scholarly journals Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models

2018 ◽  
Vol 10 (10) ◽  
pp. 1554 ◽  
Author(s):  
Tristan Goodbody ◽  
Nicholas Coops ◽  
Txomin Hermosilla ◽  
Piotr Tompalski ◽  
Gaetan Pelletier
Keyword(s):  
Vegetation Cover ◽  
Laser Scanning ◽  
Point Clouds ◽  
Vegetation Coverage ◽  
Unmanned Aerial Systems ◽  
Management Activity ◽  
Data Set ◽  
Aerial Photogrammetry ◽  
Terrain Models ◽  
Additive Mixed Models

Digital aerial photogrammetry (DAP) and unmanned aerial systems (UAS) have emerged as synergistic technologies capable of enhancing forest inventory information. A known limitation of DAP technology is its ability to derive terrain surfaces in areas with moderate to high vegetation coverage. In this study, we sought to investigate the influence of flight acquisition timing on the accuracy and coverage of digital terrain models (DTM) in a low cover forest area in New Brunswick, Canada. To do so, a multi-temporal UAS-acquired DAP data set was used. Acquired imagery was photogrammetrically processed to produce high quality DAP point clouds, from which DTMs were derived. Individual DTMs were evaluated for error using an airborne laser scanning (ALS)-derived DTM as a reference. Unobstructed road areas were used to validate DAP DTM error. Generalized additive mixed models (GAMM) were generated to assess the significance of acquisition timing on mean vegetation cover, DTM error, and proportional DAP coverage. GAMM models for mean vegetation cover and DTM error were found to be significantly influenced by acquisition date. A best available terrain pixel (BATP) compositing exercise was conducted to generate a best possible UAS DAP-derived DTM and outline the importance of flight acquisition timing. The BATP DTM yielded a mean error of −0.01 m. This study helps to show that the timing of DAP acquisitions can influence the accuracy and coverage of DTMs in low cover vegetation areas. These findings provide insight to improve future data set quality and provide a means for managers to cost-effectively derive high accuracy terrain models post-management activity.

scholarly journals An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

2021 ◽  
Vol 13 (6) ◽  
pp. 1121
Author(s):  
Raul Sampaio de Lima ◽  
Mait Lang ◽  
Niall G. Burnside ◽  
Miguel Villoslada Peciña ◽  
Tauri Arumäe ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Laser Scanning ◽  
Model Fitting ◽  
Ground Level ◽  
Point Clouds ◽  
Production Quality ◽  
Unmanned Aerial Systems ◽  
Aerial Photogrammetry ◽  
Wide Range ◽  
Scots Pine Forest

The application of unmanned aerial systems (UAS) in forest research includes a wide range of equipment, systems, and flight settings, creating a need for enhancing data acquisition efficiency and quality. Thus, we assessed the effects of flying altitude and lateral and longitudinal overlaps on digital aerial photogrammetry (DAP) processing and the ability of its products to provide point clouds for forestry inventory. For this, we used 18 combinations of flight settings for data acquisition, and a nationwide airborne laser scanning (ALS) dataset as reference data. Linear regression was applied for modeling DAP quality indicators and model fitting quality as the function of flight settings; equivalence tests compared DAP- and ALS-products. Most of DAP-Digital Terrain Models (DTM) showed a moderate to high agreement (R2 > 0.70) when fitted to ALS-based models; nine models had a regression slope within the 1% region of equivalence. The best DAP-Canopy Height Model (CHM) was generated using ALS-DTM with an R2 = 0.42 when compared with ALS-CHM, indicating reduced similarity. Altogether, our results suggest that the optimal combination of flight settings should include a 90% lateral overlap, a 70% longitudinal overlap, and a minimum altitude of 120 m above ground level, independent of the availability of an ALS-derived DTM for height normalization. We also provided insights into the effects of flight settings on DAP outputs for future applications in similar forest stands, emphasizing the benefits of overlaps for comprehensive scene reconstruction and altitude for canopy surface detection.

scholarly journals POINT CLOUD MAPPING METHODS FOR DOCUMENTING CULTURAL LANDSCAPE FEATURES AT THE WORMSLOE STATE HISTORIC SITE, SAVANNAH, GEORGIA, USA

