scholarly journals Topographic Base Maps from Remote Sensing Data for Engineering Geomorphological Modelling: An Application on Coastal Mediterranean Landscape

Geosciences ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 500 ◽  
Author(s):  
Maurizio Barbarella ◽  
Albina Cuomo ◽  
Alessandro Di Benedetto ◽  
Margherita Fiani ◽  
Domenico Guida
Keyword(s):  
Laser Scanning ◽  
Risk Mitigation ◽  
Rapid Evolution ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Feature Maps ◽  
Good Correspondence ◽  
Contour Maps ◽  
High Resolution Satellite Images ◽  
Multi Temporal

Coastal landscapes are one of the most changeable areas of the earth’s surface. Given this spatial complexity and temporal variability, the construction of reference maps useful for geo-engineering is a challenge. In order to improve the performance of geomorphic models, reliable multiscale and multi-temporal base maps and Digital Elevation Models (DEM) are needed. The work presented in this paper addresses this issue using an inter-geo-disciplinary approach to optimize the processing of multisource and multi-temporal data and DEMs by using field surveys, conceptual model, and analytical computation on a test area. The data acquired with two surveying techniques were analyzed and compared: Aerial Laser Scanning (ALS) and photogrammetry from stereo pairs of High-Resolution Satellite Images (HRSI). To assess the reliability of the DEMs produced from point clouds, the residuals between the point cloud and the interpolated filtered surface were identified and analyzed statistically. In addition to the contour maps, some feature maps such as slope, planar, and profile curvature maps were produced and analyzed. The frequency distribution of the slope and curvature values were compared with the diffusion, advection, and stream power model, revealing a good agreement with the past and present geomorphic processes acting on the different parts of the study area. Moreover, the integrated geomatics–geomorphic analysis of the outliers’ map showed a good correspondence (more than 75%) between the identified outliers and some specific geomorphological features, such as micro-landforms, which are significant for erosive and gravity-driven mechanisms. The different distribution of the above singularities by different data sources allowed us to attribute their spatial model to the temporal variation of the topography and, consequently, to the geomorphic changes, rather than to the different accuracy. For monitoring purposes and risk mitigation activities, the methodology adopted seems to meet the requirements to make a digital mapping of the coast analyzed, characterized by a rapid evolution of the surface, and can be extended to other stretches of coast with similar characteristics.

scholarly journals Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data

Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 905 ◽  
Author(s):  
Guerra-Hernández ◽  
Cosenza ◽  
Cardil ◽  
Silva ◽  
Botequim ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Basal Area ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Point Clouds ◽  
Volume Estimation ◽  
P Value ◽  
Test Statistic ◽  
Individual Tree ◽  
Significant Difference

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired from Unmanned Aerial Vehicles (UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model Efficiency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant difference was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantations.

scholarly journals THE COMBINATION OF THE IMAGE AND RANGE-BASED 3D ACQUISITION IN ARCHAEOLOGICAL AND ARCHITECTURAL RESEARCH IN THE ROYAL CASTLE IN WARSAW

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 177-184
Author(s):  
A. Bocheńska ◽  
J. Markiewicz ◽  
S. Łapiński
Keyword(s):  
Laser Scanning ◽  
Retaining Wall ◽  
Weather Conditions ◽  
Point Clouds ◽  
Sensor Data ◽  
Data Sets ◽  
Multi Temporal ◽  
Close Range ◽  
3D Acquisition ◽  
Architectural Research

<p><strong>Abstract.</strong> The paper presents archaeological and architectural research in the Royal Castle in Warsaw where a combination of image- and range-based 3D acquisition was applied. The area examined included excavations situated inside the Tower and near its outer western wall. The work was carried out at various periods and in different weather conditions. As part of the measurements, laser scanning was performed (with a Z+F 5006h scanner) and a series of close-range images were taken. It was important to integrate the data acquired to create a comprehensive documentation of archaeological excavations. When data was acquired from TLS together with photogrammetric data (in different measurement periods), the points' displacements were controlled and analysed. The process of orienting and processing the terrestrial images included photographs taken during the inventory of the tower (Canon 5D Mark II) and photographs provided by the Castle's employees (Canon PowerShot G5 X). Agisoft PhotoScan software was used to orient and process the terrestrial images, and LupoScan for the TLS data. In order to integrate the TLS data and the clouds of points from the photographs from the various stages, they were processed into a raster form; our own software (based on the OpenCV library and the Structure-from-Motion method) and LupoScan software were used to interconnect the multi-temporal and multi-sensor data sets. As a result of processing photographs and TLS data, point clouds in an external reference system were obtained. This data was then used to study the thickness of the walls of the Justice Court Tower, to analyse the course of the retaining wall, and to generate the orthoimages necessary for chronological analysis.</p>

scholarly journals ASSESSMENT OF LANDSLIDE-INDUCED DISPLACEMENT AND DEFORMATION OF ABOVE-GROUND OBJECTS USING UAV-BORNE AND AIRBORNE LASER SCANNING DATA

