The selection of appropriate spectrally bright pseudo-invariant ground targets for use in empirical line calibration of SPOT satellite imagery

The appropriate utilization of multi-temporal SPOT multispectral satellite imagery in quantitative remote sensing studies requires the removal of atmospheric effects. One widely used and potentially very accurate way of achieving absolute atmospheric correction is the calibration of at-satellite radiance data to field measures of the surface reflectance factor (ρ_s). There are a number of variations in this technique, which are known collectively as empirical line (EL) approaches. However, the successful application of an EL spectral calibration requires the presence and careful selection of appropriate pseudo-invariant ground targets within each scene area. Real surfaces, even those that are man-made and vegetation-free, display non-Lambertian reflectance behaviour to some extent. Because of the ±31° off-nadir incidence angle range of the SPOT sensors, this is a crucial consideration. In favourable circumstances, it may be possible to utilize a goniometer to collect multiangular ρ_s measurements, but for widespread lower cost application of EL approaches currently, the use of a handheld spectrometer to measure nadir only ρ_s is a more realistic proposition. In either case, the selection of targets that have more limited and stable multiangular reflectance behaviour is preferable. Details are given of the reflectance properties of a variety of spectrally bright potential calibration surface types, encompassing sands, gravel, asphalts, and managed and artificial grass turf surfaces, measured in the field using the Finnish Geodetic Institute Field Goniospectrometer (FIGIFIGO). Bright calibration site selection requirements for SPOT data are discussed and the physical mechanisms behind the varying reflectance characteristics of the surfaces are considered. The most desirable properties for useful calibration targets are identified. The results of this study will assist other workers in the identification of likely suitable EL calibration sites for medium and high resolution optical satellite data, and therefore help optimize efforts in the time consuming and costly process of measuring ρ_s in the field.     

Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis

This article's goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing the horizontal structure of different facies in forested areas (primary forests vs. secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the “Litto3D” coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1 m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentations at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height and horizontal heterogeneity. This heterogeneity was assessed using a texture index calculated from the height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.     

地面三维激光扫描技术的发展与应用前景

地面三维激光扫描技术可以快速地获取目标的高精度三维点云数据,在数字城市、数字考古、数字交通、数字管线及数字医学等行业得到了初步应用,显示了这一技术的广阔应用前景和蕴含的巨大经济价值.     

Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification

In this paper, a measurement system for the acquisition of a virtual hyperspectral LiDAR dataset is presented. As commercial hyperspectral LiDARs are not yet available, the system provides a novel type of data for the testing and developing of future hyperspectral LiDAR algorithms. The measurement system consists of two parts: first, backscattered reflectance spectra are collected using a spectrometer and a cutting-edge technology, white-light supercontinuum laser source; second, a commercial monochromatic LiDAR system is used for ranging. A virtual hyperspectral LiDAR dataset is produced by data fusion. Such a dataset was collected on a Norway spruce (Picea abies) sample. The performance of classification was tested using an experimental hyperspectral algorithm based on a novel combination of the Spectral Correlation Mapper and a region growing algorithm. The classifier was able to automatically distinguish between needles, branches and background, in other words, perform a difficult task using only traditional TLS data.     

Fusion of airborne LiDAR and multispectral sensors reveals synergic capabilities in forest structure characterization

Forest stand structure is an important concept for ecology and planning in sustainable forest management. In this article, we consider that the incorporation of complementary multispectral information from optical sensors to Light Detection and Ranging (LiDAR) may be advantageous, especially through data fusion by back-projecting the LiDAR points onto the multispectral image. A multivariate data set of both LiDAR and multispectral metrics was related with a multivariate data set of stand structural variables measured in a Scots pine forest through canonical correlation analysis (CCA). Four statistically significant pairs of canonical variables were found, which explained 83.0% accumulated variance. The first pair of canonical variables related indicators of stand development, i.e. height and volume, with LiDAR height metrics. CCA also found attributes describing stand density to be related to LiDAR and spectral variables determining canopy coverage. Other canonical variables pertained to Lorenz curve-derived attributes, which are measures of within-stand tree size variability and heterogeneity, able to discriminate even-sized from uneven-sized stands. The most relevant result was to find that metrics derived from the multispectral sensor showed significant explanatory potential for the prediction of these structural attributes. Therefore, we concluded that metrics derived from the optical sensor have potential for complementing the information from the LiDAR sensor in describing structural properties of forest stands. We recommend the use of back-projecting for jointly exploiting the synergies of both sensors using similar types of metrics as they are customary in forestry applications of LiDAR.     