2016 ◽  
Vol XLI-B5 ◽  
pp. 277-280
Author(s):  
T. R. Jordana ◽  
C. L. Goetcheus ◽  
M. Madden
Keyword(s):  
Laser Scanning ◽  
Cultural Landscape ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ease Of Use ◽  
Unmanned Aerial Systems ◽  
Historic Site ◽  
Data Set ◽  
3D Point Clouds ◽  
Park Service

Documentation of the three-dimensional (3D) cultural landscape has traditionally been conducted during site visits using conventional photographs, standard ground surveys and manual measurements. In recent years, there have been rapid developments in technologies that produce highly accurate 3D point clouds, including aerial LiDAR, terrestrial laser scanning, and photogrammetric data reduction from unmanned aerial systems (UAS) images and hand held photographs using Structure from Motion (SfM) methods. These 3D point clouds can be precisely scaled and used to conduct measurements of features even after the site visit has ended. As a consequence, it is becoming increasingly possible to collect non-destructive data for a wide variety of cultural site features, including landscapes, buildings, vegetation, artefacts and gardens. As part of a project for the U.S. National Park Service, a variety of data sets have been collected for the Wormsloe State Historic Site, near Savannah, Georgia, USA. In an effort to demonstrate the utility and versatility of these methods at a range of scales, comparisons of the features mapped with different techniques will be discussed with regards to accuracy, data set completeness, cost and ease-of-use.

scholarly journals POINT CLOUD MAPPING METHODS FOR DOCUMENTING CULTURAL LANDSCAPE FEATURES AT THE WORMSLOE STATE HISTORIC SITE, SAVANNAH, GEORGIA, USA

2016 ◽  
Vol XLI-B5 ◽  
pp. 277-280 ◽  
Author(s):  
T. R. Jordana ◽  
C. L. Goetcheus ◽  
M. Madden
Keyword(s):  
Laser Scanning ◽  
Cultural Landscape ◽  
Three Dimensional ◽  
Point Clouds ◽  
Ease Of Use ◽  
Unmanned Aerial Systems ◽  
Historic Site ◽  
Data Set ◽  
3D Point Clouds ◽  
Park Service

Documentation of the three-dimensional (3D) cultural landscape has traditionally been conducted during site visits using conventional photographs, standard ground surveys and manual measurements. In recent years, there have been rapid developments in technologies that produce highly accurate 3D point clouds, including aerial LiDAR, terrestrial laser scanning, and photogrammetric data reduction from unmanned aerial systems (UAS) images and hand held photographs using Structure from Motion (SfM) methods. These 3D point clouds can be precisely scaled and used to conduct measurements of features even after the site visit has ended. As a consequence, it is becoming increasingly possible to collect non-destructive data for a wide variety of cultural site features, including landscapes, buildings, vegetation, artefacts and gardens. As part of a project for the U.S. National Park Service, a variety of data sets have been collected for the Wormsloe State Historic Site, near Savannah, Georgia, USA. In an effort to demonstrate the utility and versatility of these methods at a range of scales, comparisons of the features mapped with different techniques will be discussed with regards to accuracy, data set completeness, cost and ease-of-use.

scholarly journals Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation

2018 ◽  
Vol 7 (9) ◽  
pp. 342 ◽  
Author(s):  
Adam Salach ◽  
Krzysztof Bakuła ◽  
Magdalena Pilarska ◽  
Wojciech Ostrowski ◽  
Konrad Górski ◽  
...  
Keyword(s):  
Land Cover ◽  
Laser Scanning ◽  
Point Clouds ◽  
Data Sources ◽  
Digital Terrain Models ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Terrain Models ◽  
Airborne Laser ◽  
Vertical Accuracy

In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

scholarly journals ON A KNOWLEDGE-BASED APPROACH TO THE CLASSIFICATION OF MOBILE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-4 ◽  
pp. 343-349 ◽  
Author(s):  
M. Lemmens
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Decision Rules ◽  
Point Clouds ◽  
Bench Mark ◽  
Automated Classification ◽  
Initial Experiment ◽  
Data Set ◽  
Knowledge Based

