Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios

Abstract

Wildfires significantly impact ecosystems, economies, and biodiversity, particularly in fire-prone regions like the Caatinga biome in Northeastern Brazil. This study integrates machine learning with climate and land use data to model current and future fire dynamics in the Caatinga. Using MaxEnt, fire probability maps were generated based on historical fire scars from Landsat imagery and environmental predictors, including bioclimatic variables and human influences. Future projections under SSP1-2.6 (low-emission) and SSP5-8.5 (high-emission) scenarios were also analyzed. The baseline model achieved an AUC of 0.825, indicating a strong predictive performance. Key drivers of fire risk included the mean temperature of the driest quarter (with an importance of 14.1%) and isothermality (12.5%). Temperature-related factors were more influential than precipitation, which played a secondary role in shaping fire dynamics. Anthropogenic factors, such as proximity to farming and urban areas, also contributed to fire susceptibility. Under the optimistic scenario, low-fire-probability areas expanded to 29.129 Mha, suggesting a reduced fire risk with climate mitigation. However, high-risk zones persisted in the Western Caatinga. The pessimistic scenario projected an alarming expansion of very-high-risk areas to 12.448 Mha, emphasizing the vulnerability of the region under severe climate conditions. These findings underline the importance of temperature dynamics and human activities in shaping fire regimes. Future research should incorporate additional variables, such as vegetation recovery and socio-economic factors, to refine predictions. This study provides critical insights for targeted fire management and land use planning, promoting the sustainable conservation of the Caatinga under changing climatic conditions.

1. Introduction

The increasing frequency and severity of wildfires globally have had significant adverse impacts on ecosystems and the vulnerability of dryland biomes, such as the Caatinga, the Sahel in Africa, and the Australian Outback, characterized by unique ecological dynamics and a high sensitivity to climatic and anthropogenic disturbances. These regions are critically compared to the Caatinga biome in Northeastern Brazil, a seasonally dry tropical forest facing similar environmental pressures from human activities and climate variability and encompassing climatic and economic dimensions [1,2,3,4,5,6,7,8]. Often exacerbated by human activities, these fires are further fueled by extreme weather conditions such as high temperatures, low rainfall, and abundant flammable material [9,10,11].

Changes in land use resulting from human intervention, particularly in areas near agriculture, infrastructure, and residential zones, are frequently the primary triggers for these fires [10,12,13]. Effectively mitigating the harmful impacts of wildfires requires addressing the complex human-driven factors contributing to their escalating occurrence.

The Caatinga biome in Northeastern Brazil faces significant environmental challenges due to human activities such as deforestation and land degradation, which have severely impacted its ecosystems and biodiversity [14,15].

The Caatinga biome, home to endemic biodiversity and vital ecosystems, is increasingly threatened by wildfires, often from agricultural burns that spiral out of control. These fires are exacerbated by factors such as deforestation, land degradation, and the projected intensification of extreme weather conditions, including prolonged droughts and rising temperatures, which mirror trends observed in other fire-prone biomes [14,15]. Wildfires threaten this biome, often originating from intentional agricultural burns that frequently spiral out of control [16]. Historical records highlight the prevalence of wildfires in the region [17], with projections suggesting that climate change will exacerbate their frequency and intensity [18]. Understanding the environmental factors influencing fire behavior, precipitation, temperature, wind, fuel loads, and topography is essential for developing accurate fire risk models. These models are vital for informing fire management strategies and regional mitigation efforts to protect the biodiversity and ecological integrity of the Caatinga.

Despite its ecological importance, the Caatinga region has seen insufficient mapping and monitoring of fire-affected areas [17,19,20,21], which impedes our understanding of its fire dynamics and increases its vulnerability to wildfires [18,22,23,24]. Uncontrolled fires pose significant threats to the environment and local communities, highlighting the need for targeted interventions. Monitoring burned areas can reveal links between human activities and wildfires, aiding in developing effective prevention strategies. Enhanced mapping and systematic monitoring are crucial for managing fire risks and ensuring the sustainability of Caatinga’s ecosystems and local livelihoods.

Recent research suggests that the risk of wildfires may increase due to the projected intensification of extreme weather conditions favorable to fire and the ongoing expansion of agricultural activities into natural areas [25,26,27]. However, some studies indicate that, despite the immediate threat posed by human-induced fires, long-term trends in tropical regions could show a decline in fire occurrences [11,28]. Predicting future fire patterns is inherently complex, depending on various interconnected factors, including climate change, land use policies, and conservation efforts.

In recent years, machine learning models have emerged as powerful tools for analyzing large environmental and climate datasets, significantly enhancing our understanding of wildfire behavior [29,30,31,32,33]. These models can integrate many factors to predict fire occurrences and their spread with a high degree of precision. Moreover, they enable the simulation of future scenarios by incorporating different land use changes and climate change projections [34,35]. Consequently, these techniques offer a valuable approach to addressing ongoing discussions about the future frequency and severity of wildfires in various biomes and regions worldwide [34,35].

Despite its ecological and socioeconomic significance, the Caatinga region requires enhanced fire monitoring and mapping, which currently hampers effective fire management and conservation initiatives. This study utilizes advanced machine learning models to analyze fire dynamics within the Caatinga biome and to forecast future wildfire risks under varying climate scenarios.

This study explores the driving forces behind wildfire dynamics in the Caatinga biome to address key questions regarding current and future fire susceptibility under various environmental and anthropogenic influences. The primary research questions include: What climatic and anthropogenic factors currently influence fire susceptibility in the Caatinga biome, and how might they evolve? How do land use changes and climatic variability interact with current and future fire susceptibility and the spatial distribution of wildfires? How will wildfire risks develop under distinct future climate scenarios, specifically SSP1-2.6 and SSP5-8.5? The study hypothesizes that climatic and anthropogenic factors are the primary driving forces behind current and future fire susceptibility in the Caatinga biome.

To enhance our understanding of wildfire dynamics in this region, this study employs advanced machine learning techniques, specifically the MaxEnt algorithm, in conjunction with a comprehensive set of environmental predictors. The research offers a nuanced understanding of fire behavior by integrating high-resolution fire scar data from Landsat imagery with environmental predictors. It identifies high-risk areas under Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5). This methodology aligns with global advancements in fire risk modeling, addressing critical knowledge gaps and providing actionable insights to inform fire prevention and sustainable land management strategies tailored to the unique challenges of the Caatinga biome.

2. Materials and Methods

2.1. Study Area

The Caatinga biome, spanning approximately 86.3 million hectares and comprising about 10% of Brazil’s land area, is situated in the country’s northeastern region, between 3° and 17° S latitudes and 35° to 45° W longitudes. The Atlantic Forest borders it to the east, the Amazon Biome to the west, and the Cerrado Biome to the south [36] (Figure 1). Renowned for its unique biodiversity, the Caatinga hosts flora and fauna that have evolved to endure its harsh climatic conditions, characterized by irregular rainfall and prolonged droughts.

