presentation document.pptx

Thesis Presentation
on
GIS and Remote Sensing based Study of Flood Risk Management
by
Amit Kumar Saha
2017GI12
M.Tech IIIrd Sem
GIS Cell
Under the supervision of
Dr. Sonam Agrawal
Assistant Professor
GIS Cell
GIS Cell
Motilal Nehru National Institute of Technology Allahabad

• Introduction
• Literature review
• Summary table
• Research objectives
• Study area
• Data Required
• Methodology
• Work done
• Expected outcomes
• References
Contents

• 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.
• 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
different states of India like Bihar, Assam, Gujarat, Maharashtra and
Uttarakhand.
Introduction

• Water flow rate change in terms of seasons and path of flow are the other
usual causes.
• Flood may spread slowly but the flash floods spread at very high speed.
• Flood is a disaster event which is both natural and manmade.
• 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 urban flood disaster.
Introduction

• Flood
• GIS analysis
• Multi Criteria Analysis
• Land use land cover
• Some Crucial Factors
• Analytic Hierarchy Process (AHP)
Literature review

• 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)
• Half of the world's population lives within 60 km of the shoreline, and it is
expected that within the next 30 years, the coastal population will double
and much of this growth will be in coastal mega-cities, like Mumbai and
Chennai(Eldho and Kulkarni, 2015)
• In recent years, humans have endured increasing numbers of natural
disasters, of which flooding is the greatest and most common throughout
the world(Safaripour et al., 2012)
Flood

• 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 shortly after its occurrence,
leading to an overall neglect in the maintenance of drainage
ways(Ghoneim and Foody, 2013)
• For developing countries (and even more so for trans-boundary
basins, as for this case), the generalized lack of hydrological and
design and implementation of
for flood-risk mitigations
topographical data makes the
reliable structural measures
difficult(Gandolfi et al., 2013)
Flood

• 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

• Multi Criteria Analysis (MCA) is a quantitative approach for integrating
multiple criteria layers for risk or vulnerability assessment.
• 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

LULC
• Land cover is the physical material at the surface of the earth. Land
covers include grass, asphalt, trees, bare ground, water, etc.
• This is the manner in which human beings employ the land and its
resources.
• 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)

• 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 flowaccumulation 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)
Some Crucial Factors

• The Analytic Hierarchy Process (AHP) was developed by Saaty in the late
1970s and originally was applied to the marketing sector (Dolan et al.
1987)
• Slowly it has been accepted in the field of multi-criteria decision making.
(https://pdfs.semanticscholar.org)
• The application of an AHP is structured into six steps, suggested by Dolan
et al., as follows: 1. define the decision goal, criteria, and alternatives, 2.
rate the criteria in pairwise comparisons, 3. calculate the relative priority
weights for the (sub-)criteria, 4. calculate the criteria’s global priority
weights and combine the alternatives’ priorities, 5. control for
inconsistency, and 6. perform sensitivity analysis.
• Only half of the pairwise matrix has to be filled in, as the other half is
obtained from the reciprocal weights. (https://pdfs.semanticscholar.org)
AHP

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

Summary table
Sl.
No.
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

Summary table
Sl.
No.
name(year)
Contribution
9 Vinod Kumar Sharma,
Nitin Mishra, Abhinav
Kumar Shukla, Anil
Yadav, G Srinivasa Rao &
V. Bhanumurthy
Satellite data planning for
flood mapping activities
based on high rainfall events
generated usin TRMM,
GEFS and disaster news
ANNALS OF
GIS(2017)
Develop a gis-based framework to
ensure proper planning of acquisition
of microwave satellite data over
indian region
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

Summary table
Sl.
No.
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 Dawod, Gomaa M
Mirza, Meraj N
Al-Ghamdi, Khalid A.
Assessment of several flood
estimation methodologies in
Makkah metropolitan area ,
Saudi Arabia
Arab J Geosci
(2013)
This paper has presented some global
and national models for estimating
flood discharge in Makkah
metropolitan area
16 Eldho, T I
Kulkarni, Anand T
Flood Management in
Coastal Cities using GIS,
Remote Sensing and
Numerical Models
International
Journal of
Scientific &
Engineering
Research
(2015)
Integrated Flood
Assessment Model (IFAM) and its
application to a coastal
urban watershed have been presented

