This thesis examines flood monitoring and mapping in Kamrup District, Assam using Sentinel-1 SAR data. The objectives are to determine the best quality data for flood risk analysis. The study area experienced heavy rainfall and flooding in June 2022. The methodology involves preprocessing Sentinel-1 images from before, during, and after the flood, and analyzing additional data sources such as DEM, rainfall, NDWI, and land cover to map the flooded areas. The results show that Sentinel-1 SAR data has potential for rapid flood mapping.
1. Under the supervision of
Dr. Arnab Kundu
Department of Geo-Informatics
Pandit Raghunath Murmu Smriti Mahavidyalaya
Bankura University
Bankura, West Bengal
Date:05.07.2023
Monitoring and Mapping of Flood Extent using Sentinel-1 SAR Data
A Case Study of Kamrup District, Assam.
Thesis submitted to the Bankura University for the award of the degree of
MASTER OF SCIENCE
in
GEO-INFORMATICS
Presented By
ANURAG GHOSH
UID NO. 21143031026
2. Introduction
Thesis Objective
Study Area
Data Used & Specifications
Methodology
Results
Conclusion
CONTENTS
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
2
3. • Floods are very serious, frequently occurring natural hazards that cause great damage to
infrastructure, lives, and economies.
• Flood usually varies from place to place and depends on various factors such as its verity and
time of occurrence.
• This study area flooded because heavy rainfall.
INTRODUCTION
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
3
4. This thesis determine the best quality data for a flood risk damage analysis the Brahmaputra River
region in kamrup.
The loss analysis aims to investigate the possibility of carrying out this investigation in a region
with data; in terms of quality and quantity and without carrying out extensive flood modelling.
The size of the basin of the study area makes it possible to do a flood hazard modelling for
different time.
ASDM in 16 June 2022 carried out a flood risk analysis in the area and most of their results and
findings would be referred to in this study to carry out a loss assessment.
THESIS OBJECTIVE
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
4
5. • The 2022 Assam state floods affected some districts.
• The study area include at Kamrup District in Assam.
• UTM location - WGS 1984 46°N.
• Latitude 26°N & Longitude 91°E – 92°E.
• This study area major river Brahmaputra river.
• This area western part of Assam.
STUDY AREA
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
5
6. DATA USED & SPECIFICATIONS
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
Product Specifications
Satellite Instrument Mod
Acquisition
Date
Use
Sentinel 1 C-SAR GRD-VV VH
28 February
2022
Pre-Flood
Image
Sentinel 1 C-SAR GRD-VV VH 06 June 2022
During-Flood
Image
Sentinel 1 C-SAR GRD-VV VH 07 Nov 2022
Post Flood
Image
Product Specifications
Spatial Resolution
1 arc-second for global coverage
(~30 meters)
3 arc-seconds for global coverage
(~90 meters)
C-band Wavelength 5.6 cm
Product Specifications
Acquisition Date Daily - 1june 2022 to 30 June 2022
Acquisition Date Monthly – June 2022
Spatial Resolution 0.05° (∼5.3 km)
Product Specifications
Acquisition Date June 2022
Spatial Resolution 10 meters
Product Specifications
Acquisition Date 16 June 2022
Spatial Resolution 30 meters
Sentinel 1 SAR Data
DEM data- Shuttle Radar Topography Mission (SRTM)
Precipitation data – CHRIPS, WRIS.