Direct georeferencing of airborne LiDAR data in national coordinates

The topographic mapping products of airborne light detection and ranging (LiDAR) are usually required in the national coordinates (i.e., using the national datum and a conformal map projection). Since the spatial scale of the national datum is usually slightly different from the World Geodetic System 1984 (WGS 84) datum, and the map projection frame is not Cartesian, the georeferencing process in the national coordinates is inevitably affected by various geometric distortions. In this paper, all the major direct georeferencing distortion factors in the national coordinates, including one 3D scale distortion (the datum scale factor distortion), one height distortion (the earth curvature distortion), two length distortions (the horizontal-to-geodesic length distortion and the geodesic-to-projected length distortion), and three angle distortions (the skew-normal distortion, the normal-section-to-geodesic distortion, and the arc-to-chord distortion) are identified and demonstrated in detail; and high-precision map projection correction formulas are provided for the direct georeferencing of the airborne LiDAR data. Given the high computational complexity of the high-precision map projection correction approach, some more approximate correction formulas are also derived for the practical calculations. The simulated experiments show that the magnitude of the datum scale distortion can reach several centimeters to decimeters for the low (e.g., 500 m) and high (e.g., 8000 m) flying heights, and therefore it always needs to be corrected. Our proposed practical map projection correction approach has better accuracy than Legat’s approach,¹ but it needs 25% more computational cost. As the correction accuracy of Legat’s approach can meet the requirements of airborne LiDAR data with low and medium flight height (up to 3000 m above ground), our practical correction approach is more suitable to the high-altitude aerial imagery. The residuals of our proposed high-precision map projection correction approach are trivial even for the high flight height of 8000 m. It can be used for the theoretical applications such as the accurate evaluation of different GPS/INS attitude transformation methods to the national coordinates.     

Calibration of area based diameter distribution with individual tree based diameter estimates using airborne laser scanning

Diameter distribution is essential for calculating stem volume and timber assortments of forest stands. A new method was proposed in this study to improve the estimation of stem volume and timber assortments, by means of combining the Area-based approach (ABA) and individual tree detection (ITD), the two main approaches to deriving forest attributes from airborne laser scanning (ALS) data. Two methods, replacement, and histogram matching were employed to calibrate ABA-derived diameter distributions with ITD-derived diameter estimates at plot level. The results showed that more accurate estimates were obtained when calibrations were applied. In view of the highest accuracy between ABA and ITD, calibrated diameter distributions decreased its relative RMSE of the estimated entire growing stock, saw log and pulpwood fractions by 2.81%, 3.05% and 7.73% points at best, respectively. Calibration improved pulpwood fraction significantly, which contributed to the negligible bias of the estimated entire growing stock.     

An automated approach to vertical road characterisation using mobile LiDAR systems: Longitudinal profiles and cross-sections

The characterisation the vertical profiles and cross-sections of roads is important for the verification of proper construction and road safety assessment. The goal of this paper is the extraction of geometric parameters through the automatic processing of mobile LiDAR system (MLS) point clouds. Massive and complex datasets provided by the MLS are processed using a hierarchical strategy that includes segmentation, principal component analysis (PCA)-based orthogonal regression, filtering and parameter extraction procedures. Best-fit geometric parameters act as a vertical road model for both linear parameters (slope and vertical curves) and cross-sections (superelevations). The proposed automatic processing approach gives satisfactory results for the analysed scenario.     

