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Fire is one of the most notorious hazards in Australia, with important economic impacts and damage to ecosystems. There is a concern of worsening fire conditions under climate variability, but there is little understanding of the variability in fire occurrence related to climate patterns. We present a statistical decomposition for spatio-temporal a...
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... 1.5°C (Grainger et al., 2022) and hot days >40°C and heat waves have doubled in frequency (Breshears et al., 2021). Climate changes have also altered the fire regime, increasing fire danger, intensity and frequency (Valente & Laurini, 2021) which in turn may change community assembly in these fire-prone plant communities (Mouillot et al., 2002). Enright et al. (2015) conceptualized the impact of climate change induced effects (e.g. less precipitation and shorter fire return intervals) on plant demographics in their theory of 'interval squeeze'. ...
Climate change, with warming and drying weather conditions, is reducing the growth, seed production, and survival of fire‐adapted plants in fire‐prone regions such as Mediterranean‐type ecosystems. These effects of climate change on local plant demographics have recently been shown to reduce the persistence time of local populations of the fire‐killed shrub Banksia hookeriana dramatically. In principle, extinctions of local populations may be partly compensated by recolonization events through long‐distance dispersal mechanisms of seeds, such as post‐fire wind and bird‐mediated dispersal, facilitating persistence in spatially structured metapopulations. However, to what degree and under which assumptions metapopulation dynamics might compensate for the drastically increased local extinction risk remains to be explored. Given the long timespans involved and the complexity of interwoven local and regional processes, mechanistic, process‐based models are one of the most suitable approaches to systematically explore the potential role of metapopulation dynamics and its underlying ecological assumptions for fire‐prone ecosystems. Here we extend a recent mechanistic, process‐based, spatially implicit population model for the well‐studied fire‐killed and serotinous shrub species B. hookeriana to a spatially explicit metapopulation model. We systematically tested the effects of different ecological processes and assumptions on metapopulation dynamics under past (1988–2002) and current (2003–2017) climatic conditions, including (i) effects of different spatio‐temporal fires, (ii) effects of (likely) reduced intraspecific plant competition under current conditions and (iii) effects of variation in plant performance among and within patches. In general, metapopulation dynamics had the potential to increase the overall regional persistence of B. hookeriana. However, increased population persistence only occurred under specific optimistic assumptions. In both climate scenarios, the highest persistence occurred with larger fires and intermediate to long inter‐fire intervals. The assumption of lower intraspecific plant competition caused by lower densities under current conditions alone was not sufficient to increase persistence significantly. To achieve long‐term persistence (defined as >400 years) it was necessary to additionally consider empirically observed variation in plant performance among and within patches, that is, improved habitat quality in some large habitat patches (≥7) that could function as source patches and a higher survival rate and seed production for a subset of plants, specifically the top 25% of flower producers based on current climate conditions monitoring data. Our model results demonstrate that the impacts of ongoing climate change on plant demographics are so severe that even under optimistic assumptions, the existing metapopulation dynamics shift to an unstable source‐sink dynamic state. Based on our findings, we recommend increased research efforts to understand the consequences of intraspecific trait variation on plant demographics, emphasizing the variation of individual traits both among and within populations. From a conservation perspective, we encourage fire and land managers to revise their prescribed fire plans, which are typically short interval, small fires, as they conflict with the ecologically appropriate spatio‐temporal fire regime for B. hookeriana, and likely as well for many other fire‐killed species.
... In this sense, to monitor the patterns of fire occurrence in the Brazilian Pantanal, we propose to model the dynamics of the point process of geolocated events (fire spots) based on remote sensing data resources through a spatiotemporal decomposition for the spatiotemporal point process,for the period 1999-2022. In particular, we use a novel dynamic representation of a Log-Gaussian Cox Process (LGCP), where the intensity function is modeled through decomposition of components in trend, seasonality, cycles, covariates, and spatial effects [17][18][19][20], assuming that spatial effects are time varying, based on an autoregressive functional structure. ...
... The Log-Gaussian Cox Process (LGCP) [34][35][36] is a spatial point process that combines elements of Gaussian processes with Cox processes to model spatial point patterns where the intensity varies smoothly over space. Applications of LGCP in health data modeling, epidemiology, and species distribution can be found at [37][38][39], and in the modeling of fire occurrence in [19,20,40]. This model is particularly useful when the intensity of point occurrences is believed to be influenced by underlying continuous spatial covariates. ...
