基于地形区域分割的复杂地区遥感影像分类

提出了一种基于地形区域分割的分类方法,在影像中利用地形特征数据预先划分出每种地物的分布区域,然后以区域为基本单位对影像进行分类,同时利用DEM数据对影像进行地形校正,减小了同种地物内部由于地形起伏造成的光谱离散的现象。利用湖北西部山区的TM影像和DEM数据的试验证明,利用地形特征数据进行分割的分类方法与仅考虑光谱特征的分类方法相比较,分类精度有了明显的提高。     

我国区域地貌数字地形分析研究进展

区域地貌研究是区域地理研究不可分割的重要组成部分。传统的基于DEM的数字地形分析方法,虽能较好提取各种地形定量因子,但由于分析算法的局限,很难实现对一个特定区域地貌的宏观形态特征与成因机理进行定量的分析。为此,近年来我国学者在该领域进行了系统的探索与创新实践,基于国家基础地形数据库多尺度、高精度的DEM数据,开展了基于DEM的区域地貌形态特征、地貌发育演化特征的研究。通过宏观形态指标分析法、地形特征要素分析法、地形信息图谱分析法等一系列方法,实现了对区域的地貌形态特征提取与分类、分区制图,取得了一批有重要国际影响的研究成果。在全国尺度以及黄土高原、青藏高原、西南喀斯特地区和月表月貌的区域数字地形分析方面,更彰显出研究的特色和优势。     

基于植被分区的多特征遥感智能分类

为了有效地提取大范围地形复杂区域的土地利用/土地覆盖遥感信息,以位居青藏高原与黄土高原过渡地带的青海东部地区为研究区,研究基于蚁群智能优化算法(ant colony intelligent optimization algorithm,ACIOA)的土地利用/土地覆盖遥感智能分类。首先选用TM图像、DEM、坡度和坡向数据作为分类的特征波段;然后利用归一化植被指数NDVI对实验区数据进行植被分区;最后利用ACIOA算法进行分类规则挖掘,并依据分类规则进行土地利用/覆盖信息的提取。研究表明,基于植被分区的多特征蚁群智能分类的总体精度为88.85%,Kappa=0.86,优于传统的遥感图像分类方法,为大范围地形复杂区域的土地利用/土地覆盖遥感信息提取提供了有效的方法。     

基于遥感技术的干旱区土壤分类研究

以新疆艾比湖地区为研究区域,以ASTER和SPOT卫星数据为基础,探讨了干旱环境下基于土壤与景观关系的土壤遥感自动分类方法.首先,研究以实地调查资料和第二次全国土壤普查数据库为基础,结合遥感图像信息分析了试验区土壤类型与景观的关系.然后,基于遥感图像和地形数据提取了分类特征,并采用Jeffries-Matusita 距离分析建立了适用遥感分类的土壤分类系统和分类特征集.最后,采用最大似然法进行了自动分类.研究证明,基于遥感信息和地形数据提取的分类特征,可有效地区分试验区9类土壤和地表覆被,主要包括:盐碱化土壤、荒漠化土壤等,总体分类精度达到了90%左右.     

青海湖流域土壤遥感分类

选择青海湖流域内一个代表性区域为试验区,以TM数据和地形数据为主要数据源,在GeoEye-1高分辨率影像和土壤图的辅助下,采用最大似然监督分类方法,探讨了遥感技术在青海湖流域土壤分类中的可行性。使用主成分分析、缨帽变换、波段组合等图像处理技术,从TM图像中提取了多种图像特征,并结合高程、坡度及坡向等地形参数,共同生成分类特征数据集进行遥感分类。研究表明,基于遥感图像和地形数据提取的分类特征,有效地区分出试验区内9个土壤亚类和1个非土壤单元,总体分类精度达到了91.76%。     

面向西北内陆复杂地形地貌区域的土地利用分类方法

中国西北地区自然环境恶劣、地形地貌复杂、植被覆盖度低,大量土地裸露,使用传统分类方法很难准确提取土地利用信息。以兰州市为研究区,基于Landsat 8 OLI遥感影像和数字高程模型（digital elevation model,DEM）数据提取指数特征、纹理特征和地形特征作为分类特征变量。首先,基于随机森林分类（random forests classification,RFC）算法对3种特征变量的分类有效性进行检验;在此基础上,构建4种特征组合实验方案,筛选出最优土地利用分类特征组合。结果表明,单一的指数特征、纹理特征和地形特征均可显著提高一种或多种土地利用类型的分类精度;最佳分类特征组合的分类精度达到90.82%,Kappa系数为0.897。     

