Estimating the Spread of Wildfires: Using Earth Observation Satellites Research Paper

Combining earth observation satellites and the current adaptation will help accurately estimate the spread of wildfires. Wildfires are an inescapable part of South Africa and are usually human-caused. In most cases, they lead to loss of life, and their financial impacts are immense. Although sending alerts concerning active fires has been working for South Africa, it has failed to estimate the severity caused by the fire properly (Artés et al., 2019). Instead, this system successfully informs the public and the appropriate department to respond to the wildfires rapidly. Therefore, combining the earth observation satellites as well as sending alerts will ensure that the approximation of the severity of the fire is almost accurate. This will happen because the earth satellites will provide high-resolution optical data to assess wildfire using a data processing toolbox.

The Sentinel-2 earth observation satellite will be the best to be combined with sending email alerts. The satellite will have two satellites with state-of-the-art Multispectral Imager (MSI) that will provide high-resolution optical imagery via spectral bands at a global scale (Ban et al., 2020). This will be the information source for land use, compositing, snow isolation, and land cover surveillance. In addition, Sentinel-2 level 2B will post-fire data to be analyzed. The most appropriate tool to analyze the collected data is statistical software such as SPSS (Artés et al., 2019). In most cases, when a statistical tool is used to analyze data, the results are almost accurate. The Sentinel-2 satellite will also provide clear images of the entire landscape affected by the fire. Therefore, combining sending alert and earth satellite observation will ensure an accurate estimation of the severity of the will fire.

Reference list

Artés, T., Oom, D., de Rigo, D., Durrant, T., Maianti, P., Libertà, G. and San-Miguel-Ayanz, J. (2019). ‘A global wildfire dataset for the analysis of fire regimes and fire behavior’, Scientific Data, 6(1).

Ban, Y., Zhang, P., Nascetti, A., Bevington, A. and Wulder, M. (2020) ‘Near real-time wildfire progression monitoring with sentinel-1 SAR time series and deep learning’, Scientific Reports, 10(1).