Forest fires pose significant concerns for the environment, economy, and human safety in a majority of forested regions globally [
1,
2,
3,
4]. Ecologically, fire serves as a pivotal factor influencing vegetation diversity and dynamics over time and space [
1,
2,
3,
4,
5]. Authorities, including civil protection agencies, governments, local authorities, and forestry corps, are compelled to effectively manage forest fires and establish preparedness strategies to preserve biome services and ensure citizen safety [
6,
7,
8,
9]. While forest fires can arise naturally due to dry weather, volcanic eruptions, or lightning, human activities stand out as the predominant factor, particularly during periods of heightened water stress [
10,
11].
The forest areas and rangelands along the western and northern expanse of the Zagros Mountains (Iraq) chain have faced a considerable number of fires since 2005. The forests in Marivan and Paveh, situated in the Kurdistan and Kermanshah provinces of western Iran, respectively, are particularly affected [
12,
13]. Furthermore, the Province of Sulaymaniyah and Halabja in the Kurdistan Region (KR) of northern Iraq has also witnessed a notable surge in fire incidents in recent years, especially after 2008 [
10]. According to both official reports and conducted studies, human activities are identified as the most frequent ignition sources in these forested areas [
13,
14]. It is also reported that more than 90% of fires in the European Union (EU) are human-caused [
15,
16]. Given the valuable opportunity presented by satellite-based indices for monitoring diverse Earth phenomena, Remote Sensing (RS) data and Geographic Information System (GIS) technology have become crucial tools for natural resource managers and researchers across government agencies, conservation organizations, and industry [
17,
18,
19]. The integration of RS data/techniques and GIS facilitates the efficient and accurate analysis of wildfire dynamics, enabling informed decision-making processes for fire management and mitigation strategies [
17,
18]. The occurrence of fires, including their severity and duration, is intricately correlated to vegetation condition [
10,
17,
18], including critical dynamic factors such as Fuel Moisture Content (FMC) and Fuel Temperature (FT) [
20,
21]. Several studies have been conducted worldwide to classify and map land cover due its wide and important role in natural resources management [
22], agriculture management [
23], biodiversity conservation [
24], among others. Amongst them, a range of researchers have tried to differentiate and map vegetation species such as trees, shrubs, and grass species using RS data and techniques. Some studies have used Light Detection and Ranging (LiDAR) data with trees height information to differentiate these species since these vegetation types have different height [
23]. However, LiDAR data is not available everywhere and it is expensive [
25]. Deep learning (DL) algorithms, on the other hand, has also been used for land cover classification in numerous recent studies [
26]. Accordingly, advanced DL techniques with high-resolution satellite data have better performance than traditional methods for classifying land cover and detecting objects. Although Dl algorithms achieved high accuracy, it needs more diverse training data to be efficient in different situations. However, most of them are applied on high resolution satellite images [
26]. In the work of Saah et al. [
22], DeepLabV3+, a semantic segmentation-based DL method, was employed to categorize three types of vegetation land covers (trees, shrubs, and grass) utilizing solely Sentinel-2 RGB images.
In contrast, there are other methods that rely on Vegetation Indices (VIs) that are generated from multispectral images [
27]. VIs allows to extract valuable information from the spectral characteristics of plants, encompassing biochemical characteristics, environmental factors, and soil properties [
27]. These indices play a crucial role in estimating vegetation biomass, canopy height, percentage vegetation cover changes, plant health, and Leaf Area Index (LAI). Additionally, they aid in distinguishing between soil and vegetation and mitigating atmospheric and topographical influences when feasible [
28]. Meshesha et al. [
29] found a strong correlation between forage biomass and spectral indices by employing Sentinel-2 Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), integrated with ground sampling in
Harshin district, Ethiopia, to develop a forage forecasting model. In Fakhri et al. [
30], a novel vegetation index-based workflow was introduced, and, within it, the multi-objective particle swarm optimization (MOPSO) algorithm was applied to optimize a set of broadband VIs to reach both objectives of greenness estimation and vegetation/non-vegetation classification in a small area in Zagros sparse woodlands. A new index was also developed by Qian et al. [
31] and applied in Beijing, China, merging spectral and texture features to differentiate tree from grass in urban areas, at a detailed level, using high resolution GeoEye-1 imagery. Another study conducted in northwest (NW) Russia utilized hyperspectral data and vegetation phenology to differentiate tree species [
32]. It was concluded that classification using multispectral data effectively improves accuracy compared to using a single hyperspectral image. In another study area in Zagros [
33], a study was conducted to generate accurate land cover map for the Shirvan County forests, a part of Zagros forests in Western Iran using Sentinel-2 derived NDVI, Google Earth imagery, and field data for protective management. The study proved that the Support Vector Machine (SVM) algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33%. In a greater area, for the entire Zagros Mountains, a new empirical model was introduced for mapping land cover for the whole Zagros mountains using Sentinel-2 derived NDVI [
34]. Despite being so challenging, this study has effectively mapped the land cover (agriculture, build-up area, wooded area, plantation, bare soil, water, and rangeland) for its wide study area. In Iraq, on the other hand, some studies were also conducted for mapping land cover in KR using VIs and DL approaches [
35,
36]. Although, several studies have employed satellite images for mapping land cover, a minority of them are dealing with differentiating grass species from wood species, and if they do, they are focusing on urban areas using high resolution images [
22,
23,
24,
25,
26,
27,
28,
29,
30,
31,
32,
33,
34,
35,
36]. However, the grass species have not been disregarded completely, and it is mostly classified as a member of rangeland class with other members such as shrubs. The land cover, vegetation dynamic, topography, distance from population center and road, besides many other static and dynamic factors, are considered as key factors in the fire susceptibility assessment [
37,
38,
39]. Deriving reliable information on fuel types is regarded as a major factor since the fires need fuel to happen and to propagate [
37,
38]. Actually, in studying fuel types, grass species have not been looked only as an ecological factor, but as the most flammable fuel type [
37,
38]. Notably, the NDVI has been widely employed to estimate vegetation phenology, as well as its quality and growth condition [
40,
41,
42]. NDVI, serving as an index of vegetation growth and coverage, finds extensive use in describing spatio-temporal characteristics of land use and land cover (LULC), including percent vegetation coverage [
41,
42,
43,
44]. However, the NDVI cannot differentiate between tree, shrub, and grass because of their similar spectral characteristics [
22,
37]. Using multi-temporal NDVI integrated with phenological information of vegetation covers is effectively helpful to differentiate vegetation cover [
27,
31,
32]. Regarding this characteristic, the NDVI has been widely used in fire susceptibility studies to represent vegetation dynamic and condition [
37,
38,
39].