Sustainable Development Practices Using Geoinformatics E-Book

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

Seitenzahl: 425

Veröffentlichungsjahr: 2020

Beschreibung

This exciting new volume will provide a comprehensive overview of the applications of geoinformatics technology for engineers, scientists, and students to become more productive, more aware, and more responsive to global climate change issues and how to manage sustainable development of Earth's resources. Over the last few years, the stress on natural resources has increased enormously due to anthropogenic activities especially through urbanization and industrialization processes. Sustainable development while protecting the Earth's environment involves the best possible management of natural resources, subject to the availability of reliable, accurate and timely information on regional and global scales. There is an increasing demand for an interdisciplinary approach and sound knowledge on each specific resource, as well as on the ecological and socio-economic perspectives related to their use. Geoinformatics, including Remote Sensing (RS), Geographical Information System (GIS), and Global Positioning System (GPS), is a groundbreaking and advanced technology for acquiring information required for natural resource management and addressing the concerns related to sustainable development. It offers a powerful and proficient tool for mapping, monitoring, modeling, and management of natural resources. There is, however, a lack of studies in understanding the core science and research elements of geoinformatics, as well as larger issues of scaling to use geoinformatics in sustainable development and management practices of natural resources. There is also a fundamental gap between the theoretical concepts and the operational use of these advance techniques. Sustainable Development Practices Using Geoinformatics, written by well-known academicians, experts and researchers provides answers to these problems, offering the engineer, scientist, or student the most thorough, comprehensive, and practical coverage of this subject available today, a must-have for any library.

Leseprobe

Table of Contents

Cover

Title page

Copyright

Preface

Acknowledgement

1 The Impact of Rapid Urbanization on Vegetation Cover and Land Surface Temperature in Barasat Municipal Area

1.1 Introduction

1.2 Study Area

1.3 Datasets and Methodology

1.4 Results and Discussion

1.5 Conclusion

Acknowledgement

References

2 Geo-Environmental Hazard Vulnerability and Risk Assessment Over South Karanpura Coalfield Region of India

2.1 Introduction

2.2 Study Area

2.3 Methodology and Data Used

2.4 Result and Discussion

2.5 Conclusion

References

Appendix: List of Abbreviations

3 Bistatic Scatterometer Measurements for Soil Moisture Estimation Using Grid Partition–Based Neuro-Fuzzy Inference System at L-Band

3.1 Introduction

3.2 Methods and Materials

3.3 Result and Discussions

3.4 Conclusions

References

4 Morphometric Analysis of Tapi Drainage Basin Using Remote Sensing and GIS Techniques

4.1 Introduction

4.2 Study Area

4.3 Methodology

4.4 Results and Discussion

4.5 Conclusion

Acknowledgments

References

5 Efficacy of GOSAT Data for Global Distribution of CO

2

Emission

5.1 Introduction

5.2 Monitoring of Greenhouse Gases From Space

5.3 GOSAT Satellite

5.4 Methodology

5.5 Results and Discussion

5.6 Conclusion

References

6 Development of a Smart Village Through Micro-Level Planning Using Geospatial Techniques—A Case Study of Jangal Aurahi Village of Gorakhpur District

6.1 Introduction

6.2 Study Area

6.3 Data Used and Methodology

6.4 Result and Discussion

6.5 Conclusion

References

7 Land Appraisal for the Growth of Potato Cultivation: A Study of Sagar Island, India

7.1 Introduction

7.2 Study Area

7.3 Materials and Method

7.4 Results and Discussion

7.5 Conclusions

References

8 Landslide Vulnerability Mapping Using Geospatial Technology

8.1 Introduction

8.2 Study Area

8.3 Materials and Methods

8.4 Summary

References

9 Assessment of Impacts of Coal Mining–Induced Subsidence on Native Flora and Native Forest Land: A Brief Review

9.1 Introduction

9.2 Material and Methods

9.3 Conclusions

References

10 Application of GI Science in Morphometric Analysis: A Case Study of the Gomati River Watershed in District Bageshwar, Uttarakhand

10.1 Introduction

10.2 Study Area

10.3 Materials and Methodology

10.4 Results and Discussion

10.5 Conclusion

References

11 Water Audit: Sustainable Strategy for Water Resource Assessment and Gap Analysis

11.1 Introduction

11.2 Material and Methodology

11.3 Result

11.4 Conclusions

References

12 Multi-Temporal Land Use/Land Cover (LULC) Change Analysis Using Remote Sensing and GIS Techniques of Durg Block, Durg District, Chhattisgarh, India

12.1 Introduction

12.2 Study Area

12.3 Materials and Methods

12.4 Result and Discussion

12.5 Conclusion

Acknowledgment

References

13 Climate Vulnerability and Adaption Assessment in Bundelkhand Region, India

13.1 Introduction

13.2 Conclusion

References

14 Suitable Zone for Sustainable Ground Water Assessment in Dhanbad Block, Jharkhand, India

14.1 Introduction

14.2 Study Area

14.3 Methodology

14.4 Results

14.5 Conclusions

References

15 Detecting Land Use/Land Cover Change of East and West Kamrup Division of Assam Using Geospatial Techniques

15.1 Introduction

15.2 Study Area

15.3 Materials and Methodology

15.4 Results and Discussion

15.5 Conclusion

References

16 Climate Resilient Housing—An Alternate Option to Cope with Natural Disasters: A Study in Fani Cyclonic Storm Affected Areas of Odisha