The Caatinga region is characterized by a semi-arid climate that diverges from typical zonal climates at similar latitudes. Annual rainfall varies between 240 mm and 1500 mm, concentrated predominantly within two to three months. High temperatures and intense evapotranspiration exacerbate frequent droughts and sustain a network of intermittent rivers [37,38]. The vegetation is primarily steppe–savanna, giving the biome its name “Caatinga”, derived from Tupi and meaning “white forest”, a reference to the pale appearance of deciduous plants during the dry season [39]. The biome also encompasses the largest seasonally dry tropical forest and woodlands ecosystem in the Americas. Savanna formations dominate the landscape, covering approximately 46.17 million hectares (53.5% of the area), followed by non-forest natural formations, such as grasslands and other vegetation types, at 3.33 million hectares (3.9%) [40]. Pasture accounts for 21.78 million hectares (25.2%), with savanna and pasture together comprising 79% of the biome. However, forested areas are limited and fragmented, covering only 4.03 million hectares (4.7%) [40].

The Caatinga’s vegetation is highly diverse, featuring species with significant drought adaptations, such as xerophytic and deciduous traits, allowing them to endure the region’s extreme environmental conditions [41,42,43]. This remarkable biodiversity, coupled with the biome’s harsh climate, underscores its ecological significance within the Americas [14,41,42].

2.2. Overview

The modeling process involved data collection and preparation from the MapBiomas Fire Collection 3 dataset for fire occurrences and environmental predictors from the WorldClim 2.1 and MapBiomas 8.0 databases, resampled to 30 arc-seconds. Euclidean distance layers were generated to incorporate anthropogenic and natural influences. Predictor variables were refined through correlation analysis, jackknife tests, and permutation importance (Figure 2).

All fire occurrence models were developed using the MaxEnt algorithm, optimized with a genetic algorithm, and validated. Future fire probabilities were projected using the MIROC6 climate model under SSP1-2.6 and SSP5-8.5 scenarios (Figure 2). Fire probabilities were classified into five risk levels, and changes between baseline and future scenarios were analyzed to identify hotspots of increased or decreased fire susceptibility. We will provide a detailed overview of the steps taken below.

During the initial phase of the analysis, we examined the probability of fires during the most recent timeframe, known as the “baseline”, to establish a model of the current spatial distribution of fire incidents (Figure 2).

Once the baseline model was validated, we projected fire probabilities for future decades, considering two distinct climate warming scenarios. These projections were mainly based on anticipated shifts in climatic patterns while maintaining all other influential factors at levels similar to present-day conditions. This methodological approach facilitated a focused exploration of how climate change could impact the future incidence of wildfires.

2.3. Fire Occurrence Data

Our study utilized the MapBiomas Fire Collection 3 (http://mapbiomas.org, accessed on 19 September 2024) dataset, derived from Landsat imagery and accessed through Google Earth Engine (GEE). This comprehensive dataset provided information on fire-affected areas from 1985 to 2023. In 2023 alone, the accumulated burned area in the Caatinga totaled 11,025,188 hectares, representing 12.8% of the biome. Additionally, the Caatinga contributed 5.5% of the total burned area in Brazil, underscoring its vulnerability to wildfires and the importance of targeted fire management strategies [44]. We employed a random sampling method, selecting 1000 points from the identified fire-impaired regions, without filtering based on fire frequency. Over 95% of the samples experienced fewer than 15 fire events during the study period (see Table S1).

2.4. Environmental and Human Predictors

To assess the influence of landscape on fire behavior, we used land cover and land use data to create a comprehensive categorical layer (Table 1). This enabled the evaluation of landscape impacts on fire patterns. The data were sourced from the MapBiomas Collection 8.0 (http://mapbiomas.org, accessed on 19 September 2024) dataset, which includes categories like forest, non-forest natural formation, farming, non-vegetated areas, and water.

We employed Euclidean distance calculations to generate distinct distance layers, allowing a refined analysis and deeper insights into how land cover types affect fire patterns. These layers highlighted fire categories associated with human activities and ignition sources. Our analysis produced four raster files, with three classes being primarily depicted (farming, non-vegetated areas, and water) as linked to human influence and the fourth representing vegetation areas less affected by human activity.

Despite the significance of soil type and elevation in fire susceptibility dynamics being well recognized, these factors were excluded from the analysis due to limitations in the available datasets. Current soil maps have a relatively coarse resolution of 1:250,000, and topographic data are only available at a scale of 1:1,000,000. Such scales may be considered inadequate for capturing the fine spatial heterogeneity of the Caatinga biome, which is essential for accurately modeling localized fire dynamics. To overcome this limitation, we incorporated proxy variables like land use and land cover, which indirectly reflect some processes influenced by soil and topography, including vegetation distribution and patterns of human activity. This approach ensures that the model considers broader environmental and anthropogenic factors that drive fire susceptibility while minimizing potential biases from using low-resolution data.

2.5. Bioclimatic Predictors

The study utilized bioclimatic data (Table 1), encompassing historical and future variables, obtained from the WorldClim version 2.1 database (http://worldclim.org, accessed on 10 April 2024) at a spatial resolution of 30 arc-seconds [45]. The baseline model employed climatic averages from 1971 to 2000. To project future fire events, we employed the MIROC6 climate model, which was recognized for its reliability and success in previous fire modeling investigations, particularly in the Amazon region [34,35]. MIROC6 has consistently exhibited robust performance in simulating climatic patterns pertinent to tropical zones, especially where climatic extremes and seasonal fluctuations significantly influence ecological and environmental processes [34,35].

The projections for fire risk in the early 21st century were developed using two distinct scenarios based on shared socioeconomic pathways. The first scenario, SSP1-2.6, represents a low-emission future, with expected temperature increases of around 1.3 °C to 1.5 °C above preindustrial levels by the 2030s. The second scenario, SSP5-8.5, is a high-emission scenario, anticipating a more substantial temperature increase of approximately 1.5 °C to 2.2 °C in the same period.

The selection of the MIROC6 model for future climate data in our modeling approach was supported by both scientific rationale and practical considerations, particularly regarding ensemble modeling and the assessment of bioclimatic variable anomalies.

Even though ensemble approaches can yield broader insights by capturing a wider array of uncertainties, their implementation demands considerable resources and significant time for data acquisition, harmonization, calibration, validation, and scenario generation. Given MIROC6′s strong performance, validation, and alignment with our study’s objectives, it emerges as the most feasible choice within our current project timeline. Utilizing a single validated model ensures consistency in scenario generation and reduces methodological variability, facilitating a more accurate interpretation of results. Although the ensemble approach has its merits, incorporating it would have led to substantial delays, rendering it impractical during the revision process.

