Figure 15.2j Distribution of HCO

3

-

for both seasons.

Figure 15.2k Distribution of SO

4

2-

for both seasons.

Figure 15.2l Distribution of NO

3

-

for both seasons.

Figure 15.2m Distribution of PO

4

3-

for both seasons.

Figure 15.2n Distribution of Cl

-

for both seasons.

Figure 15.2o Distribution of Ca

2+

for both seasons.

Figure 15.2p Distribution of Mg

2+

for both seasons.

Figure 15.2q Distribution of Na+ for both seasons.

Figure 15.2r Distribution of K+ for both seasons.

Figure 15.2s Distribution of TC for both seasons.

Figure 15.2t Distribution of FC for both seasons.

Figure 15.2u Distribution of Fe

2+

for both seasons.

Figure 15.2v Distribution of Cr

2+

for both seasons.

Figure 15.3 Percentage-wise variation due to WA WQI, SPI, NPI, CPI, and OIP.

Figure 15.4 Plot depicting WA WQI for each station.

Figure 15.5 WA WQI map for Baitarani River.

Figure 15.6 Plot showing SPI for each station.

Figure 15.7 SPI map for Baitarani River.

Figure 15.8 Plot showing WQI for each station.

Figure 15.9 NPI map for Baitarani River.

Figure 15.10 Plot showing CPI for each station.

Figure 15.11 CPI map for Baitarani River.

Figure 15.12 Plot showing OIP for each station.

Figure 15.13 OIP map for Baitarani River.

Figure 15.14 (a) Representation of a cluster map during PRM. (b) Representatio...

Chapter 16

Figure 16.1 Drone mapping applications in agriculture.

Chapter 17

Figure 17.1 Study area.

Figure 17.2 Methodology flowchart.

Figure 17.3 Field measurements with DGPS.

Figure 17.4 Drone images of the study area.

Figure 17.5 Ortho-image of the surveyed area.

Figure 17.6 Hand-made plan map of the study area.

Figure 17.7 Digitized map from ortho-mosaic of drone imageries.

Figure 17.8 Distribution of the area from K1 to K10 with the mainstream in the...

Figure 17.9 Contour map of the study area.

Figure 17.10 Grid map.

Figure 17.11 Plan map.

Figure 17.12 Diagram of the irrigation plan map in the study area.

Figure 17.13 L-Section of KG1 (kaccha/unlined gully).

Figure 17.14 L-Section of KG2 (unlined/kaccha gully).

Figure 17.15 L-Section of KG3 (unlined/kaccha gully).

Figure 17.16 L-Section of MG (main gully).

Figure 17.17 L-Section of PG (lined/pakka gully).

Guide

Cover Page

Table of Contents

Series Page

Title Page

Copyright Page

Preface

Begin Reading

Index

Also of Interest

WILEY END USER LICENSE AGREEMENT

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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106

Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])

Sustainable Development Using Geospatial Techniques

Edited by

Disha Thakur

Sanjay Kumar

Har Amrit Singh Sandhu

and

Chander Prakash

This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.

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Library of Congress Cataloging-in-Publication Data

ISBN 978-1-394-21434-1

Front cover images supplied by Adobe FireflyCover design by Russell Richardson

Preface

Sustainable development addresses pressing global challenges such as climate change, resource depletion, and social inequalities by promoting responsible consumption, renewable energy, and inclusive policies so that the needs of the present generation are met without compromising the ability of future generations to meet their own needs. By embracing such practices, societies can achieve long-term prosperity, resilience, and equity, fostering a healthier planet and equitable communities. The rapid pace of technological advancement has opened up several avenues for tackling the pressing issues associated with sustainable development. In this context, geospatial techniques have emerged as a powerful tool, offering unprecedented capabilities to monitor, analyze, and manage our natural and built environment. They enable precise mapping and analysis of climate, vegetation, water resources, and urban growth, facilitating informed decision-making for sustainable practices.

This book is a collaborative effort stemming from our diverse and vast experience that addresses the critical need for innovative solutions in the pursuit of sustainability. As environmental degradation, climate change, and resource management challenges escalate globally, the demand for effective strategies to mitigate these issues becomes increasingly urgent. The book provides a comprehensive overview of how geospatial techniques can be harnessed to achieve sustainable development goals, bridging the gap between technical know-how and practical applications. Combining theoretical foundations with real-world case studies equips practitioners, policymakers, and scholars with the necessary knowledge and tools to implement sustainable practices effectively. In the present book, readers will find detailed discussions on the various applications of geospatial techniques in sustainability initiatives. From integrating remote sensing data in agricultural applications to using Geographic Information Systems (GIS) in urban planning and resource management, each section delves into specific methodologies and their impact on promoting sustainability. The case studies included not only highlight successful implementations but also address the challenges and limitations encountered, providing a balanced perspective on the potential and constraints of these technologies.

Furthermore, the book emphasizes the importance of interdisciplinary collaboration in advancing sustainable development. Drawing on expertise from fields such as environmental science, engineering, and urban planning showcases how a holistic approach can lead to more effective and resilient solutions. This integrative perspective is crucial for addressing the complex and interconnected nature of global sustainability challenges.

Ultimately, this book aims to inspire and inform a wide range of stakeholders, from academics and researchers to practitioners and policymakers, about the transformative potential of geospatial techniques. We hope to contribute to the ongoing efforts to create a more sustainable and equitable world by fostering a deeper understanding of these tools and their applications.

Dr. Disha Thakur

Dr. Sanjay Kumar

Dr. Har Amrit Singh Sandhu

Prof. Chander Prakash