Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

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ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-797-2018 ◽

2018 ◽

Vol XLII-2 ◽

pp. 797-804 ◽

Cited By ~ 4

Author(s):

W. Ostrowski ◽

M. Pilarska ◽

J. Charyton ◽

K. Bakuła

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Large Scale ◽

Point Clouds ◽

3D Models ◽

Airborne Laser Scanning ◽

Aerial Images ◽

Statistical Parameters ◽

Airborne Laser ◽

Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-629-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 629-632 ◽

Cited By ~ 1

Author(s):

E. Hadaś ◽

A. Borkowski ◽

J. Estornell

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Field Measurements ◽

Point Clouds ◽

Average Density ◽

Geometric Parameters ◽

Airborne Laser Scanning ◽

Maximum Height ◽

Airborne Laser ◽

Crown Base Height

The estimation of dendrometric parameters has become an important issue for the agricultural planning and management. Since the classical field measurements are time consuming and inefficient, Airborne Laser Scanning (ALS) data can be used for this purpose. Point clouds acquired for orchard areas allow to determine orchard structures and geometric parameters of individual trees. In this research we propose an automatic method that allows to determine geometric parameters of individual olive trees using ALS data. The method is based on the α-shape algorithm applied for normalized point clouds. The algorithm returns polygons representing crown shapes. For points located inside each polygon, we select the maximum height and the minimum height and then we estimate the tree height and the crown base height. We use the first two components of the Principal Component Analysis (PCA) as the estimators for crown diameters. The α-shape algorithm requires to define the radius parameter R. In this study we investigated how sensitive are the results to the radius size, by comparing the results obtained with various settings of the R with reference values of estimated parameters from field measurements. Our study area was the olive orchard located in the Castellon Province, Spain. We used a set of ALS data with an average density of 4 points m<sip>−2. We noticed, that there was a narrow range of the R parameter, from 0.48 m to 0.80 m, for which all trees were detected and for which we obtained a high correlation coefficient (> 0.9) between estimated and measured values. We compared our estimates with field measurements. The RMSE of differences was 0.8 m for the tree height, 0.5 m for the crown base height, 0.6 m and 0.4 m for the longest and shorter crown diameter, respectively. The accuracy obtained with the method is thus sufficient for agricultural applications.

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QUALITY EVALUATION OF 3D BUILDING MODELS BASED ON LOW-ALTITUDE IMAGERY AND AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b2-2021-345-2021 ◽

2021 ◽

Vol XLIII-B2-2021 ◽

pp. 345-352

Author(s):

G. Gabara ◽

P. Sawicki

Keyword(s):

Urban Areas ◽

Point Cloud ◽

Laser Scanning ◽

Wide Spectrum ◽

Vertical Plane ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Airborne Laser ◽

Building Models ◽

Low Altitude

Abstract. The term “3D building models” is used in relation to the CityGML models and building information modelling. Reconstruction and modelling of 3D building objects in urban areas becomes a common trend and finds a wide spectrum of utilitarian applications. The paper presents the quality assessment of two multifaceted 3D building models, which were obtained from two open-access databases: Polish national Geoportal (accuracy in LOD 2 standard) and Trimble SketchUp Warehouse (accuracy in LOD 2 standard with information about architectural details of façades). The Geoportal 3D models were primary created based on the airborne laser scanning data (density 12 pts/sq. m, elevation accuracy to 0.10 m) collected during Informatic System for Country Protection against extraordinary hazards project. The testing was performed using different validation low-altitude photogrammetric datasets: RIEGL LMS-Q680i airborne laser scanning point cloud (min. density 25 pts/sq. m and height accuracy 0.03 m), and image-based Phase One iXU-RS 1000 point cloud (average accuracy in the horizontal and in the vertical plane is respectively to 0.015 m and 0.030 m). The visual comparison, heat maps with the function of the signed distance, and histograms in predefined ranges were used to evaluate the quality and accuracy of 3D building models. The aspect of error sources that occurred during the modelling process was also discussed.

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ALGORITHM FOR THE AUTOMATIC ESTIMATION OF AGRICULTURAL TREE GEOMETRIC PARAMETERS USING AIRBORNE LASER SCANNING DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-629-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 629-632

Author(s):

E. Hadaś ◽

A. Borkowski ◽

J. Estornell

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Field Measurements ◽

Point Clouds ◽

Average Density ◽

Geometric Parameters ◽

Airborne Laser Scanning ◽

Maximum Height ◽

Airborne Laser ◽

Crown Base Height

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Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information

AGILE: GIScience Series ◽

10.5194/agile-giss-2-10-2021 ◽

2021 ◽

Vol 2 ◽

pp. 1-14

Author(s):

Florian Politz ◽

Monika Sester ◽

Claus Brenner

Keyword(s):

Change Detection ◽

Spatial Data ◽

Image Matching ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Height Distribution ◽