We present a novel statistical methodology for analyzing shifts in spatio-temporal fire occurrence patterns within the Brazilian Pantanal, utilizing remote sensing data. Our approach employs a Log-Gaussian Cox Process to model the spatiotemporal dynamics of fire occurrence, deconstructing the intensity function into components of trend, seasonality, cycle, covariates, and time-varying spatial effects components. The results indicate a negative correlation between rainfall and fire intensity, with lower precipitation associated with heightened fire intensity. Forest formations exhibit a positive effect on fire intensity, whereas agricultural land use shows no significant impact. Savannas and grasslands, typical fire-dependent ecosystems, demonstrate a positive relationship with fire intensity. Human-induced fires, often used for agricultural purposes, contribute to an increase in both fire frequency and intensity, particularly in grassland areas. Trend analysis reveals fluctuating fire activity over time, with notable peaks in 2018–2021.
... The spatio-temporal decomposition methodology proposed in [24] was applied and generalized to other relevant contexts of spatio-temporal modeling of climate and environmental processes. Versions of this model for spatiotemporal point processes were applied to model the intensity of occurrence of tornadoes due to the effects of climate change [31], in the analysis of impacts of regulatory changes on the burning of sugar cane on the occurrence of fires in the state of São Paulo-Brazil ( [32]), in modeling the intensity of fires in Australia ( [33]) and the Brazilian Amazon ( [34]), and in modeling changes in climate patterns of rainfall, temperature, record temperatures and rainfall and other climate effects in [35], being a consolidated methodology for spatio-temporal analysis of climate and environmental effects. ...
... In this aspect we assume that the spatial random effects are constant over time, and thus the patterns of spatial variability do not change, and all the dynamics of change are given by the dynamics of the latent components of trend, seasonality and cycle. We tested this assumption by estimating a version of the model with time-varying spatial random effects, using the proposed structure in [33] and [34], but the results obtained indicate evidence in favor of the formulation with time-constant spatial effects. ...
We present a methodology designed to study the spatial heterogeneity of climate change. Our approach involves decomposing the observed changes in temperature patterns into multiple trend, cycle, and seasonal components within spatio-temporal models. We apply this method to test the hypothesis of a global long-term temperature trend against multiple trends in distinct biomes. Applying this methodology, we delve into the examination of heterogeneity of climate change in Brazil — a country characterized by a spectrum of climate zones. The findings challenge the notion of a global trend, revealing the presence of distinct trends in warming effects, and more accelerated trends for the Amazon and Cerrado biomes, indicating a composition between global warming and deforestation in determining changes in permanent temperature patterns.
... Modeling nonextreme, as well as extreme, wildfires is still crucial for hazard analysis, as they can have significant impacts on local ecosystems, homes, and communities, and can quickly develop into extreme and devastating wildfires. This is particularly relevant for Australia, where wildfires are a common occurrence (Valente and Laurini, 2021). Whilst the GPD can be fitted to the entire range of data, a more appropriate model may be one that has asymptotically-justified GPD upper-tails but a different parametric characterisation for the bulk of the distribution. ...
Recent wildfires in Australia have led to considerable economic loss and property destruction, and there is increasing concern that climate change may exacerbate their intensity, duration, and frequency. hazard quantification for extreme wildfires is an important component of wildfire management, as it facilitates efficient resource distribution, adverse effect mitigation, and recovery efforts. However, although extreme wildfires are typically the most impactful, both small and moderate fires can still be devastating to local communities and ecosystems. Therefore, it is imperative to develop robust statistical methods to reliably model the full distribution of wildfire spread. We do so for a novel dataset of Australian wildfires from 1999 to 2019, and analyse monthly spread over areas approximately corresponding to Statistical Areas Level 1 and 2 (SA1/SA2) regions. Given the complex nature of wildfire ignition and spread, we exploit recent advances in statistical deep learning and extreme value theory to construct a parametric regression model using graph convolutional neural networks and the extended generalized Pareto distribution, which allows us to model wildfire spread observed on an irregular spatial domain. We highlight the efficacy of our newly proposed model and perform a wildfire hazard assessment for Australia and population-dense communities, namely Tasmania, Sydney, Melbourne, and Perth.