Summary table
Sl.
No.
name(year)
Contribution
17 Safaripour, Mahsa
Monavari, Masoud
Zare, Mehdi
Abedi, Zahra
Gharagozlou, Alireza
Flood risk assessment using
GIS (case study: Golestan
province, Iran)
Polish Journal of
Environmental
Studies
(2012)
This study provides a new risk map
model composed of five main factors
affecting the flood in Golestan in the
form of five layers in GIS
environment
18 Ullah, Sana
Farooq, Muhammad
Sarwar, Tahir
Tareen, Mohammad Javed
Wahid, Mirza Abdul
Flood modeling and
simulations using
hydrodynamic model and
ASTER DEM — A case
study of Kalpani River
Arabian Journal of
Geosciences
(2016)
Has shown that
HEC-RAS model can be used for
flood risk management and as
decision support tool in the Kalpani
River catchment
19 Adeli, Zahra
Khorshiddoust,
Alimohammad
Application of
geomorphology in urban
planning: Case study in
landfill site selection
Procedia - Social
and Behavioral
Sciences
(2011)
Has considered Geomorphological
factor the best place of landfilling
with the less adverse effects on the
natural environment around the
landfill site.
20 Kamboj, N.
Pandey, N.
Spatial Distribution of Solid
Waste Disposal Sites in
Allahabad City, Uttar
Pradesh, India Using Gis
Approach
Archives of
Agriculture and
Environmental
Science
(2017)
Has described various land use
pattern and land cover scheme
classification for suitable sites of
waste disposal

Summary table
Sl.
No.
name(year)
Contribution
21 Singh, Ashutosh
Singh, Shalini
Kumar, Purushottam
Khanduri, Kamlesh
Land use and land cover change
detection: a comparative
approach using post classification
change matrix and discriminate
function change detection
methodology of Allahabad City
International
Journal of Current
Engineering and
Technology
(2013)
Has detected changes that has taken
place in their status particularly in the
built- up land and forest area in terms
of LULC changes in the study area
22 Anand, Jatin
Gosain, A. K.
Khosa, R.
Srinivasan
Regional scale hydrologic
modeling for prediction of water
balance, analysis of trends in
streamflow and variations in
streamflow: The case study of the
Ganga River basin
Journal of
Hydrology:
Regional Studies
(2018)
Development of a methodology
using the SWAT model for the
evaluation of spatially and temporally
explicit water balance
23 Adnan, Nor Aizam
Atkinson, Peter M.
Exploring the impact of climate
and land use changes on
streamflow trends in a monsoon
catchment
International
Journal of
Climatology
(2011)
Has suggested a clear association
between streamflow change and
precipitation change, also reveals that
land use change may be important
contributing factor, in the upstream
sub-catchment.
24 Hoque, Roxana
Nakayama, Daichi
Matsuyama, Hiroshi
Matsumoto, Jun
Flood monitoring, mapping and
assessing capabilities using
RADARSAT remote sensing, GIS
and ground data for Bangladesh
Natural Hazards
(2011)
RADARSAT inundation maps from
2000 to 2004, GIS data, and damage
data, was used to create unique flood
hazard maps

