This document outlines a thesis presentation on using GIS and remote sensing for flood risk management. It discusses flooding issues, the role of GIS and remote sensing in flood studies, and provides an introduction and literature review on relevant topics. The document then presents the study area, objectives, and methodology which involve analyzing land use/cover changes, identifying risk factors, and creating a flood risk map using analytic hierarchy process.
Surface Water modelling using Remote SensingArk Arjun
1) The document discusses remote sensing and runoff estimation using the SCS curve number method. Remote sensing involves obtaining information about objects through non-contact sensors.
2) Runoff estimation is the first step in water management. The SCS-CN method estimates runoff as a function of land use, soil type, and rainfall.
3) The study area's topographic maps, rainfall data, land use maps, and soil data were collected and used to classify land cover, model rainfall-runoff, and estimate runoff volume using the SCS-CN method.
Aims at providing expertise for preparing flood mapping and estimating flood risks.
An integrated AHP and GIS analysis techniques are utilized for the case of Gujarat state.
Use of different flood causing elements like rainfall distribution, elevation, drainage network and density, land use and land cover, and
distance from the river stream.
The index developed is shown with a varying range from high to low with changing colours.
The damaging effect of most common natural disaster flood can be minimized through the area risk assessment with the help of GIS technology and Remote Sensing techniques. With the help of Prayagraj district map and corresponding satellite images, some flood causing criteria raster layer, flood risk map can be obtained by multi-criteria evaluation approach AHP.
Application of GIS and Remote Sensing in Flood Risk ManagementAmitSaha123
Introduction to catastrophic disaster flood. Its impact on environment and human lives. GIS and Remote Sensing based solutions that can provide key approaches to mitigate flood related hazard as well as vulnerablities.
Flood risk mapping using GIS and remote sensingRohan Tuteja
This document presents a study on flood risk mapping in the Kalyan-Dombivli area of India using GIS techniques. It outlines the scope of the study, aim and objectives which are to identify low-lying areas and analyze flood risk factors. The methodology includes generating GIS data like land use/cover maps from remote sensing data and field surveys. Flood risk is assessed based on physical, demographic, and socioeconomic vulnerability indicators as well as hazard indicators like rainfall. The results found increased risk areas due to changes in land use/cover, improper drainage networks, and population growth. Recommendations include mainstreaming disaster risk reduction and using remote sensing for database management.
Iirs overview -Remote sensing and GIS application in Water Resources ManagementTushar Dholakia
Remote sensing and GIS application in Water Resources Management- By S.P. Aggarval spa@iirs.gov.in Indian Institute of Remote sensing ISRO, Department of space, Dehradun
Application of Geo-informatics in Environmental ManagementMahaMadhu2
Geo-informatics is the science and the technology which develops and uses information science, infrastructure to address the problems of geography, geosciences and related branches of engineering. “The art, science or technology dealing with the acquisition, storage, processing, production, presentation & dissemination of geo-information“. Perhaps the most important concern for all of us today is protecting the environment we live and breathe in. Climate change issues are creating havoc with erratic weather patterns affecting everything from crop production to untimely melting of ice glaciers.
There is a lot to worry about and immediate action is definitely required. It’s not that the world has not geared up to take corrective actions, but we need to do more, and Geo-informatics can help us achieve that. Geo-informatics is a powerful platform which enables every sector to perform better and the environment is no exception! Coupled with a digital map, GIS allows a user to see locations, events, features, and environmental changes with unprecedented clarity, showing layer upon layer of information such as environmental trends, soil stability, pesticide use, migration corridors, hazardous waste generators, dust source points, lake remediation efforts, and at-risk water wells. Effective environmental practice considers the whole spectrum of the environment. ArcGIS® & other GIS technologies offers a wide variety of analytical tools to meet the needs of many people, helping them make better decisions about the environment. People in the environmental management community use GIS to organize existing information and communicate that information throughout their organizations. GIS can be used as a strategic tool to automate processes, transform environmental management operations by garnering new knowledge, and support decisions that make a profound difference on our environment.
MSSRF 30 Years conference. Presented by Dr.Diwakar, Department of Space
Indian Space Research Organisation
Indian Space Research Organisation
Department of Space
Government of India
Surface Water modelling using Remote SensingArk Arjun
1) The document discusses remote sensing and runoff estimation using the SCS curve number method. Remote sensing involves obtaining information about objects through non-contact sensors.
2) Runoff estimation is the first step in water management. The SCS-CN method estimates runoff as a function of land use, soil type, and rainfall.
3) The study area's topographic maps, rainfall data, land use maps, and soil data were collected and used to classify land cover, model rainfall-runoff, and estimate runoff volume using the SCS-CN method.
Aims at providing expertise for preparing flood mapping and estimating flood risks.
An integrated AHP and GIS analysis techniques are utilized for the case of Gujarat state.
Use of different flood causing elements like rainfall distribution, elevation, drainage network and density, land use and land cover, and
distance from the river stream.
The index developed is shown with a varying range from high to low with changing colours.
The damaging effect of most common natural disaster flood can be minimized through the area risk assessment with the help of GIS technology and Remote Sensing techniques. With the help of Prayagraj district map and corresponding satellite images, some flood causing criteria raster layer, flood risk map can be obtained by multi-criteria evaluation approach AHP.
Application of GIS and Remote Sensing in Flood Risk ManagementAmitSaha123
Introduction to catastrophic disaster flood. Its impact on environment and human lives. GIS and Remote Sensing based solutions that can provide key approaches to mitigate flood related hazard as well as vulnerablities.
Flood risk mapping using GIS and remote sensingRohan Tuteja
This document presents a study on flood risk mapping in the Kalyan-Dombivli area of India using GIS techniques. It outlines the scope of the study, aim and objectives which are to identify low-lying areas and analyze flood risk factors. The methodology includes generating GIS data like land use/cover maps from remote sensing data and field surveys. Flood risk is assessed based on physical, demographic, and socioeconomic vulnerability indicators as well as hazard indicators like rainfall. The results found increased risk areas due to changes in land use/cover, improper drainage networks, and population growth. Recommendations include mainstreaming disaster risk reduction and using remote sensing for database management.
Iirs overview -Remote sensing and GIS application in Water Resources ManagementTushar Dholakia
Remote sensing and GIS application in Water Resources Management- By S.P. Aggarval spa@iirs.gov.in Indian Institute of Remote sensing ISRO, Department of space, Dehradun
Application of Geo-informatics in Environmental ManagementMahaMadhu2
Geo-informatics is the science and the technology which develops and uses information science, infrastructure to address the problems of geography, geosciences and related branches of engineering. “The art, science or technology dealing with the acquisition, storage, processing, production, presentation & dissemination of geo-information“. Perhaps the most important concern for all of us today is protecting the environment we live and breathe in. Climate change issues are creating havoc with erratic weather patterns affecting everything from crop production to untimely melting of ice glaciers.
