Automatic urban road extraction using airborne laser scanning/altimetry and high resolution satellite data

Automatic road extraction from remotely sensed images has been an active research in urban area during last few decades. But such study becomes difficult in urban environment due to mix of natural and man-made features. This research explores methodology for semiautomatic extraction of urban roads. An integrated approach of airborne laser scanning (ALS) altimetry and high-resolution data has been used to extract road and differentiate them from flyovers. Object oriented fuzzy rule based approach classifies roads from high resolution satellite images. Complete road network is extracted with the combination of ALS and high-resolution data. The results show that an integration of LiDAR data and IKONOS data gives better accuracy for automatic road extraction. The method was applied on urban area of Amsterdam, The Netherlands.     

Assessment of long-range kinematic GPS positioning errors by comparison with airborne laser altimetry and satellite altimetry

Long-range airborne laser altimetry and laser scanning (LIDAR) or airborne gravity surveys in, for example, polar or oceanic areas require airborne kinematic GPS baselines of many hundreds of kilometers in length. In such instances, with the complications of ionospheric biases, it can be a real challenge for traditional differential kinematic GPS software to obtain reasonable solutions. In this paper, we will describe attempts to validate an implementation of the precise point positioning (PPP) technique on an aircraft without the use of a local GPS reference station. We will compare PPP solutions with other conventional GPS solutions, as well as with independent data by comparison of airborne laser data with “ground truth” heights. The comparisons involve two flights: A July 5, 2003, airborne laser flight line across the North Atlantic from Iceland to Scotland, and a May 24, 2004, flight in an area of the Arctic Ocean north of Greenland, near-coincident in time and space with the ICESat satellite laser altimeter. Both of these flights were more than 800 km long. Comparisons between different GPS methods and four different software packages do not suggest a clear preference for any one, with the heights generally showing decimeter-level agreement. For the comparison with the independent ICESat- and LIDAR-derived “ground truth” of ocean or sea-ice heights, the statistics of comparison show a typical fit of around 10 cm RMS in the North Atlantic, and 30 cm in the sea-ice region north of Greenland. Part of the latter 30 cm error is likely due to errors in the airborne LIDAR measurement and calibration, as well as errors in the “ground truth” ocean surfaces due to drifting sea-ice. Nevertheless, the potential of the PPP method for generating 10 cm level kinematic height positioning over long baselines is illustrated.     

Modeling Mediterranean forest structure using airborne laser scanning data

The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters.Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using the echo-based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable results. Accuracies were greatest for dominant height (Hd) (R² = 0.91; RMSE% = 8.2%) and mean height weighted by basal area (R² = 0.83; RMSE% = 10.5%) when using the echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standard error of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m.     

Detection of gullies in roughly textured terrain using airborne laser scanning data

Airborne laser scanning data contain information about surface features, some of which are of subtle form. These features are usually embedded within the terrain, and rarely form distinct shape-transition to their surroundings. While some efforts have been made in extracting linear elements from laser scanning data, attention was mostly turned to dominant elements that are very clear and distinct. We present in this paper a detection model for gullies of various dimensions using airborne laser scanning data. Gullies are regarded as one of the main landform-reshaping agents, having a pejorative effect on the environment and on regional development. They are commonly observed along receding lakes as a common response to water-level drop. The paper demonstrates how a multi-scale approach enables the extraction of various gully forms, from well developed to subtle. It then proposes an optimization driven model for handling fragmentation in the detection. Results show that using the proposed model, gully networks can be reconstructed and ∼30 cm deep features can be identified and separated from their surroundings using moderate point density data.     

Characterization of ASTER GDEM elevation data over vegetated area compared with lidar data

Current researches based on areal or spaceborne stereo images with very high resolutions (<1 m) have demonstrated that it is possible to derive vegetation height from stereo images. The second version of the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is the state-of-the-art global elevation data-set developed by stereo images. However, the resolution of ASTER stereo images (15 m) is much coarser than areal stereo images, and the ASTER GDEM is compiled products from stereo images acquired over 10 years. The forest disturbances as well as forest growth are inevitable in 10 years time span. In this study, the features of ASTER GDEM over vegetated areas under both flat and mountainous conditions were investigated by comparisons with lidar data. The factors possibly affecting the extraction of vegetation canopy height considered include (1) co-registration of DEMs; (2) spatial resolution of digital elevation models (DEMs); (3) spatial vegetation structure; and (4) terrain slope. The results show that the accurate coregistration between ASTER GDEM and national elevation dataset (NED) is necessary over mountainous areas. The correlation between ASTER GDEM minus NED and vegetation canopy height is improved from 0.328 to 0.43 by degrading resolutions from 1 arc-second to 5 arc-second and further improved to 0.6 if only homogenous vegetated areas were considered.     