16.1 Introduction

16.2 Study Area and Methodology

16.3 Discussion

16.4 Policy Recommendation

References

17 Role of Geo-Informatics in Natural Resource Management During Disasters: A Case Study of Gujarat Floods, 2017

17.1 Background

17.2 Flood Preparedness Measures

17.3 Flood Response Measures

17.4 Gujarat Flood Case Study 2017

17.5 Preparedness Measures by State Government

17.6 Media Handling

17.7 Rescue Operation

17.8 Relief Work

17.9 Speedy Restoration of Essential Services

17.10 Use of Drones—New Initiative Adopted

References

18 Environmental Impacts by the Clustering of Rice Mills, Ernakulam District, Kerala State

18.1 Introduction

18.2 Environmental Pollution and Rice Processing Industries

18.3 Study Area

18.4 Methodology and Review of Literature

18.5 Spatial Distribution of Rice Mill Clustering

18.6 Parboiling Process and Characteristics of Rice Mill Effluents

18.7 Description of Rice Mills Taken for Assessing the Impact on Environment

18.8 First Model Cluster

18.9 Overutilization of Groundwater Resources

18.10 Physio-Chemical Analysis of Rice Mill Effluent From Second Model Cluster

18.11 Conclusion

References

19 GIS-Based Investigation of Topography, Watershed, and Hydrological Parameters of Wainganga River Basin, Central India

19.1 Introduction

19.2 Study Area

19.3 Methodology

19.4 Results and Discussions

19.5 Conclusion

Abbreviations

References

Index

Also of Interest

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Location map of Barasat municipality.

Figure 1.2 Road and railways network in Barasat municipality.

Figure 1.3 Land Use/Land Cover map of Barasat municipality (2001, 2011, and 2017...

Figure 1.4 Urban Sprawl pattern from 2001 to 2017.

Figure 1.5 (A) The Circular pattern of increasing population from center of the ...

Figure 1.6 NDVI images for the year 2001, 2011, and 2017.

Figure 1.7 Land Surface Temperature (LST) map for the year 2001, 2011, and 2017.

Figure 1.8A The relationship pattern between NDVI and LST from center to outward...

Figure 1.8B The relationship pattern between NDVI and LST from center to outward...

Figure 1.8C The relationship pattern between NDVI and LST from center to outward...

Figure 1.9 Relationship of LST with LULC classes.

Figure 1.10 Regression analysis of LST vs. NDVI.

Figure 1.11 Urban Heat Island (UHI) formation in Barasat municipality area in th...

Chapter 2

Figure 2.1 Location map of the study area showing village boundaries along with ...

Figure 2.2 Flow chart of methodology adopted for coal mining risk mapping.

Figure 2.3 Map showing spatial distribution of (a) AOT at wavelength of 340nm, (...

Figure 2.4 Land use/land cover map of South Karanpura Coalfield region.

Figure 2.5 Village-level socio-economic indicators maps of (a) population densit...

Figure 2.6 Composite geo-environmental hazard index map of the study area.

Figure 2.7 Composite socio-economic vulnerability index map of the study area.

Figure 2.8 Showing coal mining risk zone index map of the study area.

Chapter 3

Figure 3.1 Angular variation of P (dB) at different SM content.

Figure 3.2 Membership function plot for Gauss MF with P (dB).

Figure 3.3 Plot between experimentally observed and estimated SM for Gauss MF du...

Chapter 4

Figure 4.1 Location map of the study area.

Figure 4.2 Stream order map of study area.

Figure 4.3 Digital Elevation Map (DEM) of study area.

Chapter 5

Figure 5.1 Methodology flow chart for the study.

Figure 5.2 Spatial distribution of CO

2

concentration from December 2009 to March...

Figure 5.3 Spatial distribution of CO

2

concentration from December 2016 to Febru...

Figure 5.4 Monthly change in CO

2

concentration from 2009 to 2020.

Chapter 6

Figure 6.1 Shows study area where LISS IV and DEIMOS satellite data were used.

Figure 6.2 Methodology flow chart for the action plan map preparation.

Figure 6.3 Methodology flow chart for the study.

Figure 6.4 Infrastructure map.

Figure 6.5 Updated cadastral map.

Figure 6.6 LU/LC map.

Figure 6.7 Soil map.

Figure 6.8 Shows action plan map of Jangal Aurahi village of Gorakhpur district.

Figure 6.9 Groundwater prospect map.

Figure 6.10 LCC map.

Figure 6.11 Contour map.

Figure 6.12 Geotagged map of amenities.

Figure 6.13 Geotagged map of amenities.

Figure 6.14 Amenities map.

Chapter 7

Figure 7.1 Location of the study area.

Figure 7.2 Flowchart of the methodology.

Figure 7.3 Suitability levels of the six factors.

Figure 7.4 Land suitability map for potato cultivation.

Chapter 8

Figure 8.1 Key map of Western Ghats India.

Figure 8.2 Thematic layer consider in this study. (a) Elevation data; (b) Slope ...

Figure 8.3 Western Ghats landslide vulnerability zonation map.

Chapter 9

Figure 9.1 Schematic diagram showing the underground mining subsidence, pre- and...

Figure 9.2 Subsidence basin observed in an agricultural field at the Hanwang coa...

Figure 9.3 Subsidence basin observed in an agricultural field in the Witbank are...

Figure 9.4 Tension cracks associated with subsidence basin formation developed i...

Figure 9.5 Tension cracks representing secondary features of surface subsidence ...

Chapter 10

Figure 10.1 Study area map.

Figure 10.2 The process of delineation of drainage network and stream orders.

Figure 10.3 Aspect of Gomti River Basin.

Figure 10.4 Slope of Gomti River Basin.