基于信息熵和DEM地形信息量的景观预测

探讨基于DEM地形信息量的景观预测方法,为相似地区景观预测提供参考。本文基于信息熵理论和DEM地形信息量,研究不同地形信息量等级下的景观指数和景观格局的变化特征,构建标准化处理后的地形信息量和景观指数的回归模型,对具有相似地形的区域进行预测。结果表明:针对广西北部湾区域特征相似的两个流域,其景观分布规律一致,景观指数误差基本保持在0.2以内。基于DEM地形信息量的景观预测方法可有效预测相似区域的景观分布特征。     

基于DEM地形信息量的景观格局预测——以珠海市为例

针对传统景观格局分析方法的数据局限性,该文提出了一种基于数字高程模型地形信息量的景观格局预测分析方法。结合信息熵理论,利用数字高程模型计算地形信息量,研究不同分级地形信息量下景观格局特征的变化特征,以及不同景观类型在不同地形信息量等级的分布特征,对具有相似地形特征的区域建立利用地形信息量对其景观格局特征进行预测分析的方法。该文选择珠海市地形特征相似的两个区域进行了试验分析,对珠海市斗门区建立基于数字高程模型地形信息量的景观格局预测方法,预测在相似地形特征下南坪镇的景观格局特征,并以南坪镇真实的景观特征进行验证。实验结果显示,基于数字高程模型地形信息量的邻域景观预测分析方法不仅能正确预测分析不同地形信息量下的景观分析指数,同时也能正确预测不同景观类型的空间分布特征。     

高分辨率遥感影像在大比例尺土地利用图件更新中的应用探讨

通过对 IKONOS 米级高分辨率遥感影像在大比例尺土地利用图件更新中的应用技术研究,提出采用基于知识的土地利用覆盖分类以及变化监测系统方法。首先利用 NDVI 植被指数和半方差纹理特征的知识进行影像大类区域分割;其次结合光谱知识对各影像区域进行详细分类,同时利用区域生长技术与地类空间知识进行区域分类;最后是分类后处理与变化信息提取,以便利用基础图件提供的知识与各区域分类进行比较来发现变化的区域。基于知识的分类与变化信息自动提取可以为在 GIS/RS 环境下的目视数字化提供目标,缩短土地利用基础图件的更新作业过程。     

一种基于分割的机载LiDAR点云数据滤波

针对当前滤波算法在处理地形不连续区域或存在复杂建筑物区域时容易过分“腐蚀”地形并难以去除一些低矮植被的不足,提出了一种基于分割的机载LiDAR点云滤波算法。首先,对原始点云基于地表连续性进行分割;然后,在移除点数目较小的粗差点集之后采用对分割点集建立缓冲区的方法,区分地面和非地面点集;在较大地物经过迭代分割基本移除之后,使用约束平面的方法移除高度较小的地表附着物以实现滤波。实验结果表明,与经典滤波算法相比,该算法提高了地面点的分类精度,在滤除地物信息的同时能有效地保留地形特征。     

Spatial Prediction of Landslide Hazard Using Logistic Regression and ROC Analysis

An empirical modeling of road related and non‐road related landslide hazard for a large geographical area using logistic regression in tandem with signal detection theory is presented. This modeling was developed using geographic information system (GIS) and remote sensing data, and was implemented on the Clearwater National Forest in central Idaho. The approach is based on explicit and quantitative environmental correlations between observed landslide occurrences, climate, parent material, and environmental attributes while the receiver operating characteristic (ROC) curves are used as a measure of performance of a predictive rule. The modeling results suggest that development of two independent models for road related and non‐road related landslide hazard was necessary because spatial prediction and predictor variables were different for these models. The probabilistic models of landslide potential may be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