<p><strong>Abstract.</strong> A knowledge-based system exploits the knowledge, which a human expert uses for completing a complex task, through a database containing decision rules, and an inference engine. Already in the early nineties knowledge-based systems have been proposed for automated image classification. Lack of success faded out initial interest and enthusiasm, the same fate neural networks struck at that time. Today the latter enjoy a steady revival. This paper aims at demonstrating that a knowledge-based approach to automated classification of mobile laser scanning point clouds has promising prospects. An initial experiment exploiting only two features, height and reflectance value, resulted in an overall accuracy of 79<span class="thinspace"></span>% for the Paris-rue-Madame point cloud bench mark data set.</p>

scholarly journals Architecture of solution for panoramic image blurring in GIS project application

2021 ◽  
Vol 10 (2) ◽  
pp. 287-296
Author(s):  
Dejan Vasić ◽  
Marina Davidović ◽  
Ivan Radosavljević ◽  
Đorđe Obradović
Keyword(s):  
Laser Scanning ◽  
Personal Information ◽  
Point Clouds ◽  
Search Space ◽  
Legal Framework ◽  
The European Union ◽  
Data Set ◽  
General Data Protection Regulation ◽  
Panoramic Images ◽  
Spatial Entities

Abstract. Panoramic images captured using laser scanning technologies, which principally produce point clouds, are readily applicable in colorization of point cloud, detailed visual inspection, road defect detection, spatial entities extraction, diverse map creation, etc. This paper underlines the importance of images in modern surveying technologies and different GIS projects at the same time having regard to their anonymization in accordance with law. The General Data Protection Regulation (GDPR) is a legal framework that sets guidelines for the collection and processing of personal information from individuals who live in the European Union (EU). Namely, it is a legislative requirement that faces of persons and license plates of vehicles in the collected data are blurred. The objective of this paper is to present a novel architecture of the solution for a particular object blurring. The architecture is designed as a pipeline of object detection algorithms that progressively narrows the search space until it detects the objects to be blurred. The methodology was tested on four data sets counting 5000, 10 000, 15 000 and 20 000 panoramic images. The percentage of accuracy, i.e., successfully detected and blurred objects of interest, was higher than 97 % for each data set. Additionally, our aim was to achieve efficiency and broad use.

scholarly journals Terrestrial Laser Scanning and Ground Truth Data for Characterizing Downed Dead Wood

2020 ◽  
Author(s):  
Tuomas Yrttimaa ◽  
Ninni Saarinen ◽  
Ville Luoma ◽  
Topi Tanhuanpää ◽  
Ville Kankare ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Dead Wood ◽  
Terrestrial Laser Scanning ◽  
Ground Truth ◽  
Point Clouds ◽  
Data Set ◽  
Ground Truth Data ◽  
Standing Trees ◽  
Downed Dead Wood

The feasibility of terrestrial laser scanning (TLS) in characterizing standing trees has been frequently investigated, while less effort has been put in quantifying downed dead wood using TLS. To advance dead wood characterization using TLS, we collected TLS point clouds and downed dead wood information from 20 sample plots (32 m x 32 m in size) located in southern Finland. This data set can be used in developing new algorithms for downed dead wood detection and characterization as well as for understanding spatial patterns of downed dead wood in boreal forests.

scholarly journals 3D RECORDING AND MODELLING OF MIDDLE-AGE FORTRESS IN DENSE VEGETATION ENVIRONMENT

2017 ◽  
Vol XLII-2/W5 ◽  
pp. 415-420
Author(s):  
M. Koehl ◽  
Y. Courtois ◽  
S. Guillemin
Keyword(s):  
World War I ◽  
Middle Ages ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Major Step ◽  
Terrain Model ◽  
Dense Vegetation ◽  
Aerial Photogrammetry ◽  
Pass Through

