金沙江流域(云南境内)山地灾害危险性评价

云南境内的金沙江流域是斜坡不稳定的敏感区,根据1988-2000年的区域调查和统计,区内发育山地灾害点1697处,其中流域面积大于1km2的泥石流沟808条,体积大于1×104m3的滑坡580处,体积大于1000m3的崩塌309处。用于山地灾害危险性评价的主要敏感因子包括岩土体类型、山坡坡度、降雨、土地利用、地震烈度和人类活动。在对这些因子进行了敏感性评价的基础上,应用GIS对敏感因子集成评价而产生了云南金沙江流域山地灾害危险性评价图。评价结果表明:高危险区面积占全区面积6464km2的8 77%,中危险区占全区总面积的41 51%,低危险区占41 12%,无危险区占8 60%。山地灾害危险性评价图可以帮助规划者或工程师在土地发展规划中选择最佳建设场所,以减轻灾害的影响。     

基于GIS的山洪灾害风险区划

通过探讨应用地理信息系统技术编制山洪灾害风险区划图的方法。以1:25万地理底图为基础,对影响山洪形成与泛滥的地形坡度、暴雨天数、河网缓冲区、标准面积洪峰流量、泥石流分布密度和洪灾历史统计六项因子进行了分析和叠合评价,完成了红河流域的山洪灾害危险评价图。以人口密度、房屋资产、耕地百分比、单位面积工农业产值作为指标进行了易损性分析,并借助于GIS分析工具,将危险评价图与易损性图进行叠加分析,完成了红河流域的山洪灾害风险区划图。区划结果表明GIS方法能够有效地对影响山洪形成与泛滥的因子数据层进行空间集成分析。该风险区划图可通过对山洪易泛区的不同风险地带的土地利用规划的决策而减轻山洪灾害;此外,也为山洪易泛区的居民提供有关山洪风险信息。     

西藏水土流失敏感性评价及其空间分异规律

以水土流失通用方程为理论基础,运用数学模型和GIS分析相结合的方法,将降水、地貌、土壤和植被因子对西藏水土流失敏感性的影响程度,划分为极敏感、相当敏感、敏感、较敏感和不敏感5个等级,并生成各单因素评价图。在Arcinfo中完成单因素图的叠加分析,得到西藏水土流失敏感性综合评价图。在此基础上,探讨了西藏特殊高原环境下的水土流失敏感性高低的分布规律及其在不同主导因子作用下的水土流失敏感性空间分异,提出了西藏水土流失治理的优先区,为水利、公路和农牧等部门进行专题规划和治理提供依据。     

怒江流域林地景观演变过程及其驱动力研究

怒江流域地处中国云南的边境, 是全球生物多样性和生态景观保护的重要地域, 但由于人 口的增加, 粮食需求的扩大及城市的扩展, 流域林地生态系统面临着强烈的人类干扰。文中使用 中国资源环境数据库中, 用Landset TM解译获取的怒江流域1985, 1995, 2000 年1∶10 万土地利 用/覆被数据, 在GIS 空间分析基础上, 通过建立动态度和变化转移概率矩阵, 系统地研究了怒江 流域林地与其他土地覆被类型之间以及不同林地类行之间的动态变化规律及其空间格局特征, 并对造成变化的驱动力因子进行了定性分析。     

基于RUSLE的流域土壤侵蚀敏感性评价——以福建省吉溪流域为例

以修正的通用水土流失方程(RUSLE)为核心,在分析流域土壤侵蚀敏感性影响因子的基础上,运用G IS技术对各敏感因子值进行估算,结合已有研究成果探讨了定量化的流域土壤侵蚀敏感性评价指标体系的建立,并以吉溪流域为例进行土壤侵蚀敏感性评价。同时分析了该流域土壤侵蚀敏感性与土壤侵蚀量的关系。最后,针对不同的流域土壤侵蚀敏感等级提出了相应的管理措施和建议。     

