基于无人机载LiDAR的采煤沉陷监测技术方法——以宁东煤矿基地马连台煤矿为例

探索采煤地表沉陷的高新监测技术方法是推动采煤沉陷监测的重要工作,无人机载LiDAR采煤塌陷监测技术是无人机与LiDAR构建的一种新型低空三维空间测量技术。以宁东煤炭基地马莲台煤矿采煤沉陷区为例,采用无人机机载LiDAR监测技术获取了2017年4月及8月2期三维点云数据,通过数据三维建模和沉降信息提取,得到了地面沉陷情况的三维立体图,监测出了3处地面沉降区,并利用实测水准点和已有GPS自动监测站数据,对该技术监测地面沉降的精度进行评估。研究结果表明,无人机机载LiDAR监测技术方法可满足采煤塌陷的立体监测需求,具有机动灵活、成本低、效率高、精度高等特点,未来可在类似地区推广应用。     

Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

INTRODUCTIONWith the rapid development of industrialization,urbanization,andinformation technology ,as well asthe practice of “digital city”and “digital earth”( Gore , 1998) , many administrators and decision-makers are realizing the i mportance of high-resolution i mage information to urban planning andmanagement . Especially in recent years , with thefast development of remote sensing technology ,remote sensing data can be obtained that are complementaryin spatial andti me resolution…     

Extracting damages caused by the 2008 Ms 8.0 Wenchuan earthquake from SAR remote sensing data

Bad weather conditions usually limit the acquisition of optical remote sensing images, while all day and all weather synthetic aperture radar (SAR) shows the ability of providing timely remote sensing data for emergency response and rescue works after earthquake. Because SAR is sensitive to the surface changes caused by earthquake, the modified electromagnetic behaviour by geological disasters and the collapse of buildings can be recorded in SAR images as backscattering intensity changes. Absolute radiometric calibration was performed to SAR products to derive backscattering coefficient sigma nought from image digital number (DN). Based on change detection methods, Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data and TerraSAR-X data acquired for the Ms 8.0 Wenchuan earthquake were used to extract earthquake damage information. This study revealed that landslides showed stronger backscattering and barrier lakes showed lower backscattering in post-earthquake 10 m ALOS PALSAR images comparing to pre-earthquake, and collapsed buildings showed lower backscattering compared to un-collapsed buildings in 1 m TerraSAR-X image. Results showed that SAR data with different spatial resolutions are useful for different earthquake damage information extraction: medium spatial resolution SAR data, e.g. 10 m ALOS PALSAR data, were efficient for secondary geological disaster extraction; high-resolution SAR data, e.g. 1 m TerraSAR-X data, with the help of ancillary GIS data or high-resolution optical data, could be used to extract building collapse information in urban areas. This study indicates that SAR remote sensing data can provide earthquake damage information at early emergency stage and assist the field surveying, further damage assessment and post-earthquake reconstruction.     

基于GIS的矿产预测地质解译空间信息集成

利用我国海量地质标准基础数据库中的数字地质图和矿产图,通过基于GIS的地质解译空间集成地质信息,将其用于综合信息矿产预测。以地质解译系统对内蒙大兴安岭南段1∶20万成矿预测的应用为案例,阐述地质信息的空间提取与集成过程：首先在建立地质字典库实现地质空间信息共享的基础上,通过矿化密集区对地质模型的分类图层进行空间分析,建立地质成矿空间信息库和图库;然后,基于典型矿床圈定模型单元,通过模型单元与地质成矿空间信息库和图库的空间分析,建立地质找矿模型;最后,基于地质单元对地质成矿空间信息库和图库的二次空间集成,完成预测模型的地质空间信息提取与集成。将本方法应用在银矿案例的综合信息矿产预测靶区评价上,得到可供进一步查证的新增靶区比已知靶区增加了近5倍。     

“Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth: a case study in Tangshan,China

This paper presents the development of an “Internet+” approach to mapping exposure and seismic vulnerability of buildings in a context of rapid socioeconomic growth. This approach is a combination of the following interdependent components: (1) extraction of footprint areas of a large number of buildings from high-resolution Google Earth images; (2) estimation of floor numbers of these buildings with an integrated use of high-resolution Google Earth images, Tencent/Baidu Street Views, crowdsourcing data, and associated building-relevant local knowledge; and (3) identification of structural types of these buildings by a combined use of crowdsourcing data and associated building-relevant local knowledge. The efficacy of this “Internet+” approach was demonstrated through an application in Tangshan, China. Field-based verification indicated that the overall mean absolute percentage error of the proposed “Internet+” approach in assessing the total floor area of the addressed buildings was 4.64 %. The verification also showed that the overall consistency between the estimated structural types using the proposed approach and the actual structural types of the buildings with structural type uncertainties could reach 97.54 %, with a kappa coefficient of 0.94. Because of its good accuracy, noteworthy speed, substantial labor savings, negligible cost and distinctive capability in covering large areas in near real time, this “Internet+” approach might have promising prospects in actual seismic loss risk reduction challenges.