An approach to the radiometric aerotriangulation of photogrammetric images

Harnessing the radiometric information provided by photogrammetric flights could be useful in increasing the thematic applications of aerial images. The aim of this paper is to improve relative and absolute homogenization in aerial images by applying atmospheric correction and treatment of bidirectional effects. We propose combining remote sensing methodologies based on radiative transfer models and photogrammetry models, taking into account the three-dimensional geometry of the images (external orientation and Digital Elevation Model). The photogrammetric flight was done with a Z/I Digital Mapping Camera (DMC) with a Ground Sample Distance (GSD) of 45 cm. Spectral field data were acquired by defining radiometric control points in order to apply atmospheric correction models, obtaining calibration parameters from the camera and surface reflectance images. Kernel-driven models were applied to correct the anisotropy caused by the bidirectional reflectance distribution function (BRDF) of surfaces viewed under large observation angles with constant illumination, using the overlapping area between images and the establishment of radiometric tie points. Two case studies were used: 8-bit images with applied Lookup Tables (LUTs) resulting from the conventional photogrammetric workflow for BRDF studies and original 12-bit images (Low Resolution Color, LRC) for the correction of atmospheric and bidirectional effects. The proposed methodology shows promising results in the different phases of the process. The geometric kernel that shows the best performance is the Lidense kernel. The homogenization factor in 8-bit images ranged from 6% to 25% relative to the range of digital numbers (0–255), and from 18% to 35% relative to levels of reflectance (0–100) in the 12-bit images, representing a relative improvement of approximately 1–30%, depending on the band analyzed.     

Derivation of tree skeletons and error assessment using LiDAR point cloud data of varying quality

The architecture of trees is of particular interest for 3D model creation in forestry and ecolocical applications. Terrestrial (TLS) and mobile laser scanning (MLS) systems are used to acquire detailed geometrical data of trees. Since 3D point clouds from laser scanning consist of large data amounts representing uninterpreted topographical information including noise and data gaps, an extraction of salient tree structures is important for further applications. We present a fully automated modular workflow for topological reliable reconstruction of tree architecture. Object-based point cloud processing such as branch extraction is combined with tree skeletonization. Branch extraction is performed using a segmentation procedure followed by segment-based analysis of form indices derived from eigenvector metrics. Extracted branch primitives are simplified and connected to line features during skeletonization. The modular workflow allows comprehensive parameter tests and error assessments that are used for a calibration of the module parameters with respect to various characteristics of the input data (e.g noise, scanning resolution, and the number of scan positions). The estimated parameter settings are validated using an exemplary MLS data set. The quality of input point cloud data, strongly influencing the quality of the skeleton results, can be improved by the presented branch extraction procedure. The potential for data improvement increases with increasing point densities. For our object-based appoach, we can show that the presence of erroneous structures and filtering artifacts have the strongest influence onto the quality of the derived skeletons. In contrast to traditional skeletonization approaches, the existance of data gaps has less influence onto the results.     

机载激光雷达点云的阶层式分类

激光雷达是一种快速获得高密度高精度的三维数字地面信息的新技术。本文介绍了几种激光雷达数据过滤算法,提出了激光雷达点云数据的阶层式分类策略,并将基于航拍影像数据进行着色后的机载激光雷达点云数据作为研究对象,对其应用激光雷达数据过滤算法进行阶层式分类。实验结果表明,此种方法能有效地对大部分地物信息进行过滤和分类。     

手持激光雷达扫描系统在农村房地一体测量中的应用

农村房屋不动产登记是国家深化农村集体产权制度改革的重要基础性工作.房地一体测量成果是农村房屋不动产登记的基础数据,如何快速有效地采集大比例尺农村地籍及房屋数据,提高农村房屋不动产登记的效率,降低数据采集的成本,是测绘地理信息行业的使命和挑战.本文通过在广东、江苏的实际项目中使用手持激光雷达扫描采集农村房屋及附属的地籍空...     

