数字海底地形分割算法

根据不同海底地形特征,提出了基于多波束测量数据进行海底地形分割的"三态值模型"法,其步骤包括:将地形数据滤波和网格化;利用"3×3"差分算子计算各节点的梯度;利用最大梯度追踪算法检测正负地形与平缓地形的分界线;利用"三态值"算法识别正、负和平缓地形。应用该方法对胶州湾实验海区多波束实测数据处理结果表明,该方法切实可行,能够对数字海底地形准确地进行快速分割。     

一种自适应的坡度阈值地面点云分割方法

针对城市道路斜坡地形场景中地面欠分割或过分割的问题,提出了一种自适应的激光雷达地面分割算法。首先将激光点云按照水平角度分辨率进行有序组织,然后求取同一水平角度下前后扫描圈间激光点云的距离和局部坡度,最后采用自适应水平距离、局部高度和全局高度阈值区分地面点和非地面点。结合40线激光雷达进行多场景实例分析,结果表明本文算法分割的准确率更高,处理每帧数据均用时约1ms,满足无人驾驶汽车的实时性需求。提出了一种自适应的激光雷达地面分割算法,实现了对激光雷达地面点云的准确分割。     

喀喇昆仑山西北部冰川运动速度地形控制特征

为了探讨地形和海拔对冰川季节和年平均运动速度的影响程度,利用2013-2018年GoLive数据与ASTER GDEM V2数据对喀喇昆仑山西北部3 389条冰川的地形（坡度、坡向、海拔）和冰川运动速度进行了综合分析。结果表明：冰川表面运动速度在物质平衡线处（3 970~4 770 m）达到最快,是冰川积极维持物质平衡的一种体现。坡度平缓地区在不同海拔下的冰川运动速度有明显的差别,但是不同坡度地区的冰川运动速度随海拔变化的趋势基本一致,均呈现先增大后减小。北坡冰川运动速度较平稳,南坡和西南坡的冰川运动速度（均为0.25 m·d^-1）最快并且变化幅度较大,最小值与最大值相差近4倍。冰川运动速度不是呈现单一的季节性变化,同时还会受到地形的控制。低海拔区域冰川运动速度在消融期（3-6月）较快,中海拔区域在消融前（11月至次年2月）较快。     

四川地形扰动对降水分布影响

引入一维加权平均的谱分析方法定量研究四川地形强迫对该区域降水分布的影响。结果表明:纬向地形和冬季降水谱峰锁相于同一波长（475.8 km）,呈共振关系,地形与其他季节降水呈漂移关系,这与经向和纬向上环流变动有关,即冬季纬向环流占主导,纬向地形触发的大气波动对冬季降水策动作用大;夏季降水是各种不同尺度系统相互作用的结果,地形是重要因素之一。经向和纬向地形特征尺度分别为296.8 km和475.8 km,反映了地形强迫的中尺度特征,且纬向地形谱峰比经向大1个数量级,纬向强迫更明显。夏季降水谱峰比冬季大2个数量级,降水系统纬向特征尺度比冬季小约150 km,说明夏季在纬向地形强迫下,降水系统尺度减小的同时其强度大大增加,这在一定程度上可以解释中尺度对流性降水在夏季偏多。四川夏季最大降水位于雅安地区,其地形扰动比四川整体扰动更明显,故产生的降水也更大。夏季降水和经向地形锁相于同一波长（37.1 km）,经向地形对雅安夏季强降水起关键作用。     

Land surface temperature and its impact factors in Western Sichuan Plateau,China

The understanding influence of multiple factors variations on land surface temperature (LST) remains elusive. LST was retrieved by the atmospheric correction algorithms. Based on the correlation coefficients, stepwise regression analysis was developed to examine how multiple factors variability led to LST variations. The differences in LST between impact factors vary depending on time in a day. The elevation and land use types significantly affect the LST in sunny slope or shadow areas has a significantly quadratic curve correlation or a negative linear correlation with it, the influence of slope and aspect is not very significant. LST for forestland, grassland and bare land in the sunny slope and shadow area was the cubic polynomial related to its elevation. Normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) effectively express LST in mountainous. LST and NDMI or NDVI have a significantly negative correlation, NDMI is more effective and more applicable for the expression of LST.     