The Schwartzenbourg castle is a Middle-Ages fortress which was built in 1261. It is situated above the valley of Munster in Alsace, France. It was mainly used as a fortified place and a jail. In the early 15th century, the structure has deteriorated. Even after some repairs, it fell into ruins during the Thirty Years’ war (1618-1648) and stayed uninhabited. During World War I, the German army used the place as a vantage point and also built a blockhouse inside the ruins. Nowadays, the ruins are gradually collapsing and the remains of the old walls are completely covered by thick plants.<br><br> The goal of this project was to create a 3D-model of the site before closing its access, which became too dangerous for people. This modelling is divided into two elements: on one hand, a digital terrain model (DTM) of the site in order to replace the castle and to analyze the background of its original environment; on the other hand, a 3D modelling of the ruins of the castle invaded by the vegetation. Indeed, the main difficulty of the measurement is obviously the dense vegetation which hides the castle. Held back for years outside the castle, it has now become an integral part of the ruins. This vegetation is finally today usually the first threat of heritage buildings. After a preliminary inspection of the site as well as difficulties of the project, the first step consisted of the survey of the whole environment of the site. We will therefore describe the different phases of the survey with the initial implementation of a georeferenced network on site. We will present the terrestrial laser scanning (TLS) surveys, then complementary surveys carried out by aerial photogrammetry. To be implemented, we had to wait for an advanced autumn in order to have as few leaves on trees as possible. The major step of processing of point clouds described in this paper is then the extraction of a DTM by using techniques to pass through the vegetation, or better to segment the points into different classes, one of these that would be the soil i.e. DTM, another consists into wall parts of the ruins.

scholarly journals ENHANCING THE RESOLUTION OF URBAN DIGITAL TERRAIN MODELS USING MOBILE MAPPING SYSTEMS

2018 ◽  
Vol IV-4/W6 ◽  
pp. 11-18
Author(s):  
Y. Feng ◽  
C. Brenner ◽  
M. Sester
Keyword(s):  
Urban Areas ◽  
Laser Scanning ◽  
Overland Flow ◽  
Point Clouds ◽  
Digital Terrain Models ◽  
Mobile Mapping ◽  
Flow Modelling ◽  
Digital Terrain ◽  
Terrain Models ◽  
Mapping Techniques

<p><strong>Abstract.</strong> Digital Terrain Models (DTMs) are essential surveying products for terrain based analyses, especially for overland flow modelling. Nowadays, many high resolution DTM products are generated by Airborne Laser Scanning (ALS). However, DTMs with even higher resolution are of great interest for a more precise overland flow modelling in urban areas. With the help of mobile mapping techniques, we can obtain much denser measurements of the ground in the vicinity of roads. In this research, a study area in Hannover, Germany was measured by a mobile mapping system. Point clouds from 485 scan strips were aligned and a DTM was extracted. In order to achieve a product with completeness, this mobile mapping produced DTM was then merged and adapted with a DTM product with 0.5<span class="thinspace"></span>m resolution from a mapping agency. Systematic evaluations have been conducted with respect to the height accuracy of the DTM products. The results show that the final DTM product achieved a higher resolution (0.1<span class="thinspace"></span>m) near the roads while essentially maintaining its height accuracy.</p>

scholarly journals FILTERING PHOTOGRAMMETRIC POINT CLOUDS USING STANDARD LIDAR FILTERS TOWARDS DTM GENERATION

2018 ◽  
Vol IV-2 ◽  
pp. 319-326 ◽  
Author(s):  
Z. Zhang ◽  
M. Gerke ◽  
G. Vosselman ◽  
M. Y. Yang
Keyword(s):  
Laser Scanning ◽  
Type I Error ◽  
Research Question ◽  
Random Noise ◽  
Point Clouds ◽  
Type I ◽  
Low Contrast ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Matching

Digital Terrain Models (DTMs) can be generated from point clouds acquired by laser scanning or photogrammetric dense matching. During the last two decades, much effort has been paid to developing robust filtering algorithms for the airborne laser scanning (ALS) data. With the point cloud quality from dense image matching (DIM) getting better and better, the research question that arises is whether those standard Lidar filters can be used to filter photogrammetric point clouds as well. Experiments are implemented to filter two dense matching point clouds with different noise levels. Results show that the standard Lidar filter is robust to random noise. However, artefacts and blunders in the DIM points often appear due to low contrast or poor texture in the images. Filtering will be erroneous in these locations. Filtering the DIM points pre-processed by a ranking filter will bring higher Type II error (i.e. non-ground points actually labelled as ground points) but much lower Type I error (i.e. bare ground points labelled as non-ground points). Finally, the potential DTM accuracy that can be achieved by DIM points is evaluated. Two DIM point clouds derived by Pix4Dmapper and SURE are compared. On grassland dense matching generates points higher than the true terrain surface, which will result in incorrectly elevated DTMs. The application of the ranking filter leads to a reduced bias in the DTM height, but a slightly increased noise level.

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