Understanding fire drivers and relative impacts in different Chinese forest ecosystems

Guo, Futao; Su, Zhangwen; Wang, Guangyu; Sun, Lei; Tigabu, Mulualem; Yang, Xuwu; Hu, Haiqing

doi:10.1016/j.scitotenv.2017.06.219

Cited by 86 publications

(64 citation statements)

References 69 publications

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“…It can also be explained by the reduction in tree coverage, in such a way that fires do not reach larger sizes due to the lack of fuel and the climatic conditions (increase in relative humidity and temperature decrease). In this sense, the results of all models concerning altitude are consistent, showing that the higher the altitude, the lower the proportion of burnt area (similar trend than in Guo et al 2017;Pourtaghi et al 2016). This relationship is stronger in the general model and for the large fires model, whereas the response to altitude of medium and small fires is less steep.…”

Section: Discussionsupporting

confidence: 58%

Size dependency of variables influencing fire occurrence in Mediterranean forests of Eastern Spain

Peris-Llopis

Mola‐Yudego

2020

Eur J Forest Res

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Fires are among the most damaging disturbances to forests in the Mediterranean area. The study analyses the occurrence and characteristics of forest fires in Eastern Spain to identify key variables related to burnt forest land, differentiating fires according to their burnt area. Data are retrieved from digital cartography, the Spanish Forest Map and data concerning fires. Based on previous research, the variables included are altitude, slope, aspect, fuel, species, population and road density. The fires are classified in small (5-50 ha), medium (50-500 ha) and large (> 500 ha). Four models are considered to explain the proportion of burnt area based on weighted generalized linear models: a general model and one per size class. The results highlight the different relations of similar variables with fires according to the size. When a single model is considered to explain all area burnt, the relationships are mainly driven by large fires. The larger area is burnt on forests with pine, bushes and small trees, whereas smaller fires tend to occur on lower altitude, low slope, high population and road densities. There are large differences in the variables according to the fire sizes, especially for the presence of pine (negative in the medium fires model but positive for the large fires model) and Pasture (which only explains the small fires). The models can be applied to analyse occurrence by fire size in Mediterranean areas, and the results can help elaborate fire prevention strategies and land-planning schemes.

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Section: Discussionsupporting

confidence: 58%

Size dependency of variables influencing fire occurrence in Mediterranean forests of Eastern Spain

Peris-Llopis

Mola‐Yudego

2020

Eur J Forest Res

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“…China has five forest zones: (I) cold temperate deciduous coniferous forest zone (low fire frequency but high average burned area); (II) temperate deciduous mixed broadleaf and coniferous forest zone; (III) warm temperate deciduous broadleaf-mixed forest zone (low forest coverage and low forest fire frequency); (IV) subtropical evergreen broadleaf forest zone (high coverage and fire frequency, but low average burned area); and (V) tropical rainforest zone (high forest coverage, but low fire frequency). There are three other vegetation zones in China that are not typically dominated by forests: temperate grasslands (low fire frequency, but high average burned area), temperate steppes and desert region, and Qinghai-Xizang plateau alpine region (very low forest coverage and rare fires) (Guo et al 2017). Because these three vegetation zones have also experienced forest loss during our study period (2003-2015), we included them in our data analyses.…”

Section: Forest Zones and Fire Regimes In Chinamentioning

confidence: 99%

Spatiotemporal changes in forest loss and its linkage to burned areas in China

Yan

et al. 2019

J. For. Res.

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loss and burned areas using Spearman's correlation. Forest loss increased significantly (264.8 km 2 a −1 ; R 2 = 0.54, p < 0.01) throughout China, with an average annual increase of 11.4% during 2003-2015. However, the forest loss trend had extensive spatial heterogeneity. Forest loss increased mainly in the subtropical evergreen broadleaf forest zone (315.0 km 2 a −1 ; R 2 = 0.69, p < 0.01) and tropical rainforest zone (38.8 km 2 a −1 ; R 2 = 0.66, p < 0.01), but the loss of forest decreased in the cold temperate deciduous coniferous forest zone (− 70.8 km 2 year −1 ; R 2 = 0.75, p < 0.01) and the temperate deciduous mixed broadleaf and coniferous forest zone (− 14.4 km 2 a −1 ; R 2 = 0.45, p < 0.05). We found that 1.0% of China's area had a significant positive correlation (r ≥ 0.55, p < 0.05) with burned areas and 0.3% had a significant negative correlation (r ≤ − 0.55, p < 0.05). In particular, forest loss had a significant positive relationship with the burned area in the cold temperate deciduous coniferous forest zone (16.9% of the lands) and the subtropical evergreen broadleaf forest zone (7.8%). These results provide a basis for future predictions of fire-induced forest loss in China.

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“…e dependent variable in the study is a binary variable, so we need to use ArcGIS to create a certain percentage of random points (i.e., nonfire points) and assign the fire point to 1 and the nonfire point to 0 [40]. In order to ensure that the data are not excessively scattered, random points are selected at a 1 : 1 ratio according to experience [41], and in principle, double randomness in space and time should be followed [42].…”

Section: Variable Processingmentioning

confidence: 99%

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Zhong-ke

Chen

et al. 2020

Discrete Dynamics in Nature and Society

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The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

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Understanding fire drivers and relative impacts in different Chinese forest ecosystems

Cited by 86 publications

References 69 publications

Size dependency of variables influencing fire occurrence in Mediterranean forests of Eastern Spain

Size dependency of variables influencing fire occurrence in Mediterranean forests of Eastern Spain

Spatiotemporal changes in forest loss and its linkage to burned areas in China

Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

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