POINTS CLASSIFICATION BY A SEQUENTIAL HIGHER - ORDER MOMENTS STATISTICAL ANALYSIS OF LIDAR DATA

The paper deals with a new sequential procedure to perform unsupervised LIDAR points classification by iteratively studying skewness and kurtosis for elevation and intensity point distribution values. After a preliminary local shape analysis of elevation and intensity point distributions, carried out from the original discrete frequencies by a non parametric estimation of the density functions, the procedure starts by choosing the category of data (elevation or intensity) to analyse at first: the choice falls on the category better showing by a testing procedure a bi or a multi clustering distribution. The first point cluster is identified by studying the distribution skewness and kurtosis variations, after removing at each step the largest data values. The selected cluster is furthermore analysed by studying higher order moments behaviour of the complementary data category. This makes possible to find out potential sub clusters of the original selected one, permitting, in this way, a more effective point classification. Successive clusters are identified by applying the same iterative procedure to the still unclassified LIDAR points. For complex point distribution shapes or for the classification of large areas, a progressive analysis method, based on the partition of the entire data set into regular subsets, is proposed. Some real numerical experiments confirm the capability of the method proposed. The classification total errors in the experiments range from a minimum value of 1,2% to a maximum value of 8,9%

LiDAR data filtering and classification by skewness and kurtosis iterative analysis of multiple point cloud data categories

A new procedure supporting filtering and classification of LiDAR data based on both elevation and intensity analysis is introduced and validated. After a preliminary analysis to avoid the trivial classification of homogeneous datasets, a non-parametric estimation of the probability density function is computed for both elevation and intensity data values. Some statistical tests are used for selecting the category of data (elevation or intensity) that better satisfies a bi- or a multi-modal distribution. The iterative analysis of skewness and kurtosis is then applied to this category to obtain a first classification. At each step, the point with the highest value of elevation (or intensity) is removed. The classification is then refined by studying both statistical moments of the complementary data category, in order to look for potential sub-clusters. Remaining clusters are identified by applying the same iterative procedure to the still unclassified LiDAR points. For more complex point distribution shapes or for the classification of large scenes, a progressive analysis is proposed, which is based on the partitioning of the entire dataset into more sub-sets. Each of them is then independently classified by using the core procedure. Some numerical experiments on real LiDAR data confirmed the potentiality of the filtering/classification method

Remote mining: from clustering to DTM

LIDAR data acquisition is becoming an indispensable task for terrain characterization in large surfaces. In Mediterranean woods this job results hard due to the great variety of heights and forms, as well as sparse vegetation that they present. A new data mining-based approach is proposed with the aim of classifying LIDAR data clouds as a first step in DTM generation. The developed methodology consists in a multi-step iterative process that splits the data into different classes (ground and low/med/high vegetation) by means of a clustering algorithm. This method has been tested on three different areas of the southern Spain with successful results, verging on 80% hitsMinisterio de Ciencia y Tecnología TIN2007-6808

A new explainable DTM generation algorithm with airborne LIDAR data: grounds are smoothly connected eventually

The digital terrain model (DTM) is fundamental geospatial data for various studies in urban, environmental, and Earth science. The reliability of the results obtained from such studies can be considerably affected by the errors and uncertainties of the underlying DTM. Numerous algorithms have been developed to mitigate the errors and uncertainties of DTM. However, most algorithms involve tricky parameter selection and complicated procedures that make the algorithm's decision rule obscure, so it is often difficult to explain and predict the errors and uncertainties of the resulting DTM. Also, previous algorithms often consider the local neighborhood of each point for distinguishing non-ground objects, which limits both search radius and contextual understanding and can be susceptible to errors particularly if point density varies. This study presents an open-source DTM generation algorithm for airborne LiDAR data that can consider beyond the local neighborhood and whose results are easily explainable, predictable, and reliable. The key assumption of the algorithm is that grounds are smoothly connected while non-grounds are surrounded by areas having sharp elevation changes. The robustness and uniqueness of the proposed algorithm were evaluated in geographically complex environments through tiling evaluation compared to other state-of-the-art algorithms

