Design and implementation of an integrated GIS-based cellular automata model to characterize forest fire behaviour

Spatially extended radiant heat fire model

Recent wildfire prevalence and destruction have led to new initiatives in the search for better land management techniques and prescriptions for controlled burns. With limited data on low-intensity prescribed burns, finding models that can represent fire behavior is of great importance to learning how to control fires with more accuracy while also maintaining the purpose for the burn, be it reducing fuels or managing the ecosystem. Here we use a data set of infrared temperatures collected in the New Jersey Pine Barrens from 2017 through 2020 to develop a model for very fine-scale fire behavior (≈0.05 m^{2}). The model uses distributions from the data set to define five stages in fire behavior in a cellular automata framework. For each cell, the transition between each stage is probabilistically driven based on the radiant temperature values of the cell and its immediate neighbors in a coupled map lattice. With five distinct initial conditions, we performed 100 simulations and used the parameters derived from the data set to develop metrics for model verification. To validate the model, we also expanded it to include variables not in the data set that are important for fire behavior, e.g., fuel moisture levels and spotting ignitions. The model matches several metrics compared to the observational data set and exhibits behavioral characteristics expected from low-intensity wildfire behavior including a long and varied burn time for each cell after initial ignition, and lingering embers in the burn zone.

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires

Wildfires are becoming more frequent in different parts of the globe, and the ability to predict when and where they will occur is a complex process. Identifying wildfire events with high probability of becoming a large wildfire is an important task for supporting initial attack planning. Different methods, including those that are physics-based, statistical, and based on machine learning (ML) are used in wildfire analysis. Among the whole, those based on machine learning are relatively novel. In addition, because the number of wildfires is much greater than the number of large wildfires, the dataset to be used in a ML model is imbalanced, resulting in overfitting or underfitting the results. In this manuscript, we propose to generate synthetic data from variables of interest together with ML models for the prediction of large wildfires. Specifically, five synthetic data generation methods have been evaluated, and their results are analyzed with four ML methods. The results yield an improvement in the prediction power when synthetic data are used, offering a new method to be taken into account in Decision Support Systems (DSS) when managing wildfires.

Intelligent management information system of urban planning based on GIS

With the development of urbanization, the application of GIS technology is more and more extensive. This study mainly discusses the development of urban planning intelligent management information system based on GIS. To design and build a rule-detailed spatial data model, provide the physical model and the data model corresponding to the logical layer from top to bottom in all steps, based on the attribute information stored in the Geodatabase model. According to the parameters set, connect to the database through the Oracle Connection class. The defined query criteria are converted into SQL statements that are executed using the Oracle Command class. Multi-source data integration middleware integrates various data formats with a GIS software format conversion tool or direct reading tool and then uses the geometric encoding semantics of data dictionary to represent the integrated data of system data model after merging. Property queries use the interactive search function for properties and spatial information to query the land use index for a particular area of the chart. If there is a scene roaming request from the input device, the 3D scene needs to be adjusted according to the input. Display the scene effects of a 3D virtual demonstration on a computer monitor. Start the GIS management operation function to deal with the case, and realize the user’s management of the urban planning system function with the concept of stratification. Fuzzy recognition mode is applied to identify the degree of the environmental impact of eco-city planning. The impact of urban planning on the environment is H ≈ 0.11 (0.1 < H < 1), which meets the expected standard. The results show that the system demand evaluation designed in this study is good, and the overall operation of the system is relatively stable, which plays a promoting role in urban planning.

Drone Swarms in Fire Suppression Activities: A Conceptual Framework

The recent huge technological development of unmanned aerial vehicles (UAVs) can provide breakthrough means of fighting wildland fires. We propose an innovative forest firefighting system based on the use of a swarm of hundreds of UAVs able to generate a continuous flow of extinguishing liquid on the fire front, simulating the effect of rain. Automatic battery replacement and extinguishing liquid refill ensure the continuity of the action. We illustrate the validity of the approach in Mediterranean scrub first computing the critical water flow rate according to the main factors involved in the evolution of a fire, then estimating the number of linear meters of active fire front that can be extinguished depending on the number of drones available and the amount of extinguishing fluid carried. A fire propagation cellular automata model is also employed to study the evolution of the fire. Simulation results suggest that the proposed system can provide the flow of water required to fight low-intensity and limited extent fires or to support current forest firefighting techniques.

Development of forest fire risk map using geographical information systems and remote sensing capabilities: Ören case

Forest fires globally cause severe losses in vegetation, soil and habitats and inevitably have direct and indirect negative environmental impacts such as deforestation, climate change and drought. According to the official records, there has been an increase of 58% in the number of the forest fires in Turkey in the last 30 years, between 1988 and 2018. Therefore, it is vital to determine the forest fire risks in the country and develop more effective methodologies to mitigate them. From this point, in the first phase, forest fire risk map of Kütahya-Ören region was prepared via the analyses of a variety of spatial data using geographical information system capabilities. The visibility analysis for the current fire towers was also performed. The results showed that very-high and high-risk, moderate-risk and low-risk zones respectively comprised 36.86%, 60.39% and 2.76% of the total study area, and 82.8% of the region was visible from the towers. In the second phase of the study, remote sensing methods were utilized for the detection of the areas burned in October 2001 in Ören-Çamdibi region, which was officially recorded as 4 hectares. The results revealed that the actual amount of the burned area was 5.6 hectares, and 83% of the burned surfaces was classified as moderate-risk areas in the fire risk map, while 17% of it was that of very-high and high-risk zones.

Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models

Land use and land cover change (LUCC) is an important issue in global environmental change and sustainable development, yet spatial simulation of LUCC remains challenging due to the land use system complexity. The cellular automata (CA) model plays a crucial role in simulating LUCC processes due to its powerful spatial computing power; however, the majority of current LUCC CA models are binary-state models that cannot provide more general information about the overall spatial pattern of LUCC. Moreover, the current LUCC CA models rarely consider background artificial irrigation in arid regions. Here, a multiple logistic-regression-based Markov cellular automata (MLRMCA) model and a multiple artificial-neural-network-based Markov cellular automata (MANNMCA) model were developed and applied to simulate complex land use evolutionary processes in an arid region oasis (Zhangye Oasis), constrained by water resources and environmental policy change, during the period 2000–2011. Results indicated that the MANNMCA model was superior to the MLRMCA model in simulated accuracy. Furthermore, combining the artificial neural network with CA more effectively captured the complex relationships between LUCC and a set of spatial driving variables. Although the MLRMCA model also showed some advantages, the MANNMCA model was more appropriate for simulating complex land use dynamics. The two integrated models were reliable, and could reflect the spatial evolution of regional LUCC. These models also have potential implications for land use planning and sustainable development in arid regions.

Forest fire spatial pattern analysis in Galicia (NW Spain)

Knowledge of fire behaviour is of key importance in forest management. In the present study, we analysed the spatial structure of forest fire with spatial point pattern analysis and inference techniques recently developed in the Spatstat package of R. Wildfires have been the primary threat to Galician forests in recent years. The district of Fonsagrada-Ancares is one of the most seriously affected by fire in the region and, therefore, the central focus of the study. Our main goal was to determine the spatial distribution of ignition points to model and predict fire occurrence. These data are of great value in establishing enhanced fire prevention and fire fighting plans. We found that the spatial distribution of wildfires is not random and that fire occurrence may depend on ownership conflicts. We also found positive interaction between small and large fires and spatial independence between wildfires in consecutive years.

Simulation of estrogen transport and behavior in laboratory soil columns using a cellular automata model

A cellular automata model (CA model) was used to simulate the soil column leaching process of estrogens during the processes of migration and transformation. The results of the simulated leaching experiment showed that the first-order degradation rates of 17α-ethynylestradiol (EE2), 17β-estradiol (E2) and estrone (E1) were 0.131 h(-1) for E2, 0.099 h(-1) for E1 and 0.064 h(-1) for EE2 in the EE2 and E2 leaching process, and the first-order sorption rates were 5.94 h(-1) for E2, 5.63 h(-1) for EE2, 3.125 h(-1) for E1. Their sorption rates were positively correlated with the n-octanol/water partition coefficients. When the diffusion rate was low, its impact on the simulation results was insignificant. The increase in sorption and degradation rates caused the decrease in the total estrogens that leached. In addition, increasing the sorption rate could delay the emerging time of the maximum concentration of estrogen that leached, whereas increasing the degradation rate could shorten the emerging time of the maximum concentration of estrogen that leached. The comparison made between the experimental data and the simulation results of the CA model and the HYDRUS-1D software showed that the establishment of one-component and multi-component CA models could simulate EE2 and E2 soil column leaching processes, and the CA models achieve an intuitive, dynamic, and visual simulation.

Large, high-intensity fire events in southern California shrublands: debunking the fine-grain age patch model

We evaluate the fine-grain age patch model of fire regimes in southern California shrublands. Proponents contend that the historical condition was characterized by frequent small to moderate size, slow-moving smoldering fires, and that this regime has been disrupted by fire suppression activities that have caused unnatural fuel accumulation and anomalously large and catastrophic wildfires. A review of more than 100 19th-century newspaper reports reveals that large, high-intensity wildfires predate modern fire suppression policy, and extensive newspaper coverage plus first-hand accounts support the conclusion that the 1889 Santiago Canyon Fire was the largest fire in California history. Proponents of the fine-grain age patch model contend that even the very earliest 20th-century fires were the result of fire suppression disrupting natural fuel structure. We tested that hypothesis and found that, within the fire perimeters of two of the largest early fire events in 1919 and 1932, prior fire suppression activities were insufficient to have altered the natural fuel structure. Over the last 130 years there has been no significant change in the incidence of large fires greater than 10,000 ha, consistent with the conclusion that fire suppression activities are not the cause of these fire events. Eight megafires (> or = 50,000 ha) are recorded for the region, and half have occurred in the last five years. These burned through a mosaic of age classes, which raises doubts that accumulation of old age classes explains these events. Extreme drought is a plausible explanation for this recent rash of such events, and it is hypothesized that these are due to droughts that led to increased dead fine fuels that promoted the incidence of firebrands and spot fires. A major shortcoming of the fine-grain age patch model is that it requires age-dependent flammability of shrubland fuels, but seral stage chaparral is dominated by short-lived species that create a dense surface layer of fine fuels. Results from the Behave Plus fire model with a custom fuel module for young chaparral shows that there is sufficient dead fuel to spread fire even under relatively little winds. Empirical studies of fuel ages burned in recent fires illustrate that young fuels often comprise a major portion of burned vegetation, and there is no difference between evergreen chaparral and semi-deciduous sage scrub. It has also been argued that the present-day fire size distribution in northern Baja California is a model of the historical patterns that were present on southern California landscapes. Applying this model with historical fire frequencies shows that the Baja model is inadequate to maintain these fire-prone ecosystems and further demonstrates that fire managers in southern California are not likely to learn much from studying modern Baja California fire regimes. Further supporting this conclusion are theoretical cellular automata models of fire spread, which show that, even in systems with age dependent flammability, landscapes evolve toward a complex age mosaic with a plausible age structure only when there is a severe stopping rule that constrains fire size, and only if ignitions are saturating.