轨迹延续性与影像特征相似性结合的城市道路提取

城市道路区域检测是城市土地管理、交通规划等领域的迫切需求,而传统城市道路区域检测多使用轨迹提取、遥感解译、人工采集等单独方式,在自动化程度或提取质量上存在一定的局限性。本文结合GNSS轨迹点与高分遥感影像各自的数据优势,提出一种基于轨迹延续性与影像特征相似性的遥感影像道路区域检测方法。该方法以出租车GNSS轨迹点构建轨迹特征栅格,基于轨迹延续性在平均方向特征栅格中划分路段对象,利用道路对象的光谱特征向轨迹无法覆盖的小区内部进行拓展,以获得提取区域内较为完整的道路信息。试验证明：本文方法可以有效降低道路的同物异谱现象及阴影、树木遮挡的影响,高效地提取高分遥感影像中的道路区域。与传统的遥感影像分类方法相比,具有更高的精度与自动化程度,相较于深度学习模型具有更广的适应性。     

Integrating a fuzzy <Emphasis Type="Italic">k</Emphasis>-means classification and a Bayesian approach for spatial prediction of landslide hazard

A robust method for spatial prediction of landslide hazard in roaded and roadless areas of forest is described. The method is based on assigning digital terrain attributes into continuous landform classes. The continuous landform classification is achieved by applying a fuzzy k-means approach to a watershed scale area before the classification is extrapolated to a broader region. The extrapolated fuzzy landform classes and datasets of road-related and non road-related landslides are then combined in a geographic information system (GIS) for the exploration of predictive correlations and model development. In particular, a Bayesian probabilistic modeling approach is illustrated using a case study of the Clearwater National Forest (CNF) in central Idaho, which experienced significant and widespread landslide events in recent years. The computed landslide hazard potential is presented on probabilistic maps for roaded and roadless areas. The maps can be used as a decision support tool in forest planning involving the maintenance, obliteration or development of new forest roads in steep mountainous terrain.     

基于InSAR技术和光学遥感的贵州省滑坡早期识别与监测

贵州省因其复杂的地形地貌和强降水等气候特征,滑坡灾害频繁发生.亟需一种可靠的滑坡早期识别和监测方法.传统的滑坡识别和监测方法存在局限性,而InSAR技术在大规模地质灾害监测中具有独特的优势.但是,基于单一地表形变值的滑坡识别结果存在一定的不确定性.因此,本文联合InSAR技术和光学遥感,利用Sentinel-1A雷达卫...     

基于Google Earth影像的黄河上游干流地区滑坡特征分析

黄河上游干流地区由于特殊的地形地貌和地质构造使得滑坡灾害频发,对其开展滑坡灾害监测、分析研究,具有十分重要的意义。本文利用2015年间Google Earth遥感数据,提取并分析了该地区的滑坡灾害分布信息,取得了如下成果及认识:1)研究区的空间展布形态主要有7种,滑体性质类型有6种,岩质滑坡数量最多。2)从空间分布特征看,共发现研究区有各类滑坡162处,滑坡主要集中分布在群科-尖扎盆地;从滑坡类型看,研究区滑坡主要为大型滑坡和巨型滑坡。3)滑坡体长、宽主要集中在0~1 500 m和500~1 500 m之间,且长、宽呈两极化方向延伸,滑坡体面积分布不均,滑坡数量随着方量的增大呈现减少的趋势,发生的滑坡主要是滑坡体厚度在25~50 m的深层滑坡。4)滑坡数量在0°~90°之间有峰值出现,然后向两端逐渐减少。     

Spatial and temporal change in landslide hazard by future climate change scenarios using probabilistic-based frequency ratio model

The aims of this study were to apply, verify and compare a frequency ratio model for landslide hazards, considering future climate change and using a geographic information system in Inje, Korea. Data for the future climate change scenario (A1B), topography, soil, forest, land cover and geology were collected, processed and compiled in a spatial database. The probability of landslides in the study area in target years in the future was then calculated assuming that landslides are triggered by a daily rainfall threshold. Landslide hazard maps were developed for the two study areas, and the frequency ratio for one area was applied to the other area as a cross-check of methodological validity. Verification results for the target years in the future were 82.32–84.69%. The study results, showing landslide hazards in future years, can be used to help develop landslide management plans.     

