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.     

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.     

Single tree biomass modelling using airborne laser scanning

Accurate forest biomass mapping methods would provide the means for e.g. detecting bioenergy potential, biofuel and forest-bound carbon. The demand for practical biomass mapping methods at all forest levels is growing worldwide, and viable options are being developed. Airborne laser scanning (ALS) is a promising forest biomass mapping technique, due to its capability of measuring the three-dimensional forest vegetation structure. The objective of the study was to develop new methods for tree-level biomass estimation using metrics derived from ALS point clouds and to compare the results with field references collected using destructive sampling and with existing biomass models. The study area was located in Evo, southern Finland. ALS data was collected in 2009 with pulse density equalling approximately 10 pulses/m². Linear models were developed for the following tree biomass components: total, stem wood, living branch and total canopy biomass. ALS-derived geometric and statistical point metrics were used as explanatory variables when creating the models. The total and stem biomass root mean square error per cents equalled 26.3% and 28.4% for Scots pine (Pinus sylvestris L.), and 36.8% and 27.6% for Norway spruce (Picea abies (L.) H. Karst.), respectively. The results showed that higher estimation accuracy for all biomass components can be achieved with models created in this study compared to existing allometric biomass models when ALS-derived height and diameter were used as input parameters. Best results were achieved when adding field-measured diameter and height as inputs in the existing biomass models. The only exceptions to this were the canopy and living branch biomass estimations for spruce. The achieved results are encouraging for the use of ALS-derived metrics in biomass mapping and for further development of the models.     

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.     

机载激光扫描测高数据滤波

机载激光扫描测高数据的滤波和分类是获取高精度数字高程模型的关键,也是国际上目前研究的重点和难点之一。本文详细研究了机载激光扫描测高数据滤波的方法,对现有各种滤波算法进行了综合评价,指出了现有方法的不足,在此基础上首次将"移动曲面拟合预测"滤波算法用于机载激光扫描测高数据滤波处理。试验结果表明该算法自适应性强,计算速度快,滤波效果好。通过对不同测区的数据进行实验,给出了滤波前后的对比结果。     

Automated road markings extraction from mobile laser scanning data

Road markings are used to provide guidance and instruction to road users for safe and comfortable driving. Enabling rapid, cost-effective and comprehensive approaches to the maintenance of route networks can be greatly improved with detailed information about location, dimension and condition of road markings. Mobile Laser Scanning (MLS) systems provide new opportunities in terms of collecting and processing this information. Laser scanning systems enable multiple attributes of the illuminated target to be recorded including intensity data. The recorded intensity data can be used to distinguish the road markings from other road surface elements due to their higher retro-reflective property. In this paper, we present an automated algorithm for extracting road markings from MLS data. We describe a robust and automated way of applying a range dependent thresholding function to the intensity values to extract road markings. We make novel use of binary morphological operations and generic knowledge of the dimensions of road markings to complete their shapes and remove other road surface elements introduced through the use of thresholding. We present a detailed analysis of the most applicable values required for the input parameters involved in our algorithm. We tested our algorithm on different road sections consisting of multiple distinct types of road markings. The successful extraction of these road markings demonstrates the effectiveness of our algorithm.     

基于机载激光扫描数据提取建筑物的研究现状

机载激光扫描系统是集成了GPS、惯性导航系统(INS)和扫描激光测距系统并利用飞机作为运行平台,来获取地面的三维位置,进而快速生成数字表面模型(DSM)。随着机载扫描激光测距系统的不断完善和发展,获取城市DSM数据也变得越来越快速,而且方便和经济可靠,地面激光点的密度也大大提高。目前国外激光扫描系统的激光点密度一般都达到了1～20点/m2,因此利用机载激光扫描系统获取的城市DSM提取建筑物也渐渐受到重视。利用激光扫描数据提取建筑物可以分为两大类,第一类是单纯以获取的机载激光测距数据来提取建筑物,第二类是融合激光测距数据和其他相关信息的建筑物提取,如融合航空影像、融合IKONOS高分辨率卫星影像来提取建筑物。本文对国际上利用机载激光扫描测距数据进行建筑物提取的最新研究进展进行了一些分析,同时也给出了应用我国研制的机载激光扫描数据提取建筑物的试验研究和初步结果。     

Building roof modeling from airborne laser scanning data based on level set approach

This paper presents a novel approach to building roof modeling, including roof plane segmentation and roof model reconstruction, from airborne laser scanning data. Segmentation is performed by minimizing an energy function formulated as multiphase level set. The energy function is minimized when each segment corresponds to one or several roof plans of the same normal vector. With this formulation, maximum n regions are segmented at a time by applying log₂n level set functions. The roof ridges or step edges are then delineated by the union of the zero level contours of the level set functions. In the final step of segmentation, coplanar and parallel roof segments are separated into individual roof segments based on their connectivity and homogeneity. To reconstruct a 3D roof model, roof structure points are determined by intersecting adjacent roof segments or line segments of building boundary and then connected based on their topological relations inferred from the segmentation result. As a global solution to the segmentation problem, the proposed approach determines multiple roof segments at the same time, which leads to topological consistency among the segment boundaries. The paper describes the principle and solution of the multiphase level set approach and demonstrates its performance and properties with two airborne laser scanning data sets.     

An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.     

