Forest fire risk zoning based on fuzzy logic and analytical network process.

Forest fires have a significant impact on human life, property safety, and ecological environment. Deve-loping high-quality forest fire risk maps is beneficial for preventing forest fires, guiding resource allocation for firefighting, assisting in fire suppression efforts, and supporting decision-making. With a multi-criteria decision analysis (MCDA) method based on geographic information systems (GIS) and literature review, we assessed the main factors influencing the occurrences of forest fires in Youxi County, Fujian Province. We analyzed the importance of each fire risk factor using the analytic network process (ANP) and assigned weights, and evaluated the sub-standard weights using fuzzy logic assessment. Using ArcGIS aggregation functions, we generated a forest fire risk map and validated it with satellite fire points. The results showed that the areas classified as level 4 or higher fire risk accounted for a considerable proportion in Youxi County, and that the central and northern regions were at higher risk. The overall fire risk situation in the county was severe. The fuzzy ANP model demonstrated a high accuracy of 85.8%. The introduction of this novel MCDA method could effectively improve the accuracy of forest fire risk mapping at a small scale, providing a basis for early fire warning and the planning and allocation of firefighting resources.

[Applicability of mixed effect model in the prediction of forest fire]. / 混合效应模型在林火发生预测中的适用性.

Fire is an important influencing factor in forest ecosystems. Establishing an accurate forest fire forecasting model is important for forest fire management. We used different meteorological factors as predictors to construct a forest fire prediction model in Fujian Province, based on Logistic regression and generalized linear mixed effect model. We compared the fitness and prediction accuracy of the two models, judged the applicability of the mixed effect model in forest fire forecasting. The results showed that the AUC and accuracy values of the Logistic base model were 0.664 and 60.4%, respectively. Models considering random effects gave better fitting and validating statistics. Among them, the two-level mixed model containing both area and altitude difference effects performed best, with increases of 0.057 and 6.0% for the AUC and accuracy values, respectively. By applying the model to predict the probability of forest fires in Fujian Province, we found that the middle-incidence and high-incidence areas of forest fires distributed in northwest and south Fujian, whereas the low-incidence areas of forest fires distributed in southwest and east Fujian, which was consistent with the observed data. The data fitting and forest fire prediction of the mixed effects model was better than those of the Logistic basic model. Therefore, it could be used as an important tool for forest fire prediction and management.

[Seasonal variation and driving factors of forest fire in Zhejiang Province, China, based on MODIS satellite hot spots]. / 基于MODISå«æ˜Ÿç«ç‚¹çš„æµ™æ±Ÿçœæž—ç«å­£èŠ‚å˜åŒ–åŠé©±åŠ¨å› å­.

Understanding the changes and driving factors of forest fire can provide scientific basis for prevention and management of forest fire. In this study, we analyzed the changes and driving factors of forest fire in Zhejiang Province during 2001-2016 based on trend analysis and Logistic regression model with the MODIS satellite fire point data combined with meteorological (daily ave-rage wind speed, daily average temperature, daily relative humidity, daily temperature difference, daily cumulative precipitation), human activities (distance from road, distance from railway, distance from resident, population, per capita GDP), topographic and vegetation factors (elevation, slope, vegetation coverage). The results showed that the number of forest fires in spring and summer had significantly increased, while the forest fires in the autumn and winter increased first and then decreased. Forest fire in autumn significantly declined. The four seasons' fire occurrence prediction models had good prediction accuracy, reaching 75.8% (spring), 79.1% (summer), 74.7% (autumn) and 79.6% (winter). The meteorological, human activity, topographic and vegetation factors significantly affected fire occurrence in spring and summer, while meteorological factors were the main fire drivers in autumn and winter in Zhejiang. The focus of forest fire management should be on human activities. Fire prevention campaign should be done in spring and summer when high-risk forest fires were scattered in the study area. In autumn and winter, observatory and monitoring equipment could be built to facilitate fire management and detect in the area of high fire risk that was concentrated in the southwest region.

[Forest fire management in the United States]. / 美国林火管理概况及分析.

With the intensification of climate change and human activities, megafires frequently occur, with serious impacts on ecosystems, atmospheric environment, and human health. The United States has accumulated a large amount of practical experience in forest fire management. A comprehensive review of the framework of forest fire management in the United States can provide an inspiring reference for forest fire prevention in China. Starting from the process of historical evolution of forest fire policy, we systematically introduced the four stages of policy evolution and the characteristics of each stage in the US. Moreover, a comprehensive analysis of forest fire management situation in the US from four aspects was conducted, including the management of combustible fuels, administrative responsibility, fire suppression and forest fire management research support. We summarized relevant literature and proposed improvement strategies for future combustibles management, policy politics and fire fighting in the United States. Through the comprehensive analysis of forest fire management in the United States, we put forward some inspiring opinions on forest fire management in China to promote the establishment of a sound forest fire management system with Chinese characteristics.