Sentinel 2 land cover
Landsat 9 NDWI
6
7. METHODOLOGY
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
SOURCE OF DATA
SENTINEL SATELLITE DATA
DURING FLOOD IMAGE 16 JUNE 2022
PRE FLOOD IMAGE 28 FEB 2022
PREPROCESSING
SUBSET
APPLY ORBIT REDIOMETRIC-S1 THERMAL NOISE REMOVED
REDIOMETRIC CALIBRATION SINGLE PRODUCT SPECKLE FILTERING
GEOMETRIC TERRAIN CORRECTION
SENTINEL 1 TOPS BORDER NOISE REMOVED
BAND MATH
EXTRACT FLOOD AREA DURING IMAGE
LAND CLASSIFICATION RAINFALL
CHIRPS MONTHLY DATA
CHIRPS DAILY DATA
WRIS DAILY RAINFALL GRAPH
FLOOD HAZARD
DEM
ELEVATION MAP
ESRI SENTINEL 10 M
LANDSAT 8 NDWI
POST FLOOD IMAGE 07 NOV 2022
7
8. SENTINEL 1_PRE, DURING, POST FLOOD
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
This thesis paper is all about the flood that occurred in East Assam due to heavy rainfall and all the dates are based
on Sentinel 1 GRD data, SAR image. Before flood-28/02/2022, During flood-16/06/2022 & After flood-
07/11/2022.
The incessant rains during June 2022 in East Assam has caused severe floods resulting in loos of lives damage of
crops and destruction of properties at a large scale. The permanent water body pixels are reduced from the
processed maps.
8
9. ELEVATION
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
9
• In flood risk assessment studies, using a
digital elevation model of that area is very
important in demonstrating the flood
zone.
• In this study area, the digital elevation
model (DEM) of the SRTM satellite with
a spatial resolution of 90 meters was used.
• According to the maps of the elevation
classes in the study area are presented in
five classes.
10. RAINFALL
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
10
o In this region of the Assam state, up to 100 mm high
rainfall.
o First image determine June month rainfall.
o Second image determine June month 30 days rainfall as a
different image.
12. NDWI JUNE 2022
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
12
• These Landsat-8 images show the extent of the NDWI June 2022.
• The NDWI is derived from the Near-Infrared (NIR) and green:
NDWI= (Green-NIR) / (Green + NIR).
• The NDWI is efficient to detect surface water because these surfaces have
a very low reflectance in the NIR region of the spectra – in contrast to the
vegetation which is characterized by a high reflectance in the NIR.
• Some dark black patches in the image above are that might be flooded as
well.
• To highlight the extent of the flooding we can use another Sentinel-2 image,
which was acquired last year in the same period of the year:
13. LAND USE & LAND COVER MAP
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
13
• The use of accurate LULC maps can improve the results of
flood risk management. In this study area, satellite images of
Sentinel 2 A with a spatial resolution of 10, 20, and 60 m for
different bands were used to generate the LULC map.
• Considering the frequency of flooding is in the months of
June. The classification of satellite images based on spectral
information has some limitations, therefore, other information
resources should be used to increase the accuracy of
classification.
• In this research, Supervised classification was used.
• The study area was divided into four general classes in
terms of LULC including Waterbody, Forest, Agriculture,
Urban.
14. SURFACE WATER 2022
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
14
Basic subset of layers from our Risk of
Flooding from Surface Water mapping.
This layer include 10 or more month present
waterbody of the year.
This layer identifying permanent water body
and flooded water body.
15. ASDM REPORT
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
15
17. CONCLUSION
Identification and mapping of flood extent using Sentinel-1 SAR data: A case study of Kamrup District, Assam 2022.
o In this study area , a functional methodology was pressed for flood monitoring at a necessary spatial resolution derived from SAR Data.
Specifically, it aimed to highlight the potential use of the Sentinel-1 SAR data sets of the European Earth Observation mission for flood
monitoring occurred in Kamrup district June 2022.
o In SAR images, continental waters have strong contrast in the backscatter values due to their low or null roughness in the absence of waves,
behaving like a specular surface that reflects the radar return signal in a direction opposed to the sensor position. However, they need a
correction and filtering process (Copernicus Data, Descending polarization, Filter date-After flood and Before Flood, Threshold 1.2, Different
Image, Flooded Area). Evaluating the different Sentinel-1 parameters, our analysis showed that the best results were obtained using VH
polarization configuration.
o Based on the results, it is concluded that the freely available Sentinel-1 SAR data has great potential for rapid flood inundation mapping and
monitoring. Particularly, SNAP platform which is a computation environment can support operational activities for planning and disaster risk
reduction purpose. In addition, it can effectively be used for flood damage assessment by land use/ land over information as well as
embankment breach identification.
17
