Disaster susceptibility analysis in remote sensing

Natural disasters such as floods, landslides, and forest fires have caused infrastructure destruction, damage to property, injuries, and casualties worldwide. With the advent of sensing technology and data science methodologies, the impact of a disaster can be lessened through in-depth retrospective analysis. Of these, a priori hazard susceptibility analysis, which includes spatial analysis using remotely sensed images of the terrain, is widely adopted. Susceptibility analysis is thus related to spatial aspects of hazard analysis using data captured using both active and passive sensing. In the case of certain disasters, temporal information plays the role of causality owing to which spatiotemporal aspects are relevant. Historical data of occurrences play a vital role in determining the likelihood of disaster occurrence in a new area, or recurrences in an affected site. Such hazard mapping also helps in predicting the degree of the aftermath of a hazardous process in those areas. Overall, state-of-the-art susceptibility analysis uses remote sensing images, land survey data, and other related geospatial and non-spatial data in performing data preprocessing and analysis to calculate the probability of forthcoming hazard events. Data preprocessing involves noise removal, the transformation of raw data to the desired data structure or computed values, dimensionality reduction, feature ranking, and so on. Data analysis includes statistical, machine learning, and deep learning approaches. In this chapter, the widely used preprocessing and data analysis techniques applied in disaster susceptibility analysis are discussed. Additionally, disaster susceptibility analysis-related case studies, e.g. flood and landslide, are presented to elaborate the data science workflows. The resultant maps of the disaster susceptibility analysis are intended to be used by land developers, urban planners, and related authorities in innovating the land use planning and smarter disaster management.