There is a lot to worry about and immediate action is definitely required. It’s not that the world has not geared up to take corrective actions, but we need to do more, and Geo-informatics can help us achieve that. Geo-informatics is a powerful platform which enables every sector to perform better and the environment is no exception! Coupled with a digital map, GIS allows a user to see locations, events, features, and environmental changes with unprecedented clarity, showing layer upon layer of information such as environmental trends, soil stability, pesticide use, migration corridors, hazardous waste generators, dust source points, lake remediation efforts, and at-risk water wells. Effective environmental practice considers the whole spectrum of the environment. ArcGIS® & other GIS technologies offers a wide variety of analytical tools to meet the needs of many people, helping them make better decisions about the environment. People in the environmental management community use GIS to organize existing information and communicate that information throughout their organizations. GIS can be used as a strategic tool to automate processes, transform environmental management operations by garnering new knowledge, and support decisions that make a profound difference on our environment.
MSSRF 30 Years conference. Presented by Dr.Diwakar, Department of Space
Indian Space Research Organisation
Indian Space Research Organisation
Department of Space
Government of India
Flood Risk Assessment Using GIS Tools, By Dr. Omar Elbadawy, CEDARE, Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan
This document discusses the use of geographic information systems (GIS) in water resource management and assessment. It provides examples of GIS applications in watershed management, groundwater assessment, flood management, and water quality studies. It then describes a case study that developed a GIS-based decision support system to assess watershed runoff in the Kk3 Macro Watershed in India. Key steps included delineating sub-watersheds, creating soil and land use maps, determining hydrologic response units, computing runoff, and generating thematic runoff maps. The system allows users to update rainfall data and evaluate variations in spatial runoff distribution over time.
Soil erosion assessment using RUSLE and Projection Augmented Landscape Model ...ExternalEvents
Mr. José María León Villalobos, Centro de Investigación
en Ciencias de Información Geoespacia (CentroGeo),
Mexico. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.
GIS application in Natural Resource ManagementAchal Gupta
This document discusses how GIS can be used for natural resource management. It provides examples of using GIS to assess watershed management in Uttarakhand, India. Specifically, it details how GIS was used to quantify soil loss and sediment flow under different scenarios, spatially distribute those results, delineate micro-basins and watersheds, and identify suitable areas for water harvesting. The results of this analysis can help inform development actions and priorities by providing spatial information on natural resources and how they vary across a landscape.
The coastal zone of Bangladesh extends over 47,150 square kilometers and includes 147 sub-districts across 19 districts, accounting for 32% of the country's total area and home to 26% of its population. The coastal zone is divided into four main morphological zones: 1) the Ganges Tidal Floodplain west, 2) the Ganges Tidal Floodplain east, 3) the Meghna Deltaic Zone, and 4) the Eastern Hill Region. The coastal zone faces numerous vulnerabilities like sea level rise, coastal erosion, cyclones, pollution, salinity intrusion, flooding, and diseases. Integrated coastal zone management and other measures are needed to address these challenges.
This document provides an overview of remote sensing. It defines remote sensing as acquiring information about objects from a distance using sensors, without direct contact. Remote sensing occurs from various platforms, including ground, airplanes, and satellites. The history of remote sensing is discussed, beginning with aerial photography in 1909 and expanding to use of satellites, hyperspectral imaging, and more advanced technologies. A variety of applications of remote sensing are outlined, including uses in agriculture, forestry, geology, hydrology, land cover/land use mapping, and addressing national priorities such as disaster management and energy management.
SOIL MOISTURE ASSESSMENT BY REMOTE SENSING AND GISuzma shaikh
This document discusses the use of remote sensing and GIS techniques for soil moisture assessment. It provides an outline and overview of key topics including the importance of soil moisture information, conventional measurement methods, and advantages of remote sensing approaches. Two case studies are summarized that estimate soil moisture using multispectral data and analyze the relationship between NDVI and land surface temperature to estimate soil moisture levels. Remote sensing products for measuring soil moisture globally are also briefly outlined.
Applications of remote sensing in disaster managementAteeQUr2
Satellites are used across four phases of disaster management: mitigation, preparedness, rescue, and recovery. They help with tasks like risk modelling, early warning, damage assessment, evacuation planning, and monitoring weather patterns, vegetation, rainfall, and natural hazards. Specific satellites mentioned that are used include KALPANA-1, INSAT-3A, QuikScat radar, Meteosat, PALSAR, IKONOS 2, InSAR, SPOT, IRS, AMSR-E, FEWS NET, AVHRR, MODIS, Hyperion, MODIS, SERVIR, and Sentinel Asia.
The Presentation gives the overview of the process necessary for accomplishing the task for the preparation of Ground water movements and identification carried out by Rajiv gandhi national drinking water mission project.
The document provides an overview of land use and land cover (LULC) analysis using remote sensing and GIS techniques. It discusses key terminologies like land cover and land use. LULC studies are important for planning, management and monitoring programs. The methodology involves data collection, preprocessing like geometric and radiometric corrections, image classification using supervised or unsupervised methods to produce LULC maps. A case study on LULC change detection in Sikkim Himalaya, India from 1988-2017 is presented which found increases in dense forest and agriculture land areas over the study period. RS and GIS techniques are concluded to be very useful for LULC monitoring and assessment.
he management of water resources has become a critical need in Bangladesh because of growing demand for water and increasing conflict over its alternative uses. As populations expand and make various uses of water, its growing scarcity becomes a serious issue in developing countries such as Bangladesh.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
The document discusses the process of data acquisition and preparation for use in a geographic information system (GIS). It describes various data sources like maps, aerial photography, and census data. It also outlines different methods for inputting data into a GIS, such as scanning, digitizing, and field surveys. The document emphasizes that data input is often the most time-consuming and expensive part of using a GIS. It also notes that the quality of the GIS depends on the quality of the input data.
Using GIS for Water Resources Management – Selected U.S. and International Ap...Michael Baker Jr., Inc.
This document discusses the use of GIS for water resource management in the US and developing countries. In the US, GIS is commonly used for watershed management, stormwater and wastewater management, surface and groundwater management through data analysis, modeling and communication. Developing countries face challenges of limited data, expertise and resources but GIS shows promise for disaster risk reduction and basin-wide water management. The document provides examples of GIS applications in flood risk mapping, water quality assessment and decision support for water managers in Morocco.
This document discusses the role of remote sensing and GIS in disaster management. It begins with an introduction to disaster management cycles and then describes how remote sensing is used across different stages of disasters like cyclones, earthquakes, and floods for tasks such as early warning, damage assessment, and recovery planning. It provides examples of various satellites used for monitoring different disasters. The document emphasizes that while hazards cannot be prevented, remote sensing can play a key role in minimizing loss of life through preparedness, response, and rebuilding efforts after disasters strike.
Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance using aircraft or satellites. It involves the acquisition of imagery and geospatial data through the analysis of electromagnetic radiation emitted or reflected from objects such as the Earth's surface. Some key advantages of remote sensing include its ability to provide cost-effective data collection over large or inaccessible areas and to monitor changes over time. Common applications include land use mapping, agriculture, forestry, geology and natural disaster monitoring.
The document provides details about a course on fundamentals of remote sensing, including:
- The course code, module name and code, university, and department offering the course.