2019 ◽  
Vol IV-2/W5 ◽  
pp. 461-467 ◽  
Author(s):  
T. Zieher ◽  
M. Bremer ◽  
M. Rutzinger ◽  
J. Pfeiffer ◽  
P. Fritzmann ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Additional Information ◽  
3D Point Clouds ◽  
Airborne Laser ◽  
Displacement Vectors ◽  
Multi Temporal ◽  
Aerial Vehicle

<p><strong>Abstract.</strong> Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4&amp;thinsp;cm within 2.2 years, 2.9&amp;thinsp;cm a<sup>&amp;minus;1</sup>; laser scanning data: 13.2&amp;thinsp;cm within 5.4 years, 2.4&amp;thinsp;cm a<sup>&amp;minus;1</sup>) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.</p>

scholarly journals Methods for tree cover extraction from high resolution orthophotos and airborne LiDAR scanning in Spanish dehesas

2019 ◽  
pp. 17 ◽  
Author(s):  
I. Borlaf-Mena ◽  
M. A. Tanase ◽  
A. Gómez-Sal
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Tree Cover ◽  
Lidar Data ◽  
Homogeneous Tree ◽  
Visual Interpretation ◽  
Management Changes

<p>Dehesas are high value agroecosystems that benefit from the effect tree cover has on pastures. Such effect occurs when tree cover is incomplete and homogeneous. Tree cover may be characterized from field data or through visual interpretation of remote sensing data, both time-consuming tasks. An alternative is the extraction of tree cover from aerial imagery using automated methods, on spectral derivate products (i.e. NDVI) or LiDAR point clouds. This study focuses on assessing and comparing methods for tree cover estimation from high resolution orthophotos and airborne laser scanning (ALS). RGB image processing based on thresholding of the ‘Excess Green minus Excess Red’ index with the Otsu method produced acceptable results (80%), lower than that obtained by thresholding the digital canopy model obtained from the ALS data (87%) or when combining RGB and LiDAR data (87.5%). The RGB information was found to be useful for tree delineation, although very vulnerable to confusion with the grass or shrubs. The ALS based extraction suffered for less confusion as it differentiated between trees and the remaining vegetation using the height. These results show that analysis of historical orthophotographs may be successfully used to evaluate the effects of management changes while LiDAR data may provide a substantial increase in the accuracy for the latter period. Combining RGB and Lidar data did not result in significant improvements over using LIDAR data alone.</p>

scholarly journals PHOTOGRAMMETRIC POINT CLOUDS GENERATION IN URBAN AREAS FROM INTEGRATED IMAGE MATCHING AND SEGMENTATION

2017 ◽  
Vol IV-2/W4 ◽  
pp. 279-285
Author(s):  
L. Ye ◽  
B. Wu
Keyword(s):  
High Resolution ◽  
Image Matching ◽  
Urban Areas ◽  
Laser Scanning ◽  
Tall Buildings ◽  
Point Clouds ◽  
Aerial Images ◽  
High Resolution Imagery ◽  
Dense Matching ◽  
High Resolution Satellite Images