Summary table
Sl.
No.
name(year)
Contribution
25 Patel, Arun
Katiyar, S. K.
Prasad, Vishnu
Performances evaluation of
different open source DEM
using Differential Global
Positioning System (DGPS)
Egyptian Journal of
Remote Sensing and
Space Science
(2016)
Comparative analysis was made
upon open source DEM such SRTM,
ASTER and Cartosat-1 DEM for
validation and performance
evaluation
26 Arun, P. V. A comparative analysis of
different DEM interpolation
methods
Egyptian Journal of
Remote Sensing and
Space Science
(2013)
Different interpoation techniques
like IDW, kriging, ANUDEM,
Nearest Neighbor,
and Spline approaches have been
compared.
27 El Gammal, El Sayed A
Salem, S. M.
Greiling, Reinhard O.
Applications of
geomorphology, tectonics,
geology and geophysical
interpretation of, East Kom
Ombo depression, Egypt,
using Landsat images
Egyptian Journal of
Remote Sensing and
Space Science
(2013)
Made an attempt to understand the
structural evolution and genetic
development of the geomorphologic
features of the study area
28 González-Arqueros, M.
Lourdes
Mendoza, Manuel E.
Bocco, Gerardo
Solís Castillo, Berenice
Flood susceptibility in rural
settlements in remote zones:
The case of a mountainous
basin in the Sierra-Costa
region of Michoacán,
Mexico
Journal of
Environmental
Management(2018)
Has mapped land morphology and
the weighted overlay technique has
been applied in a geographic
information system to generate
maps of susceptibility to flooding

Summary table
Sl.
No.
name(year)
Contribution
29 Xiao, Yangfan
Yi, Shanzhen
Tang, Zhongqian
Integrated flood hazard
assessment based on spatial
ordered weighted averaging
method considering spatial
heterogeneity of risk
preference
Science of the Total
Environment(2017)
Has emlpoyed MCA, fuzzy analytic
hierarchy process (AHP) and spatial
orderedweighted averaging (OWA)
for flood hazard assessment
30 Singh, Omvir
Kumar, Manish
Flood occurrences, damages,
and management challenges
in India: a geographical
perspective
Arabian Journal of
Geosciences
(2017)
Have suggested river-friendly and
less-interventionist approaches to
achieve sustainable flood
management
31 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)
Has incorporated 10 susceptibility
factors: flow accumulation, annual
rainfall, slope, runoff, land
use/cover, elevation, geology, soil
type, distance from the drainage
network, and drainage density.
32 Ezzine, Ahmed
Darragi, Fadila
Rajhi, Hamadi
Ghatassi, Anis
Evaluation of Sentinel-1 data
for flood mapping in the
upstream of Sidi Salem dam
(Northern Tunisia)
Arabian Journal of
Geosciences
(2018)
Has proved that the segregation of
land/water areas with a threshold
technique is better observed in VH
polarization rather than VV
polarization

Summary table
Sl.
No.
name(year)
Contribution
33 Wan, kah mun
Billa, lawal
Post-flood land use damage
estimation using improved
normalized difference flood
index (NDFI3) on landsat 8
datasets: december 2014
floods, kelantan, malaysia
Arabian Journal of
Geosciences
(2018)
Has developed RS and GIS technique
in post-disaster damage assessment in
effective way for post-flood disaster
planning and decision-making
34 Ghoneim, eman
Foody, giles M.
Assessing flash flood hazard
in an arid mountainous region
Arabian Journal of
Geosciences
(2013)
Has studied the hydrological response
of the study basin to a rainfall event
and flood response of the town Marsa
Alam
35 Gandolfi, S.
Castellarin, a.
Barbarella, m.
Brath, a.
Domeneghetti, a.
Brandimarte, L. Di
baldassarre, G.
Rio soliette (haiti): an
international initiative for
flood-hazard assessment and
mitigation
International
Archives of the
Photogrammetry,Re
mote Sensing and
Spatial Information
Sciences - ISPRS
Archives(2013)
Has summarized the ensemble of
topographical and hydraulic analyses
carried out for the evaluation of
several flood risk mitigation
measures along the River Soliette
36 Sarath, M
Saran, sameer
Ramana, K V
Site suitability analysis for
industries using gis and multi
criteria decision making
ISPRS Annals
(2018)
Has combined MCDM and 6 AHP
criterias in GIS for decision making
as suitable site map