怒江干流（云南段）滑坡灾害危险性分析

滑坡是怒江流域主要的地质灾害,对流域内人民生命财产和生态系统安全带来了极大的危害,因此本研究针对研究区内滑坡灾害主要诱发因子进行判识。利用1991～2006年云南省减灾年鉴、长系列流域内相关站点的年平均降雨量、2006年云南省1：5万的TM影像数据等,以GIS技术为平台对其相关因子关联性进行统计与分析。研究发现：沿怒江干流发生的滑坡灾害主要受到坡度、植被盖度、降雨强度及公路建设等因子的影响,分析灾害点的分布与相关因子间的相关性,发现相关性比较密切的是坡度〉25。的地带;植被盖度为30％～70％的地带;年降水量达到1250—1500mm的地带,以及公路沿线的地带,并以相关性作为灾害发生风险度评价的权重,建立了基于GIS的滑坡灾害危险性评价模型,实现了对怒江干流区域滑坡灾害危险性区划。     

金沙江干热河谷区泥石流易发性评价模型及应用

金沙江上游奔子栏-昌波河段属典型的干热河谷区,气候干热少雨,但泥石流灾害发生频繁。采用指标熵模型对干热河谷区泥石流的影响因子进行敏感性分析,最后筛选出流域地貌熵值、岩土类型、坡向、坡度、植被归一化指数、月均降雨量等6个因子作为泥石流的易发性评价因子。将研究区划分为217个小流域,以流域单元为评价单元,采用权重系数法建立了泥石流的易发性评价模型,并利用该模型制作易发性分区图。分区结果表明:研究区泥石流极高易发区和高易发区主要分布在北部的昌波-贡波段、中东部的徐龙-曲雅贡段和南部的金沙江沿岸。极高易发区和高易发区面积占研究区总面积的36.4%,两区内的泥石流流域面积占泥石流总流域面积的58%。经检验泥石流的预测成功率为69.6%。     

昆明市东川城区后山泥石流危险性评价

云南省是中国遭受泥石流灾害最严重的省区之一,而对危险范围和危险程度进行划分和评估是泥石流减灾的重要内容。以昆明市东川城区后山泥石流为例,应用泥石流沟危险度综合评价法对泥石流危险度进行了评价;接着应用流域背景预测模型对泥石流危险范围进行预测,在这些数据和已构建的东川城区数字高程模型数据的基础上,应用GIS生成了以上泥石流危险范围预测图,研究成果为东川城区减灾防灾提供了科学依据。     

灰色关联度法在泥石流活动性评价中的应用

泥石流活动性评价是进行泥石流危险性评价的重要内容之一。本文采用处理非线性问题的有力工具——灰色系统理论中的关联度分析法，建立了泥石流活动性评价的方法，并用此方法，以云南蒋家沟为参考序列，对云南的6条典型泥石流沟进行活动性评价，评价结果与实际的情况较吻合。并应用云南保山王官屯沟和云南怒江州六库的芭蕉河沟做了检验，结果表明本方法是正确性可行的。     

基于空间分析的山地生态敏感性评价——以四川省万源市为例

以典型的秦巴山地城市万源市为例,根据山地生态系统特征,选取士地覆盖类型、高程、地质灾害、生境类型、水土流失、坡度、景观破碎度和生物多样性等8个因子,划分为极敏感、敏感、低敏感和不敏感4个等级,利用AHP确定因子权重,结合GIS空间分析技术,得到综合的生态敏感性空间分布,4个敏感级的面积比分别为8.44%、44.50%、38.31%、8.75%.根据各个因子对生态环境贡献的大小,得到了限制性敏感因子在空间上的分布.生态敏感性分析为城市规划和城市发展用地选择提供了科学依据,减小了城市发展对生态系统破坏的风险.     

