多维异构洪水过程因子相似性评价方法研究

在研究相似性概念和评价方法的基础上,针对洪水过程因子的时空分布特点,进一步提出综合性评价方法框架;针对洪水过程中水深时间序列数据和面状淹没范围等多维异构洪水过程因子,分别进行了相似性评价方法研究,并采用重要性排序法确定权重。以辽宁浑河沈阳城市段的洪水数值模拟结果和物理模型观测数据相似性分析为例,对多个观测站点的水深时间序列数据、流速数据及淹没范围数据进行相似性分析,分析洪水演进模型结果的相似性。     

渭河陕西段流域洪水淹没状况拟合研究

以渭河陕西段流域为研究区,利用2001年至2018年的水文和气象观测数据,计算出该流域十年一遇、五十年一遇和百年一遇降水量的致洪面雨量,采用水文动力学方法和分布函数,利用地理信息系统技术,拟合出该流域被十年一遇、五十年一遇和百年一遇降水量导致的洪水的淹没深度、淹没范围和淹没面积。研究结果表明,十年一遇降水量导致的洪水淹没的极高风险区(洪水淹没深度大于2.0 m)分布在渭河干流宝鸡段下游、沣河、泾河、石川河下游和渭河下游南山支流流域,其面积为42.92 km~2;五十年一遇降水量导致的洪水淹没的极高风险区分布在泾河干流、千河下游、渭河干流宝鸡段、沣河、石川河下游、北洛河下游及渭河下游南山支流流域,其面积为115.95 km~2;百年一遇降水量导致的洪水淹没的极高风险区集中分布在渭河干流宝鸡段、沣河、泾河干流、石川河、北洛河下游和渭河下游南山支流流域,其面积为183.27 km~2。     

沿海城市社区暴雨洪水风险评价——以温州龙湾区为例

从风险角度探讨了基于GIS空间网格的沿海城市社区空间尺度暴雨洪水淹没情景模拟方法。通过对温州龙湾区暴雨降水量的频率计算,得到不同频率下的降水量及其对应的径流深度。根据"雨量体积法"原理,利用编制的程序得到不同重现期暴雨洪水水面高程和不同淹没水深区间对应的淹没面积,并进行暴雨洪水淹没情景模拟和洪水风险评价。最后,根据2005年龙湾区"海棠"台风暴雨实测资料进行典型历史台风暴雨洪水事件实证研究。     

基于GIS的复杂地形洪水淹没区计算方法

利用GIS技术计算洪水淹没区范围一直是灾害评估研究中的一个热点问题。通过给出两种情形下基于种子蔓延算法的淹没区计算方法，即有源 淹没和无源淹没。淹没区计算精度主要取决于空间数据精度的优劣；种子蔓延算法及探测分辨率决定了整个模型的效率。文中最后给出了该模型在“水利综合管理信息系统”中得到验证和实现的实例。     

基于ArcGIS的渭河下游洪水淹没面积的计算

计算洪水淹没面积一直是灾害评估研究中的一个热点问题以GIS技术为基础,运用Arc-GIS软件的特殊功能,以渭河为研究背景实现了无需编程即可完成对洪水淹没面积的提取及计算。在地形图数字化基础上,分别对有源淹没和无源淹没进行分析,并运用ArcMap和ArcView软件自身的功能,对洪水淹没面积进行统计计算,并以2003年渭河下游洪水淹没数据为依据进行对比验证,取得了良好的效果。     

浙江省余姚市室内财产洪水脆弱性曲线——基于台风菲特(201323)灾后问卷调查

脆弱性用于量化承灾体在不同致灾因子强度下的损失率,是灾害系统特性的重要组成部分。灾害损失及危险性实际案例数据不足,是脆弱性定量研究的重要制约因素。研究面向台风菲特(201323)造成的余姚市室内财产洪水灾害,首先给出了脆弱性曲线及刻画二级不确定性的概念图;其次设计了灾害调查问卷并实地开展调查形成386份有效问卷;然后,基于调查数据分析了室内财产损失率随洪水淹没水深增加的变化特点;最后,用Logistic函数拟合了8条室内财产洪水脆弱性曲线,并利用Beta函数对脆弱性曲线的二级不确定性进行了量化。结果表明:室内财产较少或收入较低的家户或商户的受灾概率更高;室内财产损失率与淹没水深的关系可以分为渐增―快增―缓增3个阶段;不同水深区间的损失率不确定性分布存在差异,快增阶段的二级不确定性要高于渐增和缓增阶段;室内财产损失率与淹没时长之间缺乏统计意义上的显著相关。研究结果可以有效地描述浙江省余姚市室内财产洪水脆弱性,可为灾后损失评估、巨灾模型应用等提供有效的参数输入。     