Summary table
Sl.
No.
name(year)
Contribution
37 K. Venkatesh, H.
Ramesh
Impact of Land Use Land Cover
Change on Run Off Generation In
Tungabhadra River Basin
ISPRS Annals of the
Photogrammetry,
Remote Sensing and
Spatial Information
Sciences
(2018)
Has analysed the impact of
LULC on stream flows from
the past three decades which is
important to understand the
economic and environmental
changes in the study area
38 Roy, A. Thakur, P. K.
Pokhriyal,
N.Aggarwal, S. P.
Nikam, B. R.Garg, V.
Dhote, P. R.
Choksey, A.
Intercomparison of Different
Rainfall Products and Validation
of Wrf Modelled Rainfall
Estimation in N-W Himalaya
During Monsoon Period
ISPRS Annals of the
Photogrammetry,
Remote Sensing and
Spatial Information
Sciences
(2018)
Has obtained a precipitation
product suitable for long-term
calibration and validation of
hydrological models which can
be used to produce flood
forecast with WRF
39 Li, Peng
Jiang, Luguang
Feng, Zhiming
Cross-comparison of vegetation
indices derived from landsat-7
enhanced thematic mapper plus
(ETM+) and landsat-8 operational
land imager (OLI) sensors
Remote Sensing
(2013)
Has compared the performance
of different vegetation indices
from ETM+ and OLI imageries
for better mapping and
monitoring of land surface
changes
40 Zlatanova, Sisi
Ghawana, Tarun
Kaur, Amarjeet
Neuvel, Jeroen M.M.
Integrated flood disaster
management and spatial
information: Case studies of
Netherlands and India
ISPRS Archives
(2014)
Has analysed the components
of flood management system in
the both countries

Summary table
Sl.
No.
name(year)
Contribution
41 Balbo, S.
Boccardo, P.
Dalmasso, S.
Pasquali, P.
A public platform for
geospatial data sharing
for disaster risk
management
International Archives of
the Photogrammetry,
Remote Sensing and
Spatial Information
Sciences - ISPRS
Archives(2013)
Has included Integrated Flood Risk
Management Plan for a river basin in
the GeoNode platform can be used
for future disaster risk management
projects
42 Marzocchi, R.
Federici, B.
Cannata, M.
Cosso, T.
Syriou, A.
The contribution of GIS
in flood mapping: Two
approaches using open
source grass GIS
software
International Archives of
the Photogrammetry,
Remote Sensing and
Spatial Information
Sciences - ISPRS
Archives(2013)
Has analysed possibility of
intergrating GIS embedded hydraulic
model and 2D numerical approach for
flood mapping
43 Zhang, Zhenxing
Dehoff, Andrew D.
Pody, Robert D.
Balay, John W.
Detection of streamflow
change in the
susquehanna River Basin
Water Resources
Management(2010)
Has studied temporal change
detection over the study area for the
planning of basin management
44 Whitehead, Paul G.
Jin, Li
Macadam, Ian
Janes, Tamara
Sarkar, Sananda
Rodda, Harvey J.E.
Sinha, Rajiv
Nicholls, Robert J.
Modelling impacts of
climate change and
socio-economic change
on the Ganga,
Brahmaputra, Meghna,
Hooghly and Mahanadi
river systems in India
and Bangladesh
Science of the Total
Environment(2018)
Has considered
Regional Climate Model (RCM) and
socio-economic changes to assess
impacts on flows and water quality