- An outline of the course content and schedule, divided into 3 weeks covering topics like introduction to remote sensing, electromagnetic energy and remote sensing, satellites and image characteristics, and GPS.
- Recommended assessments including tests, lab exercises, and a group project to evaluate students' understanding of the material.
Glacial lakes are found in the Himalayan region, where water is dammed by snow in winter. The water turns to snow and the lakes appear as plains of snow. In summer, the ice caps melt and the water becomes visible, making the region look like lakes. Glacial lakes can flood due to slope movements, heavy rainfall, earthquakes, floods, landslides, lake overflows, or melting ice in moraines. Floods from glacial lakes can destroy life, property, human settlements, cultivable land, and development structures like roads and hydroelectric projects. Prevention methods include identifying avalanche-prone areas, afforestation, awareness programs, relief preparation, evacuation guidance, avalanche
There are many different means of investigating the landslide-prone areas. Two types of landslide hazard evaluation methods are available. One is the direct observation and the other one is the use of technological tools. One of the guiding principles of geology is that the past is the key to the future. In evaluating landslide hazards, the future slope failures could occur as a result of the same geologic, geomorphic, and hydrologic situations that led to past and present failures. Based on this assumption, it is possible to estimate the types, frequency of occurrence, extent, and consequences of slope failures that may occur in the future. A landslide susceptibility map goes beyond an inventory map and depicts areas that have the potential for landsliding.
APPLICATIONS OF REMOTE SENSING AND GIS IN WATERSHED MANAGEMENTSriram Chakravarthy
This document discusses watershed management and the role of remote sensing and GIS applications. It begins with defining a watershed and the watershed approach. It then discusses watershed characterization, prioritization, development activities, and monitoring. Remote sensing provides synoptic data to map natural resources within watersheds. GIS is used to integrate spatial data for watershed delineation and analysis. The goal of watershed management is sustainable development through activities like water conservation, afforestation, and improving livelihoods.
The document summarizes a thesis presentation on using GIS and remote sensing for flood risk management. It introduces the topic, literature review, objectives of studying urban flood risk in Allahabad, India through analyzing land use/cover, elevation, slope, and flow accumulation data. It will apply the Analytic Hierarchy Process to determine factor weights to create a flood risk map and assess vulnerable areas. The expected outcomes are to identify high flood risk zones and provide recommendations for flood management.
A Study of Disaster Management & Geotechnical Investigation of Landslides: A ...IRJET Journal
This document summarizes several research papers on landslide disaster management and geotechnical investigations of landslides. It discusses the causes of landslides including heavy rainfall, changes to drainage patterns from development, and construction activities disturbing slopes. Methods used to study landslides are described, such as analyzing soil properties, slope stability, and factors of safety. The use of remote sensing, GIS mapping of landslide-prone areas, and statistical modeling approaches are also summarized. Recommendations are made for landslide prevention, including slope treatment and ground improvement techniques. The document provides an overview of research on landslide hazards and susceptibility assessments.
Flood Risk Assessment Using GIS Tools, By Dr. Omar Elbadawy, CEDARE, Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan
This document discusses the use of geographic information systems (GIS) in water resource management and assessment. It provides examples of GIS applications in watershed management, groundwater assessment, flood management, and water quality studies. It then describes a case study that developed a GIS-based decision support system to assess watershed runoff in the Kk3 Macro Watershed in India. Key steps included delineating sub-watersheds, creating soil and land use maps, determining hydrologic response units, computing runoff, and generating thematic runoff maps. The system allows users to update rainfall data and evaluate variations in spatial runoff distribution over time.
Soil erosion assessment using RUSLE and Projection Augmented Landscape Model ...ExternalEvents
Mr. José María León Villalobos, Centro de Investigación
en Ciencias de Información Geoespacia (CentroGeo),
Mexico. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.
GIS application in Natural Resource ManagementAchal Gupta
This document discusses how GIS can be used for natural resource management. It provides examples of using GIS to assess watershed management in Uttarakhand, India. Specifically, it details how GIS was used to quantify soil loss and sediment flow under different scenarios, spatially distribute those results, delineate micro-basins and watersheds, and identify suitable areas for water harvesting. The results of this analysis can help inform development actions and priorities by providing spatial information on natural resources and how they vary across a landscape.
The coastal zone of Bangladesh extends over 47,150 square kilometers and includes 147 sub-districts across 19 districts, accounting for 32% of the country's total area and home to 26% of its population. The coastal zone is divided into four main morphological zones: 1) the Ganges Tidal Floodplain west, 2) the Ganges Tidal Floodplain east, 3) the Meghna Deltaic Zone, and 4) the Eastern Hill Region. The coastal zone faces numerous vulnerabilities like sea level rise, coastal erosion, cyclones, pollution, salinity intrusion, flooding, and diseases. Integrated coastal zone management and other measures are needed to address these challenges.
This document provides an overview of remote sensing. It defines remote sensing as acquiring information about objects from a distance using sensors, without direct contact. Remote sensing occurs from various platforms, including ground, airplanes, and satellites. The history of remote sensing is discussed, beginning with aerial photography in 1909 and expanding to use of satellites, hyperspectral imaging, and more advanced technologies. A variety of applications of remote sensing are outlined, including uses in agriculture, forestry, geology, hydrology, land cover/land use mapping, and addressing national priorities such as disaster management and energy management.
SOIL MOISTURE ASSESSMENT BY REMOTE SENSING AND GISuzma shaikh
This document discusses the use of remote sensing and GIS techniques for soil moisture assessment. It provides an outline and overview of key topics including the importance of soil moisture information, conventional measurement methods, and advantages of remote sensing approaches. Two case studies are summarized that estimate soil moisture using multispectral data and analyze the relationship between NDVI and land surface temperature to estimate soil moisture levels. Remote sensing products for measuring soil moisture globally are also briefly outlined.
Applications of remote sensing in disaster managementAteeQUr2
Satellites are used across four phases of disaster management: mitigation, preparedness, rescue, and recovery. They help with tasks like risk modelling, early warning, damage assessment, evacuation planning, and monitoring weather patterns, vegetation, rainfall, and natural hazards. Specific satellites mentioned that are used include KALPANA-1, INSAT-3A, QuikScat radar, Meteosat, PALSAR, IKONOS 2, InSAR, SPOT, IRS, AMSR-E, FEWS NET, AVHRR, MODIS, Hyperion, MODIS, SERVIR, and Sentinel Asia.
The Presentation gives the overview of the process necessary for accomplishing the task for the preparation of Ground water movements and identification carried out by Rajiv gandhi national drinking water mission project.
The document provides an overview of land use and land cover (LULC) analysis using remote sensing and GIS techniques. It discusses key terminologies like land cover and land use. LULC studies are important for planning, management and monitoring programs. The methodology involves data collection, preprocessing like geometric and radiometric corrections, image classification using supervised or unsupervised methods to produce LULC maps. A case study on LULC change detection in Sikkim Himalaya, India from 1988-2017 is presented which found increases in dense forest and agriculture land areas over the study period. RS and GIS techniques are concluded to be very useful for LULC monitoring and assessment.
he management of water resources has become a critical need in Bangladesh because of growing demand for water and increasing conflict over its alternative uses. As populations expand and make various uses of water, its growing scarcity becomes a serious issue in developing countries such as Bangladesh.
it is highly useful for geography students in the field of remote sensing and it is in very simple and explanatory for the purpose of simplification with relevant images in this ppt.