基于GIS技术的泥石流风险评价研究

为了满足对自然灾害预测不断增长的紧迫要求，泥石流风险评价成为帮助决策过程重要的基础工具之一。即使泥石流风险性各组分的评价很困难，但地理信息系统可辅助提出这种风险性制图的有关方法。我们以云南省为研究区，选取6个成因因子参与泥石流危险度敏感性分析，通过将研究区易损性评价图与危险性评价图叠加分析，编制出云南省泥石流风险评价图。该图描述了在现有自然条件和人类活动下的泥石流风险敏感区。研究成果为全面反映灾情，确定减灾目标，优化防御措施，进行减灾决策提供了重要依据。     

Detailed debris flow hazard assessment in Andorra: A multidisciplinary approach

In many mountainous areas, the rapid development of urbanisation and the limited space in the valley floors have created a need to construct buildings in zones potentially exposed to debris flow hazard. In these zones, a detailed and coherent hazard assessment is necessary to provide an adequate urban planning. This article presents a multidisciplinary procedure to evaluate the debris flow hazard at a local scale. Our four-step approach was successfully applied to five torrent catchments in the Principality of Andorra, located in the Pyrenees. The first step consisted of a comprehensive geomorphologic and geologic analysis providing an inventory map of the past debris flows, a magnitude–frequency relationship, and a geomorphologic–geologic map. These data were necessary to determine the potential initiation zones and volumes of future debris flows for each catchment. A susceptibility map and different scenarios were the principal outcome of the first step, as well as essential input data for the second step, the runout analysis. A one-dimensional numerical code was applied to analyse the scenarios previously defined. First, the critical channel sections in the fan area were evaluated, then the maximum runout of the debris flows on the fan was studied, and finally simplified intensity maps for each defined scenario were established. The third step of our hazard assessment was the hazard zonation and the compilation of all the results from the two previous steps in a final hazard map. The base of this hazard map was the hazard matrix, which combined the intensity of the debris flow with its probability of occurrence and determined a certain hazard degree. The fourth step referred to the hazard mitigation and included some recommendations for hazard reduction. In Andorra, this four-step approach is actually being applied to assess the debris flow hazard. The final hazard maps, at 1 : 2000 scale, provide an obligatory tool for local land use planning. Experience achieved during the study showed that the collaboration between geologists, geomorphologists, engineers, and decision makers is essential and that only a multidisciplinary approach allows for solving all the problems of such a complex process as debris flows. Finally, we propose that our approach may be applied to other mountainous areas, adapting the hazard matrix to new local conditions.     

GIS支持下三峡库区秭归县滑坡灾害空间预测

基于GIS空间分析和统计模型相结合进行区域评价与空间预测是滑坡灾害研究的重要方向之一。以三峡库区秭归县为研究区,选择坡度、坡向、边坡结构、工程岩组、排水系统、土地利用和公路开挖作为评价因子。为提高模型的预测精度、可信度和推广能力,利用窗口采样规则降低训练样本之间的空间相关性。建立Logistic回归模型,对滑坡灾害与评价因子进行定量相关性分析。计算研究区滑坡灾害易发性指数,对其进行聚类分析,绘制滑坡易发性分区图,其中高、中易发区占整个研究区面积的38.9%,主要分布在人类工程活动频繁和靠近排水系统的区域。经过验证,该模型的预测精度达到77.57%。     

汶川地震灾区滑坡空间特征变化分析简

“5.12”汶川大地震触发了大量滑坡,给人民群众生命财产和社会经济发展造成了巨大损失。基于GIS空间分析方法,结合震前和震后的滑坡编目数据,对滑坡与坡度、坡向、高程、岩土类型、道路、河流和断裂带等7个孕灾环境因素的空间分布关系进行统计分析。结果表明：滑坡与孕灾环境因素的空间分布关系受地震的影响比较大。相比于震前,震后滑坡发生的优势坡度、优势岩土类型、优势距离缓冲区等均发生了很大的变化;并且坡向、距道路距离、距河流距离等因素对滑坡有明显地趋势性影响。     

汶川地震灾区滑坡空间特征变化分析

“5.12”汶川大地震触发了大量滑坡,给人民群众生命财产和社会经济发展造成了巨大损失。基于GIS空间分析方法,结合震前和震后的滑坡编目数据,对滑坡与坡度、坡向、高程、岩土类型、道路、河流和断裂带等7个孕灾环境因素的空间分布关系进行统计分析。结果表明：滑坡与孕灾环境因素的空间分布关系受地震的影响比较大。相比于震前,震后滑坡发生的优势坡度、优势岩土类型、优势距离缓冲区等均发生了很大的变化;并且坡向、距道路距离、距河流距离等因素对滑坡有明显地趋势性影响。     