三维激光扫描技术在古建筑测绘中的应用

三维激光扫描测绘技术在古建筑测绘中的应用已渐趋成熟。全文介绍了三维激光扫描技术的工作原理,详细介绍了建立市政府6号楼三维模型的应用步骤,通过与传统测量手段获取的数据相比较,充分肯定了该技术在古建筑测绘类项目中应用的可行性及优势,三维激光扫描测绘技术在古建筑测量领域具有巨大的技术优势和应用价值。     

改进LM模型在机载小光斑LiDAR数据分解中的应用

机载小光斑Li DAR是近年来遥感领域的新兴技术,它对回波脉冲进行全数字化的记录,通过分解返回波形能够得到更加丰富的信息。针对传统LM波形分解算法容易陷入局部最优的问题,引入改进LM模型,采用信赖域技巧使得参数解全局收敛。实验证明,该方法相比传统LM能够得到更加可靠的波形分解结果,并且对城区、郊区和山地区域适应性好,具有较强实用价值。     

三维激光扫描技术在石化企业管道建模中的应用

三维激光扫描是一种速度快、效率高、实时性强并且精度高的采集目标表面点云数据的技术,在测绘领域得到了广泛的应用。本文结合工程实例,论述了三维激光扫描技术在石化企业管线测量中的应用,并提出了依据点云数据对管道进行三维模型重建的方法,包括外业踏勘测站布设、数据采集与多站数据配准等工作,最后结合相关软件实现了管道三维模型的重建。     

地面三维激光扫描技术在公路建模中的应用

为解决传统技术获取公路数据效率低,数据不够精确与详细的问题,本文提出了将地面三维激光扫描技术应用于公路建模的数据采集方案和数据处理方法。根据地面激光扫描的特点和公路建模要求,设计出按最佳扫描距离进行分段设站、分块扫描的数据采集方案。经过点云配准和滤波等预处理后,利用点云几何信息进行了数字表面模型、等高线、纵横断面等模型生成。本文以Trimble GS200地面三维激光扫描仪为例对高速公路路面进行数据采集,以Realworks Survey5.0作为建模工具进行路面建模。结果表明采集方案能提高获取原始数据的效率,并且保证了数据质量,降低了数据处理的复杂程度。     

Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces

The monitoring of different crops (cultivated plots) and types of surface (bare soils, etc.) is a crucial economic and environmental issue for the management of resources and human activity. In this context, the objective of this study is to evaluate the contribution of multispectral satellite imagery (optical and radar) to land use and land cover classification.Object-oriented supervised classifications, based on a Random Forest algorithm, and majority zoning post-processing are used. This study emerges from the experiment on multi-sensor crop monitoring (MCM'10, Baup et al., 2012) conducted in 2010 on a mixed farming area in the southwest of France, near Toulouse. This experiment enabled the regular and quasi-synchronous collection of multi-sensor satellite data and in situ observations, which are used in this study. 211 plots with contrasting characteristics (different slopes, soil types, aspects, farming practices, shapes and surface areas) were monitored to represent the variability of the study area. They can be grouped into four classes of land cover: 39 grassland areas, 100 plots of wheat, 13 plots of barley, 20 plots of rapeseed, and 2 classes of bare soil: 23 plots of small roughness and 16 plots of medium roughness. Satellite radar images in the X-, C- and L-bands (HH polarization) were acquired between 14 and 18 April 2010. Optical images delivered by Formosat-2 and corresponding field data were acquired on 14 April 2010.The results show that combining images acquired in the L-band (Alos) and the optical range (Formosat-2) improves the classification performance (overall accuracy = 0.85, kappa = 0.81) compared to the use of radar or optical data alone. The results obtained for the various types of land cover show performance levels and confusions related to the phenological stage of the species studied, with the geometry of the cover, the roughness states of the surfaces, etc. Performance is also related to the wavelength and penetration depth of the signal providing the images. Thus, the results show that the quality of the classification often increases with increasing wavelength of the images used.