Figure 10.5 Stream order of Gomti River Basin.

Figure 10.6 Drainage density of Gomti River Basin

Chapter 11

Figure 11.1 Map study area, BIT Mesra, Ranchi District, Jharkhand State, India.

Figure 11.2 Intake reservoir of Hostel 9.

Chapter 12

Figure 12.1 Location map of the study area.

Figure 12.2 Flow chart showing methodology.

Figure 12.3 LULC map of October 2005.

Figure 12.4 LULC map of October 2016.

Figure 12.5 LULC change map of October 2005 to October 2016.

Figure 12.6 LULC map (February 2006).

Figure 12.7 LULC map (February 2017).

Figure 12.8 LULC change map of pre-monsoon (February 2006 to February 2017).

Chapter 13

Figure 13.1 IPCC framework for assessing vulnerability. Source: IPCC framework r...

Chapter 14

Figure 14.1 Study area.

Figure 14.2 Slope map.

Figure 14.3 Ground water depth map.

Figure 14.4 Land use/land cover map.

Figure 14.5 Geology map.

Figure 14.6 Soil map.

Figure 14.7 Shows methodology flow chart.

Figure 14.8 Suitable site for sustainable ground water assessment.

Chapter 15

Figure 15.1 Showing study area map.

Figure 15.2 Land use and land cover map (1988).

Figure 15.3 Land use/land cover map (1998).

Figure 15.4 Land use/land cover map (2008).

Figure 15.5 Land use/land cover map (2018).

Chapter 16

Figure 16.1 Houses damaged in Puri town near the sea beach.

Figure 16.2 Affected districts due to cyclone Fani.

Chapter 17

Figure 17.1 Gujarat Flood Hazard Risk Zonation: Settlement-wise Flood Frequency....

Figure 17.2 Flood Hazard Map Gujarat (BMTPC, 2019).

Chapter 18

Figure 18.1 Location map. Prepared by authors using toposheets, Kerala 1:50,000....

Figure 18.2 District wise distribution of rice mills in Kerala. Prepared by the ...

Figure 18.3 Taluk wise distribution of rice mills in Ernakulam (2015). Prepared ...

Figure 18.4 Locations of rice mills. Prepared by the Authors on the basis of GPS...

Figure 18.5 Effluent collected points in Okkal Panchayath (field survey).

Chapter 19

Figure 19.1 Wainganga study area.

Figure 19.2 Physiographical regions.

Figure 19.3 Contour map.

Figure 19.4 Digital Elevation Models (DEM).

Figure 19.5 Wainganga River geological map.

Figure 19.6 Wainganga River sub-basin.

Figure 19.7 Land use map of Wainganga sub-basin.

List of Tables

Chapter 1

Table 1.1 List of datasets used for the study.

Table 1.2 Area statistics of LULC in Barasat municipality.

Chapter 2

Table 2.1 Area statistics of various LU/LC classes over study area.

Table 2.2 Area statistics of socio-economic vulnerability index in study area.

Table 2.3 Area statistics of geo-environmental hazard index in study area.

Table 2.4 Area statistics of coal mining risk index in study area.

Chapter 3

Table 3.1 Specification of bistatic scatterometer system.

Table 3.2 Coefficient of determination (R

2

) between P (dB) and soil moisture con...

Chapter 4

Table 4.1 Morphometric parameter calculated in the Tapi Basin.

Table 4.2 Basin Relief (R).

Table 4.3 Drainage density of all streams.

Table 4.4 Stream frequency of the study area.

Table 4.5 Form factor, circulatory ratio, and elongation ratio of basin area.

Table 4.6 Results of morphometric analysis Tapi River Basin.

Chapter 5

Table 5.1 Specifications of FTS (source: GOSAT Pamphlet 7th edition) [4].

Table 5.2 Specifications of CAI (source: GOSAT Pamphlet 7th edition) [4].

Table 5.3 List of GOSAT data products distributed/to be distributed from GOSAT D...

Chapter 6

Table 6.1 Statistics of LU/LC of Jangal Aurahi village of Gorakhpur district.

Table 6.2 Statistics of LU/LC of Jangal Aurahi village of Gorakhpur district.

Chapter 7

Table 7.1 Materials and data sources of the study area.

Table 7.2 Specific suitability level per factor for the potato crop. Sources: Si...

Table 7.3 Seven-point weighing scale for pairwise comparison.

Table 7.4 Pairwise comparison matrix of the selected criteria in AHP.

Table 7.5 Resulting overall areas for individual suitability classes of potato c...

Chapter 8

Table 8.1 Sub-class weightage index.

Table 8.2 Area of different vulnerability zones.

Chapter 10

Table 10.1 Morphometric parameters, their derivations, and references.

Table 10.2 Stream orders and their characteristics.

Table 10.3 Numeric detail of areal morphometric parameters.

Table 10.4 Numeric detail of relief morphometric parameter.

Chapter 11

Table 11.1 Bore well location of BIT Mesra. (Source: Water Supply Department BIT...

Table 11.2 Water withdrawal from Jumar River. (Source: Water Supply Department B...

Table 11.3 Water requirement.

Table 11.4 Water demand in campus facilities.

Table 11.5 Average water flow rate.

Table 11.6 Water availability based on OHT of Hostel 9.

Table 11.7 Water supply and demand gaps at Hostel 8.

Chapter 12

Table 12.1 Characteristics of Landsat satellite imageries.

Table 12.2 Eleven-year LULC change statistics during October 2005 and October 20...

Table 12.3 Eleven-year LULC change in class-wise statistics during October 2005 ...

