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.

Diurnal and Seasonal Variations of CO2Fluxes and Their Climate Controlling Factors for a Subtropical Forest in Ningxiang

In this study, the diurnal and seasonal variations of CO2 fluxes in a subtropical mixed evergreen forest in Ningxiang of Hunan Province, part of the East Asian monsoon region, were quantified for the first time. The fluxes were based on eddy covariance measurements from a newly initiated flux tower. The relationship between the CO2 fluxes and climate factors was also analyzed. The results showed that the target ecosystem appeared to be a clear carbon sink in 2013, with integrated net ecosystem CO2exchange（NEE）, ecosystem respiration（RE）, and gross ecosystem productivity（GEP） of-428.8, 1534.8 and1963.6 g C m^-2yr^-1, respectively. The net carbon uptake（i.e. the-NEE）, RE and GEP showed obvious seasonal variability,and were lower in winter and under drought conditions and higher in the growing season. The minimum NEE occurred on12 June（-7.4 g C m^-2d^-1）, due mainly to strong radiation, adequate moisture, and moderate temperature; while a very low net CO2 uptake occurred in August（9 g C m^-2month^-1）, attributable to extreme summer drought. In addition, the NEE and GEP showed obvious diurnal variability that changed with the seasons. In winter, solar radiation and temperature were the main controlling factors for GEP, while the soil water content and vapor pressure deficit were the controlling factors in summer. Furthermore, the daytime NEE was mainly limited by the water-stress effect under dry and warm atmospheric conditions, rather than by the direct temperature-stress effect.

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.