## 1 Introduction  
Fire is an important disturbance factor in ecosystems that induce land-cover modification and change [1]. Under climate change conditions, drought severity may trigger tough fire regimes, in terms of the number and dimension of fire events [2]. Fire represents a significant source of gas and aerosols worldwide [3]. The large amounts of smoke emitted from fires impact air quality and affect global warming due to rising trace gas emissions [4]. Satellite data play a major role in supporting knowledge about fire severity by delivering rapid information to map fire-damaged areas in a precise and prompt way. Accurate and rapid mapping of fire-damaged areas is fundamental to support fire management, account for environmental loss, define planning strategies, and interpret cause-effect relations of post-hazard wildfire dynamics, as the specific case of vegetation restoration dynamics [5]. From a forest management perspective, emergency maps help forest stakeholders to minimize the potentially harmful effects of excessive post-fire erosion [6], while estimating burn severity may help to identify areas that need to be recovered [7]. Fire severity is a qualitative measure of the immediate effects of fire on the non-anthropic ecosystem, related to the extent of mortality and survival of plant and animal life both aboveground and belowground and to the loss of organic matter [8]. Optical remote sensing tools have proven to be useful in the development of procedures to accurately estimate fire-affected areas and burn severity, in support of forest fire prevention, assessment, and monitoring on global, regional, and local scales [4]. These techniques have been mainly employed to address three different temporal fire-related phases: pre-fire conditions, burn severity, and post-fire ecosystem responses [9; 10]. The present research study focuses on rapid post-fire mapping, which represents an indispensable requirement to support fire management, with the scope of identifying the spatial extent of areas affected by wildfires. In general, information about post-fire mapping can be inferred from satellite remotely sensed spectra by applying one of two different approaches: (i) using a single satellite image or (ii) using multitemporal satellite images, usually adopting a combination of pre-fire and post-fire satellite acquisitions. Several methods have been developed for mapping fire-affected areas, typically defined as burned areas and known as burn scars, from multitemporal or single post-fire satellite images [11; 12; 13]: supervised classifications (i.e. maximum likelihood, decision tree, and neural network [14; 15; 16]), linear transformations (i.e., tasseled cap and principal component analysis [17; 18]), spectral unmixing techniques [19], object-based classification models [20; 21], and logistic regression models [22]. Techniques based on thresholding the spectral indices are widely applied, because they are computationally fast and efficient for detecting fire-affected areas with both medium and high resolution optical satellite data [19; 23]. Furthermore, normalized spectral indices are efficient in normalizing reflectance values related to specific acquisition conditions. An improvement to the empirical thresholding is the use of multiple spectral indices, which exploit complementary information brought by many different spectral responses. Dealing with multiple spectral indices, the adoption of a synthetic fuzzy score could reduce the effort involved in adapting thresholds from scene to scene, improving reliability when applied to different geographical areas [11; 24]. More recently a two-phase algorithm [25; 26], which first identifies clearly burned pixels and then applies a region-growing algorithm, was introduced to reduce pixel misclassification by balancing omission and commission errors. Although fire severity is a measure of fuel consumption [27], it has been typically identified with the values of fire-related spectral indices.