Our prioritization of the MIROC6 model was further bolstered by a comprehensive evaluation of bioclimatic anomalies within the CMIP6 framework, as documented on the WorldClim platform (https://www.worldclim.org/cmip6maps.html, accessed on 10 April 2024). This assessment revealed that most models exhibited similar patterns of climate anomalies within the Caatinga biome, which is the primary focus of our study. However, MIROC6 demonstrated a superior representation of climatic conditions specific to tropical arid and semi-arid ecosystems, aligning closely with our research objectives. Although some divergences were noted in regions outside the Caatinga, these variations were less pertinent to our study, reinforcing the suitability of MIROC6 for our purposes.

Furthermore, MIROC6 has consistently performed robustly in simulating climatic patterns within tropical regions, particularly in arid and semi-arid zones. Its proficiency in accurately capturing interannual and seasonal variability and its high spatial and temporal resolution renders it exceptionally practical for modeling localized ecological processes, such as vegetation dynamics and wildfire behavior. By prioritizing MIROC6, we ensured methodological consistency, minimized variability, and generated region-specific insights that aligned with the constraints and objectives of our study.

2.6. Variable Selection

The analysis began by aligning the resolution of human predictor layers, such as land use, vegetation, and anthropogenic factors, to the 30 arc-second resolutions of climatic variables from the WorldClim database, which retained its original resolution. This alignment ensured consistency across the datasets.

The variable selection process involved two sequential analyses to reduce redundancy: Spearman’s correlation analysis and a jackknife test. The variable selection process was optimized using the SDMtune package (version 1.3.1) [46] and dismo package (version 4.3.2) [47].

Spearman’s coefficient (|rs| ≤ 0.7) was used to identify and exclude highly correlated variables, generating 10,000 random background points with dismo package [47]. This ensured the selection of independent predictors. The jackknife test assessed each variable’s contribution to model performance, evaluated by the area under the curve (AUC) of the ROC using reduceVar function using SDMtune package [46]. Variables with minimal impact were iteratively removed, adhering to the correlation threshold. Additionally, variables with low permutation importance (below 2%) were considered for elimination without compromising model performance, resulting in a statistically robust and ecologically relevant selection.

2.7. MaxEnt Modeling for Fire Prediction

The research relied on the robust MaxEnt machine learning algorithm, which operates on presence-only data to model fire occurrences. MaxEnt compares predictor values at known fire ignition points against random points within the study area [48], enabling the development of models that can capture complex relationships [49]. This method is particularly beneficial for fire modeling when reliable absence data are challenging to obtain, instilling confidence in the reliability of the results. The dataset was divided; 70% was allocated for model training and 30% for testing.

The model’s performance was assessed using the area under the curve metric, which evaluates the model’s ability to differentiate between presence and absence locations.

To fine-tune the model’s hyperparameters, we implemented a hyperparameter tuning function using the optimizeModel function from the SDMtune package. It utilizes grid or random search methods to explore hyperparameter combinations and maximize model performance. Cross-validation is incorporated to evaluate configurations across multiple training data folds, resulting in robust and generalizable hyperparameter settings [46].

This algorithm generates a random set of models, evaluates their performance based on AUC scores from a validation dataset, and selects the best-performing models while retaining a smaller group of less-fit models to maintain diversity. It begins with random model generation, evaluating their performance using AUC scores, and selecting top models while keeping some less proficient ones for diversity. Selected models undergo a crossover process combined with mutation to enhance variation over multiple generations. The final optimized model predicts fire occurrences under future climate scenarios. The study employs the MaxEnt machine learning algorithm with presence-only data to identify complex relationships between predictor values at known fire ignition points and random locations. It is suitable for fire modeling when the absence of data is limited [46].

2.8. Spatial Fire Distribution of the Baseline Model and Change Analysis

The study assessed fire susceptibility using the complementary log-log transformation and classified the model’s pixels into five fire risk levels: very low, low, moderate, high, and very high. To facilitate zone-based analysis, the model’s pixels were classified into five levels of fire risk: very low (0.00 < x ≤ 0.10 → 1), low (0.10 < x ≤ 0.30 → 2), moderate (0.30 < x ≤ 0.50 → 3), high (0.50 < x ≤ 0.75 → 4), and very high (0.75 < x ≤ 1.00 → 5). The initial model computed the area covered by each risk category based on land use and cover. The researchers then investigated potential changes in fire probability under optimistic and pessimistic climate scenarios for the 2030s, identifying regions with a 10% or greater change in fire risk. This analysis allowed for the detection of potential “hotspots of change” under different climate projections.

Table 1. This table provides a comprehensive overview of the variables used in the analysis, including a concise description of each parameter, the spatial resolution of the data, the type of variable, and the corresponding data source.

Class	Variable (Unit)	Description of Data	Resolution	Type	Source
Climate normals	Tavg (°C)	Annual Mean Temperature	30 arc-seconds	Cont	Ref. [45] for the period 1971–2000 and [50] for future fire models
	ΔTdiurnal (°C)	Annual Mean Diurnal Range (Mean of monthly (max temp—min)) temp))	30 arc-seconds	Cont
	Isother (%)	Isothermality (ΔTdiurnal /ΔTannual × 100)	30 arc-seconds	Cont
	Tseason (°C)	Temperature Seasonality (Standard Deviation)	30 arc-seconds	Cont
	Tmax (°C)	Max Temperature of Warmest Month	30 arc-seconds	Cont
	Tmin (°C)	Min Temperature of Coldest Month	30 arc-seconds	Cont
	ΔTannual (°C)	Annual Temperature Range	30 arc-seconds	Cont
	Twet (°C)	Mean Temperature of Wettest Quarter	30 arc-seconds	Cont
	Tdry (°C)	Mean Temperature of Driest Quarter	30 arc-seconds	Cont
	Twarm (°C)	Mean Temperature of Warmest Quarter	30 arc-seconds	Cont
	Tcold (°C)	Mean Temperature of Coldest Quarter	30 arc-seconds	Cont
	PPT (mm)	Annual Precipitation	30 arc-seconds	Cont
	PPTwet Month (mm)	Precipitation of Wettest Month (max([PPTi, …, PPT12]))	30 arc-seconds	Cont
	PPTdry Month (mm)	Precipitation of Driest Month (min([PPTi, …, PPT12]))	30 arc-seconds	Cont
	PPTseason (%)	Precipitation Seasonality (coefficient of variation)	30 arc-seconds	Cont
	PPTwet (mm)	Precipitation of Wettest Quarter	30 arc-seconds	Cont
	PPTdry (mm)	Precipitation of Driest Quarter	30 arc-seconds	Cont
	PPTwar (mm)	Precipitation of Warmest Quarter	30 arc-seconds	Cont
	PPTcold (mm)	Precipitation of Coldest Quarter	30 arc-seconds	Cont
Land use and land cover	LULC (class)	Landsat-based classification of Caatinga Biome for 2023	30 m resampling for 30 arc-seconds	Cat	[51]
Vegetation	Dis_Veget (km)	Euclidean distance calculated from a binary vegetation raster Forest Natural Formation	30 arc-seconds	Cont	[51]
Anthropogenic factor	Dist_Nonveg (km)	Euclidian distance calculated from a binary non vegetated area raster	30 arc-seconds	Cont	[51]
	Dist_water (km)	Euclidian distance calculated from a binary water raster	30 arc-seconds	Cont	[51]

