# Automatic Methodology for Forest Fire Mapping with SuperDove Imagery
## 1 Introduction  
Global warming, and the consequent long-term increase in the Earth's average temperature, has been a topic of concern for citizens, scientists, and policy makers in recent decades [1]. One of the most alarming consequences of this warming trend is the increase in the incidence and intensity of fires, especially forest fires [2; 3; 4]. Forest fires, also known as wildfires, have been a natural part of the Earth's ecology for millions of years [5]. However, recent data suggest a worrying trend: the frequency, intensity, and total area burned by these fires have increased at an unprecedented rate. This escalation is not limited to just one or two regions; it is a global phenomenon that affects forests from the Amazon and Australia to California and Siberia [6; 7; 8; 9]. The connection between global warming and increased fire activity is complex but undeniable. Warmer temperatures lead to more frequent and longer droughts, creating drier conditions that are ideal for fires to ignite and spread. Additionally, warmer weather has led to earlier snowmelt and, consequently, a longer fire season [3; 10]. Although, as mentioned, wildfires are a natural and integral component of many ecosystems, they have significant impacts on the environment, the economy, and human health [11; 12]. This is why the ability to accurately estimate the burned area of these fires is crucial for several reasons. Wildfires can dramatically alter the landscape and disrupt ecosystems [13]. They can lead to a loss of vegetation, changes to wildlife habitats, and increased vulnerability to erosion and landslides [14]. The accurate estimation of burned areas allows for a better understanding of these environmental impacts and assists in the development of effective post-fire recovery strategies [15]. Similarly, wildfires contribute to climate change by releasing large amounts of carbon dioxide and othergreenhouse gases into the atmosphere. Therefore, an accurate estimate of the burned area is also required to quantify these emissions and improve the accuracy of climate models. Additionally, it is vital for effective fire management [16]. An accurate estimate helps evaluate the effectiveness of firefighting efforts, plan future resource allocation, and develop strategies to prevent or mitigate the impact of future fires [17; 18]. Finally, wildfires can cause substantial economic damage, including property loss and costs associated with firefighting efforts. They also pose serious health risks due to the release of harmful contaminants [19]. Generating detailed burned-area maps can help assess these health and economic impacts, inform policy decisions, and guide public health responses. The most common methodologies for determining the burned area and severity of fires using remote sensing images are based on the analysis of time series of multispectral images, statistical modeling, and unsupervised classification [20]. In particular, spectral indices are commonly considered, with the Normalized Burned Area Index (NBR) standing out as the most used, which is calculated from Near-Infrared (NIR) and Short-Wave Infrared (SWIR) bands. Determining the burned area and fire severity is usually estimated by comparing images from before and after the fire [21; 22]. CubeSats, such as those in the PlanetScope constellation (Planet Labs Inc., San Francisco, CA, USA, +- 2023 Planet Labs PBC), offer an opportunity to attain better spatial and temporal resolution to help address this problem more efficiently, despite the absence of the SWIR band. These satellites provide high-resolution images with a very short revisit time, allowing more frequent monitoring of areas affected by fires [23; 24; 25; 26]. Although the lack of the SWIR band, which is useful in distinguishing burned areas, can be a challenge, several studies have shown that accurate results can be obtained using optimal band combinations or deep learning techniques [27; 28; 29; 30; 31].