地面三维激光扫描地形测量数据粗差剔除算法及实现

本文基于地面三维激光扫描技术用于地形测量时点云数据的特点,得到引起结果存在粗差的障碍物上的点云具有坡度大于实际地形上坡度的特征,并据此提出了一种实用的粗差提取算法。对算法的原理、实现过程和其中重要参数的确定进行了分析,并编程实现了算法。最后,应用实例对粗差提取效果进行了检验,证明了算法的可行性和可靠性。     

Application of 3D triangulations of airborne laser scanning data to estimate boreal forest leaf area index

We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAI_e). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAI_e. The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAI_e and canopy volume for large areas. The LAI_e from triangulations was validated with field measured LAI_e and compared with a reference LAI_e calculated from ALS data using logarithmic model based on Beer’s law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAI_e. We investigated the influence species and season on the triangulated LAI_e and demonstrated the relationship between triangulated LAI_e and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAI_e from triangulations and field measured values agreed the most using the highest pulse density data (RMSE = 0.63, the coefficient of determination (R²) = 0.53). Yet, the LAI_e calculated using ACI-index agreed better with the field measured LAI_e (RMSE = 0.53 and R² = 0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAI_e only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.     

Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis

The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.     

利用地面激光扫描数据提取单木结构参数

针对现有提取单木结构参数的方法精度不高的现状,文章研究利用地面三维激光扫描数据提取单木的高度、胸径和冠幅,特别是将RANSAC算法用于圆拟合,提高了胸径的反演精度。最后利用野外实测数据进行验证,并比较分析了Hough变换和RANSAC算法在提取胸径中的差异。结果表明,本文方法提取的单木参数精度较高;并且无论从相关系数、平均残差还是均方根误差等方面,RANSAC算法提取的胸径精度均高于Hough变换方法。     

三维激光扫描仪平缓地形分区扫描方法研究

三维激光扫描仪对平缓地形扫描作业时,存在点密度不均匀、冗余数据多、有效扫描半径小等限制。为减少上述限制,提高作业效率,在深入分析三维激光扫描仪工作原理的基础上,提出分区扫描方法:根据到扫描仪中心距离的不同,将扫描范围分为若干环状区域,每个环状区域分别对应不同的竖直扫描夹角,各区域基于扫描点密度均衡的原则,设置不同的扫描参数。试验结果表明:竖直扫描角度分别为88°~89°、85°~88°、70°~85°、45°~70°的分区扫描方法为较理想的方法,与整体扫描方式相比,前者在作业效率、有效扫描半径等方面均有明显优势,数据点密度分布得到很大改善,利用效率明显提高。     

Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data

The use of helicopters as a sensor platform offers flexible fields of application due to adaptable flying speed at low flight levels. Modern helicopters are equipped with radar altimeters, inertial navigation systems (INS), forward-looking cameras and even laser scanners for automatic obstacle avoidance. If the 3D geometry of the terrain is already available, the analysis of airborne laser scanner (ALS) measurements may also be used for terrain-referenced navigation and change detection. In this paper, we present a framework for on-the-fly comparison of current ALS data to given reference data of an urban area. In contrast to classical difference methods, our approach extends the concept of occupancy grids known from robot mapping. However, it does not blur the measured information onto the grid cells. The proposed change detection method applies the Dempster–Shafer theory to identify conflicting evidence along the laser pulse propagation path. Additional attributes are considered to decide whether detected changes are of man-made origin or occurring due to seasonal effects. The concept of online change detection has been successfully validated in offline experiments with recorded ALS data streams. Results are shown for an urban test site at which multi-view ALS data were acquired at an interval of 1 year.     

Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery

This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km² and 487 km² in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.     

Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels — Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) — based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen’s kappa coefficient, κ). The accuracies at Levels 2–4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data.     

基于车载激光扫描数据的电线杆自动识别与定位

本文对车载激光扫描数据进行了研究,将投影点密度(DoPP)与电线杆的几何特点相结合,提出了一种基于激光扫描数据的电线杆自动识别和定位的方法。首先,将车载测量系统获取的激光点云投影到水平面,建立水平格网,设置投影点密度阈值分类出杆状地物所在的格网区域;然后将分类得到的杆状数据拟合成为一条空间直线,以电线杆的几何特性作为期望值和方差,自动识别电线杆;最后,该空间直线与地面网格相交确定电线杆的定位位置。本文算法与T3D Analyst商用软件提取的电线杆进行比较,实验结果表明本文方法行之有效。     

A method for vertical adjustment of digital aerial photogrammetry data by using a high-quality digital terrain model

The accuracy of vertical position information can be degraded by various sources of error in digital aerial photogrammetry (DAP) based point clouds. To address this issue, we propose a relatively straightforward method for automated correction of such point clouds. This method can be used in conjunction with any 3D reconstruction method in which a point cloud is generated from a pair of aerial images. The crux of the method involves separately co-registering each DAP point cloud (formed by the overlap of two or more images) to a common airborne laser scanning (ALS) based digital terrain model. The proposed method has the following essential steps: (1) Ground surface patches are identified in the normalized DAP point clouds by selecting areas in which standard deviation of vertical height is low, (2) height differences between the DAP and ALS point clouds are calculated at these patches, and (3) a correction surface is interpolated from these height differences and is then used to rectify the entire DAP point cloud. The performance of the proposed method is verified using plot data (n = 250) from a forested study area in Eastern Finland. We observed that DAP data from the area corrected using our proposed method resulted in significant increases in prediction accuracy of key forest variables. Specifically, the root mean squared error (RMSE) values for dominant height predictions decreased by up to 23.2%, while the associated model R² values increased by 16.9%. As for stem volume, RMSEs dropped by 20.6%, while the model R² improved by 14.6%, respectively. Hence, prediction accuracies were almost as good as with ALS data. The results suggest that vertically misaligned DAP data, if rectified using an algorithm such as the one presented here, could deliver near ALS data quality at a fraction of the cost.     

不同激光扫描数据组拼接的一种新方法

激光扫描测量技术在三维城市建模领域的应用具有良好的发展前景。但是,不同精度的扫描数据组的拼接是目前需要解决的主要问题之一,本文对不同精度的数据组的拼接进行了实验性研究,并提出了一种新方法。