Normalized Burn Ratio and Land Surface Temperature in Pre-and Post-Mediterranean Forests Fire

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Introduction
Forests are considered one of Earth pillars, as they play a vital role in keeping it in balance.They act as nature's air purifiers, soaking up carbon dioxide and giving us clean, fresh air to breathe [1,2].Yet recently, this pillar is confronting an increasing challenge, that is the menace of wildfires, which disrupts the stability of flora and fauna, altering the landscape's physical and ecological characteristics.Assessing the magnitude of these alterations, known as fire severity, constitutes a fundamental task in comprehending the ecological impacts of wildfires [3].
Fire severity can be estimated by using remote sensing data, which are images or data collected from satellites, airplanes, drones, or other platforms that observe the Earth from above.Remote sensing data can provide information about the spatial extent, intensity, and duration of a fire, as well as its effects on the forest [4][5][6].One way to use remote sensing data to assess fire severity is by calculating biophysical indices, which are mathematical formulas that combine different spectral bands (such as visible, near-infrared, shortwave-infrared, etc.) of the remote sensing images to highlight certain features or characteristics of the land surface.Some examples of biophysical indices that are commonly used to measure fire severity are Normalized Burn Ratio (NBR), Char Soil Index (CSI), and Burn Area Index (BAI).In addition to the measurements of Land Surface Temperature (LST).
In this article, we use seven remote sensing images from Landsat-8 satellite to assess the response of NBR and LST to the transformative force of wildfires.Taking as a study case, on a Mediterranean forest situated in the northern reaches of Morocco, a region subjected to a significant wildfire event during the summer of 2022.The images represent a comprehensive chronicle of the forest's journey over a span of three pivotal years.Among these images, three were captured in the year 2021, offering a valuable insight into the prefire conditions of the forest.Another crucial image was obtained during the peak of the wildfire in 2022.Finally, three post-fire images from 2023 complete the series, enabling us to observe the gradual process of recovery and regeneration.
This research contributes to the ongoing efforts in studying Mediterranean forests, emphasizing the essential role played by NBR and LST to assess the repercussions of wildfires on these landscapes.

Study Area
Our research is centered on Bou Jedyane, a Mediterranean forest situated in the northern region of Morocco (35.1167°N, 5.7754° W) (Figure 1), which experienced a wildfire during the summer of 2022 scorching 308 km 2 of its total area.

Satellite Data
Landsat-8 is a satellite that observes the Earth's land surfaces with two advanced sensors, the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS).It was launched in 2013 by NASA and the USGS to continue the Landsat program of collecting and archiving medium resolution multispectral image data.To compute NBR and LST, we employed a series of seven Landsat-8 images, covering a span of three years: three images taken in 2021, representing the pre-fire conditions, one image captured during the summer of 2022, corresponding to the fire period, and three additional images from 2023, signifying the post-fire phase (Table 1).We used specifically, four bands Red, NIR, SWIR2, and TIRS1, the characteristics of which are displayed in Table 2 Landsat-8 images were initially acquired from USGS website (https://earthexplorer.usgs.gov/)at level-2 processing, (i.e., calibrated and atmospherically corrected).Our pre-processing procedures involved rescaling, resampling to unify the resolution, and clipping using mask layer [7].

Results and Discussion
After computing seven images of LST and NBR (i.e., three pre-fire, one during fire period, and three post-fire) (Figure 2), we extracted their mean and then generated time series (Figure 3).From this study, we observed a consistent negative correlation between Land Surface Temperature (LST) and the Normalized Burn Ratio (NBR) in both the pre-fire (i.e., winter, spring, and summer of 2021).Same as the fire period (i.e., summer of 2022), LST increased reaching its highest temperature (i.e., 50 °C), and NBR decreased, signifying a substantial deterioration in vegetation health.
During the recovery period in 2023, LST exhibited normal seasonal fluctuations.Dropping to 12 °C in winter of 2023 after peaking in the fire period (i.e., 50 °C), only to rise in the subsequent spring and summer, reaching 36 °C and 37 °C, respectively.This pattern reflects the expected temperature variations in the study area, which is characteristic of seasonal changes in Mediterranean climates.In contrast to LST, NBR demonstrated a different behavior during the recovery phase.It gradually increased over time, moving from −0.2 in the immediate aftermath of the fire to −0.04 in winter and eventually rising to 0.03 in the summer of 2023.This upward trend indicates the gradual restoration of vegetation in the study area during the post-fire recovery period.

Conclusions
Our study provides important insights into how Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) respond to wildfires in Mediterranean forest ecosystems.We analyzed the case of Bou Jedyane forest, and found a negative correlation between these indices before and during the fire period.However, in the post-fire recovery phase, they showed different patterns: NBR increased steadily, indicating the gradual restoration of vegetation, while LST varied according to seasons.Therefore, our research highlights the robustness of NBR as an indicator of vegetation recovery after wildfires, and the need to integrate both NBR and LST assessments for a comprehensive understanding of fire impacts.This approach enhances our ability to monitor and manage Mediterranean forest ecosystems effectively, especially in the face of increasing wildfire challenges.