基于地形坝体一体化三维场景的水库洪水淹没模拟

提出一种无缝融合地形三角网与坝体虚拟现实模型、构建一体化水库库区三维场景的方法。基于真实连续的下垫面环境进行确定水位的淹没模拟,其中地形部分以迭代实现邻接扩展的淹没算法,坝体部分通过高程比较与体元轴对齐包围盒相交判断确定迎水面受淹形态。通过计算广义三棱柱及其变体的离散水体体积,求得水位对应库容,并构建二分查找判定树,以快速获取接受净入库洪量后的库区洪水淹没结果,并在三维场景中渲染表达。实验结果表明,基于地形坝体一体化三维场景的计算模拟方法,相比传统库容曲线与洪水风险图能够在展现库区洪灾影响范围的同时,兼顾坝体局部的淹没细节。     

基于数值模拟的黄河下游 不同情景溃堤洪水特性

通过研究黄河下游地区的溃堤洪水特性,为防洪减灾工作提供科学依据。本文以黄河下游地区的DEM,1958、1982、1996年典型历史洪水和2000年土地利用数据为模型初始输入,分4个模拟情景,依据黄河溃堤水流的特性,利用二维非恒定流模型对黄河下游不同位置决口情景下的洪水演进特性进行模拟,结果表明:洪水在演进过程中都会侵夺沿途河流行洪,进入下游后洪水会顺河归槽;在同一河段,同一年型洪水北岸不同位置溃口情景所淹没的面积大于南岸对应位置的淹没面积;黄河两岸洪泛区地理位置的差异,使两岸的洪灾程度具有区域差异;水深且流速大的洪水淹没主流区面积相对较小,水浅且流速小的非主流区面积相对较大。     

城市山洪灾害风险评价——以云南省文山县城为例

山洪灾害风险评价对于减灾防灾决策和管理非常重要。本文介绍基于遥感和GIS方法的应用,探讨一种快速、简便而且较为准确的城市山洪灾害风险评价方法。以发生于1998年7月26日文山城20年一遇山洪灾害为实例,将GIS的数字高程模型与实测的山洪水位和洪峰流量结合进行淹没分析,研究表明采用该方法可以模拟准确山洪泛滥范围,并计算淹没水深分布。根据不同水深指标,应用GIS工具完成了山洪灾害危险分区。本研究利用高分辨率遥感影像提供承灾体类型的可靠和准确数据用于易损性分析和期望损失评估的价值计算。根据典型区财产损失的抽样调查,建立了不同承灾体类型与水深的关系,并确定其损失率;应用GIS空间数据处理和分析的集成方法完成了复杂的损失评估。在此基础上,按期望损失程度进行分区划分而完成山洪风险评价。研究结果表明基于GIS和RS方法进行山洪风险评价效果良好,值得推广应用于其他洪水泛滥区。     

退化沼泽湿地洪水淹没数值模拟研究——以洪河国家级自然保护区为例

以三江平原洪河国家级自然保护区为研究区,通过对研究区数字高程模型与土地利用信息关系研究,模拟了不同水位洪水淹没退化沼泽湿地的过程,得到淹没过程序列图。洪水水面高程与退化沼泽淹没面积的关系表明,在洪水水面高程从50．6m升高至55．1m过程中,淹没面积以3种增长趋势变化,根据这3种趋势所对应的洪水水面高程,将研究区退化沼泽划分为轻度、中度和重度退化3种类型。研究区轻度、中度和重度退化沼泽的面积分别为0．1km^2、4．32km^2和8．22km^2。该研究结果为保护区的湿地恢复工作提供了数据基础,也为湿地恢复的区域规划与实施提供了一种新的技术指标和方法。     

Glacier extraction based on ASAR, DEM and texture feature of ASAR using SVM in the Western Qilian Mountains, Northwest China

This paper is focused on the method for extracting glacier area based on ENVISAT ASAR Wide Swath Modes (WSM) data and digital elevation model (DEM) data, using support vector machines (SVM) classification method. The digitized result of the glacier coverage area in the western Qilian Mountains was extracted based on Enhanced LandSat Thematic Mapper (ETM+) imagery, which was used to validate the precision of glacier extraction result. Because of similar backscattering of glacier, shadow and water, precision of the glacier coverage area extracted from single-polarization WSM data using SVM was only 35.4%. Then, texture features were extracted by the grey level co-occurrence matrix (GLCM), with extracted glacier coverage area based on WSM data and texture feature information. Compared with the result extracted from WSM data, the precision improved 13.2%. However, the glacier was still seriously confused with shadow and water. Finally, DEM data was introduced to extract the glacier coverage area. Water and glacier can be differentiated because their distribution area has different elevations; shadow can be removed from the classification result based on simulated shadow imagery created by DEM data and SAR imaging parameters; finally, the glacier coverage area was extracted and the precision reached to 90.2%. Thus, it can be demonstrated that the glacier can be accurately semi-automatically extracted from SAR with this method. The method is suitable not only for ENVISAT ASAR WSM imagery, but also for other satellite SAR imagery, especially for SAR imagery covering mountainous areas.     