     

基于多源遥感影像的长春市城市建成区提取

夜光遥感影像记录的城市灯光与人类活动密切相关,已广泛应用于城市信息提取。珞珈一号作为新一代夜光遥感数据源,比以往的夜光数据具有更高的空间分辨率和光谱分辨率,可以更清晰地表达城市建成区范围和内部结构。本文利用珞珈一号夜光遥感影像,通过人类居住指数（human settlement index, HSI）、植被覆盖和建筑共同校正的城市夜光指数（vegetation and build adjusted nighttime light urban index, VBANUI）及支持向量机（support vector machine, SVM）监督分类3种方法对长春市城市建成区进行提取,并与利用NPP/VIIRS（suomi national polar-orbiting partnership/visible infrared imaging radiometer suite）夜光遥感影像、采用同样方法得到的结果对比。结果显示：本文提出的VBANUI提高了传统植被覆盖校正的城市夜光指数（vegetation adjusted nighttime light urban index, VANUI）的提取精度,使用珞珈一号夜光遥感影像通过VBANUI提取的城市建成区结果最优,其Kappa系数为0.80,总体分类精度为90.74%;使用珞珈一号和NPP/VIIRS夜光遥感影像通过HSI按最佳阈值提取城市建成区的Kappa系数分别为0.75和0.72,总体分类精度分别为88.27%和86.54%;复合数据的SVM监督分类法中Landsat-NDBI、Landsat-NDBI-VIIRS、Landsat-NDBI-LJ和Landsat-NDBI-LJlog的Kappa系数分别为0.602、0.627、0.643和0.681,总体分类精度分别为81.11%、81.52%、82.25%和84.48%。研究结果表明：3种提取方法下,均为使用珞珈一号夜光遥感影像的结果优于使用NPP/VIIRS夜光遥感影像的结果,证明相比于NPP/VIIRS夜光遥感影像,珞珈一号夜光遥感影像更适用于城市尺度的建成区范围提取。     

Service-oriented approach for geospatial feature discovery

Rapid increases in remote sensing capability have made remotely sensed images an importance source for intelligence analysts to discover geospatial features. The overwhelming volume of routine image acquisition has greatly outpaced the increase in the capacity of manual image interpretation by intelligence analysts, and prompted automated methods for geospatial feature extraction from high spatial resolution images. Nevertheless, existing methods focus on automatic extraction of isolated or elementary features, such as buildings and roads. A compound geospatial feature, such as a Weapon of Mass Destruction (WMD) proliferation facility, is spatially composed of elementary features (e.g., containment buildings, cooling ponds, and fences). The spatial relations among elementary features can assist the detection of compound features from images. This paper proposes a service-oriented approach for discovering compound geospatial features. The approach includes both a chaining strategy and an architecture. The chaining strategy is to discover sites of facilities by orchestrating services that compute spatial relations among elementary features. The architecture is a service-oriented framework to support the chaining for feature discovery. The approach not only takes advantages of spatial characteristics of complex features, but also enjoys the openness and flexibility of the Service-Oriented Architecture (SOA). A prototypical implementation is provided to illustrate the applicability of the approach.     

Recognition of large scale deep-seated landslides in forest areas of Taiwan using high resolution topography

Large deep-seated landslides can be reactivated during intense events, and they can evolve into destructive failures. They are generally difficult to recognize in the field, especially when they develop in densely forested areas. A detailed and constantly updated inventory map of such phenomena, and the recognition of their topographic signatures is absolutely a key tool for landslide risk mitigation.The aim of this work is to test in forested areas, the performance of the new automatic and objective methodology developed by Tarolli et al. (2012) for geomorphic features extraction (landslide crowns) from high resolution topography (LiDAR derived Digital Terrain Models – DTMs). The methodology is based on the detection of landslides through the use of thresholds obtained by the statistical analysis of variability of landform curvature. The study was conducted in a high-risk area located in the central-south Taiwan, where an accurate field survey on landsliding processes and a high-quality set of airborne laser scanner elevation data are available. The area has been chosen because some of the deep-seated landslides are located near human infrastructures and their reactivation is highly dangerous. Thanks to LiDAR’s capability to detect the bare ground elevation data in forested areas, it was possible to recognize in detail landslide features also in remote regions difficult to access. The results, if compared with the previous work of Tarolli et al. (2012), mainly focused on shallow landslides, and in a not forested area, indicate that for deep-seated landslides, where the crowns are more evident, and they are present at large scale, the tested methodology performs better (higher quality index). The method can be used to interactively assist the interpreter/user on the task of deep-seated landslide hazard mapping, and risk assessment planning of such regions.     

