呼伦贝尔草原人为火空间分布格局

人为草原火及其影响因素的空间分布格局与相关关系研究对于草原火发生、草原火管理等研究具有重要的意义.应用Ripley's K函数对呼伦贝尔草原1976-1996年间发生的人为草原火进行了空间统计分析,确定人为草原火空间分布在年内主要发生月份和年际间均呈聚集分布；应用Kernel密度函数对人为草原火及其影响因素居民点、道路和农田的空间分布密度进行了研究,结果表明呼伦贝尔草原人为火分布广泛,热点区域主体在123.05-124.82°E、48.25-50.21°N之间；居民点、农田空间分布密度的热点区域主要分布在东部和中部地区,道路的分布密度较农田和居民点分布更加均匀.人为草原火空间分布密度与居民点、道路和农田的空间分布密度呈显著性正相关,其Pearson相关系数依次为0.448、0.236、0.602 (P＜0.001),火源因素(居民点、道路、农田)的空间分布格局是呼伦贝尔草原人为火空间分布格局的主要影响因素.

Spatial distribution pattern of human-caused fires in Hulunbeir grassland

Grassland fire is a major disturbance to ecosystems and economies throughout the world. Research on the spatial pattern of grassland fire is therefore important in understanding the dynamics of fire disturbance and providing evidence for fire management and prevention. In this study we used a spatial point process modeling approach to study the factors contributing to fire occurrence in the Hulunbeir grassland of the Inner Mongolia Autonomous Region. In previous studies, Ripley' K function, Kernel density and Poisson model have been used in the studies of spatial-temporal pattern of forest fires. But the distribution pattern of grassland fires was usually described by overlaying fire points on top of the administrative districts or study regions. The properties of spatial distribution, such as clustering, dispersion, randomness, were often omitted. In this study, Repley's K function was used to investigate the spatial distribution pattern of human-caused fires in the Hulunbeir grassland. The distribution of fire locations was found to be spatially clustered in the months of fire season and between years. The distances of spatial cluster distribution were less than 250km, 265km, 245km, 200km and 245km in April, May, June, September and October respectively. The statistical test showed that the cluster distributions were significant except for October. The distances of spatial cluster distribution were less than 210km, 280km, 260km, 220km and 220km in 1976-1980, 1981-1984, 1985-1988, 1989-192 and 1993-1996 respectively. All the cluster distributions were significant. The spatial distribution densities of human-caused grassland fires and the impacting factors including residential locations, roads and fields were calculated by the function of Kernel density. The results showed that human-caused grassland fires were widely distributed and the main "hot-spot" was in the extent of 123.05-124.82°E, 48.25-50.21°N. The second "hot-spot" had four regions and the center locations were: 117.52°E,49.51°N; 120.73°E,49.24°N;121.94°E,48.74°N;123.73°E,50.58°N. The "hot-spots" of residential points and fields were distributed in the east and middle of the study area. The "hot-spot" of roads was more widespread than the residential points and fields. The spatial distribution density of human-caused grassland fires was significantly correlated with the impacting factors (residential points, roads, fields, weather and topography) and the Pearson coefficients were 0.448, 0.236, 0.602, 0.161 and -0.042 respectively (P<0.001). The spatial patterns of fire sources (residential points, roads and fields) are found to be more closely associated with the spatial density of human-caused grassland fires. The correlation of weather with the spatial pattern of grassland fire was at a lower degree than these of residential points, roads and fields. It showed that the weather condition was not a limiting factor in affecting the occurrence of human-caused grassland fire in the fire season. Overall, the most important factors impacted human-caused grassland fire occurrence were the spatial distribution of fire sources (residential points, roads and fields). Topography has negative correlation with grassland fire occurrence. The reason may be that the human activities decreased with the increase of slope. In each month of fire seasons, the spatial distribution pattern of human-caused grassland fires was also mainly impacted by the distribution of fire source (residential points, roads and fields). The methods developed in this study can be applied to predict human-caused grassland fire risk, aiding decisions in preventative management strategies of grassland fires.