Summary table
Sl.
No.
name(year)
Contribution
45 Roy, Nanigopal
Sinha, Rajiv
Understanding confluence
dynamics in the alluvial Ganga-
Ramganga valley, India: An
integrated approach using
geomorphology and hydrology
Geomorphology(20
07)
Has examined the dynamics of the
confluence points and relates these
movements to hydrological and
sediment transport characteristics
46 Nune, Rajesh
George, Biju A.
Teluguntla,
Pardhasaradhi
Western, Andrew W.
Relating Trends in Streamflow
to Anthropogenic Influences: A
Case Study of Himayat Sagar
Catchment, India
Water Resources
Management(2014)
Has identified potential factors to
changes in catchment characteristics
due to anthropogenic activities in
detail
47 Das A.S., Dini
Kumar, Shashi
Babu, Arun
Thakur, Praveen K
Insar coherence and polarimetric
parameters based
characterization of flooded area
- case study of a natural world
heritage site kaziranga national
park
ISPRS Annals
(2018)
Has estimated the impact and flood
extent at Kaziranga during
2016 to 2017 and has carried out the
time series analysis and change
detection.
48 Paul, Daisy
Mandla, V. Ravibabu
Singh, Tejpal
Quantifying and modeling of
stream network using digital
elevation models
Ain Shams
Engineering Journal
(2017)
Has studied different relation- ships
between various morphometric
parameters obtained from the two
DEMs used

Summary table
Sl.
No.
name(year)
Contribution
49 Rastogi, A K
Thakur, P K
Rao, G Srinivasa
Aggarwal, S P
Chauhan, P
Integrated flood study of
bagmati river basin with
hydro processing, flood
inundation mapping & 1-d
hydrodynamic modeling
using remote sensing and gis
ISPRS Annals of
the
Photogrammetry,
Remote Sensing
and Spatial
Information
Sciences(2018)
Has proposed integrated framework
for flood management information
system
50 Maurya, Satya Prakash
Yadav, Akhilesh Kumar
Evaluation of course change
detection of Ramganga river
using remote sensing and
GIS, India
Weather and
Climate Extremes
(2016)
River course change detection has
been done from 1972 to 2013
with consideration of 1972 as base
year
51 Bawa, Nupur
Jain, Vikrant
Shekhar, Shashank
Kumar, Niraj
Jyani, Vikas
Controls on morphological
variability and role of stream
power distribution pattern,
Yamuna River, western India
Geomorphology(2
014)
Has characterized the geomorphic
characteristics of the Yamuna River
system in western India in a
hierarchical order, has developed
understanding of the geomorphic
controls at different scales
52 Taufik, Muhammad
Putra, Yogrema Setyanto
Hayati, Noorlaila
The Utilization of Global
Digital Elevation Model for
Watershed Management a
Case Study: Bungbuntu Sub
Watershed, Pamekasan
Procedia
Environmental
Sciences
(2015)
Has showed that DEM produced by
SRTM had better elevation accuracy
than Aster GDEM

• To determine the factors that causes urban flooding.
• 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 create the flood risk map by using AHP.
Objectives

• Survery of India topographical maps of Prayagraj district
• SRTM DEM of 30m spatial resolution
• Landsat 8 OLI/TIRS imageries
Data Required

• DEM is an array of regularly spaced elevation values referenced
horizontally either to a Universal Transverse Mercator (UTM)
projection or to a geographic coordinate system
• Surfaces are usually modeled with raster datasets
• This study uses 2.5D elevation data as SRTM from USGS
• 1 Arc-Second signifies spatial resolution as 30m (approx.)
DEM (Digital Elevation Model)

• SRTM is an international research effort for obtaining the DEM on a near-
global scale from 56°S to 60°N.
• SRTM is the digital topographic database of Earth prior to the release of
the ASTER GDEM in 2009.
• It is an active remote sensing system.
• To acquire topographic data, the SRTM payload was outfitted with two
radar antennas
• the Shuttle's payload bay
• a critical change from the SIR-C/X-SAR
• Original SRTM elevations were calculated relative to the WGS84 ellipsoid
• 1-arc second global digital elevation model (30 meters) is available since
September 23, 2014
Shuttle Radar Topography Mission

• Landsat 8 is an American Earth observation satellite launched on February
11, 2013
• It was originally called the Landsat Data Continuity Mission (LDCM)
• Utilizes a two-sensor payload( push-broom instruments)
• Operational Land Imager (OLI)
• Thermal InfraRed Sensor (TIRS)
• Provides moderate-resolution imagery, from 15 meters to 100 meters
• Operates in the visible, near-infrared, short wave infrared, and thermal
infrared spectrums
Landsat 8 OLI/TIRS

