| Popis: |
Fire is a natural disruption that affects the structure and function of forest systems by changing the vegetation composition, climatic situation, carbon cycle, wildlife habitat, and many other major properties. The measure of the degree of these changes’ degree is known as fire severity, and it can be assessed using remote sensing data (i.e., satellite images, aerial images, etc.) and various biophysical indices (such as Normalized Burn Ratio (NBR), Char Soil Index (CSI), Burn Area Index (BAI), etc.), in addition to the measurement of Land Surface Temperature (LST). This research aims to assess the response of the NBR and LST both pre- and post-forest fires, taking a Mediterranean forest located in the northern part of Morocco, which burned in the summer of 2022, as the study area. We used seven Landsat-8 images spanning three years: three images from 2021 (i.e., pre-fire), one image from the summer of 2022 (i.e., fire period), and three images from 2023 (i.e., post-fire). The results demonstrated a negative correlation between the LST and NBR in the pre-fire period; when the temperature rises, the NBR drops. The same was found for the fire period in summer 2022, in which the LST reached its peak at 50 °C, while the NBR decreased to its lowest point at −0.2, whereas in the recovery period (i.e., 2023), the LST and NBR showed changes in fluctuation patterns; the LST variated normally according to seasons, dropping from 50 °C to 12 °C in winter and reaching 37 °C in summer, while the NBR increased over time, going from −0.2 to −0.04 in winter to 0.03 in summer, which indicates the gradual restoration of vegetation in the study area. This study concludes that in the post-fire period when a forest is recovering, the NBR is unaffected by seasonal changes in temperature and is more reflective of the vegetation it projects more than the vegetation situation in the area, unlike the LST. Thus, relying only on the LST to measure fire severity can give biased results due to changes in seasons. |