The document discusses the process of data acquisition and preparation for use in a geographic information system (GIS). It describes various data sources like maps, aerial photography, and census data. It also outlines different methods for inputting data into a GIS, such as scanning, digitizing, and field surveys. The document emphasizes that data input is often the most time-consuming and expensive part of using a GIS. It also notes that the quality of the GIS depends on the quality of the input data.
Using GIS for Water Resources Management – Selected U.S. and International Ap...Michael Baker Jr., Inc.
This document discusses the use of GIS for water resource management in the US and developing countries. In the US, GIS is commonly used for watershed management, stormwater and wastewater management, surface and groundwater management through data analysis, modeling and communication. Developing countries face challenges of limited data, expertise and resources but GIS shows promise for disaster risk reduction and basin-wide water management. The document provides examples of GIS applications in flood risk mapping, water quality assessment and decision support for water managers in Morocco.
This document discusses the role of remote sensing and GIS in disaster management. It begins with an introduction to disaster management cycles and then describes how remote sensing is used across different stages of disasters like cyclones, earthquakes, and floods for tasks such as early warning, damage assessment, and recovery planning. It provides examples of various satellites used for monitoring different disasters. The document emphasizes that while hazards cannot be prevented, remote sensing can play a key role in minimizing loss of life through preparedness, response, and rebuilding efforts after disasters strike.
Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance using aircraft or satellites. It involves the acquisition of imagery and geospatial data through the analysis of electromagnetic radiation emitted or reflected from objects such as the Earth's surface. Some key advantages of remote sensing include its ability to provide cost-effective data collection over large or inaccessible areas and to monitor changes over time. Common applications include land use mapping, agriculture, forestry, geology and natural disaster monitoring.
The document provides details about a course on fundamentals of remote sensing, including:
- The course code, module name and code, university, and department offering the course.
- An outline of the course content and schedule, divided into 3 weeks covering topics like introduction to remote sensing, electromagnetic energy and remote sensing, satellites and image characteristics, and GPS.
- Recommended assessments including tests, lab exercises, and a group project to evaluate students' understanding of the material.
Glacial lakes are found in the Himalayan region, where water is dammed by snow in winter. The water turns to snow and the lakes appear as plains of snow. In summer, the ice caps melt and the water becomes visible, making the region look like lakes. Glacial lakes can flood due to slope movements, heavy rainfall, earthquakes, floods, landslides, lake overflows, or melting ice in moraines. Floods from glacial lakes can destroy life, property, human settlements, cultivable land, and development structures like roads and hydroelectric projects. Prevention methods include identifying avalanche-prone areas, afforestation, awareness programs, relief preparation, evacuation guidance, avalanche
There are many different means of investigating the landslide-prone areas. Two types of landslide hazard evaluation methods are available. One is the direct observation and the other one is the use of technological tools. One of the guiding principles of geology is that the past is the key to the future. In evaluating landslide hazards, the future slope failures could occur as a result of the same geologic, geomorphic, and hydrologic situations that led to past and present failures. Based on this assumption, it is possible to estimate the types, frequency of occurrence, extent, and consequences of slope failures that may occur in the future. A landslide susceptibility map goes beyond an inventory map and depicts areas that have the potential for landsliding.
APPLICATIONS OF REMOTE SENSING AND GIS IN WATERSHED MANAGEMENTSriram Chakravarthy
This document discusses watershed management and the role of remote sensing and GIS applications. It begins with defining a watershed and the watershed approach. It then discusses watershed characterization, prioritization, development activities, and monitoring. Remote sensing provides synoptic data to map natural resources within watersheds. GIS is used to integrate spatial data for watershed delineation and analysis. The goal of watershed management is sustainable development through activities like water conservation, afforestation, and improving livelihoods.
The document summarizes a thesis presentation on using GIS and remote sensing for flood risk management. It introduces the topic, literature review, objectives of studying urban flood risk in Allahabad, India through analyzing land use/cover, elevation, slope, and flow accumulation data. It will apply the Analytic Hierarchy Process to determine factor weights to create a flood risk map and assess vulnerable areas. The expected outcomes are to identify high flood risk zones and provide recommendations for flood management.
A Study of Disaster Management & Geotechnical Investigation of Landslides: A ...IRJET Journal
This document summarizes several research papers on landslide disaster management and geotechnical investigations of landslides. It discusses the causes of landslides including heavy rainfall, changes to drainage patterns from development, and construction activities disturbing slopes. Methods used to study landslides are described, such as analyzing soil properties, slope stability, and factors of safety. The use of remote sensing, GIS mapping of landslide-prone areas, and statistical modeling approaches are also summarized. Recommendations are made for landslide prevention, including slope treatment and ground improvement techniques. The document provides an overview of research on landslide hazards and susceptibility assessments.
Comparison of multi‑infuence factor, weight of evidence and frequency ratio ...nitinrane33
Groundwater is the largest available reservoir of freshwater. But the rapid increase in the
population and urbanisation, has led to over exploitation of groundwater which imposed
tremendous pressure on global groundwater resources. Because of the hidden and dynamic
nature of groundwater, it requires appropriate quantifcation for the formulation of ground-
water planning and management strategies. The present study evaluates the efcacy of
geospatial technology based Multi Infuence Factor (MIF), Weight of Evidence (WofE)
and Frequency Ratio (FR) technique to evaluate groundwater potential using a case study
of basaltic terrain. The thematic layers infuencing the groundwater occurrence viz. rain-
fall, slope, geomorphology, soil type, land use, drainage density, lineament density, and
elevation were prepared using satellite images, hydrologic, hydrogeologic and relevant
feld data. Based on the conceptual frameworks of MIF, WofE and FR techniques these
thematic layers and their features were assigned with appropriate weight and then inte-
grated in the ArcGIS platform for the generation of aggregated raster layer which portray
the groundwater potential zones. The results of validation showed that the groundwater
potential delineated using MIF technique has a prediction accuracy of 81.94%, followed by
WofE technique (76.19%) and FR techniques (71.43%). It is concluded that for evaluation
of groundwater potential, the MIF technique is most reliable, followed by the WofE tech-
nique. The evaluated groundwater potential zones are useful as a scientifc guide to identify
the suitable location of wells and recharge structure in a cost-efcient way and also for the
development of structured and pragmatic groundwater management strategies.
This document summarizes a study that assesses flood risk in Ambala City, India using geospatial modeling. The study analyzed natural and human factors contributing to flooding. A Geographic Information System (GIS) was used to model flood risk for different return periods using hydrologic and hydraulic models. Model results showed increasing flood inundation areas from 690 to 2300 hectares with return periods from 2 to 20 years. The 5-year flood extent was validated using remote sensing imagery and field data from a 2010 flood. The flood risk modeling can help urban planners make risk-informed land use and development decisions to mitigate flooding impacts.