Table 1. This table provides a comprehensive overview of the variables used in the analysis, including a concise description of each parameter, the spatial resolution of the data, the type of variable, and the corresponding data source.

Class	Variable (Unit)	Description of Data	Resolution	Type	Source
Climate normals	Tavg (°C)	Annual Mean Temperature	30 arc-seconds	Cont	Ref. [45] for the period 1971–2000 and [50] for future fire models
	ΔTdiurnal (°C)	Annual Mean Diurnal Range (Mean of monthly (max temp—min)) temp))	30 arc-seconds	Cont
	Isother (%)	Isothermality (ΔTdiurnal /ΔTannual × 100)	30 arc-seconds	Cont
	Tseason (°C)	Temperature Seasonality (Standard Deviation)	30 arc-seconds	Cont
	Tmax (°C)	Max Temperature of Warmest Month	30 arc-seconds	Cont
	Tmin (°C)	Min Temperature of Coldest Month	30 arc-seconds	Cont
	ΔTannual (°C)	Annual Temperature Range	30 arc-seconds	Cont
	Twet (°C)	Mean Temperature of Wettest Quarter	30 arc-seconds	Cont
	Tdry (°C)	Mean Temperature of Driest Quarter	30 arc-seconds	Cont
	Twarm (°C)	Mean Temperature of Warmest Quarter	30 arc-seconds	Cont
	Tcold (°C)	Mean Temperature of Coldest Quarter	30 arc-seconds	Cont
	PPT (mm)	Annual Precipitation	30 arc-seconds	Cont
	PPTwet Month (mm)	Precipitation of Wettest Month (max([PPTi, …, PPT12]))	30 arc-seconds	Cont
	PPTdry Month (mm)	Precipitation of Driest Month (min([PPTi, …, PPT12]))	30 arc-seconds	Cont
	PPTseason (%)	Precipitation Seasonality (coefficient of variation)	30 arc-seconds	Cont
	PPTwet (mm)	Precipitation of Wettest Quarter	30 arc-seconds	Cont
	PPTdry (mm)	Precipitation of Driest Quarter	30 arc-seconds	Cont
	PPTwar (mm)	Precipitation of Warmest Quarter	30 arc-seconds	Cont
	PPTcold (mm)	Precipitation of Coldest Quarter	30 arc-seconds	Cont
Land use and land cover	LULC (class)	Landsat-based classification of Caatinga Biome for 2023	30 m resampling for 30 arc-seconds	Cat	[51]
Vegetation	Dis_Veget (km)	Euclidean distance calculated from a binary vegetation raster Forest Natural Formation	30 arc-seconds	Cont	[51]
Anthropogenic factor	Dist_Nonveg (km)	Euclidian distance calculated from a binary non vegetated area raster	30 arc-seconds	Cont	[51]
	Dist_water (km)	Euclidian distance calculated from a binary water raster	30 arc-seconds	Cont	[51]

3. Results

The baseline model for fire occurrence achieved an average AUC test value of 0.825, reflecting a strong predictive capability consistent with the high-performance standards of MaxEnt models. For the 2030s validation phase, the optimistic model (SSP1-2.6) produced an AUC of 0.818, while the pessimistic model (SSP5-8.5) yielded an AUC of 0.813. These findings confirm the robustness and reliability of the MaxEnt model in capturing fire dynamics, aligning with research indicating its effectiveness in environmental modeling [52].

The analysis of permutation importance revealed that temperature-related factors are dominant determinants of fire occurrence, with the mean temperature of the driest quarter demonstrating the highest contribution at 14.1%. This underscores the critical influence of thermal conditions during dry periods on the fire risk. Similarly, isothermality accounted for 12.5%, emphasizing the influence of daily temperature variations relative to annual fluctuations. The mean temperature of the wettest quarter, with a value of 11.5%, highlights the continued significance of temperature fluctuations even in wetter periods. Other temperature-related variables, including the mean diurnal range (8.5%), temperature seasonality (7.4%), and the temperature annual range (7%), collectively reinforce the importance of understanding temperature variability across different timescales (Figure 3).

While less impactful, precipitation variables still play a role in shaping fire occurrence. For instance, the precipitation of the warmest quarter (2.2%) and the precipitation of the wettest quarter (1.9%) provide moderate contributions, indicating that rainfall during these periods influences fire dynamics. However, variables such as the precipitation of the driest month (0.6%) and the precipitation of the driest quarter (0.3%) exhibit minimal impacts, further emphasizing the dominance of temperature over precipitation in determining fire susceptibility.

Anthropogenic factors also contribute significantly to fire dynamics. The distance to urban areas, with a permutation importance of 2.8%, reflects the role of human activities in shaping fire patterns, mainly through urban expansion and associated land use changes. Similarly, the distance to water bodies contributed 2.5%, highlighting the indirect influence of resource availability on fire risk. The distance to agricultural areas (1.9%) underscores the effects of farming practices, while non-vegetated areas, with a value of 1.7%, indicate that barren landscapes also play a role in fire occurrence. The land use and land cover (LULC) variable, with the lowest contribution at 0.1%, suggests that while land cover classifications matter, they are less influential than climatic and proximity-related variables.

The comparison of AUC metrics further validates the predictive superiority of temperature-related variables, including the mean temperature of the driest quarter and isothermality, which achieved high AUC values, indicative of their strong correlation with fire occurrence (Figure 4). Precipitation-related variables, such as PPTseason and PPTwetMonth, achieved notable AUC scores but were slightly less predictive. Proximity factors, such as Dist_urban and Dist_farming, achieved moderate AUC values, suggesting some contribution to fire susceptibility modeling, although their impact was secondary compared to climatic variables.