Table 12.4 Eleven-year LULC change statistics during February 2006 and February ...

Table 12.5 Eleven-year LULC change in class-wise statistics during February 2006...

Chapter 13

Table 13.1 Major components and sub-components comprising Livelihood Vulnerabili...

Chapter 14

Table 14.1 Pairwise comparison matrix.

Table 14.2 Saaty’s ratio index for different values of n.

Table 14.3 Weights of the thematic maps of the potential groundwater.

Chapter 15

Table 15.1 Area of LULC classes in km

2

.

Table 15.2 LULC transition matrix (1988–1998).

Table 15.3 LULC Transition matrix (1998–2008).

Table 15.4 LULC transition matrix (2008–2018).

Table 15.5 Theoretical error matrix of the change detection (1988–2018).

Chapter 16

Table 16.1 Damage caused to houses due to cyclone Fani in Odisha. Source: Odisha...

Table 16.2 Assessment of damage caused to housing due to Fani. Source: Odisha Di...

Chapter 18

Table 18.1 District wise number of modern rice mills in Kerala (2016). Source: R...

Table 18.2 Taluk wise distribution of rice mills in Ernakulam (2015). Source: RM...

Table 18.3 Figure showing requirement of water and the amount of effluent genera...

Table 18.4 Anticipatory figure showing requirement of water and amount of efflue...

Table 18.5 Physio-chemical analysis of rice mill effluent from second model clus...

Chapter 19

Table 19.1 Wainganga River Basin watershed area.

Table 19.2 The land use pattern of the Wainganga sub-basin.

Table 19.3 Inflows.

Table 19.4 Average observed (monsoon) runoff at CWC sites in the Wainganga River...

Guide

Cover

Table of Contents

Title page

Copyright

Preface

Acknowledgement

Begin Reading

Index

Also of Interest

End User License Agreement

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Sustainable Development Practices Using Geoinformatics

Edited by

Shruti Kanga

Varun Narayan Mishra

Suraj Kumar Singh

This edition first published 2021 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

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Preface

The sustainable development refers to the qualitative and quantitative stability in the use of natural resources. It involves equilibrium between anthropogenic activities as influenced by social activities, acquired knowledge, applied technology, and food production. Sustainability attempts to address the issues such as resource degradation, deforestation, ecosystem loss, and environmental deterioration from global to local scale. The sustainable use and management of essential natural resources cannot be done without considering the direct and indirect impacts of human. It is required to apply an interdisciplinary approach in order to ensure long-term conservation of natural resources and its sustainable use at ecological and socioeconomic perspectives.

Geoinformatics, including Remote Sensing (RS), Geographical Information System (GIS), and Global Positioning System (GPS), has tremendous potential to effectively monitor the natural resources and addressing the concerns related to sustainable development and planning of society. RS is a quick and cost-effective technique to measure the location and spectral properties of earth surface features in comparison to traditional ground-based surveying. It provides reliable geospatial information for comprehensive sustainable development plans, policy making, and decision. GIS is a computer-based system used to digitize remotely sensed data matched with various ground-truth data, which are geo-coded using a GPS. It is able to manipulate, analyze, and display spatial database. Applications of Geoinformatics include land use change and planning, agriculture and soil, water resource management, forest resource mapping and management, glacier mapping and monitoring, climate change, disaster management, and many more.

Sustainable applications of Geoinformatics have become more essential in understanding various characteristics of Earth surfaces with the launch of Landsat mission in the 1970s. Many studies of direct relevance to the sustainable development and management have been reported. However, few studies have been reported using the harmonized approach of core science and research basics, as there are larger concerns of capacity building to use Geoinformatics in sustainable development practices and management. This could be overcome by taking the advantages of Geoinformatics into consideration to the scientific and research communities. The book entitled “Sustainable Development Practices Using Geoinformatics” contains chapters written by well-known researchers, academicians, and experts. The potential readers of this book are scientists, environmentalists, ecologists, policy makers, administrators, university students, urban planners, land managers, and professionals working in the field of sustainable development and management of natural resources.

In Chapter 1, multi-temporal Landsat images are used to investigate the change in variability of surface temperature in the Barasat municipal area, West Bengal, India. A correlation analysis is performed between Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST) to show the urban growth and its pattern and trend in relation to surface temperature variation. This study is very useful for investigating the changes in environmental condition due to human activity in an urban area.

In Chapter 2, attempts are made to estimate the geo-environmental hazards and risks in South Karanpura Coalfield region using information on land use/land cover (LU/LC), aerosol optical thickness (AOT), precipitable water vapor (PWV), and temperature conditions integrated with socio-economic vulnerability using Geoinformatics approach. Most of the risk-prone zones are found to present in the vicinity of industry and mining areas with higher population density. This study provides a basis to allocate resources for risk mitigation, improve community preparedness, and prepare cost-effective emergency planning.

In Chapter 3, a co-polarized radar system is investigated for the estimation of soil moisture along specular direction. The data are collected by indigenously designed ground-based scatterometer system for 20°–60° incidence angles at steps of 10° in the specular direction for HH- and VV-polarizations at L-band. In this study, a hybrid machine learning algorithm combined with fuzzy inference system and artificial neural network called neuro-fuzzy inference system were evaluated for the estimation of soil moisture. The performance index Root Mean Squared Error (RMSE) was used to evaluate the estimation efficiency of the algorithm. This study is very useful for accurate and timely soil moisture estimation for agricultural practices.