High-resolution imagery is an attractive option for surveying and mapping applications due to the advantages of high quality imaging, short revisit time, and lower cost. Automated reliable and dense image matching is essential for photogrammetric 3D data derivation. Such matching, in urban areas, however, is extremely difficult, owing to the complexity of urban textures and severe occlusion problems on the images caused by tall buildings. Aimed at exploiting high-resolution imagery for 3D urban modelling applications, this paper presents an integrated image matching and segmentation approach for reliable dense matching of high-resolution imagery in urban areas. The approach is based on the framework of our existing self-adaptive triangulation constrained image matching (SATM), but incorporates three novel aspects to tackle the image matching difficulties in urban areas: 1) occlusion filtering based on image segmentation, 2) segment-adaptive similarity correlation to reduce the similarity ambiguity, 3) improved dense matching propagation to provide more reliable matches in urban areas. Experimental analyses were conducted using aerial images of Vaihingen, Germany and high-resolution satellite images in Hong Kong. The photogrammetric point clouds were generated, from which digital surface models (DSMs) were derived. They were compared with the corresponding airborne laser scanning data and the DSMs generated from the Semi-Global matching (SGM) method. The experimental results show that the proposed approach is able to produce dense and reliable matches comparable to SGM in flat areas, while for densely built-up areas, the proposed method performs better than SGM. The proposed method offers an alternative solution for 3D surface reconstruction in urban areas.

scholarly journals An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

2019 ◽  
Vol 11 (6) ◽  
pp. 647 ◽  
Author(s):  
Yufu Zang ◽  
Bisheng Yang ◽  
Jianping Li ◽  
Haiyan Guan
Keyword(s):  
Laser Scanning ◽  
Detection Method ◽  
Three Dimensional ◽  
Point Clouds ◽  
Image Point ◽  
Numerical Representation ◽  
Registration Method ◽  
3D Point Clouds ◽  
Multi Temporal ◽  
Difficult Terrain

Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs.

scholarly journals AUTOMATIC DETECTION AND CHARACTERIZATION OF GROUND OCCLUSIONS IN URBAN POINT CLOUDS FROM MOBILE LASER SCANNING DATA

2020 ◽  
Vol VI-4/W1-2020 ◽  
pp. 13-20
Author(s):  
J. Balado ◽  
E. González ◽  
E. Verbree ◽  
L. Díaz-Vilariño ◽  
H. Lorenzo
Keyword(s):  
Case Studies ◽  
Point Cloud ◽  
Laser Scanning ◽  
Automatic Detection ◽  
Point Clouds ◽  
Real Case ◽  
Occlusion Detection ◽  
Object Class ◽  
Multi Temporal

Abstract. Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.

scholarly journals Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data

2019 ◽  
Vol 11 (20) ◽  
pp. 2417 ◽  
Author(s):  
Zhenchao Zhang ◽  
George Vosselman ◽  
Markus Gerke ◽  
Claudio Persello ◽  
Devis Tuia ◽  
...  
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Recall Rate ◽  
Airborne Laser Scanning ◽  
Data Set ◽  
Multimodal Data ◽  
Airborne Laser ◽  
Dense Matching

Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.

scholarly journals VERTICAL ORIENTATION CORRECTION OF UAV IMAGE-BASED POINT CLOUDS USING STATISTICAL MODELING OF GABLE ROOF GEOMETRY

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 519-523 ◽  
Author(s):  
P. Polewski ◽  
W. Yao ◽  
L. Fang
Keyword(s):  
Rural Areas ◽  
Laser Scanning ◽  
Low Cost ◽  
Remote Sensing Data ◽  
Point Clouds ◽  
Density Estimator ◽  
Vertical Orientation ◽  
Gable Roof ◽  
The World ◽  
Uav Image

<p><strong>Abstract.</strong> Coregistration of point clouds obtained from various sensors is an important part of workflows for automatic building reconstruction from remote sensing data. Many approaches assume a common Z axis between the coordinate systems, and perform coregistration in 2D. While this assumption is usually valid for laser scanning (LS) data, for photogrammetric point clouds the Z axis is in general different from the world Z axis, and requires correction e.g. by manually measured ground control points (GCP). In this paper, we propose a fully automatic, GCP-free procedure for finding the world Z axis in rural areas, based on the relationships of planar surfaces in building gable roofs. Instead of performing direct gable line detection, we derive these lines as theoretical intersections between adjacent roof planes from 3D shape fitting. Each gable roof then casts a vote for both the Z axis direction and sign based on roof convexity constraints, and the votes are aggregated through a non-parametric kernel density estimator model. Experiments on two real world UAV image-based point clouds show that the Z axis recovered by our method leads to high-accuracy planimetric coregistration, with a median distance over 89 as well as 149 matched linear feature pairs (respectively for dataset 1 and 2) lying below 1&amp;thinsp;cm. Our results indicate that a high-quality vertical orientation can be achieved without using any GNSS or IMU hardware, which enables the use of low-cost UAV platforms for suburban and rural mapping tasks.</p>

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