This study identifies landslide prone zones in Sakleshpur Talluk, Western Ghats, Karnataka, India using a frequency ratio model in GIS. Five factors were considered: slope, soil, drainage density, distance from drainage, and rainfall. The study area experienced 25 landslides in 2018 due to heavy rainfall. Frequency ratio values identified red gravelly clay soil, slopes of 20-30 degrees, distances of 250m from drainage, rainfall of 1976mm, and drainage densities of 1.3-2.3 km/km2 as most correlated with landslides. The area was classified into high, moderate, and low hazard zones, with 75.36 sq km in the high zone having a landslide density of
This document presents a quantitative morphometric analysis of the Adhala river basin in Maharashtra, India using GIS tools. It analyzes various linear, areal, and relief aspects of the basin's morphology. Key findings include:
- The trunk stream order was found to be 6th order, with a total of 3145 streams in the basin. The total stream length was calculated to be 985.20 km.
- Bifurcation ratio values indicated an elongated basin shape. Drainage density was 4.54 km/km2, suggesting high drainage.
- Relief aspects showed an absolute relief of 552 m and a relief ratio of 0.02, indicating overall low relief due to the
Evaluation of Groundwater Resource Potential using GIS and Remote Sensing App...IJERA Editor
Environment and Development are the two wheels of the cart. However, they become antagonists at some
points. It has been witnessed many a times that development is done at the cost of environment. Analysis and
assessment tools like GIS along with Remote Sensing have proved to be very efficient and effective and hence
useful for management of natural resources. Groundwater is a precious resource of limited extent. In order to
ensure a judicious use of groundwater, proper evaluation is required. There is an urgent need of planned and
optimal development of water resources. An appropriate strategy is required to develop water resources with
planning based on conjunctive use of surface and subsurface water resources. Integrated remote sensing and GIS
can provide the appropriate platform for convergent analysis of diverse data sets for decision making in
groundwater management and planning. Sustainable water resources development and management necessarily
depends on proper planning, implementation, operation and maintenance. The interpretation of remote sensing
data in conjunction with conventional data and sufficient ground truth information makes it possible to identify
and outline various ground features such as geological structures, geomorphic features and their hydrologic
characters that may serve as direct or indirect indicators of the presence of ground and surface water. Remotely
sensed data provides unbiased information on geology, geomorphology, structural pattern and recharging
conditions, which logically define the groundwater regime of an area. Groundwater resource potential has been
evaluated in Pulivendula-Sanivaripalli, Kadapa district, Andhra Pradesh, India, using remote sensing and
Geographic information system. Under this study, three thematic maps viz. Geological map (Lithology and
Structure), Geomorphological map and Hydro morphological maps were prepared. These thematic maps have
been integrated with the help of GIS. Appropriate weightage has been assigned to various factors controlling
occurrence of groundwater to assess the groundwater potential in each segment of the study area. The area has
been classified into high potential, moderate potential, low potential and non-potential zones landforms ground
water development on the basis of hydromorphological studies. Some of the favorable locations have been
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1. Thesis Presentation
on
GIS and Remote Sensing based Study of Flood Risk Management
by
Amit Kumar Saha
2017GI12
M.Tech IVth Sem
GIS Cell
Under the supervision of
Dr. Sonam Agrawal
Assistant Professor
GIS Cell
GIS Cell
Motilal Nehru National Institute of Technology Allahabad
2. • Introduction
• Literature review
• Summary table
• Research gap
• Research objectives
• Study area
• Data Required
• Methodology
• Result and Discussion
• Conclusions
• References
Contents
3. • Flood is an overflow of water that submerges usually dry area.
• Flood is a hydrological and meteorological disaster.
• Flood occurs due to overflow of water bodies by high precipitation and
snow melt.
• Water flow rate change in terms of seasons and path of flow are the other
usual causes of flooding.
• There are different types of floods as flash flood ,river flood , coastal flood,
urban flood.
• Flood is one of the most the most re-occurring natural hazard in the states
of India like Bihar, Assam, Gujarat, Maharashtra and Uttarakhand.
• Remote sensing and GIS plays an important role in mapping, monitoring
and providing spatial database for flood related studies.
• A recent catastrophic flood event in Kerala has taken more than 400 lives
and affected a total of more than a million people.
• This work is oriented towards waterlog flood disaster in the study area.
Introduction
4. • Flood
• GIS analysis
• Multi Criteria Analysis
• Land use land cover
• Factors that Effects Flood
• Analytical Hierarchy Process (AHP)
Literature review
5. • Flood is one of the common hydrological phenomena which is to a large
extent unpredictable and uncontrollable (Seenirajanet et al., 2017).
• Floods are one of the most common and devastating hydro meteorological
hazards worldwide, causing both economic losses and human fatalities
(González-Arqueros et al., 2018).
• The main causes of changes in flood risk are climatic change, changes in
land use and other anthropogenic interventions(George P. Karatzas, 2011).
• Recently, a study of 616 cities around the world indicated that floods
endanger more cities than any other natural hazard, followed by
earthquakes and storms(Xiao et al., 2017).
• India is one of the most flood-prone country in the world after Bangladesh,
and roughly one eighth of the country’s geographical area, is prone to
floods (Singh and Kumar, 2017).
• The extremely long series of floodless years may encourage the locals to
downplay the flood damage, leading to an overall neglect in the
maintenance of drainage ways(Ghoneim and Foody, 2013).
Flood
6. • Applications of geospatial science allow analysis on
four stages in flood management: flood prediction, flood preparation, flood
prevention and flood damage assessment(Wan and Billa, 2018).
• Utilization of recent technologies, such as the Geographic Information
Systems (GIS) and remote sensing, in developing flood hazard
maps is gaining increasing attention in the last couple of
decades(Dawod et al., 2013).
• Areas with higher flooding susceptibility based on input factors can be
defined by means of the weighted overlay technique in GIS(González-
Arqueros et al., 2018).
• The relative importance of the criteria can be estimated through fuzzy
analytic hierarchy process (AHP) method followed by ordered weighted
averaging(Xiao,Yi et al.,2017).
GIS analysis
7. • Multi Criteria Analysis (MCA) is a quantitative approach for integrating
multiple criteria layers for risk or vulnerability assessment(Luu et al., 2018;
Tang et al.,2018).
• MCA is a decision-making tool developed for solving complex multi-
criteria problems that include qualitative and/or quantitative aspects of the
problem(Roy, Chakravarthi et al.,2018).
• Multi Criteria Analysis(MCA) method includes the ideas of ranking and
weighting with the knowledge of experts is an index-based method which
provides an effective way of estimating the flood hazard(Xiao et al., 2017).
• Flood risk assessment is realized by the product of flood hazard zonation
and the sum of vulnerabilities derived from various vulnerability indicators
and criteria followed in deducing the social, infrastructure and land use
parameters, (Roy, Chakravarthi et al.,2018) represented by
Flood risk assessment = Hazard X f {Social, Infrastructure, Land use
Vulnerabilities}
Multi Criteria Analysis
8. LULC
• Land cover is the physical material at the surface of the earth. Land
covers include grass, asphalt, trees, bare ground, water, etc. (Cherqui
et al., 2015).