Response curves illustrate the relationships between fire probability and the top five influential variables. For instance, fire probability increases with the mean temperature of the driest quarter, particularly at moderate to high temperatures, peaking between 20 °C and 30 °C. However, fire probability decreases at extreme temperatures, suggesting a threshold effect. Isothermality shows that moderate values, between 40% and 60%, are associated with elevated fire risk, whereas extremely stable or highly variable temperatures reduce fire susceptibility. The mean temperature of the wettest quarter reveals the highest fire probability when temperature fluctuations are significant, peaking between 20 °C and 22 °C, with the fire risk declining beyond 26 °C.

Regarding temperature seasonality, the fire risk peaks at moderate variability levels but declines in highly seasonal regions, where extreme seasonality may suppress fire occurrence. The annual temperature range shows a steady increase in fire probability as the range expands, stabilizing at higher levels beyond 25 °C (see details in Supplementary Materials Figure S1).

Temperature-related variables collectively account for over 60% of the model’s explanatory power, highlighting their dominant role in determining fire susceptibility in the Caatinga biome. Precipitation and proximity factors, while secondary, collectively contribute approximately 10%, underscoring their relevance in specific contexts. These quantitative insights validate the hypothesis that climatic conditions, particularly temperature extremes, are the primary drivers of fire risk. The results also emphasize the importance of integrating high-resolution fire scar data with advanced modeling techniques, such as MaxEnt, to enhance fire risk predictions and support sustainable land management strategies under varying future climate scenarios (SSP1-2.6 and SSP5-8.5).

3.1. Baseline Model of Fire Probability

The baseline model analysis indicates that approximately 32.12% of the Caatinga biome falls into the “high” to “very high” fire susceptibility categories, revealing a complex spatial pattern of fire activity across the region. These high-risk zones are primarily concentrated in the Western Caatinga, which shares ecological boundaries with the Cerrado and connects to the MATOPIBA region. This area’s combination of climatic and environmental conditions increases the likelihood of fire occurrences, emphasizing the need for region-specific mitigation strategies (Figure 5A,B).

The southern and southeastern borders of the Caatinga also exhibit an elevated fire susceptibility, attributed to their geographic proximity and ecological connectivity to surrounding regions, particularly those experiencing higher fire frequencies. These areas underscore a critical need for targeted fire prevention and management interventions to protect the fragile Caatinga ecosystem (Figure 5A,B). Conversely, the eastern, east/central, southeastern, and northern regions of the Caatinga demonstrate relatively lower fire susceptibility, with most areas classified as having “very low” to “low” fire probabilities. This geographic disparity highlights a distinct gradient of fire risk across the biome, influenced by climatic and land use factors.

High-probability fire zones are particularly evident in the savanna vegetation of the central Caatinga biome, which encompasses states such as Ceará, Paraíba, and Pernambuco. Isolated high-risk areas are also observed in specific municipalities, including Limoeiro do Norte, Tabuleiro do Norte, and Quixeré in Ceará, as well as Governador Rosado and Baraúna in Rio Grande do Norte. These localized fire hotspots necessitate vigilant monitoring and proactive fire management measures to mitigate potential ecological and socioeconomic impacts.

Land use and land cover (LULC) types of distribution across fire susceptibility classes reveal significant quantitative trends (Figure 6A,B).

The “vegetation” class exhibits notable area variations across fire probability levels. In the “very low” fire susceptibility category, vegetation covers 8.2 million hectares, peaking at 13.3 million hectares in the “low” category. However, as the fire susceptibility increases, vegetation cover declines, decreasing to 9.7 million hectares at “moderate”, 11.3 million hectares at “high”, and further dropping to 9 million hectares at “very high” fire susceptibility levels. This trend demonstrates that vegetation predominates in areas with a lower fire risk but diminishes in more fire-prone regions.

In parallel, the proportional distribution of LULC types provides further insights into fire susceptibility patterns (Figure 6B). The “vegetation” class accounts for 41% of the area at the “very low” fire probability level, rising to 47% at “low”, 60% at “moderate”, 65% at “high”, and peaking at 78% in the “very high” fire probability zones. This pattern suggests that regions with elevated fire probabilities are dominated by fire-adapted or fire-prone ecosystems, highlighting a critical interaction between vegetation cover and fire risk.

In contrast, the “farming” class consistently declines with increasing fire susceptibility. Farming areas represent 43% of the total area at both the “very low” and “low” fire probability levels but decrease sharply to 32% at “moderate”, 30% at “high”, and further to 17% at “very high” fire susceptibility levels. This decline underscores the vulnerability of agricultural activities to fire risk and highlights the need for sustainable land management practices in fire-prone areas.

The “non-vegetated” class follows a similar downward trajectory, decreasing from 16% at the “very low” probability level to 10% at “low”, 7% at “moderate”, and stabilizing at 5–6% for both “high” and “very high” fire probability zones. This trend indicates that barren landscapes, although less dominant in fire-prone areas, continue to exert some influence on fire dynamics.

These findings illustrate a clear shift in land use and land cover (LULC) patterns across fire susceptibility gradients. Vegetation increasingly dominates regions with elevated fire probabilities, supporting the hypothesis that fire-prone ecosystems exhibit adaptations to frequent fire events. Conversely, farming and non-vegetated areas become less prominent as the fire risk intensifies, underscoring the ecological and socioeconomic challenges associated with fire dynamics in the Caatinga biome.

3.2. Projected Future Fire Probabilities

Analyzing fire probability distributions reveals critical spatial and temporal trends under the baseline and future scenarios (Figure 7A,B). The baseline model classifies fire probabilities into five categories, ranging from “very low” (0.00 < x ≤ 0.10) to “very high” (0.75 < x ≤ 1.00), providing a detailed assessment of fire risk across the Caatinga biome.

The largest fire suitability category for the baseline period is “very low”, which spans 17.741 million hectares. This is followed by the “low” category, which covers 25.981 million hectares and accounts for a substantial portion of the biome with a relatively low fire risk. The “moderate” and “high” categories occupy 14.957 million hectares and 16.584 million hectares, respectively, while the “very high” probability zone, representing the most fire-prone areas, accounts for 10.992 million hectares (Figure 7A). Spatially, the “high” and “very high” probability zones are concentrated in the western and northern borders, depicted in red, where fire risks are more intense due to environmental and climatic conditions. In contrast, “very low” to “low” probability zones are predominantly located in the central and eastern regions, characterized by lighter yellow tones on the map (Figure 7B).

A clear shift toward lower fire probabilities is observed under the “Optimistic 2030s” scenario (SSP1-2.6). The “low” fire probability class expands significantly to 29.129 million hectares, indicating a potential reduction in fire-prone regions under less severe climate projections. However, the “very high” probability zone exhibits a slight increase to 11.338 million hectares, suggesting that some areas remain vulnerable despite overall reductions in fire risk. This scenario is visually represented by more widespread yellow zones in central areas, indicating reduced fire risks, and localized red zones where fire susceptibility persists (Figure 7C).

