Effects of climate, vegetation, and topography on spatial patterns of burn severity in the Great Xing'an Mountains

Jingjing, 付婧婧 Fu; Zhiwei, 吴志伟 Wu; Saijia, 闫赛佳 Yan; Yujing, 张宇婧 Zhang; Xianli, 顾先丽 Gu; Linhan, 杜林翰 Du

doi:10.5846/stxb201902140264

Cited by 3 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The "bag fraction", which specifies the proportion of data to be selected at each step, was set to 75% for model training to reduce stochastic errors. The resulting BRT RF [19,33] uses a bootstrap sampling method to randomly extract 2/3 of the data from the sample for modeling, with the remaining data, termed "out-of-bag" (OOB) data, used for self-verification of the model. It is powerful for handling nonlinearity and interactions among variables [34].…”

Section: Fire Modelsmentioning

confidence: 99%

“…Ke et al [18] analyzed the correlation between forest fire burned area and climate in China, and showed that forest fireburned area is highly correlated with temperature and precipitation. Finally, Fu et al [19] analyzed forest fire intensity in the Greater Khingan Range through a Random Forest Model, and concluded that altitude plays an important role. Forest burned area accounts for 23.4-27.7% of the total annual burned area in China [3].…”

Section: Introductionmentioning

confidence: 99%

“…The large area affected makes it important to strengthen research into the factors that drive forest fire in China. Research on this topic is mostly regional and concentrated in the northeastern forests [19][20][21], while research across a large geographic scale is still relatively scarce, and the differences between the variables that influence forest fire in different climate regions are not clear.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the Spatial Distribution of Predictors of Fire Regimes in China from 2003 to 2016

Su,

Liu,

Wang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Wildfire has extensive and profound impacts on forest structure and function. Therefore, it is important to study the spatial and temporal patterns of forest fire regimes and their drivers in order to better understand the feedbacks between climate change, fire disturbance, and forest ecosystems. Based on the Global Fire Atlas dataset, three forest fire regime components (fire occurrence density, burned rate, and median fire size) were extracted for China from 2003 to 2016. Three statistical models (Boosted Regression Tree, Random Forest, and Support Vector Machine) were used to systematically analyze the relationships between patterns of forest fire disturbance and climate, human activities, vegetation, and topography in China, as well as their spatial heterogeneity in different climatic regions. The results indicate that the spatial distribution of forest fires is heterogeneous, and different forest fire regime components are predicted by different factors. At the national level, the distribution of forest fire regimes mainly corresponds to climatic factors, although the relationship between median fire size and predictors is obscure. At the scale of each ecoregion, the main climate predictors of forest fire occurrence density and burned rate change from temperature in the north to temperature and precipitation in the south. Median fire size varies with elevation and temperature in the south. These results demonstrate that the spatial heterogeneity of predictors and scaling effects must be fully considered in the study of forest fire disturbance.

show abstract

Section: Fire Modelsmentioning

confidence: 99%