• This is the manner in which human beings employ the land and its
resources (Hong et al., 2017).
• This description of the appearance of the landscape and is generally
classified by the amount and type of vegetation, which is a reflection of its
use, environment, cultivation and seasonal phenology. Land cover is other
essential factor influences on runoff (Alexakis et al., 2014).
• Human activity have caused a net loss of 7 to 11million sq.km of forest in
the past 300 years, thus reducing the forest cover worldwide (Foley et al.,
2005).
• Many land-use practices, such as fuel-wood collection, forest grazing, and
road expansion can degrade forest ecosystem conditions even without
changing forest area (Nepstad et al., 1999).
9. • Elevation is the fundamental presentation of the topographic characteristic.
In many previous studies on flood risk assessment, DEM was directly used
as an assessment layer (Sarker and Sivertun, 2011).
• Slope indicates a degree of elevation variability in adjacent grid cells. The
flood is affected by slope. The steeper the terrain slope is, the lower the
hazard of area is (Wu et al., 2015).
• If a cell is surrounded by higher cells, the water will be stuck in it.(Xiao et
al. 2017).
• The flow accumulation is calculated by a cumulative count of the number
of grids that naturally drain into outlets. High values of flow accumulation
indicate high concentration of the water and consequently high possibility
of flooding (Papaioannou et al., 2015).
• The region near the river is easy to be flooded. The distance to river is
calculated using the Euclidean distance to the closest river channel (Y.
Chen et al., 2015).
Factors that Effects Flood
10. • Digital elevation model can be used as perfect source to derive factors as
slope, altitude, flow accumulation, TWI (Arabameri et al., 2018).
• Flow accumulation, elevation, drainage density, land use, slope, and has
been used as flood susceptibility factors(Mahmoud and Gan, 2018).
• TWI, slope percent, altitude, drainage density, land use have been found as
flood conditioning factors in Khiyav-Chai watershed in Iran(Choubin et al.,
2018).
• Reducing channel roughness in terms of hydrodynamic friction results in
faster stream flow velocities and less infiltration(Elkhrachy, 2015).
• If the drainage network is dense at any area, it will be a good indicator to
high flow accumulation path and more likely to get flooded (Islam and
Sado, 2000).
• Elevation, slope, TWI have been used for urban flood hazard zoning in
Argentina(Lutz and Fernandez, 2010).
Factors that Effects Flood
11. • The Analytic Hierarchy Process (AHP) was developed by Prof. Thomas L.
Saaty in the late 1970s and originally was applied to the marketing sector
(Dolan et al. 1987).
• Saaty (2008) proposed a procedure that involves arranging variables in a
hierarchy from which the best possible solution is determined via pairwise
comparison.
• AHP is one of the most popular methods in applying a multi criteria
decision support system to optimize decision making(Mahmoud and Gan,
2018).
• Application of AHP and GIS in an integrated manner has been successful in
various studies such as flood susceptibility mapping (Kazakis et al., 2015).
• AHP has been used widely in recent decades for flood hazard susceptibility
modelling (Masselink et al., 2017; Keesstra et al., 2018).
AHP
12. Summary table
Sl.
No.
Author Title Jrnl/Conf.
name(year)
Contribution
1 K. Sowmya ,C. M. John,
N. K. Shrivasthava
Urban flood vulnerability
zoning of Cochin City,
southwest coast of India,
using remote sensing and
GIS
Nat Hazards
(2015)
Identified various zones vulnerable to
urban flood in Cochin City
2 Shivaprasad Sharma SV,
Parth Sarathi Roy,
Chakravarthi V,
Srinivasarao G &
Bhanumurthy V
Extraction of detailed level
flood hazard zones using
multi-temporal historical
satellite data-sets –
a case study of Kopili River
Basin, Assam, India
Geomatics,
Natural Hazards
And Risk (2017)
Utilized the historical spatial data for
identifying villages falling in various
flood hazard severity zones
3 Shivaprasad Sharma S V,
Parth Sarathi Roy,
Chakravarthi V &
Srinivasa Rao G
Flood risk assessment using
multi-criteria analysis:
a case study from Kopili
River Basin, Assam, India
Geomatics,
Natural Hazards
And Risk(2018)
Described the effective utilization
of geospatial techniques for disaster
risk reduction
4 C.M. Bhatt & G.S. Rao Ganga floods of 2010 in
Uttar Pradesh, north
India: a perspective analysis
using satellite remote
sensing data
Geomatics,
Natural Hazards
And Risk(2016)
Generated flood hydrograph for five
gauge stations, anticipated the areas
to be affected in situations where
satellite images cannot be
effectively utilized
13. Summary table
Sl.
No.
Author Title Jrnl/Conf.
name(year)
Contribution
5 Arpit Aggarwal, Sanjay K.
Jain, Anil K. Lohani &
Neha Jain
Glacial lake outburst flood
risk assessment using
combined approaches of
remote sensing, GIS and
dam break modelling
Geomatics,
Natural Hazards
and Risk(2016)
Focuses on accurate mapping of the
glaciers and glacial lakes using
multispectral satellite images of
landsat and indian remote
sensing satellites
6 Dhruvesh P. Patel &
Prashant K. Srivastava
Flood Hazards Mitigation
Analysis Using Remote
Sensing and GIS:
Correspondence with Town
Planning Scheme
Water Resource
Manage (2013)
Identified the flood susceptible area
of the various zones, detected the
most vulnerable areas in terms of
submergence
7 Sanjay K. Jain • Anil K.
Lohani • R. D. Singh •
Anju Chaudhary •
L. N. Thakural
Glacial lakes and glacial lake
outburst flood in a
Himalayan basin using
remote sensing and GIS
Nat Hazards
(2012)
In the study basin, total lakes found
and flood peaks for those lakes are
determined
8 M. V. Aswathy & H. Vijith
& R. Satheesh
Factors influencing the
sinuosity of Pannagon River,
Kottayam, Kerala, India: An
assessment using remote
sensing and GIS
Environ Monit
Assess (2008)
The controls on the channel
morphology of the pannagon
river has been understood
14. Summary table
Sl.
No.
Author Title Jrnl/Conf.
name(year)
Contribution
9 Mahmoud, Shereif H.
Gan, Thian Yew
Multi-criteria approach to
develop flood susceptibility
maps in arid regions of
Middle East
Journal of Cleaner
Production
(2018)
Used potential physical risk factors to
generate flood susceptibility map for
the study area
10 R. Chacko, A.T. Kulkarni
and T.I. Eldho
Urban coastal flood
inundation modelling: a case
study of Thane City, India
ISH Journal of
Hydraulic
Engineering
(2012)
Flood simulation of a coastal urban
city is presented using the integrated
approach of hydrological model,
remote sensing and GIS
11 Muthusamy Seenirajan,
Muthusamy Natarajan,
Ramasamy Thangaraj,
Murugesan Bagyaraj
Study and Analysis of
Chennai Flood 2015 Using
GIS and Multicriteria
Technique
Journal of
Geographic
Information
System
(2017)
Generated a flood risk map in
accordance to the flood hazard
regions in chennai
12 George P. Karatzas,
Nektarios N. Kourgialas
Flood management and a
GIS modelling method to
assess flood-hazard areas —
a case study Flood
management and a GIS
modelling method to assess
flood-hazard areas — a case
study
Hydrological
Sciences Journal
(2011)
Has divided koliaris river basin into
five regions characterized by
different degrees of flood hazard
ranging from very low to very high
15. Summary table
Sl.