[Prediction model of human-caused fire occurrence in the boreal forest of northern China].

The Chinese boreal forest is an important forest resource in China. However, it has been suffering serious disturbances of forest fires, which were caused equally by natural disasters (e.g., lightning) and human activities. The literature on human-caused fires indicates that climate, topography, vegetation, and human infrastructure are significant factors that impact the occurrence and spread of human-caused fires. But the studies on human-caused fires in the boreal forest of northern China are limited and less comprehensive. This paper applied the spatial analysis tools in ArcGIS 10.0 and Logistic regression model to investigate the driving factors of human-caused fires. Our data included the geographic coordinates of human-caused fires, climate factors during year 1974-2009, topographic information, and forest map. The results indicated that distance to railway (x1) and average relative humidity (x2) significantly impacted the occurrence of human-caused fire in the study area. The logistic model for predicting the fire occurrence probability was formulated as P= 1/[11+e-(3.026-0.00011x1-0.047x2)] with an accuracy rate of 80%. The above model was used to predict the monthly fire occurrence during the fire season of 2015 based on the HADCM2 future weather data. The prediction results showed that the high risk of human-caused fire occurrence concentrated in the months of April, May, June and August, while April and May had higher risk of fire occurrence than other months. According to the spatial distribution of possibility of fire occurrence, the high fire risk zones were mainly in the west and southwest of Tahe, where the major railways were located.

[Selection of advantage prediction model for forest fire occurrence in Tahe, Daxing'an Mountain].

This study chose zero-inflated model and Hurdle model that have been widely used in economic and social fields to model the fire occurrence in Tahe, Daxing'an Mountain. The AIC, LR and SSR were used to compare the models including zero-inflated Poisson model (ZIP), zero-inflated negative binomial model (ZINB), Poisson-Hurdle model (PH) and negative Binomial Hurdle (NBH) (two types, four models in total) so as to determine a better-fit model to predict the local fire occurrence. The results illustrated that ZINB model was superior over the other three models (ZIP, PH and NBH) based on the result of AIC and SSR tests. LR test revealed that the negative binomial distribution was suitable to both the "count" portion of zero-inflated model and hurdle model. Furthermore, this paper concluded that the zero-inflated model could better fit the fire feature of the study area according to the hypotheses of the two types of models.

[Applicability of different models in simulating the relationships between forest fire occurrence and weather factors in Daxing' an Mountains].

The Poisson's and Zero Inflated Poisson (ZIP) models that meet the data structure of forest fire occurrence were used to explore the relationships between the forest fire occurrence and climate factors in Daxing' an Mountains in 1980-2005. Compared with the ordinary least squares (OLS) model which often produced poor fitting results (R2 = 0.215), the Poisson's and ZIP models operated better, and had better prediction ability on the forest fire occurrence. The AIC and Vuong tests further indicated that ZIP model produced better fitting results, and thus, had better prediction ability than Poisson model.

[Estimation of total carbon-containing gas emission from main tree species in forest fires in Daxing' an mountains].

By the method of emission factor (EF), this paper estimated the total carbon-containing gas emission from five main tree species in Daxing' an Mountains in forest fires from 1980 to 2005. The results showed that different tree species had different EF. Pinus sylvesstris var. mongolica and Populus davidiana had the maximum and minimum EF of CO2, respectively. Larix gmelinii and Betula platyphylla had the maximun EF of CO and C(x)H(y), while B. platyphylla and L. gmelinii had the minimum EF of CO and C(x)H(y). Based on the carbon storage in different organs and the total biomass of the tree, it was estimated that the total emission of CO2, CO and C(x)H(y) from the five tree in the 25 years was 16.58 Tg, 1.61 Tg and 0.54 Tg, and the contributions of L. gmelinii, P. sylvesstris var. mongolica, B. platyphylla, P. davidiana, and Quercus mongolica were 5.00 Tg, 0.63 Tg and 0.05 Tg, 0.225 Tg, 0.023 Tg and 0.003 Tg, 11.22 Tg, 0.83 Tg and 0.41 Tg, 0.0022 Tg, 0.004 Tg and 0.00034 Tg, and 3.12 Tg, 0.13 Tg and 0.062 Tg, respectively.