基于概率指数模型的区域滑坡危险性评价

影响滑坡的因子很多,区域滑坡危险性因子的确定是治理滑坡的关键。以马来西亚金马仑高原作为研究区,选取岩性、地形、地貌、道路、土地利用等环境因子,在GIS平台上运用双变量统计方法,分析了滑坡与各环境因子之间的关系,根据滑坡面积密度建立了概率指数评价模型,进行了研究区滑坡危险性评价,结果表明,此模型具有较好的评价精度。     

基于WSNs的定位策略在滑坡监测中的应用

将无线传感器网络(WSNs)应用在滑坡监测上,通过对传感器节点位置的监测关注坡体运动从而预测滑坡的发生.针对传统定位算法对坡体节点定位精度低的问题,提出一种改进的基于RSSI的加权质心定位算法(IWCLA),该算法考虑坡体节点所处的外部环境,引入锚节点误差系数,实时调整RSSI测距,同时引入锚节点环境因子的概念,将环境因子和修正距离的商作为算法权值,增强权值选取的合理性,降低外部环境的影响力.仿真结果表明:该算法明显地提高了坡体传感器节点的定位精度.     

基于人工神经网络和AMSR2多频微波亮温的北疆地区雪深反演

被动微波遥感数据是进行积雪深度反演的重要资料,不同频率微波信号对积雪响应不同。利用人工神经网络(Artificial Neural Network,ANN)方法结合新疆北部地区积雪观测资料建立AMSR2(Advanced Microwave Scanning Radiometer 2)微波亮温(Brightness Temperature,TB)、地理位置、地形因子与雪深的隐含关系,以实现通过亮温、地理位置、地形因子估算北疆地区积雪深度,并分析微波极化方式、位置以及地形的不同组合方式对雪深反演效果的影响。实验结果表明:水平极化对雪深反演的影响大于垂直极化,纬度对雪深的影响大于经度,地表粗糙度和坡向对雪深的影响大于高程和坡度,并且位置和地形因子对雪深影响作用相当。最终通过4种优选模型的误差空间分布对比发现,综合亮温、经纬度、坡度、坡向的ANN输入模型能够较好的反映北疆地区积雪分布状况,训练集的站点平均误差在-7~6cm之间,该组合模型作为神经网络的输入能够较为合理地获取北疆地区雪深模拟值。     

装配零件位置关系求解新方法

本文研究装配零件之间内在的几何连接关系，提出一种求解相对位置关系新方法。     

遥感和地理信息系统在半干旱地区降雨-径流关系模拟中的应用

为解决黄土高原半干旱地区农业和生活缺水问题 ,许多地方实施了集水工程以收集雨水。为分析集水潜力 ,以黄土高原半干旱地区的 6个自然集水区为研究对象 ,利用遥感和地理信息系统获得研究区地形、植被、流域特征等参数。通过统计分析的办法得出该区域多年平均年径流量与降雨量、地形、植被等因素的关系模型。该模型将影响降雨 -径流关系的几个主要因素定量表示出来 ,可以快速、准确地计算一个集水区的径流量 ,克服采用径流等值线计算径流带来的误差     

遥感和地理信息系统在半干旱地区降雨-径流关系模拟中的应用

为解决黄土高原半干旱地区农业和生活缺水问题,许多地方实施了集水工程以收集雨水。为分析集水潜力,以黄土高原半干旱地区的6个自然集水区为研究对象,利用遥感和地理信息系统获得研究区地形、植被、流域特征等参数。通过统计分析的办法得出该区域多年平均年径流量与降雨量、地形、植被等因素的关系模型。该模型将影响降雨-径流关系的几个主要因素定量表示出来,可以快速、准确地计算一个集水区的径流量,克服采用径流等值线计算径流带来的误差。     

基于GIS的区域坡度坡长因子提取算法

为提高基于地理信息系统的区域土壤侵蚀研究、水土保持环境效应评价、流域水文分析等的应用效率,设计新的坡度坡长(LS)因子算法,利用正向-反向遍历算法取代原累积坡长算法,以获取区域尺度下的LS因子。实验结果表明,在计算精度允许的范围内,新算法使计算机运行效率有较大幅度的提高。     

基于遥感技术的滑坡识别及滑坡检测系统开发

遥感技术及图像处理技术的快速发展提高了滑坡自动化解译的能力。由于能够综合考虑遥感影像中的光谱、纹理、形态和地形特性，近年来面向对象的图像分析技术(Object-Based Image Analysis, OBIA)在滑坡自动化解译中得到广泛利用。当前许多相关研究都是基于已有商业软件开展的，为降低滑坡检测技术的使用门槛，在本研究中利用OBIA和机器学习创建了一个基于开源Python包的半自动滑坡检测系统。基于该系统对精伊霍铁路沿线约330 km²的面积开展滑坡检测，将检测结果与人工野外调查结果相对比并进行定量精度评价，结果表明kappa系数为0.803,该系统在滑坡检测中具有较高的精度，可以适用于大范围快速滑坡提取。     