No.
Author Title Jrnl/Conf.
name(year)
Contribution
13 Prasad, N. N.Rama
Narayanan, Priya
Vulnerability assessment of
flood-affected locations of
Bangalore by using multi-
criteria evaluation
Annals of GIS
(2016)
Has created a vulnerability map using
the topographical layers by MCE
within the city limits of Bangalore
and categorize the flooded locations
14 Dewan, Ashraf M.
Kabir, Humayun
Islam, M. Monirul
Kumamoto, T.
Nishigaki, M.
Delineating flood risk areas
in greater dhaka of
bangladesh using
geoinformatics
Georisk
(2007)
Has presented the results of a study to
determine the flood hazard and risk
areas in Greater Dhaka using
integrated GIS and remote sensing
techniques. The greatest flood of
1998 was considered for the analysis
15 Weerasinghe, Kumari M.
Gehrels, Hans
Arambepola, N. M.S.I.
Vajja, Hari Prasad
Herath, J. M.K.
Atapattu, K. B.
Qualitative Flood Risk
assessment for the Western
Province of Sri Lanka
Procedia
Engineering
(2018)
Used weighted overlaying for flood
risk zoning and proposed flood
mitigation measures
16 Fernández, D. S.
Lutz, M. A.
Urban flood hazard zoning
in Tucumán Province,
Argentina, using GIS and
multicriteria decision
analysis
Engineering
Geology
(2010)
Used multi-criteria AHP with few
risk factors for urban flood zoning
16. • Allahabad district has been affected by flood earlier in this decade, which
should lead to detailed analysis of its recent hazard zones.
• Physical factors need to be evaluated which are responsible for flooding in
Prayagraj district.
• No risk assessment work has been done in study area by AHP multi-criteria
decision making (MCDM).
Research gap
17. • To identify flood vulnerable zones in the study area.
• To identify the area of land use land cover which is effected by the flood.
• To determine the factors that causes flooding.
• To create the flood risk map by using AHP.
Objectives
21. • Survey of India topographical maps of Prayagraj district
• SRTM DEM of 30m spatial resolution
• Entry ID: SRTM 1N24E081V3, SRTM 1N24E081V3, SRTM
1N24E081V3 and SRTM 1N24E081V3
• Publication Date: 23-SEP-14
• Landsat 8 OLI/TIRS imageries of 30m spatial resolution
• LC08_L1TP_143042_20160414_20170326_01_T1 and
LC08_L1TP_143043_20160820_20180524_01_T1 from year 2016 as
dry and wet season images
Data Required
22. • Landsat 8 OLI/TIRS imageries
• LC08_L1TP_143042_20170316_20170328_01_T1 and
LC08_L1TP_143043_20170908_20170926_01_T1 from year 2017 as
dry and wet season images
• LC08_L1TP_143042_20180303_20180319_01_T1 and
LC08_L1TP_143043_20181029_20181115_01_T1 from year 2018 as
dry and wet season images
Data Required
23. • Following tasks are performed to achieve objective 1
• Collection of Landsat data of dry and wet season for 3 years
• Stacking and mosaicking of images
• Clipping of AOI using ArcGIS
• Supervised image classification for water identification
• Reclassification and subtraction for each year
• Identification of waterlogged area
Objective 1 Methodology
24. Objective 1 Methodology Flowchart
Landsat images of dry and wet seasons of year 2016,
2017 and 2018
Stacking & mosaicking
Clipping
Supervised image classification for water identification
Delineation of water logged area
Reclassify and Subtraction
25. • Following tasks are performed to achieve objective 2
• Identified waterlog images of 3 years
• Union of waterlog raster layers
• Reclassification for single class waterlog
• Conversion from raster to polygon feature
• Editing of polygon for waterlog area feature
• Generation of LULC map through image classification
• Identification of LULC raster affected by flood
• Area calculation of each LULC class that is affected by the flood
Objective 2 Methodology
26. Objective 2 Methodology Flowchart
Water logged raster from year 2016, 2017
and 2018
Generation of LULC map through
image classification
Area calculation of each LULC class that
is affected by the flood
Union of three year waterlog layers
Raster to Polygon conversion and editing
Reclassification
27. • Following tasks are performed to achieve objective 3
• Collection of SRTM data for study area
• Mosaic of 4 DEM files
• Clipping of Prayagraj district AOI
• Projection of DEM
• Assessment of elevation of study area
• Generating of slope
• Generating of flow accumulation
• Generating of TWI
• Generating of drainage density
• Generating of roughness
• Generating of LULC map
Objective 3 Methodology
28. Objective 3 Methodology Flowchart
DEM data from SRTM
Mosaicking and clipping
Elevation
map
Slope
map
Drainage
density
map
TWIFlow
accumulation
Roughness
map
Projection from GCS to PCS
29. Objective 3 Methodology Flowchart
LULC
map
Multispectral Image from Landsat 8
from dry season of 2018
Stacking, Mosaicking and
clipping with AOI
Anderson
Classification
30. • Following tasks are performed to achieve objective 4
• Finalizing weightage for each factor through reclassification
• Pairwise comparison of all the factors
• AHP comparison matrix preparation
• Priority and weight determination
• Generating of flood risk layer through overlaying
Objective 4 Methodology
31. Objective 4 Methodology Flowchart
Weight assignment of each factor through reclassification
Pairwise comparison of all factors
AHP comparison matrix and weight calculation
Flood risk layer generate through overlaying
Elevation
map
Slope
map
Drainage
density
map
TWI
map
Flow
accumulation
map
Roughness
map
LULC
map
32. • All the proceeding for objective 1 methodology have been performed in
step-by-step manner as initially proposed.
• Waterlogged area in three different years i.e. 2016, 2017 and 2018 have
been identified.
• The mainly effected areas as hazard zones are easily observable at the
near proximity of Ganga and Tauns river.
Result and Discussion
33.
34.
35.
36. • Waterlogging area detail
Result and Discussion
Years Area of waterlog (Km2)
2016 591.74
2017 131.69
2018 47.31
37.
38. • 3 years of waterlogging have given a big picture for hazard prone zones.
• It is easily observable that majority of hazard prone zones have a good
proximity with main channels and closed water bodies.
• Other areas with dense spots of waterlogging areas are observable
between the main channels as Yamuna and Tauns.
• The union of three years of waterlog identification is capable of giving a
very good assessment information about hazard zones which can be used
further for identifying flood effected land covers and also verifying risk
prediction result.
Result and Discussion
39. • For 2nd objective, union of 3 years of waterlogging have been used for the
extraction of hazard area feature.