Methods for calculating glacier area and length in a mountainous area based on remote-sensing data and a digital elevation model

In a mountainous region, the glacier area and length extracted form the satellite imagery data is the projected area and length of the land surface, which can’t be representative of the reality; there are always some errors. In this paper, the methods of calculating glacier area and length calculation were put forward based on satellite imagery data and a digital elevation model (DEM). The pure pixels and the mixed pixels were extracted based on the linear spectral un-mixing approach, the slop of the pixels was calculated based on the DEM, then the area calculation method was presented. The projection length was obtained from the satellite imagery data, and the elevation differences was calculated from the DEM. The length calculation method was presented based on the Pythagorean theorem. For a glacier in the study area of western Qilian Mountain, northwestern China, the projected area and length were 140.93 km2 and 30.82 km, respectively. This compares with the results calculated by the methods in this paper, which were 155.16 km2 and 32.11 km respectively, a relative error of the projected area and length extracted from the LandSat Thematic Mapper (TM) image directly reach to -9.2 percent and -4.0 percent, respectively. The calculation method is more in accord with the practicality and can provide reference for some other object’s area and length monitoring in a mountainous region.     

基于FloodArea模型新疆山洪淹没模拟及致灾临界雨量阈值的研究——以皮里青河流域为例

以皮里青河流域为研究区,运用小时降水、土地利用类型、数字高程（DEM）、实测淹没深度等数据,基于FloodArea模型对研究区2010年5月2日、2012年6月3日、2016年5月9日、2016年6月17日洪水过程进行再现模拟,通过精度验证并建立了降水－淹没深度的关系,在此基础上确定了4个淹没等级对应的致灾临界雨量。相关分析得出喀拉亚尕奇乡累计8 h降雨量与模拟洪水淹没深度的相关性最好,达到了0.96,潘津乡降雨累计5 h的相关性最好,为0.99;通过实测数据对模拟淹没深度进行精度检验得出,喀拉亚尕奇乡和潘津乡两个考察点相对误差分别为0.47 m和0.1 m,误差率分别为31.33%和7.69%,FloodArea模型对研究区洪水过程模拟的效果较好,可以反映出该区域的洪水淹没情况,能为无水文资料的山区流域的山洪过程进行较为精准的模拟;按照山洪灾害等级划分标准和降水-淹没深度的关系得出,预警点累计5 h降水得到对应4个等级的致灾临界雨量阈值分别为：四级17.84 mm、三级32.39 mm、二级54.21 mm、一级76.04 mm。     

洪泛平原农村居民地空间分布特征定量研究及应用探讨

通过对影响居民地空间分布因素的分析,探讨了洪泛平原农村居民地空间分布特征;利用地理信息系统技术对不同高程带内居民地分布规模、等级和空间分布密度作了定量研究,得到了居民地空间分布密度和人口空间分布密度参数;并结合洪灾淹没区居民地损失的估算,对研究结果的应用作了探讨。     

A predictive model of archaeological potential: An example from northwestern Belize

Binary Logistic Regression is used to identify areas of high archaeological potential in a portion of northwestern Belize. The predictive modeling process involves remotely sensed imagery, Geographic Information System (GIS) data and techniques, and multivariate statistical approaches. Predictive variables represent both the pre-historic current landscape of the ancient Maya and the present day physical landscape. An optimal predictive model obtained using logistic regression includes one variable derived from a Landsat image representing contemporary vegetation patterns associated with Maya settlement and two variables derived from a digital elevation model (DEM) and an analog hydrography map representing resource endowments relevant to the ancient Maya. The predictive model identifies several areas of high archaeological probability as well as areas that are unlikely to contain any archaeological remains. Results can be used to inform future field surveys in a more cost efficient manner. Prior research has utilized remote sensing and GIS approaches for Maya site identification in the southern lowlands region of the Mexican Yucatan peninsula and the northern lowlands of Peten, Guatemala. This research represents the first landscape archaeological approach using satellite imagery for the Maya region in northwestern Belize.     

A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Flood is one of the major recurrent natural disasters faced by the state of Bihar in north India. In the present study the authors assess the severity of flood hazard in Bihar, using 128 decadal historical satellite datasets acquired during different flood magnitudes during 1998 to 2010. The satellite‐based observations have been analysed in conjunction with the hydrological data, for assessing the frequency of inundation, severity of flood hazard and cropped land under flood hazard. This study assesses the spatial distribution of flooding and creation of systematic flood hazard database, which can be analysed from a spatial dimension in GIS. It is observed that about 24.56 lakh ha of the state's area and about 15.85 lakh ha of the cropped area are vulnerable to flood hazard. North Bihar is more vulnerable to flooding; 8 of the 10 areas identified as worst flood‐affected districts lie in this region.     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

VEGETATION MAPPING USING REMOTE SENSING AND GIS

I.StduyAreaPUschRjdgeoftheSantaCatalinaMountains,CoronadONaionalForest,SoutheastArizona,wasselectedasastudyareafOrvegetationmopingandatestoftheroleOfGISinaidingrem0teIysenseddataclassificati0n.BeinganepitOmeoftheSantaCatalinaMountains,PUscllmdgeiscomprisedOf23O.65sqUarelQnoflandIocatedonthesouthwesternPOrti0noftheSantaCarelinaRangerDistrictOftheCoronadoNationalForest.ltprovidesasharPcontrastbebeenthenamralruggdnessOftheSantaCatalinaMountainsandtheCityOfTucson,Arizona,araPdl…