Fire from policy, human interventions, or biophysical factors? Temporal–spatial patterns of forest fire in southwestern China

Xiong, Qinli; Luo, Xiaojin; Liang, Pinghan; Xiao, Yang; Xiao, Qiang; Sun, Hui; Pan, Kaiwen; Wang, Lixia; Li, Lingjuan; Pang, Xiaoyun

doi:10.1016/j.foreco.2020.118381

Cited by 47 publications

(23 citation statements)

References 48 publications

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“…On the one hand, biophysical factors affect the spatial distribution of forest fires, including climate and weather conditions, topography characteristics, and vegetation type and continuity [14,[30][31][32]. Specifically, climate and weather conditions consist of temperature, precipitation, humidity, wind speed, and solar radiation [18]. On the other hand, human beings may affect regional fire distribution through forest management [26], forest degradation [33], fuel type and quantity selection [34,35], and fire prevention and suppression education [18].…”

Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

2021

Sustainability

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As forest fires are becoming a recurrent and severe issue in China, their temporal-spatial information and risk assessment are crucial for forest fire prevention and reduction. Based on provincial-level forest fire data during 1998–2017, this study adopts principal component analysis, clustering analysis, and the information diffusion theory to estimate the temporal-spatial distribution and risk of forest fires in China. Viewed from temporality, China’s forest fires reveal a trend of increasing first and then decreasing. Viewed from spatiality, provinces characterized by high population density and high coverage density are seriously affected, while eastern coastal provinces with strong fire management capabilities or western provinces with a low forest coverage rate are slightly affected. Through the principal component analysis, Hunan (1.33), Guizhou (0.74), Guangxi (0.51), Heilongjiang (0.48), and Zhejiang (0.46) are found to rank in the top five for the severity of forest fires. Further, Hunan (1089), Guizhou (659), and Guanxi (416) are the top three in the expected number of general forest fires, Fujian (4.70), Inner Mongolia (4.60), and Heilongjiang (3.73) are the top three in the expected number of large forest fires, and Heilongjiang (59,290), Inner Mongolia (20,665), and Hunan (5816) are the top three in the expected area of the burnt forest.

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Section: Literature Reviewmentioning

confidence: 99%

RETRACTED: The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

2021

Sustainability

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“…In addition, several research institutes have meteorological monitoring stations in natural reserves, such as Jiuzhaigou and Baodinggou Nature Reserves, where CIB, CAS has established a long‐term monitoring station in the 1980s. For each weather station, monthly meteorological data were interpolated to a spatial resolution of 30 m × 30 m for the whole study area, and climatic driver factors of each plot were estimated by using the Kriging interpolation method which was widely used for regionalizing various variables at different scales (Piao et al., 2011; Xiong et al., 2020; Xiong, Xiao, et al., 2019; Zhu et al., 2006). According to the Sichuan Statistical Yearbook and Meteorological Bureau historical records in the study area during past 40 years, no pest outbreaks, or fires, had occurred in sample plots during the 40‐year study period from 1974 to 2017.…”

Section: Methodsmentioning

confidence: 99%

“…where CIB, CAS has established a long-term monitoring station in the 1980s. For each weather station, monthly meteorological data were interpolated to a spatial resolution of 30 m × 30 m for the whole study area, and climatic driver factors of each plot were estimated by using the Kriging interpolation method which was widely used for regionalizing various variables at different scales (Piao et al, 2011;Xiong et al, 2020;Zhu et al, 2006).…”

Section: Meteorological Datamentioning

confidence: 99%

Spatial heterogeneity of tree diversity response to climate warming in montane forests

Luo

Xiong

et al. 2020

Ecology and Evolution

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“…No entanto, nos últimos anos têm-se constatado um aumento significativo no número de FC, possivelmente relacionados às alterações no padrão climático, em virtude da intensificação das mudanças climáticas e da atuação do modo da variabilidade climática El Niño-Oscilação Sul (ENOS) - (Caúla et al, 2015;Clemente et al, 2017;Urrutia-Jalabert et al, 2018). Outros fatores climáticos também intensificam os incêndios, por exemplo, períodos prolongados de estiagem, secas severas, velocidade do vento e o relevo, influenciam na propagação e determinam os seus efeitos devastadores (Xiong et al, 2020). Neste sentido, o uso de técnicas de Sensoriamento Remoto e Geoprocessamento tem sido eficazes no monitoramento do FC, devido à sua agilidade e praticidade e, assim primordiais para a prevenção e controle de incêndio em áreas de vegetação (Caúla et al, 2015;Chuvieco et al, 2020).…”

Section: Introductionunclassified

Diagnóstico espaço-temporal dos focos de calor no Brasil

Santos

Filho

Júnior

et al. 2021

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O surgimento de incêndios florestais pode ser de origem antrópica ou natural, ambas causam grandes prejuízos socioeconômico e ambiental, e em boa parte dessas ocorrências são resultantes da ocorrência de Focos de Calor (FC). Nos últimos anos, o Brasil tem sofrido com o aumento significativo de FC, ao qual resultaram em grandes incêndios. Desta maneira, o objetivo do presente estudo foi diagnosticar o comportamento espaço-temporal dos FC no Brasil entre 1999 e 2020, baseados nos dados de dados BDQueimadas do CPTEC/INPE. Para a manipulação e o processamento dos dados, utilizou-se o software de ambiente R versão 3.4-1. Após o armazenamento dos dados, calculou-se os registros totais, médias anual e mensal, e a composição dos anos mais significativos, neste caso, os anos de 2015, 2017, 2019 e 2020. Os resultados apontaram que os maiores acumulados totais e médios anuais variaram entre 10-50 mil FC e 0,5-1,5 mil FC, concentrados na região centro-norte do Brasil, principalmente nos estados do Maranhão, Pará e Tocantins. Este padrão de alto registros de FC está relacionado ao desmatamento e expansão agrícola nessas regiões. Em escala mensal, as maiores ocorrências de FC ocorrem entre os meses de agosto e novembro, com valores de 0,20-0,45 mil FC, devido ao período de estiagem. Verificou-se que nos últimos anos, o El Niño-Oscilação Sul influenciou a incidência dos FC através da persistência de longos períodos de estiagem, que resultaram em escassez de chuvas e grandes incêndios verificados em 2020 no bioma Pantanal.

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Fire from policy, human interventions, or biophysical factors? Temporal–spatial patterns of forest fire in southwestern China

Cited by 47 publications

References 48 publications

RETRACTED: The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

RETRACTED: The Temporal-Spatial Distribution and Information-Diffusion-Based Risk Assessment of Forest Fires in China

Spatial heterogeneity of tree diversity response to climate warming in montane forests

Diagnóstico espaço-temporal dos focos de calor no Brasil

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