A Quantitative Framework for Analyzing Spatial Dynamics of Flood Events: A Case Study of Super Cyclone Amphan

Abstract

Identifying the flooding risk hotspot is crucial for aiding a rapid response and prioritizes mitigation efforts over large disaster impacted regions. While climate change is increasing the risk of floods in many vulnerable regions of the world, the commonly used crisis map is inefficient and cannot rapidly determine the spatial variation and intensity of flooding extension across the affected areas. In such cases, the Local Indicators of Spatial Association (LISA) statistic can detect heterogeneity or the flooding hotspot at a local spatial scale beyond routine mapping. This area, however, has not yet been studied in the context of the magnitude of the floods. The present study incorporates the LISA methodology including Moran’s I and Getis–Ord Gi* to identify the spatial and temporal heterogeneity of the occurrence of flooding from super cyclone Amphan across 16 coastal districts of Bangladesh. Using the Synthetic Aperture Radar (SAR) data from Sentinel-1 and a Support Vector Machine (SVM) classification, “water” and “land” were classified for the pre-event (16 May 2020) and post-events (22 May, 28 May, and 7 June 2020) of the area under study. A Modified Normalized Difference Water Index (MNDWI), and visual comparison were used to evaluate the flood maps. A compelling agreement was accomplished between the observed and predicted flood maps, with an overall precision of above 95% for all SAR classified images. As per this study, 2233 km2 (8%) of the region is estimated to have been inundated on 22 May. After this point, the intensity and aerial expansion of flood decreased to 1490 km2 by 28 May before it increased slightly to 1520 km2 (2.1% of the study area) on 7 June. The results from LISA indicated that the main flooding hotspots were located in the central part, particularly in the region off the north-east of the mangrove forest. A total of 238 Unions (smallest administrative units) were identified as high flooding hotspots (p < 0.05) on 22 May, but the number of flooding hotspots dropped to 166 in the second week (28 May) after Amphan subsided before it increased to a further 208 hotspots (p < 0.05) on 7 June due to incessant rainfall and riverbank failure in the south-west part of the study area. As such, an appropriate, timely, and cost-effective strategy would be to assess existing flooding management policies through the identified flooding hotspot regions. This identification would then allow for the creation of an improved policy to help curtail the destructive effects of flooding in the future.

Flood Mapping in the Coastal Region of Bangladesh Using Sentinel-1 SAR Images: A Case Study of Super Cyclone Amphan

“…Generally, the driest period is preferred for the reference image collection (Clement, Kilsby & Moore, 2018, Singha et al, 2020. Average rainfall of 200mm is observed depending on the CHIRPS data (Hassan et al, 2020). In this reported study, the period was selected before the Amphan event to avoid heavy rainfall in the research location.…”

“…Inundations between 2004-2007 have been analyzed using MODIS and RADARSAT images (Islam, Bala, &Haque, 2010), while LANDSAT-7 data were utilized alongside RADARSAT for mapping floods from 2000-2004 in the northeastern zone (Hoque et al, 2011) (Chowdhury & Hassan, 2017. Similarly, Sentinel-1 SAR photos were adopted in 2017 (Uddin, Matin &Meyer, 2019) to analyze the spatial flood dynamics (Hassan et al, 2020). These images also aided the detection of spatiotemporal patterns of the flood events between 2014-2018, with a focus on paddy rice fields (Singha et al, 2020).…”

Land cover change and its impact on human–elephant conflict: a case from Fashiakhali forest reserve in Bangladesh

“…As the accuracy in land cover classification is fundamentally significant for remote sensing analysis, several studies evaluated the support vector machine (SVM), maximum likelihood classifier (MLC), decision tree (DT), and ensembles classifier such as Random Forest (RF) for monitoring land-cover change and these algorithms have shown a quantum increase in current times [87]. In particular, SVM [88] and Random Forest [89] have recently received considerable attention in remote sensing image classification, RF have been applied relatively extensively, offer suitable levels of accuracy [90,91], and both are supervised, nonparametric statistical learning techniques [92,93]. However, the images of the study were classified using a supervised maximum likelihood classification (MLC) algorithm [90], and the most practical algorithms for parametric classification rule [75,94,95] usually been proven to achieve (2021) 3:649 | https://doi.org/10.1007/s42452-021-04625-1 the best results if each class of the remotely sensed data has a Gaussian distribution [11,96].…”

Pandemic or Environmental Socio-Economic Stressors Which Have Greater Impact on Food Security in the Barishal Division of Bangladesh: Initial Perspectives from Agricultural Officers and Farmers

“…The first tropical cyclone of 2020, Amphan, reached Bangladesh on the 20 May and caused widespread devastation [21]. This "super cyclone" wreaked havoc across lowlying/coastal areas especially, with a five-meter (16-foot) storm surge, heavy precipitation, and strong winds (up to 260 km/h) [21,34]. The cyclone affected crops on some 176,007 hectares of land in 17 coastal districts [35].…”

“…The cyclone affected crops on some 176,007 hectares of land in 17 coastal districts [35]. Heavy downpours continued as the aftermath of the tropical cyclone caused flooding in several districts, which extended into the first week of June 2020 [34]. Furthermore, Bangladesh endured its worst flooding in a decade with persistently heavy monsoon rains [36].…”