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.

Understanding the distribution and drivers of PM2.5 concentrations in the Yangtze River Delta from 2015 to 2020 using Random Forest Regression.

Understanding the drivers of PM2.5 is critical for the establishment of PM2.5 prediction models and the prevention and control of regional air pollution. In this study, the Yangtze River Delta is taken as the research object. Spatial cluster and outlier method was used to analyze the temporal and spatial distribution and variation of surface PM2.5 in the Yangtze River Delta from 2015 to 2020, and Random Forest was utilized to analyze the drivers of PM2.5 in this area. The results indicated that (1) based on the spatial cluster distribution of PM2.5, the northwest and north of Yangtze River Delta region were mostly highly concentrated and surrounded by high concentrations of PM2.5, while lowly concentrated and surrounded by low concentrations areas were distributed in the southern; (2) the relationship between PM2.5 concentrations and drivers in the Yangtze River Delta was modeled well and the explanatory rate of drivers to PM2.5 were more than 0.9; (3) temperature, precipitation, and wind speed were the main driving forces of PM2.5 emission in the Yangtze River Delta. It should be noted that the repercussion of wildfire on PM2.5 was gradually prominent. When formulating air pollution control measures, the local government normally considers the impact of weather and traffic conditions. In order to reduce PM2.5 pollution caused by biomass combustion, the influence of wildfire should also be taken into account, especially in the fire season. Meanwhile, high leaf area was conducive to improving air quality, and the increasing green area will help reduce air pollutants.

[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.

Understanding fire drivers and relative impacts in different Chinese forest ecosystems.

In this study, spatial patterns and driving factors of fires were identified from 2000 to 2010 using Ripley's K (d) function and logistic regression (LR) model in two different forest ecosystems of China: the boreal forest (Daxing'an Mountains) and sub-tropical forest (Fujian province). Relative effects of each driving factor on fire occurrence were identified based on standardized coefficients in the LR model. Results revealed that fires were spatially clustered and that fire drivers vary amongst differing forest ecosystems in China. Fires in the Daxing'an Mountains respond primarily to human factors, of which infrastructure is recognized as the most influential. In contrast, climate factors played a critical role in fire occurrence in Fujian, of which the temperature of fire season was found to be of greater importance than other climate factors. Selected factors can predict nearly 80% of the total fire occurrence in the Daxing'an Mountains and 66% in Fujian, wherein human and climate factors contributed the greatest impact in the two study areas, respectively. This study suggests that different fire prevention and management strategies are required in the areas of study, as significant variations of the main fire-driving exist. Rapid socio-economic development has produced similar effects in different forest ecosystems within China, implying a strong correlation between socio-economic development and fire regimes. It can be concluded that the influence of human factors will increase in the future as China's economy continues to grow - an issue of concern that should be further addressed in future national fire management.

[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.