Study Of Distribution Characteristics Of Forest Surface Fuel Load And Moisture Content

Forest fire is the most serious disasters threatened forestry. In recent years, the amounts of forest fire are increasing as global warming and the increase of human activities. Surface fire, crown fire and special fire behavior such as fly fire are the three main forms of forest fire and surface fire account for more than 90%. So, the current forest fire forecast and spread calculation is mainly aimed at the surface fire.Forest surface fuel load is one of the most important factors for forest fire spread predicting. In existing fire spread models, fuel load is usually assumed to be uniform in a region with the same forest type, although its spatial distribution is complex even in one kind of forest with different forest stand factors. In this article, a method for surface fuel load estimation by the forest stand factors is established based on the cluster analysis method. The stand factors used in cluster analysis include forest age, canopy density, average tree height and diameter at breast height which are all easy to be obtained. 35 Larix gmelinii plots and 21 Pinus sylvestris plots of Da Hinggan Mountains forests were used to carry on centroid cluster analysis and in result they were divided into 5 and 7 clusters, respectively. The center of each cluster was calculated and its corresponding fuel load was represented by the average load of the plots grouped in this cluster. Finally, three statistical indicators, including the mean absolute error of the estimate, MAE, the standard error of the estimate, SEE, and the stable indicators of the estimate, SIE, were used to contrast the fitting errors of the cluster analysis method and the multiple linear regressions method. The results show that the cluster analysis method is better than the latter one.Forest fine fuel moisture content is one of the most important factors for forest fire forecast. In the existing Catchpole's direct estimation method for fine fuel moisture content prediction, crown density is not considered and the temperature and humidity are measured in the forest. In this paper, a series of outdoor experiments were carried out under the conditions of different crown density. The dynamic variations of the moisture content of Pinus Sylvestris needles were captured, and the local meteorological data, including temperature, humidity and winds velocity, are also collected from the Weather China homepage at the same time. Based on the experiments, a modified model for fine fuel moisture content prediction is achieved, in which the crown density is used to adjust the temperature and the humidity near the fuel surface. The comparison results show that under different conditions of crown densities, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) of the modified model are decreased significantly, and the Mean Absolute Percentage Errors (MAPE) for different crown densities are all less than 6%. In contrast with Catchpole's direct estimation method, the prediction precision of the modified method is doubled. The contrast validation between forest field observation and the prediction values of the modified method indicates that the absolute errors are less than 10%. This modified method can effectively reduce the prediction errors due to different crown densities; meanwhile, the calculation is directly using meteorological forecast data, so it has better applicability.By using the Arc /INFO AML language, combined with the surface fuel load estimation method based on cluster analysis and Catchpole's direct estimation method modified by crown density, the visualization of forest surface fine fuel load and moisture content are realized. The result can reflect the inhomogeneous distribution of fuel load and moisture content in the same forest. Meanwhile, it can improve the accuracy of forest fire forecast and spread calculation.