顾及地形特征的机载LiDAR数据桥梁提取算法研究

提出了一种从机载LiDAR点云数据中提取水陆桥梁的新算法,采用先滤波分类再识别桥梁的策略用于桥梁主体及其与地面连接处脚点的提取。首先对机载LiDAR点云数据进行严格滤波,在此基础上利用多个阈值对非地面脚点进行初步提取;然后引入Alpha-shapes算法对初步提取结果进行分割,并辅以面积阈值提取出属于桥梁主体部分的脚点;最后提出一种顾及地形特征的点云区域生长算法,用于准确提取桥梁主体与地面连接部分的脚点。实验证明,本文算法对水面和陆地桥梁提取均有很好的效果。     

Terrain Pattern Recognition and Spatial Decision Making for Regional Slope Stability Studies

A terrain partition scheme is presented that allows the identification of regions with high landslide risk in natural terrain zones on the basis of geomorphometric criteria from moderate resolution DEMs. The key factor being the terrain segmentation to aspect regions (regions formed by points preserving the same aspect direction) instead of using an artificial regular-grid terrain partition scheme. The study area is in western Greece (NW Peloponnesus) whereas a moderate resolution digital elevation model with spacing 75 m is used. Landslide inventory analysis and knowledge conceptualization identified that the landslide susceptibility of a particular aspect region is high, if the mean elevation is low and the mean gradient is high. Each aspect region was parametrically represented on the basis of its mean gradient and elevation. The domain of each parameter was divided to seven slices (classes) on the basis of the observed density. Subsequent knowledge based mapping identified aspect regions with high landslide susceptibility for the following spatial rule: (a) “mean slope in class 6 or 7” and (b) “mean elevation in class 1 to 5”. Alternatively the rule is expressed as mean slope to be equal or greater than 15^∘ whereas mean elevation to be in the range 0 to 750 m. These identified zones correspond to regions where historical landslides occurred (populated coastal areas in the North) as well as to south regions (natural terrain zone) where no landslide record is available, because of the limitations posed by the natural terrain landslide mapping program in Greece. The presented terrain segmentation technique combined to the spatial decision-making process, provided both an object framework for integrating geomorphometric parameters and a method for landslide risk analysis in natural terrain zones.     

基于GPU的地形可视化加速算法研究

地形可视化是利用数字高程模型DEM,采用计算机图形学和图像处理技术进行三维地形模拟显示。该技术在深部矿产预测、矿产资源评价、虚拟现实、娱乐游戏、飞行模拟等诸多领域有着广泛的应用。随着数据量的增大,三维地形可视化的实时、流畅视觉效果受到当前的计算机硬件技术水平限制。针对这一问题,本文运用ROAM算法进行地形建模,利用GPU高速并行运算性能加速地形可视化建模速度,加速模型显示效果。实验对比表明:当计算量比较小时,加速效果不显著;随着计算量的增大,计算效果越来越明显;当计算量达到一定值时,加速效果达到一个稳定的加速趋势。研究结果为地形可视化及矿产资源评价等类似工作提供了原创性可视化技术支撑。     

The reduction of aliasing in gravity anomalies and geoid heights using digital terrain data

Observations of gravity can be aliased by virtue of the logistics involved in collecting these data in the field. For instance, gravity measurements are often made in more accessible lowland areas where there are roads and tracks, thus omitting areas of higher relief in between. The gravimetric determination of the geoid requires mean terrain-corrected free-air anomalies; however, anomalies based only on the observations in lowland regions are not necessarily representative of the true mean value over the topography. A five-stage approach is taken that uses a digital elevation model, which provides a more accurate representation of the topography than the gravity observation elevations, to reduce the unrepresentative sampling in the gravity observations. When using this approach with the Australian digital elevation model, the terrain-corrected free-air anomalies generated from the Australian gravity data base change by between 77.075 and −84.335 mgal (−0.193 mgal mean and 2.687 mgal standard deviation). Subsequent gravimetric geoid computations are used to illustrate the effect of aliasing in the Australian gravity data upon the geoid. The difference between 'aliased' and 'non-aliased' gravimetric geoid solutions varies by between 0.732 and −1.816 m (−0.058 m mean and 0.122 m standard deviation). Based on these conceptual arguments and numerical results, it is recommended that supplementary digital elevation information be included during the estimation of mean gravity anomalies prior to the computation of a gravimetric geoid model.