Temporal Resolution, a Key Factor in Environmental Risk Assessment II - Integrating Data from Multiple Data Sources

Dear Colleagues,

Scientists can benefit from a vast database of satellite imagery, covering the entire surface of the globe, spanning over 40 years of our timeline. Considering the large number of different types of satellites orbiting the Earth, the available data are not always homogeneous and comparable, but each space mission has managed to collect large packages of systematic data. In recent years, spatial analysis instruments have diversified and evolved significantly from a technological point of view, so we can benefit from satellite images with better spatial, spectral and temporal resolutions. Therefore, we can now easily evaluate the impact of natural or anthropic events on the environment and society, and we can easily estimate the repercussions and provide appropriate solutions.

Good temporal resolution and good-quality satellite images allow for scientists to evaluate the effects of droughts, hails, hurricanes, tornadoes, floods, deforestation, forest fires, mining accidents, pollution, Hazmat accidents, land-use changes, social events, urbanization, wars, etc. Furthermore, having a consistent long-term database of satellite images provides researchers with the opportunity to analyse these phenomena from a historic perspective, and it is possible to evaluate long-term changes in natural local parameters in relation to recent changes in the environment at the global scale.

When we analyse phenomenon over a long period of time, it is necessarily to use various data sources, such as old maps, field analyses or other types of data. If we analyse a natural phenomenon with disastrous effects in detail, we can benefit from data from sources other than remote sensing, such as: Doppler weather radar, ground-penetrating radar (GPR), 3D laser scanning, electromagnetic resistivity surveys, etc. Therefore, this Special Issue focuses on TIME as the determinant factor in the analysis of various phenomena at various spatial scales, but aims also to integrate data from multiple sources.

Dr. Adrian Ursu
Dr. Cristian Constantin Stoleriu
Dr. Marian Mierlă
Guest Editors

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