Abstract
It is challenging to detect burn severity due to the long-time period needed to capture the ecosystem characteristics. Whether there is a warning from the IoT devices, multitemporal remote sensing data is being received via satellites to contribute to multitemporal observations before, during and after a bushfire, and enhance the difference on detection accuracy. In this study, we strive to design an infrastructure to fire detection, to perform a qualitative assessment of the condition as quickly as possible in order to avoid a major disaster. Studying the multitemporal spectral indicators such as Normalized difference vegetation index (NDVI), Enhanced vegetation index (EVI), Normalized burn ratio (NBR), Soil-adjusted vegetation index (SAVI), Normalized Difference Moisture (Water) Index (NDMI or NDWI) and Normalized wildfire ash index (NWAI), we can draw reliable conclusions about the seriousness of an area. The scope of this project is to examine the correlation between multitemporal spectral indices and field-observed conditions and provide a practical method for immediate fire detection to assess its burn severity in advance. Furthermore, a quantified mapping model is presented to illustrate the spatial distribution of fire severity across the burnt area. The study focuses on the recent bushfire that took place in Mati Athens in Greece on 24 July 2018 which had as a result not only material and environmental disaster but also was responsible for the loss of human lives.
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Athanasaki, D.E., Mastorakis, G., Mavromoustakis, C.X., Markakis, E.K., Pallis, E., Panagiotakis, S. (2020). IoT Detection Techniques for Modeling Post-Fire Landscape Alteration Using Multitemporal Spectral Indices. In: Mastorakis, G., Mavromoustakis, C., Batalla, J., Pallis, E. (eds) Convergence of Artificial Intelligence and the Internet of Things. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-44907-0_14
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