Classification of airborne laser scanning point clouds based on binomial logistic regression analysis

This article presents a newly developed procedure for the classification of airborne laser scanning (ALS) point clouds, based on binomial logistic regression analysis. By using a feature space containing a large number of adaptable geometrical parameters, this new procedure can be applied to point clouds covering different types of topography and variable point densities. Besides, the procedure can be adapted to different user requirements. A binomial logistic model is estimated for all a priori defined classes, using a training set of manually classified points. For each point, a value is calculated defining the probability that this point belongs to a certain class. The class with the highest probability will be used for the final point classification. Besides, the use of statistical methods enables a thorough model evaluation by the implementation of well-founded inference criteria. If necessary, the interpretation of these inference analyses also enables the possible definition of more sub-classes. The use of a large number of geometrical parameters is an important advantage of this procedure in comparison with current classification algorithms. It allows more user modifications for the large variety of types of ALS point clouds, while still achieving comparable classification results. It is indeed possible to evaluate parameters as degrees of freedom and remove or add parameters as a function of the type of study area. The performance of this procedure is successfully demonstrated by classifying two different ALS point sets from an urban and a rural area. Moreover, the potential of the proposed classification procedure is explored for terrestrial data

An Automatic Digital Terrain Generation Technique for Terrestrial Sensing and Virtual Reality Applications

The identification and modeling of the terrain from point cloud data is an important component of Terrestrial Remote Sensing (TRS) applications. The main focus in terrain modeling is capturing details of complex geological features of landforms. Traditional terrain modeling approaches rely on the user to exert control over terrain features. However, relying on the user input to manually develop the digital terrain becomes intractable when considering the amount of data generated by new remote sensing systems capable of producing massive aerial and ground-based point clouds from scanned environments. This article provides a novel terrain modeling technique capable of automatically generating accurate and physically realistic Digital Terrain Models (DTM) from a variety of point cloud data. The proposed method runs efficiently on large-scale point cloud data with real-time performance over large segments of terrestrial landforms. Moreover, generated digital models are designed to effectively render within a Virtual Reality (VR) environment in real time. The paper concludes with an in-depth discussion of possible research directions and outstanding technical and scientific challenges to improve the proposed approach

Terrain Mapping From Unmanned Aerial Vehicles

In land surveying, digital terrain model (DTM) and digital surface model (DSM) have long been benefitted in many applications related to terrain mapping. Conventional methods of generating DTM and DSM have limitations in terms of practicality, time consumption and costing. The problems are much more serious for tropical regions where clouds are persistence and tend to affect the accuracy of most of these devices. This study aims to propose a novel way of generating DTM and DSM by utilizing unmanned aerial vehicle (UAV) for different land covers including forest, plantation and developed areas in the tropical region of Malaysia. The aerial images obtained from non-matrix digital compact camera payload on UAV were processed photogrammetrically to produce terrain mapping products including DTM, DSM and orthophoto. A detailed survey is also conducted at these areas to produce contour map as benchmark data in which is less being practiced by UAV mappers. To determine the accuracy, quantitative and qualitative analysis were carried out by means of root mean square error (RMSE) and visual inspection. The results show that the RMSE of DTM for forest, plantation and developed area are ± 1.806m, ± 0.938m and ± 0.549m, respectively while for DSM are ± 3.143m, ± 0.637m and ± 0.276m respectively. This study has determined that, the development area gives the highest accuracy compared to the plantation and forested area in which for developed and plantation area the DSM is better than DTM while vice-versa for forested area. It can be concluded that the complexity if terrain is found to be one of the key factors that influences the accuracy of the generated DSM and DTM

LEAST SQUARES MATCHING FOR COMPARISON OF DIGITAL TERRAIN MODELS AND ITS APPLICATION POTENTIAL FOR THE BRAZILIAN MODELS AND THE SRTM MODEL

Digital Terrain Models are being used for planning and hydrological applications, but also for visualization and many other tasks. For all applications, it is necessary to know the model quality, because it has an impact on the quality of the decisions that are drawn from the terrain model applications. In this paper we present a method that is suitable for comparing two terrain models to each other. Vertical, but also horizontal displacement of terrain features can be found automatically, which are systematic errors and are in the main focus of this paper. However, random errors can be quantified, too. This method allows establishing a vector field of differences between two models, measuring the deviation from one to the other. These deviations are a measure of quality of one model against the other. Emphasis will be put on comparing terrain model from NASAs Shuttle Radar Topographic Mission to terrain models of known quality in Brazil