Underlying topography extraction over forest areas from multi-baseline PolInSAR data

In this paper, the digital elevation model (DEM) for a forest area is extracted from multi-baseline (MB) polarimetric interferometric synthetic aperture radar (PolInSAR) data. On the basis of the random-volume-over-ground (RVoG) model, the weighted complex least-squares adjustment (WCLSA) method is proposed for the ground phase estimation, so that the MB PolInSAR observations can be constrained by a generalized observation function and the observation contribution to the solution can be adjusted by a weighting strategy. A baseline length weighting strategy is then adopted to syncretize the DEMs estimated with the ground phases. The results of the simulated experiment undertaken in this study demonstrate that the WCLSA method is sensitive to the number of redundant observations and can adjust the contributions of the different observations. We also applied the WCLSA method to E-SAR L- and P-band MB PolInSAR data from the Krycklan River catchment in Northern Sweden. The results show that the two extracted DEMs are in close agreement with the Light Detection and Ranging (Lidar) DEM, with root-mean-square errors of 3.54 and 3.16 m. The DEM vertical error is correlated with the terrain slope and ground-cover condition, but not with the forest height.     

Bathymetric inversion of South China Sea from satellite altimetry data

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高分七号卫星激光测高数据处理与精度初步验证

装备在高分七号卫星上的是我国首个具备全波形记录功能的激光测高仪,主要用于获取地面稀疏的高程控制点,提高了同平台立体影像无地面控制点的立体测图精度.高分七号卫星激光测高标准化处理是测绘应用的关键步骤,所生成的激光测高标准产品是后续对外分发和业务化应用的重要前提.本文围绕高分七号卫星的激光数据,研究了激光测高数据处理方法,验证了激光测高标准产品的几何精度.选择几何定标区以及陕西华阴、德国北威州等多个验证区,结合高精度外业测量点和机载LiDAR-DSM数据,对高分七号卫星激光测高标准产品开展精度验证.验证结果表明,高分七号SLA03产品定标区两波束激光的平面精度分别为(3.896±1.029)m和(3.286±0.337)m、高程精度分别为(0.018±0.099)m和(-0.017±0.096)m.采用高程控制点质量控制参数ECP_Fl a g能有效标识出可用于高程控制的激光点,其中陕西华阴验证区两波束激光总体精度分别为(-0.113±2.519)m和(0.191±1.071)m,经质量控制后ECP_Fl a g标记为1的激光点高程精度为(0.111±0.152)m和(-0.064±0.115)m;德国北威州总体精度为(-0.897±5.485)m和(-0.202±6.207)m,ECP_Flag标记为1的激光点高程精度为(-0.304±0.190)m和(-0.279±0.220)m.目前高分七号卫星激光测高标准产品已在自然资源部国土卫星遥感应用中心实现业务化生产.     

亚热带森林参数的机载激光雷达估测

通过应用机载激光雷达数据,在分析云南省中部的78块样地的基础上提出2个预测森林不同生物特性的统计模型(加权平均高度的预测模型和生物量的预测模型),并讨论了预测结果及其精确性。从激光雷达数据中提取了2组变量(树冠高度变量组和植被密度变量组)作为自变量,采用逐步回归方法进行自变量选择。结果表明,激光雷达数据与森林的平均树高和地上各部分生物量有很强的相关性。对于3种不同森林类型(针叶林,阔叶林和混交林),平均树高估测均能达到比较高的精度;生物量的估测结果是针叶林优于阔叶林,混交林的生物量与激光雷达数据则没有明显相关性。最后,对回归分析的结果和影响预测精度的因素进行讨论,认为预测结果的精度可能与森林类型、激光雷达采样时间和采样密度以及坐标误差等因素有关。     