In Chapter 4, a study is conducted for detailed morphometric analysis of Tapi basin using Geographic Information System (GIS) technique. Different morphometric parameters analyzed, viz., stream order, stream length, bifurcation ratio, drainage density, relief ratio, drainage density, stream frequency, texture ratio, form factor, circulatory ratio, elongation ratio, etc., are calculated. The stream order of the basin is mainly controlled by lithological and physiographic conditions of the area. The present study will be helpful for sustainable water resource management and agricultural applications.

In Chapter 5, the demand for fossil fuels is increasing speedily with the rapid population growth and development. It is a leading factor of greenhouse gases emission, global warming, and climate change. There are some satellites are available to monitor the concentration of these gases in the atmosphere. This chapter described the importance and capacity of GOSAT satellite to observe and monitor the global distribution of carbon dioxide (CO2). The kriging method is applied to analyze the global distribution of CO2 during 2009 to 2020 for the months of December, January, February, and March.

In Chapter 6, a study is performed for micro-level planning and development of natural resources available in Jangal Aurahi village, Gorakhpur district, using high resolution satellite images like CARTOSAT-I, LISS IV merged, and DEIMOS. The basic objectives are to map, monitor, and manage existing resources, facilities, and infrastructures of a village. This kind of study will be very useful for the decision makers and planners to prepare the action plans for all the resources available within the rural area

In Chapter 7, land suitability evaluation has been performed for potato crop in the Sagar Island using multi-criteria decision-making (MCDM) and Analytical Hierarch Process (AHP) methods. To find out more accurate suitability for potato crops, the derived suitability zones for the have been veteran by compared criteria-based suitability map and present land-use map using weighted sum overlay techniques in spatial analysis method. The techniques employed in this study provide valuable information that could be utilized by farmers to choose the suitable cultivation areas for potatoes at local level.

In Chapter 8, a geospatial technology assisted overlay and index approach is applied to derive a landslide susceptibility zonation map for Western Ghats, India. Different thematic layers responsible for landslide are developed in GIS platform. The sub-class weightage indexes are feed in to the respective thematic layer in the GIS platform to generate landslide vulnerability zonation map into very low, low, moderate, high, and very high categories. An accurate spatial mapping of landslide vulnerability is important for disaster mitigation and regional planning.

In Chapter 9, the underground mining activities may have devastating effect on the forest land and its soil. This chapter provided the review of existing information of the subsidence impacts on forest lands. It showed that there are reasonably impacts on the topography, hydrology, and soil properties of the area. These multiple impacts need to be considered at local level with particular concern to the interaction of subsidence disturbances with the forest ecosystems. This work can be useful to suggest appropriate adaptation strategies during subsidence for the suitable sustenance of healthy forest environments.

In Chapter 10, an approach based on GI Science is demonstrated for Morphometric analysis of Gomati watershed from the lesser Himalaya terrain in district Bageshwar, Uttarakhand. Several morphometric parameters are calculated and analyzed. The drainage density for Gomati river basin is found to be 0.81 km/km2 which show the high runoff in the channels. The methods utilized in this study will be helpful for the planners and decision makers in the development and management of the basin.

In Chapter 11, water is an essential natural resource for human being. The adequate supply of water is of highest importance for survival. In this paper, water audit has been attempted for the campus of Birla institute of Technology, Mesra, Ranchi with case studies of two hostels. The water audit is assessed lobby wise to conclude the gaps. Water harvesting potentials was assessed for the study area, and recommendations were made for water management and planning.

In Chapter 12, this study is conducted to analyze LULC changes during the period of 2006 to 2017 in Durg block of Chhattisgarh state, India using multi-temporal Landsat satellite imageries. Thematic layers and maps for the year of 2005 and 2016 (post-monsoon) and 2006 and 2017 (pre-monsoon) are prepared. A map is generated for LULC change analysis with the help of the intersection tool. The LULC categories showed changing patterns during the period. This type of study can be very useful for policy makers and planners for the management of land resources.

In Chapter 13, this study attempts to apply livelihood vulnerability index (LVI) for the assessment of the livelihood risks of the vulnerable communities because of climate change. The socio-economic vulnerabilities suggested by IPCC’s three contributing factors such as exposure, sensitivity, and adaptive capacity of the region are taken into consideration. The study revealed that livelihood options in the region are limited and mainly dependent on agriculture and labor sector. The communities in the region are highly vulnerable due to changing climatic conditions.

In Chapter 14, this work is carried out for suitable site selection for the sustainable urban groundwater management in the Dhanbad Block in Jharkhand state, India. Different datasets such as Landsat 8 satellite image, DEM, Toposheet, and secondary data are used in this study. It facilitated to know the complexities of a dynamic phenomenon like suitability site sustainable water management, land use/land cover benefits, and urban development planning pattern. The weights have been assigned to different layers as per the need for the acceptable site selection for the sustainable groundwater management planning.

In Chapter 15, this paper presents a study to detect changes in land use and land cover over a period of 30 years from 1988 to 2018 in the Kamrup district of Assam, India. Multi-temporal Landsat satellite images of year 1988, 1998, 2008, and 2018 are used in this study. The images are classified into different categories using visual interpretation and manual digitization methods. The change matrix approach is used for evaluating the net loss and gain of different land use and land cover classes. This study can be useful for sustainable urban management and land use planning in the region.

In Chapter 16, on May 03, 2019, a rare summer cyclone named “Fani” hit Puri, a small coastal town of Odisha, India. This cyclone resulted into the loss of 64 human lives and affected about 16.5 million people in 18,388 villages of the entire state. It also severely affected power, telecommunication infrastructure, and road services. The damage to housing has been extensive, particularly in the Puri district of Odisha. This examines how climate resilient houses with “Build Back Better” features can save valuable human lives through use of eco-friendly, durable, cost effective, and non-pollutant building materials.

