Remote Sensing of Natural Hazards

• Eará dahkkit: Ahmed, Bayes (Doaimmaheaddji), Alam, Akhtar (Doaimmaheaddji)
• Materiálatiipa: Elektrovnnalaš Girjji oassi
• Giella: eaŋgalasgiella
• Almmustuhtton: Basel 2022
• Fáttát: Research & information: general; Geography; sequential estimation; InSAR time series; groundwater; land subsidence and rebound; earthquake; rapid mapping; damage assessment; deep learning; convolutional neural networks; ordinal regression; aerial image; landslide; machine learning models; remote sensing; ensemble models; validation; ice storm; forest ecosystems; disaster impact; post-disaster recovery; ice jam; snowmelt; flood mapping; monitoring and prediction; VIIRS; ABI; NUAE; flash flood; BRT; CART; naive Bayes tree; geohydrological model; landslide susceptibility; Bangladesh; digital elevation model; random forest; modified frequency ratio; logistic regression; automatic landslide detection; OBIA; PBA; random forests; supervised classification; landslides; uncertainty; K-Nearest Neighbor; Multi-Layer Perceptron; Random Forest; Support Vector Machine; agriculture; drought; NDVI; MODIS; landslide deformation; InSAR; reservoir water level; Sentinel-1; Three Gorges Reservoir area (China); peri-urbanization; urban growth boundary demarcation; climate change; climate migrants; natural hazards; flooding; land use and land cover; night-time light data; Dhaka
• Liŋkkat: DOAB: download the publication; DOAB: description of the publication

Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human...