机载激光雷达在密林山区测绘中的应用

机载激光雷达是近年来发展迅速的高新测绘技术,具有机动性高、数据覆盖量大、作业效率高和精度可靠等特点。针对当前山区沟壑且有大量植被覆盖区域进行传统测量作业较为困难,危险性大的问题,采用机载激光雷达技术获取研究区原始点云数据,在此基础上,对比分析四种滤波算法的点云分类效果,得到适用于密林沟壑区的点云滤波方法,进而通过人机交互和地面点内插实现了测区高精度数字高程模型（digital elevation model,DEM）的构建,最终获得的DEM高程中误差为0.09 m,满足实际测绘生产需求,生产效率大大提高。研究结果表明,机载激光雷达技术应用于复杂危险地形测绘具有极大优势。     

State-of-the-art of elevation extraction from satellite SAR data

Relative or absolute elevation extraction from satellite radar data has been an active research topic for more than 20 years. Various investigations have been made on different methods depending on the predominant “fashion” and data availability, leading each time to new developments to improve the capability and the applicability of each method. The paper presents an update of the state-of-the-art of elevation extraction from satellite SAR data. The performance and limitations of four different methods (clinometry, stereoscopy, interferometry and polarimetry) are reviewed, as well as their applicability to different satellite SAR sensors. Their advantages and disadvantages and how they are addressed during the data processing are also analysed. Finally, concluding remarks look at the complementarity aspects of each method to make the best use of the existing and future radar data for elevation extraction.     

Multiple-entity based classification of airborne laser scanning data in urban areas

There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information.     

卫星测高数据监测青藏高原湖泊2010年—2018年水位变化

青藏高原湖泊水位变化是气候变化和生态环境变化研究的重要指标。随着Cryosat-2观测数据的日益丰富和处理技术的提升,可以有效监测更多湖泊的水位变化信息。本研究构建了基于噪声去除技术、改进的波形重跟踪处理算法（ImpMWaPP）和误差混合动态模型为一体的高精度湖泊水位序列提取方法,利用Cryosat-2 SARIn数据获取到133个青藏高原湖泊2010年—2018年的高精度水位序列,并分析了这些湖泊水位变化的时空变化特征。总体上,青藏高原湖泊的水位继续呈上升趋势,但上升速度较2003年—2009年趋缓,年均变化率0.159 m/a。从地域分布上,北部湖泊的水位上升最为显著,而南部湖泊的水位则趋于稳定。从时间上,2010年—2012年和2016年—2018年,大多数湖泊的水位呈现快速上涨,而其他时间水位相对稳定或略有下降。     

机载WIDAS数据的Landsat卫星反照率初步验证

随着精细化监测的需求,中高空间分辨率的地表反照率产品逐渐成为气候模型的主要输入。目前,中高空间分辨率反照率产品的验证主要基于地表站点的通量塔观测数据,区域机载飞行数据的验证依然相对较少。因此,本文基于区域机载数据验证Landsat反照率产品。针对内蒙古自治区根河森林试验区所获取的机载红外广角双模式成像仪(WIDAS)多角度反射率数据,应用BRDF原型反演算法估算其反照率,分析了应用机载数据验证中高空间分辨率反照率产品的潜力。2016年内蒙古根河森林试验区机载WIDAS飞行多角度观测的可用多角度范围为25°,以前的研究表明BRDF原型反照率反演算法表现出对小观测角度的反照率反演结果的鲁棒性。因此,机载WIDAS反照率在一定程度可用于星载反照率的验证。首先,基于核驱动模型和各向异性平整指数(AFX)提取了试验区4种MODIS二向性反射分布函数(BRDF)原型;然后,将其作为先验知识应用到根河森林WIDAS机载数据的反照率反演中;最后,用WIDAS反照率和单个地面通量塔观测的反照率对Landsat卫星数据的反照率进行初步验证。验证结果表明Landsat反照率与WIDAS反照率结果较为一致,但略有低估,总体均方根误差(RMSE)约为0.02,偏差为0.0057。在多角度观测范围较小时,BRDF原型的反照率反演算法可为星载地表反照率的验证提供了一种有效的验证手段。     

