A forest fire can be a real ecological disaster regardless of whether it is caused by natural forces or human activities, it is possible to map forest fire risk zones to minimize the frequency of fires, avert damage, etc. A method integrating remote sensing and GIS was developed and applied to forest fire risk zone mapping for Baihe forestry bureau in this paper. Satellite images were interpreted and classified to generate vegetation type layer and land use layers （roads, settlements and farmlands）. Topographic layers （slope, aspect and altitude） were derived from DEM. The thematic and topographic information was analyzed by using ARC/INFO GIS software. Forest fire risk zones were delineated by assigning subjective weights to the classes of all the layers （vegetation type, slope, aspect, altitude and distance from r3ads, farmlands and settlements） according to their sensitivity to fire or their fire-inducing capability. Five categories of forest fire risk ranging from very high to very low were derived automatically. The mapping result of the study area was found to be in strong agreement with actual fire-affected sites.
By integrating forest inventory data with remotely sensed data, new data layers for factors that affect forest fire potentials were generated for Baihe Forestry Bureau in Jilin Province of China. The principle component analysis was used to sort out the relationships between forest fire potentials and environmental factors. The classifications of these factors were performed with GIS, generating three maps: a fuel-based fire risk map, a topography-based fire risk map, and an anthropogenic-factor fire risk map. These three maps were then synthesized to generate the final fire risk map. The linear regression method was used to analyze the relationship between an area-weighted value of forest fire risks and the frequency of historical forest fires at each forest farm. The results showed that the most important factor contributing to forest fire ignition was topography, followed by anthropogenic factors.
XU Dong, Guofan Shao, DAI Limin, HAO Zhanqing, TANG Lei & WANG Hui Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
We investigated the effects of climate on Yeddo spruce (Picea jezoensis)radial growth along altitudinal gradients in the subalpine forests of Changbai Mountains using dendroclimatic analyses. Yeddo spruce at its lower and upper distribution limits was more sensitive to the climate. Despite precipitation being generally considered sufficient, we found that precipitation significantly affected Yeddo spruce radial growth. Yeddo spruce at its lower distribution limit was much more affected by precipitation while Yeddo spruce at its upper distribution limit was much more affected by minimum temperature. Yeddo spruce at its medial altitude was affected by sunshine ratio. These results demonstrated that climate affected Yeddo spruce growth differently depending on its altitudinal distributions in the Changbai Mountains. Both temperature and precipitation in the annualization period significantly correlated with Yeddo spruce radial growth. However, warmer signals were not reflected in radial growth trend during the past 20 years because annual total precipitation declined during the same period. It appeared that the climate affected tree rings growth by altering soil moisture availability.
The broadleaved-Korean pine mixed forest is a native vegetation in the Changbai Mountains, northeast China. The probability density functions including the normal, negative exponential, Weibull and finite mixture distribution, were used to describe the diameter distributions of the species groups and entire forest stand. There is a strong correlation between parameters and mean DBH except the shape parameters in the mixture distribution. The diameter classes of species and entire forest stand showed not negative exponential but normal and "S" distribution. The mixture function was better than normal and Weibull to describe the model distribution. The location parameter had an effect on the estimated frequency in the first diameter class, when the estimated location parameter was bigger than the lower limit of the first diameter class.
WANG Shunzhong, DAI Limin, LIU Guohua, YUAN Jianqiong, ZHANG Hengmin & WANG Qingli Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