Airborne LiDAR for DEM generation: some critical issues

Airborne LiDAR is one of the most effective and reliable means of terrain data collection. Using LiDAR data for DEM generation is becoming a standard practice in spatial related areas. However, the effective processing of the raw LiDAR data and the generation of an efficient and high-quality DEM remain big challenges. This paper reviews the recent advances of airborne LiDAR systems and the use of LiDAR data for DEM generation, with special focus on LiDAR data filters, interpolation methods, DEM resolution, and LiDAR data reduction. Separating LiDAR points into ground and non-ground is the most critical and difficult step for DEM generation from LiDAR data. Commonly used and most recently developed LiDAR filtering methods are presented. Interpolation methods and choices of suitable interpolator and DEM resolution for LiDAR DEM generation are discussed in detail. In order to reduce the data redundancy and increase the efficiency in terms of storage and manipulation, LiDAR data reduction is required in the process of DEM generation. Feature specific elements such as breaklines contribute significantly to DEM quality. Therefore, data reduction should be conducted in such a way that critical elements are kept while less important elements are removed. Given the highdensity characteristic of LiDAR data, breaklines can be directly extracted from LiDAR data. Extraction of breaklines and integration of the breaklines into DEM generation are presented

PATHWAY DETECTION AND GEOMETRICAL DESCRIPTION FROM ALS DATA IN FORESTED MOUNTANEOUS AREA

International audienceIn the last decade, airborne laser scanning (ALS) systems have become an alternative source for the acquisition of altimeter data. Compared to high resolution orthoimages, one of the main advantages of ALS is the ability of the laser beam to penetrate vegetation and reach the ground underneath. Therefore, 3D point clouds are essential data for computing Digital Terrain Models (DTM) in natural and vegetated areas. DTMs are a key product for many applications such as tree detection, flood modelling, archeology or road detection. Indeed, in forested areas, traditional image-based algorithms for road and pathway detection would partially fail due to their occlusion by the canopy cover. Thus, crucial information for forest management and fire prevention such as road width and slope would be misevaluated. This paper deals with road and pathway detection in a complex forested mountaneous area and with their geometrical parameter extraction using lidar data. Firstly, a three-step image-based methodology is proposed to detect road regions. Lidar feature orthoimages are first generated. Then, road seeds are both automatically and semi-automatically detected. And, a region growing algorithm is carried out to retrieve the full pathways from the seeds previously detected. Secondly, these pathways are vectorized using morphological tools, smoothed, and discretized. Finally, 1D sections within the lidar point cloud are successively generated for each point of the pathways to estimate more accurately road widths in 3D. We also retrieve a precise location of the pathway borders and centers, exported as vector data

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

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Sensitivity analysis of automatic landslide mapping: numerical experiments towards the best solution

The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline

LiDAR based Biomass Estimation System for Forested Areas

In continental Portugal, forest fires are considered the biggest and most serious cause of forest deterioration and therefore the introduction of forest management mechanisms and biomass monitoring are imperative for a better future. However, conducting field studies on a large scale is a very expensive and time-consuming task. Alternatively, through remote sensing via a LiDAR, it becomes possible to map, with high accuracy, forest parameters such as tree height, diameter at breast height or tree canopy length in order to carry out other relevant estimates such as above ground biomass. In this sense, this dissertation aims to develop a system capable of, through algorithms and filters of point cloud processing, as statistical outlier removal, progressive morphological filters and region growing segmentation, extract in detail,a digital terrain model and correctly detect the number of trees in a given area, proceeding to the measurement of some interesting variables from the point of view of a forest inventory. Thus, testing data of different characteristics, our detection method obtained positive results, with all the average detection rates above 80 %.Em Portugal continental, os incêndios florestais são considerados a maior e mais grave causa de deterioramento da floresta e por isso a introdução de mecanismos de gestão florestal e monitorização da biomassa são imperativos para um futuro melhor. No entanto, realizar estudos de campo em grande escala é uma tarefa muito dispendiosa e demorosa. Em alternativa, através da deteção remota por vias de um LiDAR torna-se possível mapear, com elevado rigor, parâmetros florestais como altura das arvores, diâmetro do tronco ou comprimento da copa da arvore de modo a proceder a outras relevantes estimações como a biomassa. Neste sentido, esta dissertação teve como objetivo o desenvolvimento de um sistema capaz de, através de algoritmos e filtros de processamento de nuvens de pontos, como remoção de outliers estatístico, filtros morfologicos progressivos e segmentação por crescimento de regiões anexas , extrair com detalhe, um modelo digital do terreno e detetar corretamente o número de arvores numa determinada área, procedendo à medição de algumas variáveis interessantes do ponto de vista do inventário florestal. Assim, testando dados de diferentes características, o nosso método de deteção obteve resultados positivos, com todas as taxas deteção média superiores a 80 %