复杂地形影响下雷达探测范围表现算法研究

通过分析雷达探测范围边界与数字地形高程的相对位置关系,提出一种基于几何光学原理的雷达探测范围受复杂地形影响的修正算法。采用雷达传播衰减模型和混合采样方法,应用该修正算法,实现了对复杂地形影响下雷达探测范围的修正。实验结果表明,该算法不仅对复杂地形适应性强,而且表现效果真实直观。     

利用地形参数提高土地覆被分类精度方法的改进

为提高MODIS卫星影像土地覆被产品的分类精度,以京津冀为研究区,在1∶25万土地覆被数据与MODIS土地覆被产品(MCD12Q1)分类一致区内,构建土地覆被类型面积占比与地形因子之间的多元回归模型,并据此改进MODIS土地覆被产品中分类精度较低区域的分类。用面积构成比例和空间一致性比率两个评价指标对改进结果进行评价。结果表明:林地、草地、耕地三种地类的回归模型适合用来改进MODIS土地覆被产品的分类,三种地类与参考数据的空间一致性比率比改进前分别提高了30.02%、40.87%和4.94%;对于与地形因子关系密切的林地和草地,两个评价指标均显示,基于分类一致区建模来改进目标产品的分类精度,比基于整个区域建模改进目标产品的分类精度的效果更加明显。其中,林地的空间一致性比率的提升幅度由8.55%升到30.02%,草地由27.44%升到40.87%。由此可见,地形地貌对土地覆被类型的形成具有重要影响,土地覆被类型面积占比与地形因子之间具有很强的相关关系,基于这种定量关系对土地覆被分类进行改进是完全可行的。     

基于迁移学习的低空摄影测量滑坡方量估算方法

针对现有方法难以准确地估算山体滑坡体积的问题,引入人工智能算法,提出耦合迁移学习与微分算法的低空摄影测量山体滑坡方量估算方法。首先,利用SfM与SGM密集匹配等算法从低空无人机立体影像中解算出高精度三维密集点云,结合可见光植被指数和双边滤波算法从密集点云中剥离出目标区地面点云;然后,构建深度神经网络插值模型来表征二维坐标与高程之间的非线性映射关系,并基于参数共享的迁移学习来自适应优化深度神经网络以实现滑坡目标区高程值预测,进而重构滑坡区域的数字地表模型;最后,基于目标区滑坡前后数字地表模型高程差值和微分算法实现山体滑坡方量估算。实验结果表明,该方法平均相对误差为2.7%,相比常用的方法,显著提高了滑坡方量估计精度,并能适应不同地形条件下滑坡方量估算。     

Semi-automatic approach for identifying locations of shallow debris slides/flows based on lidar-derived morphological features

Identification of landslides at the regional scale has always been a challenging problem. Various automatic landslide identification methods, mainly relying on spectral information from aerial photographs or satellite imagery, have been developed. This paper proposes a semi-automatic approach to identify locations of small-sized shallow debris slides and flows using airborne lidar (light detection and ranging) data. Cells related to landslide components were first extracted by using a new method based on local Moran’s I (LMI). Subsequently, cell clusters representing landslide components and other terrain objects were discriminated through geometric and contextual analysis at cluster level. The approach was tested in a study area in Hong Kong and the identification result was verified by a landslide inventory. Locations of 93.5% of recent landslides and 23.8% of old landslides were identified by the proposed approach. The result indicates that the proposed approach is able to identify both recent and old landslides with distinct morphological features. However, the proposed approach also identified a large number of locations (77.6% of all locations) unrelated to landslides. These locations may correspond to terrain objects with similar morphology to debris slides or flows, and indicate rough terrain in the study area. In addition, the effects of DEM (digital elevation model) resolution on landslide identification were analysed by applying the LMI-based method to digital elevation models (DEMs) at different resolutions. The results indicate that the smoothing effect caused by lowering DEM resolution led to extraction of fewer landslide components.     

Landslide mapping in the coastal area between the Strymonic Gulf and Kavala (Macedonia,Greece) with the aid of remote sensing and geographical information systems

The main aim of this study is to photointerpret land cover change along hill slopes in order to detect existing landslides with the aid of methodologies such as false colour composites (FCCs), principal component analysis (PCA) and the normalized difference vegetation index (NDVI). Then, by combining geological data (lithology, vegetation cover), and geomorphologic factors (slope, aspect, distance from the rivers), landslide susceptibility maps were produced. The region where the study took place was the coastal area between the Strymonic Gulf and southwest Kavala prefecture, which was selected because of its intense landslide activity. The identification of locations where landslides occurred was achieved with the use and processing of TERRA/ASTER satellite images, while the data, which were mainly collected from the digitization of contours from topographical maps at 1 : 50 000 scale, were used to construct the final landslide susceptibility maps. The resulting FCC images provided satisfactory information about the locations of landslide sites and the landslide susceptibility maps indicated areas that were more prone to produce such phenomena.     