利用GLAS激光测高数据评估DSM产品质量及精度优化

提出了一种利用卫星激光测高数据直接优化提升数字表面模型（DSM）产品精度的方法。选取境外中亚地区的资源三号DSM开展试验,通过采用多准则约束方法提取激光高程控制点,分别利用偏度、中值、线性、二次多项式等进行DSM误差修正,发现4种模型均能有效消除DSM系统误差,其中基于二次多项式的方法更适用于平地和丘陵地貌,线性模型更适用于高山地貌。试验验证了采用卫星激光测高数据优化境外DSM技术流程的可行性,最终可提高DSM的绝对高程精度。     

Mapping individual trees with airborne laser scanning data in an European lowland forest using a self-calibration algorithm

Traditional field-based forest inventories tend to be expensive, time-consuming, and cover only a limited area of a forested region. Remote sensing (RS), especially airborne laser scanning (ALS) has opened new possibilities for operational forest inventories, particularly at the single-tree level, and in the prediction of single-tree characteristics. Throughout the world, forests have varying characteristics that necessitate the development of modern, effective, and versatile tools for ALS data processing. To address this need, we aimed to develop a tool for individual tree detection (ITD) utilising a self-calibrating algorithm procedure and to verify its accuracy using the complicated forest structure of near natural forests in the temperate zone.This study was carried out in the Polish part of the Białowieża Forest (BF). The airborne laser scanner (ALS) and color-infrared (CIR) datasets were acquired for more than 60 000 ha. Field-based measurements were performed to provide reference data at the single tree level. We introduced a novel ITD method that is self-calibrated and uses a hierarchical analyses of the canopy height model.There were more than 20 000 000 of trees in first layer in BF above 7 m height. Trees visible from above were divided into coniferous, deciduous and mixed trees that were then matched with an accuracy of 85 %, 85 % and 75 %, respectively. Compared to existing methods, the proposed method is more flexible and achieves better results, especially for deciduous species. Before application of the presented method to other regions, the calibration based on the developed optimisation procedure is needed.     

Effect of field plot location on estimating tropical forest above-ground biomass in Nepal using airborne laser scanning data

The prediction of tropical forest attributes using airborne laser scanning (ALS) is becoming attractive as an alternative to traditional field measurements. Area-based ALS inventories require a set of representative field plots from the study area, which may be difficult to obtain in tropical forests with limited accessibility. This study investigates the effect of sample-plot selection in Nepal, based on two accessibility factors: distance to road and degree of slope. The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation. Study findings showed that the sample plot distance and slope had a considerable effect on the accuracy of the AGB estimation, because the forest structure varied according to the level of accessibility. Thus, the field sample plots that are used in model construction should cover the full range of sample plot distances and slopes occurring within the area.     

Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests

Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregularly distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework using aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm the relevance of input features for urban classification. The quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.     

Most similar neighbor imputation of forest attributes using metrics derived from combined airborne LIDAR and multispectral sensors

In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.     

Assessment of ZTD derived from ECMWF/NCEP data with GPS ZTD over China

The accuracy and feasibility of computing the zenith tropospheric delays (ZTDs) from data of the European Center for Medium-Range Weather Forecasts (ECMWF) and the United States National Centers for Environmental Prediction (NCEP) are studied. The ZTDs are calculated from ECMWF/NCEP pressure-level data by integration and from the surface data with the Saastamoinen model method and then compared with the solutions measured from 28 global positioning system (GPS) stations of the Crustal Movement Observation Network of China (CMONOC) for 1 year. The results are as follows: (1) the error of the integration method is 1–3 cm less than that of the Saastamoinen model method. The agreement between the ECMWF ZTD and GPS ZTD is better than that between NCEP ZTD and GPS ZTD; (2) the bias and root mean square difference (RMSD), especially the latter, have a seasonal variation, and the RMSD decreases with increasing altitude while the variation with latitude is not obvious; and (3) when using the full horizontal resolution of 0.5° × 0.5° of the ECMWF meteorological data in place of a reduced 2.5° × 2.5° grid, the mean RMSD between GPS and ECMWF ZTD decreases by 4.5 mm. These results illuminated the accuracy and feasibility of computing the tropospheric delays and establishing the ZTD prediction model over China for navigation and positioning with ECMWF and NCEP data.