Deep Learning Methods of Satellite Image Processing for Monitoring of Flood Dynamics in the Ganges Delta, Bangladesh

Water, Journal Year: 2024, Volume and Issue: 16(8), P. 1141 - 1141

Published: April 17, 2024

Mapping spatial data is essential for the monitoring of flooded areas, prognosis hazards and prevention flood risks. The Ganges River Delta, Bangladesh, world’s largest river delta prone to floods that impact social–natural systems through losses lives damage infrastructure landscapes. Millions people living in this region are vulnerable repetitive due exposure, high susceptibility low resilience. Cumulative effects monsoon climate, rainfall, tropical cyclones hydrogeologic setting Delta increase probability floods. While engineering methods mitigation include practical solutions (technical construction dams, bridges hydraulic drains), regulation traffic land planning support systems, geoinformation rely on modelling remote sensing (RS) evaluate dynamics hazards. Geoinformation indispensable mapping catchments areas visualization affected regions real-time monitoring, addition implementing developing emergency plans vulnerability assessment warning supported by RS data. In regard, study used monitor southern segment Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated (March) post-flood (November) periods analysis extent landscape changes. Deep Learning (DL) algorithms GRASS GIS modules qualitative quantitative as advanced image processing. results constitute a series maps based classified

Language: Английский

Climate Change Induced Risks Assessment of a Coastal Area: A “Socioeconomic and Livelihood Vulnerability Index” Based Study in Coastal Bangladesh

Natural Hazards Research, Journal Year: 2024, Volume and Issue: unknown

Published: June 1, 2024

While climate change impacts the entire world, people of Bangladesh bear a disproportionately heavy burden. Situated at forefront extreme climatic events such as cyclones, floods, saltwater intrusion, droughts, and rainfall, they face severe vulnerabilities. Coastal communities have been facing livelihood threats for some time now. Hatiya – coastal Upazila (sub-district) Noakhali District in faced socio-economic challenges recent past. To understand change-induced risks vulnerabilities Upazila, it is vital to socioeconomic vulnerability index this area. In study, Livelihood Vulnerability Index (LVI), Socioeconomic (SeVI) LVI-IPCC analyzed evaluate on profile affected Hatiya. A total 150 household surveys 11 Focus Group Discussions conducted purpose following purposive random sampling. The collected data included strategies, social network & communications, food, health, water, social, economic, physical, disaster variability. All these indicators were divided into 7 sub-components LVI, 5 subcomponents SeVI, forming measure desired index. was formed by three IPCC endorsed i.e., exposure, sensitivity, adaptive capacity. LVI value found be 0.495, which indicated that has medium terms livelihood. Based weighted average scores, most vulnerable due natural hazards (0.729), while within domain revealed highest percentage (64.6%) households lost their property other resources during hazards. addition, possessed high level (0.704). Strategies become less diversified with increased deterioration rate fishing, agriculture, forest resources, etc. Most weak Social Networks Communication did not go local government or others any kind help, so score components (0.722) highly range LVI. However, study area 0.027, indicating vulnerability. SeVI 0.704 economic mostly influenced indexed values contributing factors capacity, sensitivity 0.631 0.573, 0.465 respectively. This can baseline assessment change-affected take proper initiatives facilitate capacity reduce communities.

Language: Английский

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Forests, Journal Year: 2023, Volume and Issue: 14(12), P. 2352 - 2352

Published: Nov. 29, 2023

Sichuan Province preserves numerous rare and ancient species of plants animals, making it an important bio-genetic repository in China even the world. However, this region is also vulnerable to fire disturbance due rich forest resources, complex topography, dry climate, thus has become one main regions needing wildfire prevention. Analyzing driving factors influencing incidence can provide data policy guidance for management Province. Here we analyzed spatial temporal distribution characteristics wildfires based on spot during 2010–2019. Based 14 input variables, including vegetation, human factors, applied Pearson correlation analysis Random Forest methods investigate most occurrence. Then, Logistic model was further predict occurrences. The results showed that: (1) southwestern a high-incidence area wildfires, fires occurred from January June. (2) factor affecting occurrence monthly average temperature, followed by elevation, precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI previous month, Road kernel density. (3) prediction yielded good performance, with under curve (AUC) values higher than 0.94, overall accuracy (OA) 86%, true positive rate (TPR) 0.82, threat score (TS) 0.71. final selected AUC 0.944, OA 87.28%, TPR 0.829, TS 0.723. (4) indicate that extremely high danger (probability 0.8) concentrated southwest, which accounted about 1% study region, specifically Panzhihua Liangshan. These findings demonstrated effectiveness predicting Province, providing valuable insights regarding prevention efforts region.

Language: Английский