Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea

Piao, Yong; Lee, Dongkun; Park, Sangjin; Kim, Ho; Jin, Yihua

doi:10.1080/19475705.2022.2030808

Cited by 29 publications

(3 citation statements)

References 48 publications

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“…Google Earth Engine (GEE) is a cloud‐based platform with high computational speed, is easily applicable to broad areas as well as performs successful analyses and visualization of big earth datasets, which can easily be made available online (Mehmood et al., 2021; Piao et al., 2022; Vanama et al., 2020). GEE was used to extract and process various satellite imageries such as Sentinel, Landsat, and LST.…”

Section: Methodsmentioning

confidence: 99%

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Kibirige,

Gokool,

Mkhize

2023

Vadose Zone Journal

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Soil moisture (SM) is a fundamental constituent of the terrestrial environment and the hydrological cycle. Owing to its significant influence on catchment hydrological responses, it can be utilized as an indicator of floods and droughts to aid early warning systems. This study aimed to develop a field‐scale method to estimate SM using parametric and machine learning‐based methods to compare whether advanced artificial intelligence methods can give similar results as traditional methods. Considering this, monthly observed SM data (from the top 10 cm), environmental covariates, and remotely sensed data from March 2019 to July 2021 for the Cathedral Peak Research Catchments VI and IX in South Africa were obtained. From the 241 observations obtained across 12 sites, 160 (∼66%) were used for model training, while the remaining 81 (∼34%) were used for model testing. Employing 10‐fold cross‐validation, the individual machine learning models (viz., support vector machine [SVM], random forest (RF), k‐nearest neighbor, classification and regression trees [Rpart], and generalized linear model) displayed a satisfactory performance (R2 = 0.52–0.79; root mean square error = 3.79–5.80). In the validation phase, the RF model displayed a superior performance, followed by the SVM. Subsequent SM estimation using the hybrid model produced satisfactory results in training (R2 = 0.90) and testing (R2 = 0.45). The results obtained from this study can aid in predicting SM variations in catchments with limited monitoring. Furthermore, this model can be applied in drought monitoring, forecasting, and informing agricultural management practices.

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Section: Methodsmentioning

confidence: 99%

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Kibirige,

Gokool,

Mkhize

2023

Vadose Zone Journal

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“…Globally, droughts and forest fires are natural disasters that are increasing due to the climate crisis, causing serious damage such as human and ecological damage, economic damage, and forest damage 9 . In the case of Indonesia, many mountainous areas are experiencing illegal land conversion into farmland, in which artificial fires are occurring and government efforts are being thwarted by widespread damage 10 , 11 .…”

Section: Introductionmentioning

confidence: 99%

Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia

Heo,

Park,

Lee

2023

Sci Rep

Self Cite

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Numerous natural disasters that threaten people’s lives and property occur in Indonesia. Climate change-induced temperature increases are expected to affect the frequency of natural hazards in the future and pose more risks. This study examines the consequences of droughts and forest fires on the Indonesian island of Kalimantan. We first create maps showing the eleven contributing factors that have the greatest impact on forest fires and droughts related to the climate, topography, anthropogenic, and vegetation. Next, we used RF to create single and multi-risk maps for forest fires and droughts in Kalimantan Island. Finally, using the Coupled Model Intercomparison Project (CMIP6) integrated evaluation model, a future climate scenario was applied to predict multiple risk maps for RCP-SSP2-4.5 and RCP-SSP5-8.5 in 2040–2059 and 2080–2099. The probability of a 22.6% drought and a 21.7% forest fire were anticipated to have an influence on the study’s findings, and 2.6% of the sites looked at were predicted to be affected by both hazards. Both RCP-SSP2-4.5 and RCP-SSP5-8.5 have an increase in these hazards projected for them. Researchers and stakeholders may use these findings to assess risks under various mitigation strategies and estimate the spatial behavior of such forest fire and drought occurrences.

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“…Therefore, the modeling results are not sufficiently accurate and the prediction results are poor. The second method commonly used to determine forest fire drivers and make spatial predictions of forest fires is the machine learning approach, using the common random forest model 5 , 12 , 13 , artificial neural networks 14 , logistic regression, among other methods 15 , 16 . The idea behind this approach is to combine artificial intelligence to learn the complex spatial relationships between forest fires and their drivers, identify the main drivers, and make spatial predictions of forest fires, mainly as adjustments to the model parameters to determine the effect of the drivers.…”

Section: Introductionmentioning

confidence: 99%

Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China

et al. 2022

Sci Rep

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Forest fires are among the major natural disasters that destroy the balance of forest ecosystems. The construction of a forest fire prediction model to investigate the driving mechanism of fire drivers on forest fires can help reveal the mechanism of forest fire occurrence and its risk, and thus contribute to the prevention and control of forest fires. However, previous studies on the mechanisms of forest fire drivers have not considered the effect of differences in spatial scale of action of forest fire drivers on the predicted effect. Therefore, the present study proposes a spatial prediction model of forest fires that considers the spatial scale effect of forest fire drivers to predict forest fire risk. First, based on historical forest fire data and geographic environmental data in the Yunnan Province, geographically weighted logistic regression (GWLR) was used to determine the forest fire drivers and to estimate the probability of forest fire occurrence at locations where fire observations are absent. Then, multi-scale geographically weighted regression (MGWR) was used to explore the spatial scales of action of different drivers on forest fires. The results show that meteorological factors such as relative humidity, air temperature, air pressure, sunshine hours, daily precipitation, wind speed, topographic factors such as elevation, slope, and aspect, anthropogenic factors such as population density and road network, as well as vegetation type, were significantly correlated with forest fires; thus, they are identified as important factors influencing occurrence of forest fires in the Yunnan Province. The MGWR model regression results show that the role of different forest fire drivers on forest fire occurrence has spatial scale differences. The spatial scale of drivers such as altitude, aspect, wind speed, temperature, slope, and distance from the road to the fire point was larger and their spatial influence was relatively stable, with spatial heterogeneity having less influence on the model evaluation results. The spatial scale of drivers such as relative humidity, sunshine, air pressure, precipitation, population density, and vegetation type were smaller, and spatial heterogeneity had a more obvious influence on the model evaluation results. This study provides a reference for selecting drivers and evaluating their spatial scale effects to construct predictive regional forest fire models.

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Forest fire susceptibility assessment using google earth engine in Gangwon-do, Republic of Korea

Cited by 29 publications

References 48 publications

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Estimation of soil moisture using environmental covariates and machine learning algorithms in Cathedral Peak Catchment, South Africa

Multi-hazard exposure mapping under climate crisis using random forest algorithm for the Kalimantan Islands, Indonesia

Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China

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