Conversely, the “Pessimistic 2030s” scenario (SSP5-8.5) projects a substantial worsening of fire probabilities under severe climate conditions. The “very low” probability zone contracts sharply to 14.569 million hectares, representing a loss of over 3.1 million hectares compared to the baseline model. Simultaneously, the “very high” fire probability zone expands alarmingly to 12.448 million hectares, reflecting a net increase of 1.456 million hectares in the most fire-prone regions (Figure 7A). The spatial patterns in Figure 7D vividly illustrate this trend, with intensified red zones dominating the western, northern, and southern regions. Central regions, which previously exhibited moderate fire risks, experience a notable contraction of low-risk zones, signaling an escalation in fire vulnerability due to severe climatic conditions.

Figure 7 also highlights key spatial dynamics under both climate scenarios. Grey areas represent regions where the fire risk remains minimal and is unlikely to change, while red zones depict areas of increasing fire probability, particularly along the western and northern borders. Salmon-colored zones indicate persistent fire risks, especially across the central and southwestern portions, underscoring areas requiring ongoing fire management interventions. Meanwhile, blue zones, which represent decreasing fire probabilities, are sparse and concentrated in central/eastern areas under the optimistic scenario but remain largely absent under the pessimistic scenario.

Figure 8, which details the fire suitability dynamics, provides further insights into the projected changes.

Under the optimistic scenario, red zones of increasing fire probabilities are concentrated in the northern and northwestern boundaries and southwestern clusters, highlighting localized hotspots with an elevated fire risk. Salmon-colored regions, denoting persistently high fire probabilities, dominate the central and western portions of the biome. In contrast, blue zones reflecting reduced fire probabilities are restricted to small, isolated patches in the central/eastern areas (Figure 8A,B).

In the pessimistic scenario, red zones expand dramatically across the western, southern, and central regions (Figure 8B). The widespread increase in high-risk areas indicates a substantial deterioration in fire conditions, exacerbated by extreme climate projections. Persistent salmon-colored zones continue to dominate the central, southwestern, and northern regions, emphasizing ongoing challenges for fire management in these areas. Meanwhile, the blue zones indicating decreasing fire probabilities remain limited and are concentrated in the eastern and southeastern regions, where climatic influences may mitigate fire risks to some extent.

The pessimistic scenario underscores an expansive increase in fire-prone areas, with “very high” fire risk zones expanding by 13.3% compared to the baseline. In contrast, the optimistic scenario projects a potential decrease in overall fire probabilities, with “low” fire risk zones increasing by 12.1%. These findings reflect the critical influence of future climate trajectories on fire dynamics in the Caatinga biome and reinforce the need for region-specific fire management strategies to mitigate escalating risks under severe climate conditions.

4. Discussion

The findings of this study highlight the robust predictive accuracy of the fire modeling approach, as demonstrated by high AUC values for baseline (0.825) and future scenarios (optimistic: 0.818; pessimistic: 0.813). The slight decline in future model performance reflects inherent uncertainties in projecting climate-driven events but aligns with prior research validating MaxEnt as an effective tool for modeling fire [52,53,54]. These results emphasize the importance of incorporating diverse environmental and anthropogenic predictors to address the complexities of fire dynamics under changing climates [16,17,55].

Temperature was identified as the primary driver of fire risk, with the mean temperature of the driest quarter (Tdry) and isothermality (Isother) showing the highest contributions to the model. This aligns with patterns observed in other semi-arid regions, where elevated temperatures accelerate vegetation desiccation, increasing fuel availability and fire susceptibility [56,57]. The influence of temperature variability, including consistent heat extremes and daily fluctuations, further underscores its critical role in shaping ignition conditions.

In contrast, precipitation-related variables, such as precipitation seasonality (PPTseason) and precipitation of the wettest month (PPTwet), had lower but complementary impacts on the fire dynamics. These variables affect vegetation growth and fuel accumulation, which are indirectly tied to fire ignition after dry periods. This pattern mirrors global findings from other fire-prone ecosystems, where precipitation primarily governs long-term vegetation patterns rather than immediate fire conditions [58,59].

Anthropogenic factors, such as proximity to urban and agricultural areas, also influenced fire risk, demonstrating the interaction between human activities and natural fire dynamics. These results underscore the need for integrated fire management strategies that address climatic and human-induced factors. Future research should focus on refining models by incorporating additional bioclimatic datasets, exploring alternative modeling techniques, and integrating socio-economic variables. Such efforts are crucial to improving fire risk predictions and guiding sustainable land management practices in vulnerable ecosystems like the Caatinga [60,61,62].

Understanding how variables interact with land management and vegetation regeneration is crucial for assessing fire risk and ecological dynamics. These interactions link climatic factors with human practices to create effective sustainable management strategies.

The variables isothermality (Isother, %), mean temperature of the driest quarter (Tdry, °C), mean temperature of the wettest quarter (Twet, °C), diurnal temperature range (ΔTdiurnal, °C), and temperature seasonality (Tseason, °C) interact intricately with land management practices and vegetation regeneration by influencing thermal regimes that are critical for plant growth and fuel desiccation. For instance, the high-temperature variability captured by Tdry and ΔTdiurnal exacerbates vegetation desiccation during dry periods, increasing the availability of flammable materials. Conversely, Twet and Tseason underscore how seasonal shifts in temperature affect vegetation regrowth and resilience, with extreme variations potentially hindering regeneration in degraded areas. Managing these thermal dynamics through adaptive practices, such as selecting heat-tolerant species for reforestation or optimizing irrigation during peak dry seasons, can mitigate fire risk and promote sustainable land use.

Precipitation-related variables, such as precipitation seasonality (PPTseason, %), precipitation of the wettest month (PPTwet, mm), precipitation of the driest month (PPTdry, mm), and precipitation of the warmest quarter (PPTwar, mm), play a dual role in shaping vegetation growth and fire dynamics. High precipitation during wet seasons (PPTwet) fosters rapid vegetation growth, which, if unmanaged, contributes to fuel accumulation. On the other hand, PPTdry and PPTwar emphasize the scarcity of moisture during critical periods, limiting vegetation recovery and making landscapes more susceptible to fire ignition. Effective land management strategies must consider these precipitation patterns, implement controlled burns during wet seasons, or preserve riparian buffers to stabilize water availability and enhance vegetation resilience during droughts.