... An alternative approach to address these issues is to combine elements of structural time series decomposition with spatiotemporal models that incorporate continuous spatial random effects. This approach can be seen as a process of decomposing geostatistical time series into a combination of trend, seasonal, and cyclical components, as well as the effects of additional covariates 25,27,28 . ...
... To achieve this, we propose a methodology that extends the trend-cycle decomposition in spatio-temporal models to spatio-temporal point pattern data. Specifically, we suggest using a dynamic representation of a Log Gaussian Cox process (LGCP), where the intensity function is modeled by decomposing the components into trends, seasonality, cycles, covariates, and spatial effects 25,27,28 . This formulation is valuable for identifying potential changes in the occurrence intensity over time, such as permanent changes in fire occurrence, and capturing seasonal and cyclical effects. ...
... To perform inference procedures, we employ the SPDE approach, allowing us to utilize Bayesian inference methods based on INLA. Our implementation follows the general structure proposed in Valente et al. 28 , which uses a basic version of the model without the inclusion of covariates to analyze spatio-temporal patterns of fires in Australia, and thus our formulation can be interpreted as a generalization of 28 . We provide a brief description of the SPDE approach, and further details can be found in Lindgren et al. 30 and Simpson et al. 29 . ...
Wildfires in the Amazon significantly impact the forest structure and carbon cycle. Understanding the patterns of fire occurrence is crucial for effective management. A novel spatio-temporal point process framework was used to analyze changes in fire occurrence patterns in the Brazilian Amazon. A dynamic representation of a Log Gaussian Cox process was used to model the intensity function, which was decomposed into trend, seasonality, cycles, covariates, and spatial effects. The results show a marked decrease in long-term fire occurrence movements between the start of the sample and 2012, followed by an increase until the end of the sample, attributed to governance measures and market mechanisms. Spatial variability of fire occurrence rates in the Brazilian Amazon was successfully captured, with regions having more dry seasons experiencing higher fire occurrence rates. This analysis provides valuable insights into fire occurrence patterns in the Amazon region and the factors driving them.
... This study selected the number of forest fires as the dependent variable. Previous studies often acquired fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) [4,17,26,32,[41][42][43][44][45][46][47]. Compared with MODIS, Visible Infrared Imaging Radiometer Suite (VIIRS) fire data has significant improvement in spatial resolution, observation amplitude, and data quality. ...
A forest fire is a destructive disaster that is difficult to handle and rescue and can pose a significant threat to ecosystems, society, and humans. Since driving factors and their effects on forest fires change over time and space, exploring the spatiotemporal patterns of forest fire occurrence should be addressed. To better understand the patterns of forest fire occurrence and provide valuable insights for policy making, we employed the Geographically and Temporally Weighted Regression (GTWR) model to investigate the varying spatiotemporal correlations between driving factors (vegetation, topography, meteorology, social economy) and forest fires in Anhui province from 2012 to 2020. Then we identified the dominant factors and conducted the spatiotemporal distribution analysis. Moreover, we innovatively introduced nighttime light as a socioeconomic driving factor of forest fires since it can directly reflect more comprehensive information about the social economy than other socioeconomic factors commonly used in previous studies. This study applied remote sensing data since the historical statistic data were not detailed. Here, we obtained the following results. (1) There was a spatial autocorrelation of forest fires in Anhui from 2012 to 2020, with high-high aggregation of forest fires in eastern cities. (2) The GTWR model outperformed the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression model (GWR), implying the necessity of considering temporal heterogeneity in addition to spatial heterogeneity. (3) The relationships between driving factors and forest fires were spatially and temporally heterogeneous. (4) The forest fire occurrence was mainly dominated by socioeconomic factors, while the dominant role of vegetation, topography, and meteorology was relatively limited. It’s worth noting that nighttime light played the most extensive dominant role in forest fires of Anhui among all the driving factors in the years except 2015.
