Prediction and driving factors of forest fire occurrence in Jilin Province,China

Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.

Effects of Topographical and Edaphic Factors on the Distribution of Plant Communities in two Subtropical Karst Forests,Southwestern China

Relationships between topography,soil properties and the distribution of plant communities on two different rocky hillsides are examined in two subtropical karst forests in the Maolan National Natural Reserve,southwestern China.Surveys of two 1-ha permanent plots at each forest,and measurements of four topographic and thirteen edaphic factors on the slopes were performed.Twoway Indicator Species Analysis(TWINSPAN) and Detrended Canonical Correspondence Analysis(DCCA) were used for the classification of plant communities and for vegetation ordination with environmental variables.One hundred 10m×10m quadrats in each plot were classified into four plant community types.A clear altitudinal gradient suggested that elevation was important in community differentiation.The topography and soil explained 51.06% and 54.69% of the variability of the distribution of plant species in the two forest plots,respectively,indicating both topographic factors(eg.elevation,slope and rock-bareness rate) and edaphic factors(e.g.total P,K and exchangeable Ca) were the important drivers of the distribution of woody plant species in subtropical karst forest.However,our results suggested that topographical factors were more important than edaphic ones in affecting local plant distribution on steep slopes with extensive rock outcrops,while edaphic factors were more influential on gentle slope and relatively thick soil over rock in subtropical karst forest.Understanding relationships between vegetation and environmental factors in karst forest ecosystems would enable us to apply these findings in vegetation management strategies and restoration of forest communities.

Developing a USLE cover and management factor(C)for forested regions of southern China

The Universal Soil Loss Equation model is often used to improve soil resource conservation by monitoring and forecasting soil erosion.This study tested a novel method to determine the cover and management factor(C)of this model by coupling the leaf area index(LAI)and soil basal respiration(SBR)to more accurately estimate a soil erosion map for a typical region with red soil in Hetian,Fujian Province,China.The spatial distribution of the LAI was obtained using the normalized difference vegetation index and was consistent with the LAI observed in the field(R^2=0.66).The spatial distribution of the SBR was obtained using the Carnegie-Ames-Stanford Approach model and verified by soil respiration field observations(R^2=0.51).Correlation analyses and regression models suggested that the LAI and SBR could reasonably reflect the structure of the forest canopy and understory vegetation,respectively.Finally,the C-factor was reconstructed using the proposed forest vegetation structure factor(Cs),which considers the effect of the forest canopy and shrub and litter layers on reducing rainfall erosion.The feasibility of this new method was thoroughly verified using runoff plots(R2=0.55).The results demonstrated that Cs may help local governments understand the vital role of the structure of the vegetation layer in limiting soil erosion and provide a more accurate large-scale quantification of the C-factor for soil erosion.