Methods to Detect Edge Effected Reductions in Fire Frequency in Simulated Forest Landscapes

Wei, Xinyuan; Larsen, C. T.

doi:10.3390/ijgi8060277

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…Despite these promising results, there might be several uncertainties in such susceptibility maps. One possible source of uncertainty is the edge effect, which happens when some fires ignited outside the study area spread to the study area and may alter the level of susceptibility near the boundaries [98,99]. Since no information is available for fires spreading from the other areas to the Pu Mat National Park, we failed to analyze the potential edge effect in this study.…”

Section: Robustness Analysismentioning

confidence: 99%

“…Since no information is available for fires spreading from the other areas to the Pu Mat National Park, we failed to analyze the potential edge effect in this study. When the required information is available, the application of the edge detection methods [98] can help researchers ensure that edge effected regions are identified and removed.…”

Section: Robustness Analysismentioning

confidence: 99%

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Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

et al. 2020

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Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

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Section: Robustness Analysismentioning

confidence: 99%

Section: Robustness Analysismentioning

confidence: 99%

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

et al. 2020

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“…Therefore, the congruence between our results based on LANDIS-II simulations and actual observations in old-growth forests highlights the ability of this model to accurately describe forest structural changes over long timescales. It is noteworthy that LANDIS-II could not simulate possible fires that started from outside the study area and that spread to the study area [73,74], which may have affected the simulation of the conditions of the boundary of forest landscapes.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Impacts of Forest Management Practices under Climate Change on Structure, Composition, and Fragmentation of the Canadian Boreal Landscape

Molina

Valeria²,

Martin³

et al. 2022

Forests

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Forest harvesting and fire are major disturbances in boreal forests. Forest harvesting has modified stand successional pathways, which has led to compositional changes from the original conifer-dominated forests to predominantly mixed and hardwood forests. Boreal fire regimes are expected to change with future climate change. Using the LANDIS-II spatially explicit landscape model, we evaluated the effects of forest management scenarios and projected fire regimes under climate change in northeastern Canadian boreal forests, and we determined the subsequent alteration in stand- and landscape-level composition, succession, and spatial configuration of boreal forests. We observed that, in contrast to successional pathways that followed fire, successional pathways that followed forest harvesting favored mixed forests with a prevalence of shade-intolerant hardwoods for up to 300 y after harvesting. This trend was exacerbated under climate change scenarios where forests became dominated by hardwood species, particularly in ecoregions where these species were found currently in low abundance. Our results highlight the failure of existing forest management regimes to emulate the effects of natural disturbance regimes on boreal forest composition and configuration. This illustrates the risks to maintaining ecosystem goods and services over the long term and the exacerbation of this trend in the context of future climate change.

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“…The absolute difference index (AD, %, Equation ( 4)) [31] was used to access the performance of the WRTDS-hurricane model, which can evaluate the difference between recorded riverine nitrogen or phosphorus concentration and estimated concentration for a given day.…”

Section: Riverine Nitrogen and Phosphorus Exports Estimatementioning

confidence: 99%

Hurricanes Substantially Reduce the Nutrients in Tropical Forested Watersheds in Puerto Rico

Sun

Wei

Zhou

et al. 2022

Forests

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Because nutrients including nitrogen and phosphorus are generally limited in tropical forest ecosystems in Puerto Rico, a quantitative understanding of the nutrient budget at a watershed scale is required to assess vegetation growth and predict forest carbon dynamics. Hurricanes are the most frequent disturbance in Puerto Rico and play an important role in regulating lateral nitrogen and phosphorus exports from the forested watershed. In this study, we selected seven watersheds in Puerto Rico to examine the immediate and lagged effects of hurricanes on nitrogen and phosphorous exports. Our results suggest that immediate surges of heavy precipitation associated with hurricanes accelerate nitrogen and phosphorus exports as much as 297 ± 113 and 306 ± 70 times than the long-term average, respectively. In addition, we estimated that it requires approximately one year for post-hurricane riverine nitrogen and phosphorus concentrations to recover to pre-hurricane levels. During the recovery period, the riverine nitrogen and phosphorus concentrations are 30 ± 6% and 28 ± 5% higher than the pre-hurricane concentrations on average.

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Methods to Detect Edge Effected Reductions in Fire Frequency in Simulated Forest Landscapes

Cited by 11 publications

References 40 publications

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Long-Term Impacts of Forest Management Practices under Climate Change on Structure, Composition, and Fragmentation of the Canadian Boreal Landscape

Hurricanes Substantially Reduce the Nutrients in Tropical Forested Watersheds in Puerto Rico

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