... Wildfires have been analysed quantifying their frequency across time and space, giving information about the probability of occurrence as well as determining the effect of some covariates on the trend in the intensity of fire location. In light of this, it is known that the majority of fires are caused by human ignitions and that climatological variables and weather conditions have a high influence on determining wildfires occurrence (Eskandari et al. 2020;Valente and Laurini 2021). In addition, the literature also includes studies focusing on wildfire drivers such as landscapes or socioeconomic factors which may change over time and are important on the occurrence of wildfires (McWethy et al. 2018;Viedma et al. 2018). ...
In the last decades, wildfire hazards have increased to dangerous levels, becoming the focus of debate among policymakers both at the local and national levels. This paper proposes a Spatio-temporal approach to study the determinants of fire size distributions taking Sardinia as a case study in the time span 1998–2009. Special attention is devoted to socio-economic factors of local communities where wildfires occurred. The main finding of this study is that the proportion of public lands in a given municipality tends to mitigate the extent of the burned area. In addition, communities with a higher percentage of people employed in the primary sector are less likely to experience large burned extents.
... Forest fires are a global natural disaster with a greater impact on forest ecosystem carbon cycling, and they are due to global warming, the annual increase in the amount of combustible materials and difficulties in controlling fire sources [1][2][3]. The climate and weather conditions are the dominant drivers of forest fires. ...
... The group G1 is located on the coast and in the interior of the NEB, and consists of the Caatinga, the Atlantic Forest and the Amazon biomes, emphasizing the lower occurrence in Cerrado. Group G1 also showed the highest population density, the lowest Human Development Indexes (HDI) [47,48] and the strongest LULC [49,50], being identified with the influence of socioeconomic data and LULC in other studies on fires around the world [2,[4][5][6]10,11], since it is a region where large and rain-fed agriculture is concentrated [51,52]. The group G2 is located on a part of the north and inland NEB coast, corresponding to the Caatinga, the Cerrado and the Amazon biomes. ...
Forest fires destroy productive land throughout the world. In Brazil, mainly the Northeast of Brazil (NEB) is strongly affected by forest fires and bush fires. Similarly, there is no adequate study of long-term data from ground and satellite-based estimation of fire foci in NEB. The objectives of this study are: (i) to evaluate the spatiotemporal estimation of fires in NEB biomes via environmental satellites during the long term over 1998-2018, and (ii) to characterize the environmental degradation in the NEB biomes via orbital products during 1998-2018, obtained from the Burn Da-Citation: Oliveira-Júnior, J.F.d.; Shah, M.; Abbas, A.; Correia Filho, W.L.F.; Silva Júnior, C.A.d.; Santiago, D.d.B.; Teodoro, P.E.; Mendes, D.; Souza, A.d.; Aviv-Sharon, E.; et al. Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil. Sustainability 2022, 14, 6935. https://
... Identification and mapping of burned areas are essential tasks for monitoring the Earth surface, where satellite images are crucial sources of information [1,2]. More specifically, the generation of global burned area (BA) products has been an important issue since the late 1990s, but only since the 2000s we can find periodical and global BA products routinely derived with different degrees of accuracy, yet not for all over the Earth. ...
Wildland fires are one of the most important disturbances on Earth ecosystems, where the combination of remote sensing data and modern machine learning techniques offer a great potential for detection and monitoring of burned areas. Many algorithms have been developed in parallel to locate burned areas at local and global scales, but more accurate methodologies are still needed to provide precise and valuable information to climate users, environmentalists and public administrations. Gradient boosting-based methodologies have shown a good performance when dealing with segmentation of burned areas. In this paper, we propose a new method, called MBAGB, based on these methodologies for detecting and mapping burned areas in a multi-temporal setting of satellite imagery. We illustrate the procedure with the fires occurred in October 2017 in a region covering the North-Central Portugal and the North-West of Spain. Daily satellite images used for the definition of spectral indexes are taken from MODIS satellite products, between September and November 2017. The MBAGB accuracy metrics show the overall goodness of this method, and in particular, a very small number of false negatives in the identification of burned areas.
... Forest fire is a global natural disaster [1]. In recent years, the frequent occurrence of forest fires has been caused by global warming, the annual increase in the amount of combustible materials, and the difficulty controlling fire source [2,3]. There are many complex factors influencing the spread of forest fires [4,5]. ...
The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.