图层约束下的点状自然灾害风险地图综合

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。     

Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images

We present a method for the semi-automatic recognition and mapping of recent rainfall induced shallow landslides. The method exploits VHR panchromatic and HR multispectral satellite images, and was tested in a 9.4 km² area in Sicily, Italy, where on 1 October 2009 a high intensity rainfall event caused shallow landslides, soil erosion, and inundation. Pre-event and post-event images of the study area taken by the QuickBird satellite, and information on the location and type of landslides obtained in the field and through the interpretation of post-event aerial photographs, were used to construct and validate a set of terrain classification models. The models classify each image element (pixel) based on the probability that the pixel contains (or does not contain) a new rainfall induced landslide. To construct and validate the models, a procedure in five steps was adopted. First, the pre-event and the post-event images were pan-sharpened, ortho-rectified, co-registered, and corrected for atmospheric disturbance. Next, variables describing changes between the pre-event and the post-event images attributed to landslide occurrence were selected. Next, three classification models were calibrated in a training area using different multivariate statistical techniques. The calibrated models were then applied in a validation area using the same set of independent variables, and the same statistical techniques. Lastly, combined terrain classification models were prepared for the training and the validation areas. The performances of the models were evaluated using four-fold plots and receiver operating characteristic curves. The method proved capable of detecting and mapping the new rainfall induced landslides in the study area. We expect the method to be capable of detecting analogous shallow landslides caused by similar (rainfall) or different (e.g. earthquake) triggers, provided that the event slope failures leave discernable features captured by the post-event satellite images, and that the terrain information and satellite images are of adequate quality. The proposed method can facilitate the rapid production of accurate landslide event-inventory maps, and we expect that it will improve our ability to map landslides consistently over large areas. Application of the method will advance our ability to evaluate landslide hazards, and will foster our understanding of the evolution of landscapes shaped by mass-wasting processes.     

Digital orthophotography: mapping with pictures

Aerial photographs and maps would seem a natural combination to record and analyze geographical information: maps provide geometric information and photographs add realistic, timely detail. But cameras record images on a flat plane, whereas the earth is curved and its terrain takes on many varied shapes-all of which distort the image geometry and render it invalid for mapping and geographic analysis. Digital orthophotographs solve this problem. A digital orthophoto starts with a rasterized (scanned) aerial photograph; a process called rectification (described below) removes distortions arising from the camera lens, the aircraft's position, and elevation and other topographical features. This transforms aerial photos into high-resolution digital images that correctly represent the geometry of an area and its terrain. These images can be used as standard true-scale representations of geographic sectors-a function already served by analog orthophotos, which must be painstakingly scanned and rectified in small strips or patches. Their fully digital format makes digital orthophotos useful as base maps in geographical information systems (GIS) used for creating and revising topographic and planimetric maps, vegetation and timber management, environmental impact assessments, and infrastructure assessment     

基于GIS和Logistic回归模型的兰州市滑坡灾害敏感性区划研究

针对兰州市脆弱的地质环境和频繁发生的滑坡灾害,采用Logistic回归模型,以ArcGIS和SPSS软件为工具,选取地层岩性、断层构造、坡度、地貌、植被覆盖度、7~9月平均降水、道路(公路、铁路)作为滑坡灾害影响因子。首先对每个影响因子分级并计算每个因子指标值,然后在ArcMap中对影响因子图层进行叠加操作,最后在SPSS软件中运用Logistic回归方法,计算出每个影响因子的系数值并建立Logistic回归模型。根据Logistic回归模型在ArcMap中绘制兰州市滑坡灾害敏感性区划图,区划图和实际的滑坡分布情况基本吻合。模型的Kappa系数值和ROC曲线下面积值(AUC值)分别为0.623和0.709,两种方法的检验结果均表明模型模拟效果较好,能应用于兰州市滑坡灾害敏感性区划研究中。     

A spatial–temporal analysis of impacts from human development on the Shih-men Reservoir watershed,Taiwan

Human activity such as the development of slope land around watersheds has dramatically affected the ecological environment in Taiwan. This situation has been aggravated by heavy precipitation from typhoons in the summertime. The results include serious soil erosion and mass movement in the Shih-men Reservoir watershed. In order to identify the most fragile areas and seek the triggering factors of landslide changes that can cause turbid currents in the Shih-men Reservoir watershed, this study integrates different types of satellite imagery and geographic information system data to determine changes in land cover and vegetation cover since the early 1970s. Results from spatial regression models indicate road and land uses are the main factors that lead to slope failure along roads and contribute to a large number of landslides in environmental hotspots like the Baishih River sub-watershed. Soil erosion estimates indicate a positive relationship between the increases in landslide and soil loss areas and the road system development. Therefore, human development has a significant negative influence both on sensitive mountainous watersheds and on critical environmental hotspots.