Building Damage Extraction from Post-earthquake Airborne LiDAR Data

Building collapse is a significant cause of earthquake-related casualties; therefore, the rapid assessment of buildings damage is important for emergency management and rescue. Airborne light detection and ranging(Li DAR) can acquire point cloud data in combination with height values, which in turn provides detailed information on building damage. However, the most previous approaches have used optical images and LiDAR data, or pre- and post-earthquake LiDAR data, to derive building damage information. This study applied surface normal algorithms to extract the degree of building damage. In this method, the angle between the surface normal and zenith(θ) is used to identify damaged parts of a building, while the ratio of the standard deviation to the mean absolute deviation(σ/δ) of θ is used to obtain the degree of building damage. Quantitative analysis of 85 individual buildings with different roof types(i.e., flat top or pitched roofs) was conducted, and the results confirm that post-earthquake single LiDAR data are not affected by roof shape. Furthermore, the results confirm that θ is correlated to building damage, and that σ/δ represents an effective index to identify the degree of building damage.     

地理信息系统与主成分分析在多年气象观测数据处理中的应用

探讨一种处理多年气象观测数据的新方法,该方法以地理信息系统为技术支撑,以数字图像处理和标准主成分分析为核心,适用于处理空间分布广、时间序列长的多类型气象观测数据。在实例研究中,首先对中国160个站10年气温、降水观测数据进行空间双线性插值,得到一系列数值图像;并对这些数值图像进行标准主成分分析,得到反映多年气候状况且相互独立的各主成分;然后选取其中前6个主成分进行空间迭代聚类分析,得到中国气候分类图;通过对所得类别进行多年气候指标统计分析,得到中国气候区划图;最后将所得气候区划图与传统气候区划图进行比较分析,并指出该方法在处理多年气象观测数据工作中的可行性与局限性。     

Lidar点云数据中建筑物的快速提取

Lidar技术可快速获取地表的高精度三维点云数据, 目前对此类数据的分类却是速度慢、精度低, 尤其是城市区域建筑物和树木靠得较近时更是难以准确提取建筑物.介绍了一种基于点云数据生成距离影像, 而后引入对比度纹理辅助的点云数据建筑物快速提取方法.结果证明, 该方法不需要其他辅助数据就能实现点云数据中建筑物的快速提取.      

Organizing earth observation data inside a spatial data infrastructure

Scientists as well public institutions dealing with geospatial data often work with a large amount of heterogeneous data deriving from different sources. Without a well-defined, organized structure they face problems in finding and reusing existing data, and as consequence this may cause data inconsistency and storage problems. A catalog system based on the metadata of spatial data facilitates the management of large amount of data and offers service to retrieve, discover and exchange geographic data in an quick and easy fashion. Currently, most online catalogs are more focusing on the geographic data and there has been only few interests in catalogizing Earth observation data, in which in addition the acquisition information matters. This article presents an automatic metadata extraction approach that creates from different optical data deriving from various satellite missions of scientific interest (i.e. MODIS, LANDSAT, RapidEye, Suomi-NPP VIIRS, Sentinel-1A, Sentinel-2A) metadata information, based on an extended model of the standard ISO 19115. The XML schema ISO 19139-2 with the support of gridded and imagery information defined in ISO 19115-2 was examined, and based on the requirements of experts working in the research field of Earth observation the schema was extended. The XML schema ISO 19139-2 and its extension has been deployed as a new schema plugin in the spatial catalog Geonetwork Open Source in order to store all relevant metadata information about satellite data and the appropriate acquisition and processing information in an online catalog. A real-world scenario that is productively used in the EURAC research group institute for Applied Remote Sensing illustrates a workflow management for Earth observation data including data processing, metadata extraction, generation and distribution.     