Airborne Laser ◽

Dense Image

Abstract. Detecting changes is an important task to update databases and find irregularities in spatial data. Every couple of years, national mapping agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) as well as from Dense Image Matching (DIM) using aerial images. Besides deriving several other products such as Digital Elevation Models (DEMs) from them, those point clouds also offer the chance to detect changes between two points in time on a large scale. Buildings are an important object class in the context of change detection to update cadastre data. As detecting changes manually is very time consuming, the aim of this study is to provide reliable change detections for different building sizes in order to support NMAs in their task to update their databases. As datasets of different times may have varying point densities due to technological advancements or different sensors, we propose a raster-based approach, which is independent of the point density altogether. Within a raster cell, our approach considers the height distribution of all points for two points in time by exploiting the Jensen-Shannon distance to measure their similarity. Our proposed method outperforms simple threshold methods on detecting building changes with respect to the same or different point cloud types. In combination with our proposed class change detection approach, we achieve a change detection performance measured by the mean F1-Score of about 71% between two ALS and about 60% between ALS and DIM point clouds acquired at different times.

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COMPARISON OF SOFTWARE FOR AIRBORNE LASER SCANNING DATA PROCESSING IN SMART CITY APPLICATIONS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-w2-9-2019 ◽

2019 ◽

Vol XLII-5/W2 ◽

pp. 9-13 ◽

Cited By ~ 1

Author(s):

V. Badenko ◽

D. Zotov ◽

N. Muromtseva ◽

Y. Volkova ◽

P. Chernov

Keyword(s):

Data Processing ◽

Smart City ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Test Point ◽

Airborne Laser Scanning ◽

Cloud Processing ◽

Airborne Laser ◽

Erdas Imagine

Abstract. Problems of processing of point clouds of airborne laser scanning using different software for Smart City projects are considered. Results of comparison of suitable software on the base of a test point cloud are presented. For comparison we had chosen a criterion for how results of point cloud processing can be used in the smart city application. The following software were chosen for comparison: Erdas IMAGINE, ENVI Lidar, TerraSolid (without Terraslave), GlobalMapper, Autodesk InfraWorks. Comparison have been conducted in qualitative and quantitative terms. The results presented allowed us to create recommendations on the usage of specific software for airborne laser scanning data processing for Smart City projects.

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A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-131-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 131-137

Author(s):

A. V. Vo ◽

D. F. Laefer

Keyword(s):

Point Cloud ◽

Input Data ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Data Set ◽

Cloud Data ◽

Airborne Laser ◽

Alternative Approach ◽

Hadoop Cluster

Abstract. Because of the importance of access to sunlight, shadow analysis is a common consideration in urban design, especially for dense urban developments. As shadow computation is computationally expensive, most urban shadow analysis tools have to date circumvented the high computational costs by representing urban complexity only through simplified geometric models. The simplification process removes details and adversely affects the level of realism of the ultimate results. In this paper, an alternative approach is presented by utilizing the highest level of detail and resolution captured in the geometric input data source, which is an extremely high-resolution airborne laser scanning point cloud (300 points/m2). To cope with the high computational demand caused by the use of this dense and detailed input data set, the Comprehensive Urban Shadow algorithm is introduced to distribute the computation for parallel processing on a Hadoop cluster. The proposed comprehensive urban shadow analysis solution is scalable, reasonably fast, and capable of preserving the original resolution and geometric detail of the original point cloud data.

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3D Reconstruction of Bridges from Airborne Laser Scanning Data and Cadastral Footprints

Journal of Geovisualization and Spatial Analysis ◽

10.1007/s41651-021-00076-9 ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Steffen Goebbels

Keyword(s):

3D Reconstruction ◽

Point Cloud ◽

Laser Scanning ◽

Medial Axis ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Consensus Algorithm ◽

Airborne Laser ◽

German Cities ◽

City Models

AbstractThe given paper describes a method for automatic 3D reconstruction of bridges from cadastral footprints and airborne laser scanning point clouds. The reconstructed bridges are used to enrich 3D city models. Unlike roofs, decks of bridges are typically smooth without ridge lines or step edges. Therefore, established methods for roof reconstruction are not suitable for bridges. The standard description language for semantic city models is CityGML. This specification of the Open Geospatial Consortium assumes that surfaces are composed of planar polygons. The approximation of smooth decks by planar polygons is achieved by using a medial axis tree. Instead of the medial axis of the footprint, a modified medial axis is computed that does not consider counter bearing edges. The resulting tree represents centerline connections between all counter bearing edges and, in conjunction with filtered height values of a point cloud, serves as the basis for approximation with polygons. In addition to modeling decks, superstructures such as pylons and cables are also derived from the point cloud. For this purpose, planes carrying many superstructure points are detected using the Random Sampling Consensus Algorithm (RANSAC). Images are generated by projecting points onto these planes. Then, image processing methods are used to find connected contours that are extruded to form 3D objects. The presented method was successfully applied to all bridges of two German cities as well as to large bridges built over the Rhine River.

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An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Remote Sensing ◽

10.3390/rs13152938 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2938

Author(s):

Feng Li ◽

Haihong Zhu ◽

Zhenwei Luo ◽

Hang Shen ◽

Lin Li

Keyword(s):

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Superior Performance ◽

Cloth Simulation ◽

Cloud Data ◽

Interpolation Filter ◽

Landscape Characteristics ◽

Surface Interpolation ◽

Airborne Laser

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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