Areal extent of the 1988 flood in Bangladesh: How much did the satellite imagery show?

Rasid, Harun; Pramanik, M. A. H.

doi:10.1007/bf00605441

Cited by 38 publications

(15 citation statements)

References 9 publications

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“…One cause for this discrepancy might be that the timing of the image data and the damage data were different. Nevertheless, results of this hazard map found to be somewhat similar to those of Rasid and Pramanik (1993). They identified that the actual extent of flooding was larger than the satellite images suggested.…”

Section: Discussionsupporting

confidence: 62%

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Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh

et al. 2010

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Remote sensing is the most practical method available to managers of floodprone areas for quantifying and mapping flood impacts. This study explored large inundation areas in the Maghna River Basin, around the northeastern Bangladesh, as determined from passive sensor LANDSAT data and the cloud-penetrating capabilities of the active sensors of the remote imaging microwave RADARSAT. This study also used passive sensor LANDSAT wet and dry images for the year 2000. Spatial resolution was 30 m by 30 m for comparisons of the inundation area with RADARSAT images. RA-DARSAT images with spatial resolution of 50 m by 50 m were used for frequency analysis of floods from 2000 to 2004. Time series images for 2004 were also used. RADARSAT remote sensing data, GIS data, and ground data were used for the purpose of flood monitoring, mapping and assessing. A supervised classification technique was used for this processing. They were processed for creating a maximum water extent map and for estimating inundation areas. The results of this study indicated that the maximum extent of the inundation area as estimated using RADARSAT satellite imaging was about 29, 900.72 km 2 in 2004, which corresponded well with the heavy rainfall around northeast region, as seen at the Bhairab Bazar station and with the highest water level of the GangesBrahmaputra-Meghna (GBM) Rivers. A composite of 5 years of RADARSAT inundation maps from 2000 to 2004, GIS data, and damage data, was used to create unique flood hazard maps. Using the damage data for 2004 and the GIS data, a set of damage maps was also created. These maps are expected to be useful for future planning and flood disaster management. Thus, it has been demonstrated that RADARSAT imaging data acquired over the Bangladesh have the ability to precisely assess and clarify inundation areas allowing for successful flood monitoring, mapping and disaster management.

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Section: Discussionsupporting

confidence: 62%

“…Their study, however, could not be supplemented with conventional data of surface measurements. Rasid and Pramanik (1993) also applied AVHRR satellite imaging data to estimate the areal extent of the 1988 flood in Bangladesh. One limitation was that cloud-free images were not available for the peak flood period.…”

Section: Introductionmentioning

confidence: 99%

Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh

et al. 2010

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“…Furthermore, accurate estimates of the flooded area in Bangladesh are not available for many years (Rashid and Pramanik 1993). We focus on Brahmaputra River discharge as recorded in northern Bangladesh rather than flooded area or similar metrics because the flooded area is strongly influenced by factors such as local precipitation, preexisting water table variations, and local topography.…”

Section: Introductionmentioning

confidence: 99%

A Simple Predictive Tool for Lower Brahmaputra River Basin Monsoon Flooding

et al. 2007

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The Brahmaputra River of South Asia is the fourth largest river in the world in terms of annual discharge. The lower Brahmaputra River basin is susceptible to catastrophic flooding with major social, economic, and public health impacts. There is relatively little rainfall and snowpack informa-tion for the watershed, and the system is poorly understood hydrologically. Using a combination of available remotely sensed and gauge data, this study analyzes snow cover, rainfall, and monsoon period discharge for a 14-yr time period . It is found that interannual rainfall variability is low and is a weak predictor of monsoon discharge volumes. Strong evidence is found, however, that maximum spring snow cover in the upper Brahmaputra basin is a good predictor of the monsoon flood volume. Despite the temporal and spatial limitations of the data, this study's analysis demonstrates the potential for developing an empirical tool for predicting large flood events that may allow an annual early window for mitigating flood damages in the lower Brahmaputra basin, home to 300 million people.

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“…The existence of cloud cover/shadows is the most significant impediment for capturing the progress of floods during bad weather conditions (Lowry et al 1981;Rashid and Pramanik 1993;Melack et al 1994; see Sanyal and Lu 2004). Cloud-free data acquisition for a single date is difficult and even in this study, although the cloud cover on the entire during-flood image was <20%, clouds and shadows were sporadically observed in the study domain ( Figure 3).…”

Section: Cloud Cover Correctionmentioning

confidence: 70%

“…The existence of cloud cover/shadows is the most significant impediment for capturing the progress of floods during bad weather conditions (Lowry et al. ; Rashid and Pramanik ; Melack et al. ; see Sanyal and Lu ).…”

Section: Methodsmentioning

confidence: 99%

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

Munasinghe

Cohen

Huang

et al. 2018

J American Water Resour Assoc

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The objective of this study was to determine the accuracy of five different digital image processing techniques to map flood inundation extent with Landsat 8–Operational Land Imager satellite imagery. The May 2016 flooding event in the Hempstead region of the Brazos River, Texas is used as a case study for this first comprehensive comparison of classification techniques of its kind. Five flood water classification techniques (i.e., supervised classification, unsupervised classification, delta‐cue change detection, Normalized Difference Water Index [NDWI], modified NDWI [MNDWI]) were implemented to characterize flooded regions. To identify flood water obscured by cloud cover, a digital elevation model (DEM)–based approach was employed. Classified floods were compared using an Advanced Fitness Index to a “reference flood map” created based on manual digitization, as well as other data sources, using the same satellite image. Supervised classification yielded the highest accuracy of 86.4%, while unsupervised, MNDWI, and NDWI closely followed at 79.6%, 77.3%, and 77.1%, respectively. Delta‐cue change detection yielded the lowest accuracy with 70.1%. Thus, supervised classification is recommended for flood water classification and inundation map generation under these settings. The DEM‐based approach used to identify cloud‐obscured flood water pixels was found reliable and easy to apply. It is therefore recommended for regions with relatively flat topography.

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Areal extent of the 1988 flood in Bangladesh: How much did the satellite imagery show?

Cited by 38 publications

References 9 publications

Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh

Flood monitoring, mapping and assessing capabilities using RADARSAT remote sensing, GIS and ground data for Bangladesh

A Simple Predictive Tool for Lower Brahmaputra River Basin Monsoon Flooding

Intercomparison of Satellite Remote Sensing‐Based Flood Inundation Mapping Techniques

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