Quantifying and relating land-surface and subsurface variability in permafrost environments using LiDAR and surface geophysical datasets

The value of remote sensing and surface geophysical data for characterizing the spatial variability and relationships between land-surface and subsurface properties was explored in an Alaska (USA) coastal plain ecosystem. At this site, a nested suite of measurements was collected within a region where the land surface was dominated by polygons, including: LiDAR data; ground-penetrating radar, electromagnetic, and electrical-resistance tomography data; active-layer depth, soil temperature, soil-moisture content, soil texture, soil carbon and nitrogen content; and pore-fluid cations. LiDAR data were used to extract geomorphic metrics, which potentially indicate drainage potential. Geophysical data were used to characterize active-layer depth, soil-moisture content, and permafrost variability. Cluster analysis of the LiDAR and geophysical attributes revealed the presence of three spatial zones, which had unique distributions of geomorphic, hydrological, thermal, and geochemical properties. The correspondence between the LiDAR-based geomorphic zonation and the geophysics-based active-layer and permafrost zonation highlights the significant linkage between these ecosystem compartments. This study suggests the potential of combining LiDAR and surface geophysical measurements for providing high-resolution information about land-surface and subsurface properties as well as their spatial variations and linkages, all of which are important for quantifying terrestrial-ecosystem evolution and feedbacks to climate.     

LiDAR技术在地质环境中的主要应用与展望

介绍了LiDAR激光探测与测距系统的组成和基本原理, 并对LiDAR技术在地质灾害、活动断裂、冰川及海岸线测绘等地质环境领域的国内外应用现状与进展进行了系统分析和总结, 对该技术的应用前景进行了展望。LiDAR系统集激光、GPS和惯性导航系统(Inertial Navigation System, INS)三种技术于一身, 能够快速、精确地获取地面目标的三维空间信息, 是继GPS空间定位系统之后又一项测绘技术新突破。LiDAR作为一种新型的遥感测量技术未来在自动、快速提取滑坡体、自动提取与断裂相关的微地貌结构信息、海岸带附近精细地物分类、海岸带调查以及潮间带生物多样性研究等方面具有很大的发展空间。      

面向基元的高空间分辨率矿区遥感影像土地利用分类

为了合理开发矿产资源和有效监测矿区生态环境,采用面向基元的分类方法,对广西横县某矿区的高分辨率航空遥感影像进行了土地利用分类.通过优化分形网络演化多尺度分割方法,高效提取了矿区两个尺度上的影像基元层;基于基元信息,详细分析了各地表地物光谱特征、空间特征以及类相关特征,建立了研究区土地利用的分类知识库;采用决策支持的模糊逻辑推理法进行分类,使分类的精度从53%提高到了90%.表明面向基元的方法能较好地利于高空间分辨率矿区影像的各种特征进行高精度的土地利用分类.      

Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China

The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.     

同震崩塌滑坡的光学遥感影像多特征融合解译方法

同震崩塌滑坡的解译及定位是震区灾后恢复工作中需要重点解决的问题,如何在灾害快速、自动解译的基础上,不断提高解译精度,是目前同震崩塌滑坡解译的研究热点之一,也是促使地质灾害早期识别向智能化、科学化发展的必要前提。文章在团队前期所提出的遥感影像局部阈值二值化方法的基础上,针对同震崩塌滑坡解译结果假阳率偏高的问题,分析了假阳性地物的光学和几何特点,提出了融合目标区域光学影像灰度特征、区域坡度信息、NDVI指数特征及解译地物主轴特征的同震崩塌滑坡多特征融合解译方法。为验证所提出模型的准确性,以2014年云南鲁甸地震龙头山镇为研究区,利用震后获取的高分一号（GF-1）卫星影像数据及数字高程模型对该同震崩塌滑坡进行了解译识别,结果表明,文中提出的方法准确解译出了同震崩塌滑坡区域,并有效去除了假阳性地物干扰,提高了解译精度。     

Landslide vulnerability and risk assessment for multi-hazard scenarios using airborne laser scanning data (LiDAR)

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.     

一种基于光谱角和光谱距离自动加权融合分类方法

高分辨率遥感影像分类一直是业内研究的热点之一,考虑到影像地物光谱角和光谱距离在分类中具有较好的互补性,提出了一种基于光谱角和光谱距离自动加权融合的分类方法,对传统多分类器分类的融合策略进行改进,能够在训练阶段根据样本自动地调整好各分类器对各类别进行分类的权重系数,使得融合后的分类结果更加科学和准确。QuickBird影像的分类实验表明,方法的分类精度明显优于单纯的光谱角或距离法,可广泛用于各种高分辨率影像的分类识别。     

Solution of Multiple——Point Statistics to Extracting Information from Remotely Sensed Imagery

Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information．Therefore,there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery．In the current article,a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification．The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese YeHow River delta on August 8,1999． The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier (MLC)