大尺度平原城市精细汇水区分级划分研究

在城市内涝模拟研究中,汇水区划分是十分重要的环节,同时对大尺度平原城市进行汇水区精细划分也是研究者共同研究的技术难点。针对现有基于DEM流向分析的划分方法存在的无法正确反映实际城市复杂地形和流向问题,本文提出了一种顾及地类和流向,适用于大尺度平原城市的精细汇水区分级划分方法。首先,从城市自然地形和主干河流出发,进行一级宏观尺度划分;然后,依据城市主次干道,干渠和管网实现二级中观尺度划分;最后,在二级划分的基础上,结合流向和地类做精细的三级子汇水区微观尺度划分。本文选取东营市30 km²核心主城区进行了验证分析,研究结果表明,划分结果跟实际地物类型和真实流向相吻合,该方法对于大尺度平原城市具有良好的适用性。     

Geo-environment-aware adversarial transfer learning method for landslide susceptibility evaluation of complex mountainous areas

Landslide susceptibility evaluation (LSE) is a critical issue for disaster prevention. Limited by labor cost and observation technology, landslide samples are extremely limited in dense vegetation-covered and remote areas, making the common supervised learning model underfit with limited samples. Therefore, the reliability of analysis results in mountainous areas is low. Transfer learning can achieve reliable assessment without the need for representative samples. However, transfer learning suffers from environmental heterogeneity in regional LSE and may transfer incorrect classification knowledge of landslide features from dissimilar environments. Aiming at these challenges, we proposed a geo-environment-aware LSE method based on unsupervised adversarial transfer learning. The key is to consider the difference in landslide features in different geo-environments. The study areas were first divided into multiple sub-environments, and the similarity between the sub-environments was calculated. Then an environment-aware adversarial transfer model was built for fine-grained aligning of the landslide feature with similar sub-environments and for reducing negative transfer between dissimilar environments. The fitted classification model was employed to predict the target regions and to generate the final LSE. The experimental results indicated that the proposed method achieves reliable LSE for sample-free regions. The accuracy of the proposed method is 7–12% better than commonly used methods such as support vector machines, random forests, and artificial neural networks. The performance of the proposed method is even close to the results of supervised learning with the presence of representative samples, and it also performs more globally and objectively in susceptibility mapping. These results reveal that the proposed method effectively transfers the knowledge of landslide susceptibility from other regions to the sample-free region.     

Landslide inventory validation and susceptibility mapping in the Gerecse Hills,Hungary

Landslides pose a threat to property both in the populated and cultivated areas of the Gerecse Hills (Hungary). The currently available landslide inventory database holds the records from many sites in the area, but the database is out-of-date. Here we address the problem of revising the National Landslides Cadastre landslide inventory database by creating a landslide suscept- ibility map with a multivariate model based on likelihood ratio functions. The model is applied to the TanDEM-X DEM (0.4″ res.), the current landslide inventory of the area, and data acquired from geological maps. By comparing the distributions of four variables in the landslide and non-landslide area with grid computation methods, the model yields landslide susceptibility estimates for the study area. The estimations show to what extent a certain area is similar to the sample areas, therefore, its likelihood to be affected by landslides in the future. The accuracy of the model predictions was checked in the field and compared to the results of our previous study using the SRTM-1 DEM for a similar analysis. The model gave accurate estimates when certain correction measures were applied to the input datasets. The limitations of the model, the input datasets, and the suggested correction measures are also discussed.     

尼泊尔Ms 8.1级地震活动构造及次生地质灾害研究

2015年尼泊尔Mw 7.8大地震诱发了大量的山体滑坡,对尼泊尔境内与周边地区造成了严重的影响。选取离震中较近的辛杜帕尔乔克地区作为研究区,基于L波段ALOS-2和C波段Sentinel-1A两种合成孔径雷达数据,采用堆叠合成孔径雷达干涉测量（interferometric synthetic aperture radar,InSAR）技术开展震后滑坡的探测与识别,结合光学影像圈定出滑坡隐患点14处。在此基础上,联合升降轨数据和多维小基线集（multidimensional small baseline subset,MSBAS)-InSAR技术获取了典型滑坡的二维时间序列形变特征,结果表明,该典型滑坡的主要形变发生在水平东西向,最大形变速率为-69 mm/a。同时,通过对该典型滑坡时间序列中的趋势项与周期项形变信号进行分析,发现地震对于滑坡运动具有明显的加速作用,且降雨量的增加使得滑坡位移在每年的8月-11月呈现出周期性变化,可为震后滑坡监测研究提供参考。