基于GIS和双变量分析模型的三峡库区滑坡灾害易发性制图

在GIS技术的支持下,以三峡库区忠县-石柱河段为研究区域(面积260.9km2,滑坡分布面积5.3km2),建立了地质、地形数据库等滑坡因子空间数据库和滑坡空间分布数据库(数据比例尺均为1∶10万);在进行滑坡影响因子敏感性分析的基础上;对双变量分析模型进行了改进应用,对滑坡影响定量因子采用滑坡种子网格数据驱动的分级新方法。在GIS系统中进行了滑坡危险度评价成果图制图,将评价结果分为很低、低、中等、高、很高5个等级,依次占研究区域19.9%、31.69%、27.95%、17.1%和3.6%。评价结果显示危险性高和很高的区域主要分布在长江两岸,这与实际的滑坡分布吻合。研究结果对在三峡库区推广应用、防灾减灾具有实际指导意义。     

Spatial patterns of old, deep-seated landslides: A case-study in the northern Ethiopian highlands

During the last decade, slope failures were reported in a 500 km² study area in the Geba–Werei catchment, northern Ethiopia, a region where landslides were not considered an important hazard before. Field observations, however, revealed that many of the failures were actually reactivations of old deep-seated landslides after land use changes. Therefore, this study was conducted (1) to explore the importance of environmental factors controlling landslide occurrence and (2) to estimate future landslide susceptibility. A landslide inventory map of the study area derived from aerial photograph interpretation and field checks shows the location of 57 landslides and six zones with multiple landslides, mainly complex slides and debris flows. In total 14.8% of the area is affected by an old landslide. For the landslide susceptibility modelling, weights of evidence (WofE), was applied and five different models were produced. After comparison of the models and spatial validation using Receiver Operating Characteristic curves and Kappa values, a model combining data on elevation, hillslope gradient, aspect, geology and distance to faults was selected. This model confirmed our hypothesis that deep-seated landslides are located on hillslopes with a moderate slope gradient (i.e. 5°–13°). The depletion areas are expected on and along the border of plateaus where weathered basalts rich in smectite clays are found, and the landslide debris is expected to accumulate on the Amba Aradam sandstone and upper Antalo limestone. As future landslides are believed to occur on inherently unstable hillslopes similar to those where deep-seated landslides occurred, the classified landslide susceptibility map allows delineating zones where human interventions decreasing slope stability might cause slope failures. The results obtained demonstrate that the applied methodology could be used in similar areas where information on the location of landslides is essential for present-day hazard analysis.     

Simulation of event-based landslides and debris flows at watershed level

A coupled model has been developed to simulate, at watershed level, landslides and debris flows induced by a severe typhoon (tropical cyclone) in Taiwan. The model comprises a landslide susceptibility model to predict landslide occurrence, an empirical model to select debris-flow initiation points, and a debris flow model to simulate the transport and deposit of failed materials from the identified source areas. In raster format with a 10 m spatial resolution, the model output includes unstable cells, debris-flow initiation cells, debris-flow velocities, runout paths, and deposition zones. The model was first tested and calibrated in a small area, where the damage by landslides had been investigated and recorded. It was then applied to a watershed, and the simulation results were validated by comparing them with a landslide/debris-flow inventory map prepared from satellite images using a multiple change detection technique. Model test and validation results confirm the usefulness of the model in predicting the number and size of affected areas (landslides and runouts combined), runout path, and volume of runout deposits. It is a common practice in Taiwan to separate landslide and debris-flow inventories and to study debris flows only in select drainage basins. This study suggests that landslide and debris flow should be modeled as a sequential process for efficient watershed management.     

GIS and ANN model for landslide susceptibility mapping

Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage line are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.