Temperature-related predictors influence fire occurrence more than precipitation-related predictors due to their direct and immediate role in shaping fuel availability and flammability. In semi-arid ecosystems like the Caatinga, the mean temperature of the driest quarter (Tdry) and temperature seasonality (Tseason) are pivotal in driving vegetation desiccation during dry periods, creating conditions conducive to fire ignition and spread. High temperatures exacerbate biomass drying, reducing moisture content in vegetation and increasing its combustibility. This effect is particularly pronounced during periods of extreme temperature variability, such as those captured by isothermality (Isother) and diurnal temperature range (ΔTdiurnal), in which rapid transitions between daytime heating and nighttime cooling further stress vegetation. These thermal dynamics provide a robust explanatory power for fire susceptibility, as they directly influence the availability and continuity of flammable material.

In contrast, while precipitation-related predictors such as precipitation seasonality (PPTseason) and precipitation of the wettest month (PPTwet) are crucial for understanding long-term fuel accumulation and vegetation growth, their effects on fire dynamics are more indirect and dependent on temporal lags. For example, wet-season precipitation fosters vegetation growth, increasing fuel loads, but the fire risk materializes only after sufficient drying. Conversely, limited precipitation during the dry season (e.g., PPTdry) has a relatively lower impact on mitigating fire risk in regions where high temperatures drive fuel desiccation. This indirect relationship explains why precipitation variables often have a lower relative importance in predictive models than temperature.

Moreover, the dominance of temperature-related predictors may also reflect their strong interaction with other variables, such as land use and vegetation types, which amplify fire risks in areas undergoing agricultural expansion or deforestation. Elevated temperatures in disturbed landscapes can further reduce vegetation resilience and hinder natural regeneration, creating a feedback loop that increases fire susceptibility.

Focusing on temperature-related dynamics is important when modeling fire risk in semi-arid biomes. However, precipitation’s complementary role in influencing long-term vegetation patterns and fuel availability highlights the need for an integrated approach that considers both immediate thermal effects and broader hydrological processes. Future models may benefit from refining the spatial and temporal representation of these interactions to better capture the complexity of fire regimes under changing climatic conditions.

These findings align with a broader global understanding that temperature extremes and seasonal variability are increasingly pivotal determinants of wildfire dynamics in semi-arid and transitional ecosystems. As is similarly observed in other fire-prone regions worldwide, elevated and fluctuating thermal conditions facilitate vegetation desiccation, enhance fuel continuity, and create ignition environments conducive to widespread fire events. Although precipitation contributes to long-term fuel accumulation and landscape structure, its indirect effects are often overshadowed by the more immediate influence of temperature, underscoring a fundamental ecological tension between climate-driven fuel desiccation and plant community resilience. Furthermore, the spatial heterogeneity of fire susceptibility, with distinct high-risk zones and less vulnerable areas, mirrors patterns documented in ecotonal interfaces globally, where shifts in climatic and land use regimes heighten instability and threaten biodiversity and ecosystem function.

Anthropogenic factors, represented by variables such as distance to urban areas (Dist_urban, km), distance to water bodies (Dist_water, km), distance to farming areas (Dist_Farming, km), and land use and land cover (LULC), directly influence the interaction between human activities and natural processes like vegetation regeneration and fire occurrence. Proximity to urban and agricultural areas often correlates with increased fire ignition due to human activity, while areas closer to water bodies may serve as natural barriers to fire spread. LULC captures the broader context of land conversion, where deforestation or monoculture farming reduces vegetation diversity, hindering natural regeneration. Incorporating these variables into land management plans can improve fire prevention strategies by identifying high-risk zones and promoting reforestation or agroforestry practices that balance human needs with ecological sustainability.

The findings from this study emphasize the uneven spatial distribution of fire susceptibility across the Caatinga biome, highlighting the western region, particularly near the Cerrado and MATOPIBA borders, as a high-risk area. This region’s elevated fire probability is strongly influenced by agricultural expansion and land conversion, aligning with studies in other fire-prone biomes where transitional zones exhibit heightened fire risks due to intensified human activity and environmental pressures [63,64,65,66,67]. Conversely, lower fire probabilities in the eastern and northern parts of the biome reflect a reduced anthropogenic influence and more stable climatic conditions, underscoring the spatial heterogeneity of fire dynamics in the region.

The optimistic 2030 scenario reveals potential reductions in fire risk, with a significant expansion of low-probability zones covering 29.129 million hectares. However, the persistence of high-risk areas, particularly in the central and western regions, suggests that localized vulnerabilities may remain despite improved climatic conditions. These findings highlight the need for targeted fire prevention and management strategies to address persistent risk zones, even under favorable climate scenarios (Figure 7A).

In contrast, the pessimistic scenario underscores the severe implications of worsening climate conditions, with a contraction of low-risk areas and an expansion of high-risk zones to 12.448 million hectares. This scenario reflects a critical need for adaptive fire management strategies for intensifying risks [26] in the western and northern regions, which emerge as consistent hotspots under the projected conditions. The study emphasizes the importance of proactive fire mitigation efforts and long-term planning to address these vulnerabilities, particularly climatic and anthropogenic pressures.

The reliance of our study on a single climate model, specifically MIROC6, while operationally beneficial due to its known efficacy in tropical and semi-arid contexts, introduces significant uncertainty in our projections of fire dynamics under future climate scenarios. The inherent limitations of using just one model are particularly poignant given the complex, nonlinear nature of climate impacts on fire risk. Expanding the model ensemble to include alternatives like HadGEM3, GFDL-CM4, and BCC-CSM2-MR would not only diversify the range of climatic inputs and outcomes but also enhance the robustness of our predictions by encapsulating a broader spectrum of potential climatic conditions. Such an approach would address the critical need for a more comprehensive assessment of uncertainties and variability in fire risk, potentially reducing biases linked to any single model’s assumptions. It is essential for future research to leverage multiple models to capture extensive climatic variability better and enhance the reliability of fire risk assessments under diverse future scenarios.

Furthermore, our reliance on the MaxEnt model, predominantly due to its strength in handling presence-only data, may limit our ability to accurately model the intricate interactions between fire dynamics and environmental and anthropogenic factors. The introduction of alternative modeling techniques, such as random forests and generalized additive models (GAMs), which efficiently integrate both presence and pseudo-absence data, could substantially widen the analytical framework. These models are particularly adept at capturing nonlinear relationships and complex interactions between multiple predictors, offering a more dynamic and detailed exploration of fire dynamics. This methodological diversification would allow for a more nuanced understanding of fire risks, uncovering patterns and relationships that MaxEnt might overlook due to its methodological constraints.

Moreover, excluding critical environmental variables such as soil type and high-resolution elevation data from our analysis is a notable limitation. These factors significantly impact fire behavior, influencing fuel moisture content, fire spread, and intensity through their effects on microclimate conditions and vegetation structure. The coarse resolution or limited variability of such data within the study area might justify their omission to some extent. However, their inclusion in future studies could provide insights into the localized processes that govern fire dynamics. Incorporating these alongside additional biophysical and socio-economic predictors such as land management practices, human settlement patterns, and economic activities could enhance the model’s applicability and accuracy. This holistic approach would refine the spatial and temporal resolution of fire risk projections and support more targeted and effective fire management and mitigation strategies in the Caatinga and similar biomes globally.