• Land cover areas as flood prone zones have been identified.
• The result of this objective will help to assess the damage that can be
caused by the flood as it gives the LULC classes that get submerged
during the rainy season.
• Using the hazard area feature of three years of waterlog raster has given a
primary way of assessing recent time hazard conditions over the study
area.
Result and Discussion
40.
41. • LU/LC of study area affected by flooding
Result and Discussion
Land cover Area affected (km2)
Percentage of
Submerged Land
Agriculture 232.25 37.60
Soil area 186.92 30.25
Other fallow land 103.35 16.71
Sand 54.48 8.81
Built up area 25.93 4.20
Open scrub land 15.00 2.43
Forest 0.56 0.0009
SUM 618.49 100
42. • It has been observed and also has been identified that agriculture followed
by soil area are the worst effected land covers.
• The other fallow land is the next immediate major land cover effected by
waterlog with more than 100 sq km. area span.
• The result of this objective gives necessary estimation of prioritized
LULC areas under the demand of better care for waterlogging disaster.
• More than 25 sq km. area of developed areas as built up ones are there
under hazard effect, which are sparsely located over study area.
Result and Discussion
43. • LULC of tehsils effected by flooding (in sq. km.)
Result and Discussion
LULC Allahabad Karchana Karaon Meja Saraon Phulpur Handia Bara
Agriculture 15.06 28.59 24.72 25.88 37.89 23.36 19.04 57.69
Soil area 27.66 19.25 16.13 22.27 26.58 20.65 15.51 38.85
Other
fallow
10.20 22.37 8.18 12.77 4.33 19.33 7.29 18.89
Sand 10.44 8.23 0.02 4.10 7.15 18.01 3.53 3.00
Built up
Area
8.39 3.45 0.386 1.52 3.93 4.82 1.83 1.61
Open
Scrub
0.33 1.92 2.14 5.69 0.06 0.88 0.05 3.93
Forest 0 0.02 0.20 0.22 0 0 0.01 0.10
SUM 72.08 83.83 51.78 72.45 79.94 87.05 47.26 124.07
44. • As the next part of work, tehsil wise distribution of affected LULC have
been done for detailed analysis.
• This result shows Bara, Phulpur and Karchana as top three tehsils which
are effected by flooding.
• Bara, followed by Saraon and Karchana tehsils has got more amount of
agricultural areas effected by flood.
• Another part of this result reveals Bara, followed by Allahabad and
Saraon tehsils has got major distribution of affected soil area or sparsely
vegetated land covers.
• Allahabad has got more effected built up areas than other tehsils.
Result and Discussion
45. • Seven factors have been considered as risk factors
• Elevation map, a high risk factor at low magnitude
• Slope map, a high risk factor at low magnitude
• TWI map, a high risk factor high magnitude
• Drainage density map, a high risk factor at high magnitude
• Roughness map, a high risk factor at low magnitude
• Flow accumulation map, a high risk factor at high magnitude
• Land Use Land Cover map, gives high risk regions for built up area
and low risk region for forest
Result and Discussion
46.
47.
48.
49.
50.
51.
52.
53. • AHP pairwise comparison have been done with 21 pairwise comparisons.
• Flow accumulation, TWI and drainage density have been given strong to
extreme importance in pairwise comparisons with slope and LULC.
• Pairwise comparison and normalized matrix have been formed.
• Consistency and priority vector have been obtained.
• Weightage assigning for criteria classes have been done in scale 1-10.
• Five factors were derived with four reclassified different impact classes of
weightages other than slope and LULC.
Result and Discussion
56. • AHP Consistency Index (CI) and Consistency Ratio (CR)
CI =
𝜆 𝑚𝑎𝑥 − 𝑛
𝑛 − 1
Where, 𝜆 𝑚𝑎𝑥 is the principal Eigen value and n is the number of factors
𝐶𝑅 =
𝐶𝐼
𝑅𝐼
Where, permissible inconsistency is less than 0.1 or 10%
Random Index is obtainable from table by Prof. Thomas L. Saaty,
Result and Discussion
n 1 2 3 4 5 6 7 8 9 10
RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49
57. • Weightage determination of factors(While, CR = 5.64% < 10%)
Result and Discussion
Criteria Weight
Flow accumulation 0.347
TWI 0.245
Drainage density 0.181
Elevation 0.110
Roughness 0.066
Slope 0.030
LULC 0.022
58. • Weightage assigning for factor classes in 1-10 scale
Result and Discussion
Factor Classes Weightage
Flow
acaccumulation
<5,000 2
5,000-50,000 4
50,000-100,000 8
>100,000 10
TWI <1.5 1
1.5-2.5 3
2.5-3.5 7
>3.5 10
Drainage density <0.6 2
0.6-0.9 5
0.9-1.2 8
>1.2 10
60. • Weightage assigning for factor classes in 1-10 scale
Result and Discussion
Factor Classes Weightage
LULC Water 10
Built up 7
Open scrub + Sand 5
Agriculture + Other fallow land 3
Forest + Soil area 1
61. • It have been quite evident how much flow accumulation, TWI and
drainage density have more importance in risk mapping.
• Elevation has been set with moderate priority because of its specific
classes but large spanning over study area.
• LULC have been the least priority layer for the case of risk estimation.
• Finally the flood risk index map has been generated through weighted
sum overlaying.
Result and Discussion
62. • Determining the Flood risk layer
Flood Risk Index = 𝑖=1
𝑛
𝑃𝑖 𝑊𝑖
where, Pi is the rating of the parameter in each point
Wi is the weight of each parameter
n is the number of the criteria
FRI = Flow accumulation x 0.347 + TWI x 0.245 +
Drainage density x 0.181 + Elevation x 0.110 +
Roughness x 0.066 + Slope x 0.030 + LULC x
0.021
Result and Discussion
63.
64.
65. • The risk map has been generated with 5 different classes.
• High and moderate risk zones have been subjected to verification with the
previously generated hazard layer.
• Overlaying hazard layer with appropriate transparency have been done for
verification of risk mapping.
• Verified high risk areas are showing very good alignment with hazard
areas while has its good proximity with the main channels of Ganga,
Yamuna, Tauns and Belan.
Result and Discussion
66. • Major portions that is actually effected by flood have been identified.
• Through this work the majorly effected LULC have been identified.
• As irrigated lands and soil areas are severely affected and can be located
distant from main channels, these areas needs effective remedial steps for
disaster risk management.
• For major sand area water may have got down in summer, thus subjected
to flooding in monsoon.
• Allahabad followed by Phulpur and Saraon tehsils needs good care for
planning the span of built up or developed areas.
Conclusions
67. • Major factors that are effecting the flood situation have been identified.
• Flood risk factors as flow accumulation, topographic wetness index,
drainage density and elevation can be very useful for flood risk estimate
over any similar type of study area.
• Flood risk zone mapping over study area has been done through AHP,
which has been validated against the waterlogging hazard layer.
• The decision making study with AHP demands more than six factors for
disaster risk estimation.
• A better result with AHP may demand inclusion of more potential factor
as well as better comparisons by expert consultant of hydrology.
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References
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