• Planned parameters for Landsat 8 standard products:
• Product type: Level 1T (terrain corrected)
• Output format: GeoTIFF
• Pixel size: 15 meters/30 meters/100 meters
(panchromatic/multispectral/thermal)
• Map projection: UTM (Polar Stereographic for Antarctica)
• Datum: WGS 84
• Orientation: North-up (map)
• Resampling: Cubic convolution
• Accuracy:
• OLI: 12 meters circular error, 90-percent confidence
• TIRS: 41 meters circular error, 90-percent confidence
• It images the entire Earth every 16 days
Landsat 8 OLI/TIRS

Spectral Band Wavelength Resolution Solar Irradiance
Band 1 - Coastal / Aerosol 0.433 – 0.453 µm 30 m 2031 W/(m²µm)
Band 2 - Blue 0.450 – 0.515 µm 30 m 1925 W/(m²µm)
Band 3 - Green 0.525 – 0.600 µm 30 m 1826 W/(m²µm)
Band 4 - Red 0.630 – 0.680 µm 30 m 1574 W/(m²µm)
Band 5 - Near Infrared 0.845 – 0.885 µm 30 m 955 W/(m²µm)
Band 6 - Short Wavelength
Infrared
1.560 – 1.660 µm 30 m 242 W/(m²µm)
Band 7 - Short Wavelength
Infrared
2.100 – 2.300 µm 30 m 82.5 W/(m²µm)
Band 8 - Panchromatic 0.500 – 0.680 µm 15 m 1739 W/(m²µm)
Band 9 - Cirrus 1.360 – 1.390 µm 30 m 361 W/(m²µm)
Landsat 8 OLI/TIRS
• OLI Spectral Bands:

• Objective 1 Methodology
• Objective 1 Methodology Flowchart
Methodology

• Following tasks are performed to achieve objective 1
• Collection of SRTM data for study area
• Mosaic of 4 DEM files
• Clipping of Prayagraj district AOI using ArcGIS 10.3
• Assessment of elevation of study area
• Defining elevation classes
• Generation of contour using contour tool
• Generation of slope using slope tool
• Generation of fill using fill tool
• Generation of flow direction using fill
• Generation of flow accumulation using flow direction
Objective 1 Methodology

Objective 1 Methodology Flowchart
DEM data
from
SRTM
Mosaicing
and clippping
Elevation
map
Slope map Contour map Sink
Flow
accumulation

• Following tasks are performed to achieve objective 2
• Collection of Landsat data for study area
• Stacking of individual files
• Mosaic of files
• Clipping of AOI using ArcGIS
• Assessment of major land covers of study area
• Supervised image classification in Erdas Imagine
• Waterlogged area determination by change detection

Landsat images of
dry and wet
seasons
Stacking &
mosaicing
Clipping
Supervised image
classification
Identification of water
Delineation of water logged
area by change detection

• Following tasks are performed to achieve objective 3:
• Collection of Landsat images of dry and wet seasons.
• Anderson classification of both.
• Identification of water from classified images.
• Performing of subtraction
• Extraction of shapefile from raster
• Extraction of raster from classified image of dry season
• Generation of LULC raster affected by flood
• Calculation of area of affected land cover

Landsat images of
dry and wet
seasons
Supervised image classification
Water identification
Subtraction
Polygon shapefile
Extraction of raster from
LULC classified image
Area calculation

• Elevation map
• Slope map
• Tehsil Map
• Contour Map
• Flow Direction Map
• Flow Accumulation Map
• Land Use Land Cover Map
Work done

• Potential factors causing urban flood disaster in the study area will be
determined.
• Flood vulnerable zones in the study area will be detected.
• Land use land cover which is effected by the flood hazard will be
determined.
• Methodology of 4th objective will be decided.
• Flood risk map with the help of supportive factors will be generated.
• Further distribution of LULC types in different tehsils will be shown for
more detailed interpretation.
Expected outcomes

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