In Chapter 17, disasters resulting in substantial loss of deaths, disruption of normal life, and the developmental process for years to come. This paper systematically describes the application Geoinformatics technique for disaster management. It has robust data handling capabilities that is ideal for disaster risk reduction, mitigation, and management from global to local scales. This technique is capable to create awareness to dissemination of information during disaster mitigation, preparedness, and response as part of disaster management measures.

In Chapter 18, the food processing industries play a key role in economic development of any country. This work analyzes the locational factors how favored in rice mill clustering in Ernakulam district, Kerala state, India. The environmental concerns were identified through field and house hold survey in the select areas or panchayats of Kalady, Okkal, and Koovappady. The physio-chemical analysis of waste water effluent carried out revealed the organic and inorganic presence of the pollutants and its extent.

In Chapter 19, this study demonstrates the importance of the Digital Elevation Model (DEM) and satellite images for evaluation of drainage and extraction of their relative parameters for the Wainganga River watershed area of the Godavari River, India. Several hydrological parameters including drainage analysis, topographic parameters, and land use patterns were evaluated and interpreted. The climatic condition based on hydrological investigation, of the basin is characterized by hot summer from March to May followed by a rainy season from June to September using.

This edited book entitled “Sustainable Development Practices Using Geoinformatics” contains chapters written by prominent researchers and experts. The key focus of this edited book entitled “Sustainable Development Practices Using Geoinformatics” is to replenish the available resources on the topic by integrating the concepts, theories, and experiences of the experts and professionals in this field.

Acknowledgement

The completion of this edited book entitled “Sustainable Development Practices Using Geoinformatics” could not have been possible without the grace of almighty God.

We are grateful to Hon’ble Sunil Sharma, Chairperson, Suresh Gyan Vihar University, Jaipur for his encouragement and support. The words cannot express our indebtedness to Hon’ble Dr. Sudhanshu, Chief Mentor, Suresh Gyan Vihar University, Jaipur for his continuous guidance, expert suggestions and motivation during the completion of this edited book.

Special thanks are due to all the reviewers for their time to review the chapters. The editors would like to express heartfelt gratitude to all the members of editorial advisory board for their endless support and valuable instructions at all stages of the preparation of this edited book. We would like to mention the names of the members of editorial advisory board as Prof. M. S. Nathawat, IGNOU New Delhi, India; Dr. (Mrs) Tapati Banerjee, NATMO, Kolkata, India; Prof. Milap Punia, JNU, New Delhi, India; Prof. Rajendra Prasad, IIT (BHU), Varanasi, India; Dr. Devendra Pradhan, IMD, Government of India, New Delhi, India; Prof. Manoj K. Pandit, University of Rajasthan, Jaipur, India, Dr. Snehmani, SASE, DRDO, Chandigarh, India, Prof. Shakeel Ahmed, Jamia Millia Islamia, New Delhi, India; Prof. Suresh Prasad Singh, Himalayan University, Itanagar, India; Mr. Peeyush Gupta, NMCG, Ministry of Jal Shakti, Government of India, New Delhi, India

To all the colleagues, friends, and relatives who in one way or another shared their constant and moral support. The editors are eternally thankfully to Scrivener Publishing for giving the opportunity to publish with them.

Dr. Shruti KangaDr. Varun Narayan MishraDr. Suraj Kumar SinghEditorsJune 2020

1The Impact of Rapid Urbanization on Vegetation Cover and Land Surface Temperature in Barasat Municipal Area

Aniruddha Debnath, Ritesh Kumar*, Taniya Singh and Ravindra Prawasi

Haryana Space Applications Centre, Hisar, Haryana, India

Abstract

India is a developing country and its growing phase is facing the trio of urbanization, modernization, and globalization. The study pertains to find out the impacts of rapid urban development on vegetation cover and its inter-relationship with the variability of Land Surface Temperature (LST). The study area, Barasat municipality, is facing rapid urbanization since mid of 1990s; hence, the number of people residing in Barasat is increasing rapidly, resulting in dense, concrete, and high-rise buildings. The Barasat city is adjacent to Kolkata metropolitan city and is a part of Greater Kolkata. Therefore, there is escalation in number of multi-storied buildings along with proliferating population leading to urban sprawl in the study area. These facts promote Barasat to be an Urban Heat Island (UHI). The study aims to show the change in variability of surface temperature from 2001 to 2017 with the help of geospatial techniques and using Landsat data of multiple dates in order to uncover the modification/variation in the urbanization and then correlate it with NDVI (Normalized Difference Vegetation Index), and LST. The 17 years’ time scale is very small period for change detection of urban land use change but enough to show the urban growth and its pattern and trend in relation to surface temperature variation. The remote sensing and GIS provides very useful tool for the analysis of changes in environmental condition due to human activity in the study area.

Keywords: Urbanization, UHI, NDVI, LST

1.1 Introduction

Urban land is primary resultant feature on the Earth surface, induced by human activities from centuries. Urban area is defined as the area having facilities of higher administrative departments in which most of the population belong to secondary and tertiary division, these segments comprises a city or a town, etc. (McGranahan, Satterthwaite, and International Institute for Environment and Development 2014). Urbanization can be simply defined as the conversion of any spatial entity from rural to urban with the help of technology and sustainable uses of resources (Datta 2007). Since ancient era, modification, and transformation of the geographical areas are steady, and great example of this is urban landform. World’s earliest industrial revolution took place in Britain in the 18th century, which caused the rural mass movement toward cities. This era was considered to be the footstep of urbanization. However, in India, the wind of urbanization was initiated by the Britishers, while India being once a domicile of British Empire. The modification in the settlements and settlement zone continue to vary till date, which commences urban sprawl (Narayan 2014).

