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wildfires; hillslope erosion; satellite imagery; rainfall erosivity; RUSLE; rockfall source areas; identification; relief; slope angle; rock mass strength; rockfall susceptibility; land subsidence; Geographic Information System (GIS); InSAR; machine learning algorithm; meta-heuristics; Iran; k-Nearest Neighbor; Random Forest; fires; Landsat 8; Sentinel 2; Terra; ASTER; MODIS; burned; mapping; hazard chain; turbidity; suspended sediment detection; extreme climate events; tailing dam risk management; spatiotemporal pattern mining; El Niño; remote sensing; geographic information system; flash floods; visual analysis; SAR offset tracking; glacier surface velocity; glacier instability; glacier hazards; ice avalanches; ENSO; glacier mass balance; glacier surface energy; earthquake; coseismic effects; field line resonance; acoustic gravity waves; lithosphere-magnetosphere coupling; burnt area monitoring; Australia; Sydney; wildfire; earth observation; mid-resolution sensors; time series analysis; burn severity; climate zones; deep learning; PRISMA; burned area; Sentinel-2; morphological operator; convolutional neural network; casualty prediction; importance assessment; spatial division; support vector regression; digital image correlation; phase correlation; optical flow; time series image stack; landslides; ground motion identification; displacement mapping; UAS; risk assessment; random forest; DInSAR; Yan’an city; settlement prediction; reclaimed land; exponential model; Asaoka method; wide-area deformation; deformation detection; time-series InSAR; stacking; Turpan–Hami basin; heavy rainfall; shallow landslides; TRIGRS model; spatial distribution; susceptibility assessment; Longchuan County; Guangdong Province; MT-InSAR; ground deformation monitoring; Sentinel-1A/B; image partition; block adjustment; Gaofen-2; Interferometric synthetic aperture radar (InSAR); freeze–thaw processes; permafrost; Qilian Mountains; natural hazards; hazard; vulnerability