Assessing the Effectiveness of Green Landscape Buffers to Reduce Fire Severity and Limit Fire Spread in California: Case Study of Golf Courses

Herbert, Claudia; Butsic, Van

doi:10.3390/fire5020044

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Previous studies modeling fire cessation have compared burned and unburned locations at a spatial scale. This scale is chosen to be close enough to observe fire behavior processes, yet distant enough to ensure that distinct states are seen on each side of the fire edge [40]. These models are typically accomplished by predefining the spatial extent or span of the fire boundaries and subjectively setting the proximity between burned and unburned observation points [41].…”

Section: Statistical Modellingmentioning

confidence: 99%

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

Sun

Zhang

et al. 2023

Forests

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Forest fires create burned and unburned areas on a spatial scale, with the boundary between these areas known as the fire boundary. Following an analysis of forest fire boundaries in the northern region of Yangyuan County, located in the Liangshan Yi Autonomous Prefecture of Sichuan Province, China, several key factors influencing the formation of fire boundaries were identified. These factors include the topography, vegetation, climate, and human activity. To explore the impact of these factors in different spaces on potential results, we varied the distances between matched sample points and built six fire environment models with different sampling distances. We constructed a matched case-control conditional light gradient boosting machine (MCC CLightGBM) to model these environment models and analyzed the factors influencing fire boundary formation and the spatial locations of the predicted boundaries. Our results show that the MCC CLightGBM model performs better when points on the selected boundaries are paired with points within the burned areas, specifically between 120 m and 480 m away from the boundaries. By using the MCC CLightGBM model to predict the probability of boundary formation under six environmental models at different distances, we found that fire boundaries are most likely to form near roads and populated areas. Boundary formation is also influenced by areas with significant topographic relief. It should be noted explicitly that this conclusion is only applicable to this study region and has not been validated for other different regions. Finally, the matched case-control conditional random forest (MCC CRF) model was constructed for comparison experiments. The MCC CLightGBM model demonstrates potential in predicting fire boundaries and fills a gap in research on fire boundary predictions in this area which can be useful in future forest fire management, allowing for a quick and intuitive assessment of where a fire has stopped.

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Section: Statistical Modellingmentioning

confidence: 99%

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

Sun

Zhang

et al. 2023

Forests

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Modeling the Ignition Risk: Analysis before and after Megafire on Maule Region, Chile

et al. 2022

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Wildland fires are a phenomenon of broad interest due to their relationship with climate change. The impacts of climate change are related to a greater frequency and intensity of wildland fires. In this context, megafires have become a phenomenon of particular concern. In this study, we develop a model of ignition risk. We use factors such as human activity, geographic, topographic, and land cover variables to develop a bagged decision tree model. The study area corresponds to the Maule region in Chile, a large zone with a Mediterranean climate. This area was affected by a megafire in 2017. After generating the model, we compared three interface zones, analyzing the scar and the occurrences of ignition during and after the megafire. For the construction of georeferenced data, we used the geographic information system QGIS. The results show a model with high fit goodness that can be replicated in other areas. Fewer ignitions are observed after the megafire, a high recovery of urban infrastructure, and a slow recovery of forest plantations. It is feasible to interpret that the lower number of ignitions observed in the 2019–2020 season is a consequence of the megafire scar. It is crucial to remember that the risk of ignition will increase as forest crops recover. Wildland fire management requires integrating this information into decision-making processes if we consider that the impacts of climate change persist in the area.

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Delineating Fire-Hazardous Areas and Fire-Induced Patterns Based on Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires in Northeast China

Zhu

2022

Remote Sensing

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(1) Background: Fire affects global agricultural and/or forest ecosystems with high biomass accumulation. However, the delineation of fire-hazardous areas based on satellite-derived active fire intensity is not well-studied. Therefore, examining the characteristics of fire occurrence and development plays an important role in zoning fire-hazardous areas and promoting fire management. (2) Methods: A fire intensity (FI) index was developed with Visible Infrared Imaging Radiometer Suite (VIIRS) active fires and then applied to identify fire-hazardous areas in Northeast China. Combined with terrain, land cover and net primary productivity (NPP), the spatial and temporal characteristics of active fire occurrence were consistently analyzed. Next, a conceptual decision tree model was constructed for delineating fire-induced patterns impacted by varied factors in Northeast China. (3) Results: The accidental, frequent, prone and high-incidence areas of active fires defined by the FI index accounted for 31.62%, 30.97%, 26.23% and 11.18%, respectively. More than 90% of active fires occurred in areas with altitude <350 m above sea level (asl), slope <3° and NPP between 2500–5000 kg·C/m2. Similarly, about 75% occurred in cropland and forest. Then, four fire-induced conceptual patterns driven by different factors were classified, including the agricultural and forest active fire-induced patterns (i.e., the Agri-pattern and FRST pattern) with NPP ranging 2500–5000 kg·C/m2, and two others related to settlements and unused land with an altitude <350 m asl. The Agri-pattern dominates in Northeast China because of agricultural straw burning. (4) Conclusions: Despite the national bans of open burning of straws, active fires due to agricultural production have occurred frequently in Northeast China in the last decade, followed by small and sporadic forest fires. The approach for defining fire-hazardous areas and varied fire occurrence patterns is of significance for fire management and risk prediction at continental to global scales.

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Assessing the Effectiveness of Green Landscape Buffers to Reduce Fire Severity and Limit Fire Spread in California: Case Study of Golf Courses

Cited by 3 publications

References 27 publications

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

Modeling the Ignition Risk: Analysis before and after Megafire on Maule Region, Chile

Delineating Fire-Hazardous Areas and Fire-Induced Patterns Based on Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fires in Northeast China

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