USE OF UNMANNED AERIAL VEHICLES (UAV) FOR URBAN TREE INVENTORIES

In contrast to standard aerial imagery, unmanned aerial systems (UAS) utilize recent technological advances to provide an affordable alternative for imagery acquisition. Increased value can be realized through clarity and detail providing higher resolution (2-5 cm) over traditional products. Many natural resource disciplines such as urban forestry will benefit from UAS. Tree inventories for risk assessment, biodiversity, planning, and design can be efficiently achieved with the UAS. Recent advances in photogrammetric processing have proved automated methods for three dimensional rendering of aerial imagery. Point clouds can be generated from images providing additional benefits. Association of spatial locational information within the point cloud can be used to produce elevation models i.e. digital elevation, digital terrain and digital surface. Taking advantage of this point cloud data, additional information such as tree heights can be obtained. Several software applications have been developed for LiDAR data which can be adapted to utilize UAS point clouds. This study examines solutions to provide tree inventory and heights from UAS imagery. Imagery taken with a micro-UAS was processed to produce a seamless orthorectified image. This image provided an accurate way to obtain a tree inventory within the study boundary. Utilizing several methods, tree height models were developed with variations in spatial accuracy. Model parameters were modified to offset spatial inconsistencies providing statistical equality of means. Statistical results (p = 0.756) with a level of significance (α = 0.01) between measured and modeled tree height means resulted with 82% of tree species obtaining accurate tree heights. Within this study, the UAS has proven to be an efficient tool for urban forestry providing a cost effective and reliable system to obtain remotely sensed data

Volume Component Analysis for Classification of LiDAR Data

One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach. This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block. This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%

Mapping Riparian Habitats of Natura 2000 Network (91E0*, 3240) at Individual Tree Level Using UAV Multi-Temporal and Multi-Spectral Data

Riparian habitats provide a series of ecological services vital for the balance of the environment, and are niches and resources for a wide variety of species. Monitoring riparian environments at the intra-habitat level is crucial for assessing and preserving their conservation status, although it is challenging due to their landscape complexity. Unmanned aerial vehicles (UAV) and multi-spectral optical sensors can be used for very high resolution (VHR) monitoring in terms of spectral, spatial, and temporal resolutions. In this contribution, the vegetation species of the riparian habitat (91E0*, 3240 of Natura 2000 network) of North-West Italy were mapped at individual tree (ITD) level using machine learning and a multi-temporal phenology-based approach. Three UAV flights were conducted at the phenological-relevant time of the year (epochs). The data were analyzed using a structure from motion (SfM) approach. The resulting orthomosaics were segmented and classified using a random forest (RF) algorithm. The training dataset was composed of field-collected data, and was oversampled to reduce the effects of unbalancing and size. Three-hundred features were computed considering spectral, textural, and geometric information. Finally, the RF model was cross-validated (leave-one-out). This model was applied to eight scenarios that differed in temporal resolution to assess the role of multi-temporality over the UAV’s VHR optical data. Results showed better performances in multi-epoch phenology-based classification than single-epochs ones, with 0.71 overall accuracy compared to 0.61. Some classes, such as Pinus sylvestris and Betula pendula, are remarkably influenced by the phenology-based multi-temporality: the F1-score increased by 0.3 points by considering three epochs instead of two

Forest Road Detection Using LiDAR Data and Hybrid Classification

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2This research was supported by: (1) the Project “Sistema de ayuda a la decisión para la adaptación al cambio climático a través de la planificación territorial y la gestión de riesgos (CLIMAPLAN) (PID2019-111154RB-I00): Proyectos de I+D+i - RTI”; and (2) “National Programme for the Promotion of Talent and Its Employability” of the Ministry of Economy, Industry, and Competitiveness (Torres-Quevedo program) via a postdoctoral grant (PTQ2018-010043) to Juan Guerra HernándezS

A fast and optimal pathfinder using airborne LiDAR data

Determining the optimal path between two points in a 3D point cloud is a problem that have been addressed in many different situations: from road planning and escape routes determination, to network routing and facility layout. This problem is addressed using different input information, being 3D point clouds one of the most valuables. Its main utility is to save costs, whatever the field of application is. In this paper, we present a fast algorithm to determine the least cost path in an Airborne Laser Scanning point cloud. In some situations, like finding escape routes for instance, computing the solution in a very short time is crucial, and there are not many works developed in this theme. State of the art methods are mainly based on a digital terrain model (DTM) for calculating these routes, and these methods do not reflect well the topography along the edges of the graph. Also, the use of a DTM leads to a significant loss of both information and precision when calculating the characteristics of possible routes between two points. In this paper, a new method that does not require the use of a DTM and is suitable for airborne point clouds, whether they are classified or not, is proposed. The problem is modeled by defining a graph using the information given by a segmentation and a Voronoi Tessellation of the point cloud. The performance tests show that the algorithm is able to compute the optimal path between two points by processing up to 678,820 points per second in a point cloud of 40,000,000 points and 16 km² of extensionThis work has received financial support from the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019-2022 ED431G-2019/04, reference competitive group 2019-2021, ED431C 2018/19) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System. This work was also supported by the Ministry of Economy and Competitiveness, Government of Spain (Grant No. PID2019-104834 GB-I00). We also acknowledge the Centro de Supercomputación de Galicia (CESGA) for the use of their computersS