图层约束下的点状自然灾害风险地图综合

Generalization for point features in risk mapping of natural hazards based on layer constraint

点状自然灾害现象如地震、滑坡等,由于其特殊性,灾害风险与其周边的地理环境有着复杂的联系和相互作用,孕灾环境对灾情具有放大或缩小的效应,在制图综合过程中,不能只考虑单个灾害点个体,而因将与之相关的各因素综合考虑,从而判定其风险范围。基于此,在地理学与地图学的基础上,从灾害系统的角度考虑,探讨基于图层约束(LC)和模糊推理系统(FIS)相结合的点状现象自动综合的适用性问题,重点阐述基于图层约束理论和FIS算法相结合的滑坡灾害自动综合技术,并以滑坡灾害为例,构建了基于滑坡灾害程度区划、地形坡度、地貌区划、地震长期烈度区划、年暴雨日数、年均降水量等为约束图层的自动综合应用;通过多尺度综合分析结果表明,中国存在三大重点滑坡区:即青藏高原东部斜坡带、黄土高原滑坡区和太行山东麓、巫山、武陵山脉一线滑坡带。本研究为多尺度、多图层约束下的自然灾害风险地图自动综合提供了一种有效途径,同时对不同区域尺度下的灾害风险管理提供了更高效、更准确的决策支持和技术支撑。

Point-like natural disaster phenomena, such as earthquakes, landslides, etc. , have complex linkages and inter- actions with their surrounding geographical environment. Disaster environment on the disaster may enlarge or reduce effect. In the process of cartographic generalization, only considering a single point of individual disasters is not enough. Based on Geography and Cartography, hut from the perspective of a disaster system, the applicability issues of automatic generaliza- tion of point phenomena are explored by combing the layer constraint （LC） and Fuzzy Inference System （FIS）. A land- slide-disaster automatic-generalization technology, based on LC and FIS, is discussed. Taking landslides as an example, an automated comprehensive application of the constraint layers, including landslide hazard zoning, terrain slope, landform regionalization, long-term seismic intensity zoning, annual torrential rain days, and annual precipitation was developed. The experiment results showed that there are three key landslide areas in China, which are the eastern slope of the Qinghai-Tibet Plateau, the Loess Plateau, and the Taihang Mountain landslide area, Wushan-Wuling Mountain Area landslide. Our re- search can provide an effective way for automatic generalization of natural disaster risk maps under the constraints of multi- scales and multi-layers. It can also provide a more efficient, more accurate decision-making support and technical support for disaster risk management of different regional scales.