By addressing these methodological and conceptual uncertainties, future research can more effectively support land managers, policymakers, and conservation practitioners in designing targeted interventions. Integrating advanced models with scenario-based projections could inform adaptive management strategies that anticipate shifting fire regimes and identify areas where interventions may be most beneficial, such as mitigating ignition sources, restoring native vegetation, or establishing strategic firebreaks. Moreover, extending investigations to include socio-economic dimensions, local ecological knowledge, and policy frameworks could improve the relevance of modeling outcomes, ensuring that future fire risk analyses not only enhance predictive accuracy but also guide the development of sustainable and context-specific land use policies. Acknowledging and working within these uncertainties will strengthen the scientific foundation for adaptive management and long-term conservation planning.

Future research should enhance fire risk prediction models by integrating comprehensive data on fire behavior, land use changes, and vegetation dynamics. Detailed temporal and spatial projections are essential for understanding the complex interactions between climate, human activities, and land cover, especially under changing climate scenarios. Incorporating variables such as vegetation recovery, fuel loads, and fire propagation will provide a deeper insight into fire behavior, particularly in areas experiencing rapid land conversion. Multi-scale models that account for fine-resolution land use changes, including agricultural expansion and reforestation, can offer a broader perspective on fire management strategies.

From a practical perspective, these insights underscore the urgency of integrating nuanced climate projections with targeted land management strategies to mitigate escalating fire risks. By recognizing that climatic parameters, notably temperature-related variables, influence fire probabilities, policymakers and land managers can refine interventions, such as vegetation management, firebreak planning, and restoration activities, to the areas most susceptible to thermal extremes. Moreover, incorporating anthropogenic factors, including proximity to urban and agricultural zones, provides opportunities to mitigate ignition sources and to harmonize agricultural expansion with ecological resilience goals. Leveraging advanced modeling tools that account for climatic and socio-environmental drivers can guide robust, evidence-based decision making to safeguard semi-arid ecosystems and ensure more sustainable and fire-adapted landscapes under future climate scenarios.

Integrating advanced scientific research into practical policy frameworks to enhance fire management in semi-arid regions like the Caatinga is crucial. This requires expanding the array of climate models beyond a single-model approach to include diverse models that can account for a broader range of potential climate scenarios. Adopting dynamic modeling frameworks such as random forests or generalized additive models, which utilize both presence and absence data, is essential. These models are better equipped to capture the complex interactions between fire risks and environmental variables, improving fire risk assessment accuracy and applicability. Additionally, incorporating overlooked predictive variables like soil types and high-resolution elevation data would refine our understanding of localized fire behaviors, which is crucial for developing targeted fire management strategies.

Moreover, integrating socio-economic factors and detailed land management practices into these models is vital for crafting effective fire management policies. Sustainable practices such as controlled burning, the establishment of strategic firebreaks, and reforestation with fire-resilient species must be tailored to the region’s unique ecological and economic contexts. Engaging local communities through educational programs is also fundamental, ensuring local knowledge is harnessed and fire management becomes a cooperative endeavor. Continuous adaptation and refinement of these strategies in response to new research and environmental changes will maintain their effectiveness against evolving fire regimes, ensuring that fire management strategies are scientifically robust, ecologically sustainable, and socially inclusive.

5. Conclusions

This research offers a thorough analysis of the main factors influencing the frequency of wildfires in the Caatinga biome and forecasts future fire risk patterns under various climate change scenarios. The findings from this study highlight new pathways for improving fire management practices in the Caatinga biome, suggesting a shift towards more integrated and dynamic modeling approaches that consider the multifaceted influences on fire risk. By employing machine learning algorithms, we have developed spatial models that accurately depict current fire dynamics and predict upcoming wildfire incidents. By integrating an extensive temporal dataset of fire scars obtained from Landsat imagery and climate projections, we have been able to generate highly detailed predictions of fire risk probability. These models pinpoint areas with a heightened susceptibility to fire and provide valuable insights into the primary environmental and human-induced factors contributing to fire occurrences in the Caatinga. Our discoveries provide knowledge to guide fire management strategies to reduce the escalating threat of wildfires in this delicate biome. This nuanced understanding allows for the development of predictive models that identify areas at risk and suggest specific times when the risk is most acute due to climatic conditions. This temporal specificity in fire risk prediction can significantly enhance the effectiveness of fire prevention strategies, enabling timely interventions that could mitigate potential damage.

The spatial maps created from this research highlight specific areas requiring preventive measures and increased monitoring due to their heightened susceptibility to fires. The study found that temperature-related factors significantly influence fire risk, particularly those associated with the driest and hottest periods. Notably, variables such as the mean temperature of the driest quarter and isothermality consistently emerged as strong predictors of fire probability. Additionally, while playing a secondary role, changes in land use and proximity to human activities, such as agriculture and urban development, were also identified as significant contributors to fire susceptibility. These findings emphasize the importance of targeted fire prevention strategies considering both climatic conditions and human-induced changes to the landscape.

Moving forward, adopting a more holistic approach in modeling efforts that incorporate the immediate predictors of fire and the indirect factors such as ecological changes, human settlement patterns, and economic activities that influence fire dynamics over more extended periods is essential. Research should also explore the impact of post-fire landscapes on environmental recovery and resilience, which could provide insights into more sustainable land management practices. Given the complexities revealed in this study, there is a clear need to develop adaptive management strategies responsive to the predicted trends in climate change and the socio-economic developments within the biome. Collaborative efforts involving ecologists, climatologists, policymakers, and local stakeholders will be crucial in crafting policies that address the current fire risks and aim to maintain the ecological balance and ensure the sustainability of the Caatinga ecosystem.

Future research needs to focus on refining and expanding predictive models by integrating additional data on fire behavior, post-fire recovery, and projected land use changes. The increasing expansion of agricultural activities and rising deforestation rates pose a growing threat to the Caatinga biome. Therefore, future modeling efforts should incorporate these socio-economic factors and ecological responses to climate change to understand wildfire risks comprehensively. Additionally, integrating fine-scale changes in vegetation cover and fuel loads will improve the predictive capability of models, facilitating more effective fire management and conservation planning.

Given the mounting pressures on this ecosystem due to climate change and human activities, it is essential for future conservation efforts to build on these findings. Using scientific models and predictive frameworks, stakeholders can make well-informed decisions supporting the Caatinga’s long-term sustainability and biodiversity. Strong collaborations between researchers, policymakers, and local communities will ensure the preservation and protection of this unique biome for future generations.