In the phases of urban development, continuous changes on land surface are observed, from small houses to tall buildings, agriculture to industry, pervious surface to impervious (paved) surface, kaccha road to highway, etc. (Grimmond 1998; Gál and Unger 2009). The two most important controlling factors responsible for the development and also retreat of urban region are pull factors and push factors. With the rapid urban sprawl, it results in the increase of inhabitants with a balance of demand and supply. Along with the proliferation of the crowd toward a separate area, there is burgeoning demand of supply for the inhabitants, which further entice entrepreneurs. The urban sprawl cannot be controlled; hence, it appears as an interrelated network of a complex system. The socio-economic development of an urban area is an impact of migration that escalates the growth of urban society. The constant process of growth leads to urban spread and agglomeration, which is continually an ongoing process (Yeh and Li 2001).

The scope of application of “Remote Sensing and GIS” is widening day by day from cryosphere to biosphere to hydrosphere to atmosphere, etc. Subject to mankind, most of application parts are broadly used like study of land cover dynamics, spatial growth, trend analysis, rainfall monitoring, zoning of hazard risk assessment mapping, global climatic imbalance, atmospheric phenomenon, etc. (Wijeatne and Bijker 2006). Contemplating the urban application part, it is largely used in the fields of urban morphology structure, urban flooding, urban planning, ventilation mapping, urban climatic zones, urban pollution, urban population, urban growth modelling, etc. (Grimmond and Oke 1999; Gál and Unger 2009; Mirzaei 2015; Wong, Nichol, and Ng 2011). With the advancement in technologies, it is aimed to gather data from the underground and under water also. Various endeavors were done to discover the prototype of urban growth and examine the several spatial patterns of urban area with the help of various algorithms including geographical weighted regression, Sleuth model, multivariate regression, etc. In India, the urban growth scenario is changing rapidly and poses complexity in measuring urban growth parameters, but use of remote sensing and GIS techniques are becoming handy in to perform analysis on urban growth and its impact on natural vegetation and local surface air temperature.

Urban sprawl is a continuous process, which leads to decrease in the amount of green space and increase in the density of concrete garden of buildings (Capozza and Helsley 1989). To demarcate the consistency of vegetation canopy layer, NDVI (Normalized Difference Vegetation Index) is a very useful index (Bhandari, Kumar, and Singh 2012; Volcani, Karnieli, and Svoray 2005). The increase density of buildings is the major cause of increasing surface air temperature that is trapped by the building infrastructure (Unger, Sümeghy, and Zoboki 2001). From this point of view, the concept of Land Surface Temperature (LST) is inspired, which is the temperature of the near surface area within specified limit, but it is entirely different from atmospheric temperature. The LST is a new emerging concept in the field of remote sensing and it plays a key role in establishing an inter-relation between NDVI and LST (Deng et al., 2018). The relationship between LST and NDVI ponders on the concept of surface temperature in cram-full areas (Yuan and Bauer 2007). From this point of view, the area can be delineated as a Heat Island as the core area of the city experiences relatively high temperature than the surrounding and rural areas. The domain of UHI can be easily detected using these two crucial indices. The urban area that is comparatively hotter than the surrounding area can be considered as a UHI (Tso 1996).

India is home of 1,210,193,422 people (Census of India, 2011) and having a population density about 382 persons/km2, which represent a mass of population pressure on less amount of land. As a developing country, India is bound to see increase in urban area or converting land use into boundless built-ups. India is facing force of population toward urban areas and converting land use into boundless built-ups. Since independence, the growth rate of urban population is gradually rising and recorded 17.46% as per Census of India, 2011. Kolkata is one of the renowned metropolitan cities having the population density of approximately 24,000 persons/km2 (Census of India, 2011), one of the highest in the world. The suburbs (Barasat, Barracpore, Kalyani, Kashba, Rajarhat, etc.) are having density of almost 9,000 persons/km2, which is increasing rapidly. Because Kolkata is having limited land, the suburbs are developing at faster pace than the core city from last few decenniums. Barasat city is adjacent to Kolkata; therefore, the branches of Kolkata city are expanding toward the outskirt areas at faster rate and that can be clearly estimated from the difference in 2001 and 2011 Census.

1.2 Study Area

Barasat city is in the northern outer periphery of Kolkata city, in West Bengal, India, facing problems of unplanned urbanization in a short span of time after getting declared as district head quarter town within the jurisdiction of Kolkata Metropolitan Development Authority (KMDA). It has a total area of 31.41 km2 and extends between 88°27’ E and 88°31’E longitude and between 22°40’58” N and 22°44’44”N latitude (Figure 1.1). There are 32 wards in Barasat municipality. The growth rate of population in this town is very high and approximates around 3.5% per year. As per the provisional reports of census of India, population of Barasat in 2011 is 283,443. Barasat has a population density of 9,023 persons/km2.

1.3 Datasets and Methodology

1.3.1 Datasets

The satellite images of Landsat 5 TM for the year 2001, Landsat 5 TM for the year 2011, and Landsat 8 OLI and TIRS for the year 2017 were obtained from USGS official website and processed for analysis (Table 1.1), and to quantify the changes due to urbanization. The datasets used for various analyses and for preparing different maps along with census data are listed in Table 1.1.

