Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh (original) (raw)
Related papers
Rapid Flood Mapping Using Multi-temporal SAR Images: An Example from Bangladesh
Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region, 2021
In the HKH region, large areas in Afghanistan, Bangladesh, China, India, Myanmar, Nepal, and Pakistan get inundated by floodwater during every rainy season. Among them, Bangladesh has been experiencing record-high floods where four types prevail: flash flood, local rainfall flood, monsoon river flood, and storm-surge flood; and these occur almost every year due to Bangladesh’s unique geographical setting as the most downstream country in the HKH region.
Real time flood mapping using SAR imagery
2021
Synthetic Aperture Radars (SAR) are the active remote sensing sensor and used for terrestrial remote sensing from the space platforms. In this paper the SAR imageries are used for the application of flood mapping and monitoring during the monsoon season when the optical remote sensing sensor fails to provide the cloud free clear imageries of parts of flood ridden Assam, India. Using pre-flood SAR imagery, permanent water bodies are delineated and cross validated using Sentinel 2B optical imagery with overall classification accuracy of 94.06%. SAR imagery during flood is processed to derive inundation map, cross validation is done using ISRO BHUVAN portal flood data. Radar backscatter variation in pre-flood and during flood period is studied for croplands and grasslands. Finally village level inundation map is prepared for the study area using village data available in District Census Handbook.
Bangladesh is a flood-prone area among Asia for the geological location. Due to this natural disaster, infrastructure damages and human life losses occur every year in this country. Efficient monitoring and prediction of the flood in this county are very difficult without using satellite data. Flood mapping is a process which uses for damage assessment and risk management and helping rescuers during the flood. Somehow, Rainfall associated with flooding affects the study area with life loss and crop loss. The study area is the north western part (Rajshahi, Naogaon and Natore) of Bangladesh. The objective of this study is to define the extent of the flood in the study area during rainy season comparing two years of 2016 and 2017 and to identify the characterization of rainfall distribution during this period. The study also illustrates the links between evolving rainfall structure and spatial extent of flooding. The whole analysis is based on SAR (synthetic aperture radar) satellite images from Sentinel-1, have free access from ESA. The software is used for SAR imagery processing using threshold method to derive the flood extent and google earth is used to visualize the result of image processing. The results help operating estimation and detection of flooded area and determine the extent of flood causing damages in the study area. Finally, the study explores that due to the changing contribution of rainfall distribution, the extension of flooding is increased to a great extend or not in Rajshahi, Naogaon and Natore. It also helps to find that the rainfall distribution does really responsible for the flood in the study area or other reasons do responsible for it.
Flood Mapping and Permanent Water Bodies Change Detection Using Sentinel Sar Data
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Producing flood maps that can be carried out quickly for disaster management applications is essential to reduce the human and socioeconomic losses. In addition, mapping and change detection of water bodies as an essential natural resource is imperative for robust operations and sustainable management. Synthetic Aperture Radar (SAR) sensors with long wavelengths have a high potential for delineating the extent of the flooded areas and providing timely and accurate maps of surface water for risk mitigation and disaster or sustainable management. In this study, multi-temporal Sentinel-1 C-band SAR images were utilized to investigate the performance of the sensor backscatter image on permanent water bodies monitoring and flooded areas mapping. Lake Urmia as a permanent water system and two floods in Golestan and Khuzestan provinces of Iran have been investigated. The backscatter values of an image acquired before the event that is referred as an Archive image and another one after the event as a Crisis image are analysed. As a preliminary result, it is concluded that with overlaying of the two bands from Archive and Crisis images and creating a color composite image, the permanent water bodies have a uniformly dark return due to the very low backscatter in both images. The flooded areas and changes in water level show relatively higher backscatter in the Crisis image, whereas the other land cover features indicate very high backscatter values with tones of grey. Therefore, Sentinel-1 SAR data provides beneficial information on flood risk management and change detection.
Journal of the Civil Engineering Forum
Floods are triggered by water overflow into drylands from several sources, including rivers, lakes, oceans, or heavy rainfall. Near real-time (NRT) flood mapping plays an important role in taking strategic measures to reduce flood damage after a flood event. There are many satellite imagery based remote sensing techniques that are widely used to generate flood maps. Synthetic aperture radar (SAR) images have proven to be more effective in flood mapping due to its high spatial resolution and cloud penetration capacity. This case study is focused on the super cyclone, commonly known as Amphan, stemming from the west Bengal-Bangladesh coast across the Sundarbans on 20 May 2020, with a wind speed between 155 -165 gusting up to 185 . The flooding extent is determined by analyzing the pre and post-event synthetic aperture radar images, using the change detection and thresholding (CDAT) method. The results showed an inundated landmass of 2146 on 22 May 2020, excluding Sundarban. However, ...
Environmental Science and Pollution Research, 2023
Brahmaputra is one of the perennial rivers in India which causes floods every year in the northeast state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and nonflooded water. From this method, the range of backscatter values for normal water are identified as − 18 to − 30 dB and the range is identified as − 19 to − 24 dB for flooded water. The results showed that the method is able to separate the flooded and nonflooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km 2 on peak flood date for the chosen study area.
Environmental Science and Pollution Research, 2019
Brahmaputra is one of the perennial rivers in India which causes floods every year in the northeast state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and nonflooded water. From this method, the range of backscatter values for normal water are identified as − 18 to − 30 dB and the range is identified as − 19 to − 24 dB for flooded water. The results showed that the method is able to separate the flooded and nonflooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km 2 on peak flood date for the chosen study area.
Flood Detection and Flood Mapping Using Multi-temporal Synthetic Aperture Radar and Optical Data
Advances in geographical and environmental sciences, 2023
This paper discusses the thresholding and unsupervised classification methodologies, in order to find the inundated areas due to incessant rains and rise of water level in Rapti and Ghaghara Rivers during the month of August 2017. Used high resolution multi-temporal Synthetic Aperture Radar (SAR) and Optical images captured during the August 2017 floods in the state of Uttar Pradesh, India. The zonal statistics are calculated to find the inundated area in each district of the selected Area of Study (AOS). District-wise flood water mapping is done by superimposing the extracted water layer. The obtained results are validated against the meteorological observations. From the findings, it is evident that SAR data can effectively be used for flood water mapping and flood monitoring. These findings will therefore help to minimize the flood hazard impact and aid in augmenting the flexibility in flood management.
AGU Fall Meeting 2021, 2021
Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.
ISPRS Journal of Photogrammetry and Remote Sensing, 2020
Globally, flooding is the leading cause of natural disaster related deaths, especially in Bangladesh where approximately one third of national area gets flooded annually by overflowing rivers during the monsoon season, which drastically affects paddy rice agriculture and food security. However, existing studies on the pattern of floods and their impact on agriculture in Bangladesh are limited. Here we examined the spatiotemporal pattern of floods for the country during 2014-2018 using all the available Sentinel-1 Synthetic Aperture Radar (SAR) images and the Google Earth Engine (GEE) platform. We also identified the flood-affected paddy rice fields by integrating the flooding areas and remote sensing-based paddy rice planting areas. Our results indicate that flooding is frequent in northeastern Bangladesh and along the three major rivers, the Ganges, Brahmaputra, and Meghna. Between 2014 and 2018, the flood-affected paddy rice areas accounted for 1.61-18.17% of the total paddy rice area. The satellite-based detection of floods and flood-affected paddy rice fields advance our understanding of the annual dynamics of flooding in Bangladesh, which is essential for adaptation and mitigation strategies where severe annual floods threaten human lives, properties, and agricultural production.