1.3.2 Methodology

Landsat images of different time are very helpful for the analysis of land use and land cover change pattern and to measure the increase in urban built-up area (Song et al., 2001). To meet the objectives of the study, the following procedures were done. Pre-processing is an essential step for removing the atmospheric noise and haze, which is there in the image due to atmospheric scattering of solar radiation due to atmospheric elements (Chander, Markham, and Helder 2009). Satellite images were classified using Maximum Likelihood classification algorithm because it gives better accuracy than other available techniques like box classifier, minimum distance to mean, etc., available in published literature (Lyon et al., 1998; Reis 2008; Patidar and Sankhla 2015).

Figure 1.1 Location map of Barasat municipality.

Table 1.1 List of datasets used for the study.

Satellite/Sensor

Date

Source

Landsat 5 (TM)

January, 2001

USGS

Landsat 5 (TM)

January, 2011

USGS

Landsat 8 (OLI and TIRS)

January, 2017

USGS

MOD11A1

January: 2001, 2011, 2017

USGS

Census

2011

Census of India

Barasat municipality boundary

2014

Barasat municipality

The study area was classified into six classes inclusive of built-up area, agricultural fallow, bare land, water body, green space, and built up with green space. The main aim was to measure the increase in built-up area, which is an indicator of urbanization. Further, accuracy of the classified images was calculated, and confusion matrix generated to highlight the user accuracy, producer accuracy, and Kappa statistics thus obtained (Foody 2002; Berberoglu and Akin 2009).

The urban growth is a process of urbanization, which always follows a pattern of development. In urban areas, the pattern differs from core to periphery region. In this study, two different types of pattern had been observed, one was concentric, and another one was linear. The concentric circular pattern was found in the areas of tri-junction of roads or in the “Y” point where the development is very rapid and shows multiplier effect of growth. The linear pattern of development was observed from 2001 to 2017 along the sides of roads and railway lines (Figure 1.2), which is very common in this region. Hence, both the developmental patterns are simultaneously affecting the environment of the area significantly.

Figure 1.2 Road and railways network in Barasat municipality.

NDVI is a very popular vegetation index used to measure the biomass content of vegetation with respect to its spatial entity (Volcani, Karnieli, and Svoray 2005). Two different bands, NIR and Red of remote sensing data, are used for calculating the NDVI (Bhandari, Kumar, and Singh 2012; Yin et al., 2012). The formula for NDVI (Eq. 1.1) calculation is as follows:

(1.1)

Remote sensing helps in deriving LST, which is used in various studies related to local climatology, meteorology, and climate change, etc., as the observations collected from the ground cannot provide much detailed information over a larger area (Wu et al., 2015). LST is the temperature radiated by the surface and measured within limited boundary of lower atmosphere from the surface, which is proportionally dependent on land surface emissivity (Wan and Dozier 1996; Wang et al., 2015; Isaya Ndossi and Avdan 2016). The LST is technically different from the Atmospheric temperature and is largely affected by the urban canopy layer. LST calculation was done with the help of Inversion of Planck’s Function (Isaya Ndossi and Avdan 2016; Zhang, Wang, and Li 2006; Srivastava et al., 2014; Artis and Carnahan 1982; Sobrino, Caselles, and Becker 1990). The formula for LST calculation (Eq. 1.2) is as follows:

(1.2)

where Ts = land surface temperature (°C), BT = brightness temperature (K), λ = wavelength of the emitted radiance, ρ = (h * c/σ) = 1.438 *10−2mK, and ε = land surface emissivity

1.4 Results and Discussion

The results of the data analyzed for this study are discussed in four sections.

1.4.1 Pattern of LULC in Barasat

The temporal images of study area were classified in six classes with the help of Maximum Likelihood algorithm. The classification scheme of Land Use/Land Cover (LULC) includes the classes (1) built-up area, (2) built-up with green space, (3) agricultural fallow, (4) bare land, (5) green space, and (6) water bodies (Figure 1.3). A drastic change is observed in the ratio of built-up area and built-up area with green vegetation in the center of the city. Moreover, the aggregation and expansion in built-up area changes rapidly after 2011.

The total area of the Barasat municipality is about 34.5 km2 as obtained by the digitized vector layer. The observed change in built-up area is ranging from 6.79% in 2001 to 29.23% in 2017, and simultaneously, there is highest decrease observed in green space ranging from 27.99% in 2001 to 11.50% in 2017 (Table 1.2). Built-up area with green space is increasing, although at a slower rate than the pure built-up area, which implies that the municipality or concerned agency for greenery in urban space is not giving serious thought to the importance of green space in urbanization. There is decrease in surface area of water body and reduction in number of water body within the study area, which may lead to water crisis in near future.

Figure 1.3 Land Use/Land Cover map of Barasat municipality (2001, 2011, and 2017).

Table 1.2 Area statistics of LULC in Barasat municipality.

Class Name

Area in %

2001

2011

2017

Bare land

16.70

13.39

8.25

Water body

2.40

2.36

1.47

Agricultural fallow

7.92

4.75

3.74

Built-up area

6.79

16.38

29.23

Built-up with green space

38.20

40.79

45.80

Green space

27.99

22.33

11.50

1.4.2 Urban Sprawl

It is imperative from above classified (Figure 1.3) remote sensing images of different time period that the LULC in Barasat municipality area is more inclined toward urbanization. Moreover, the rate of urbanization in the study area has changed rapidly within second decade than the first decennia. It is clear from Figure 1.4 that there are two peculiar patterns of urban growth found in the study area. Initially, the urbanization in the study area shows the linear pattern followed by concentric development in built-up area. The Figure 1.4