scholarly journals Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 35 ◽  
Author(s):  
Jonathan P. Resop ◽  
Laura Lehmann ◽  
W. Cully Hession
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Lidar Data ◽  
Lower Altitude ◽  
Aerial Laser Scanning ◽  
Digital Terrain ◽  
Terrain Models ◽  
Data Points ◽  
Aerial Vehicle ◽  
Complete Survey

Lidar remote sensing has been used to survey stream channel and floodplain topography for decades. However, traditional platforms, such as aerial laser scanning (ALS) from an airplane, have limitations including flight altitude and scan angle that prevent the scanner from collecting a complete survey of the riverscape. Drone laser scanning (DLS) or unmanned aerial vehicle (UAV)-based lidar offer ways to scan riverscapes with many potential advantages over ALS. We compared point clouds and lidar data products generated with both DLS and ALS for a small gravel-bed stream, Stroubles Creek, located in Blacksburg, VA. Lidar data points were classified as ground and vegetation, and then rasterized to produce digital terrain models (DTMs) representing the topography and canopy height models (CHMs) representing the vegetation. The results highlighted that the lower-altitude, higher-resolution DLS data were more capable than ALS of providing details of the channel profile as well as detecting small vegetation on the floodplain. The greater detail gained with DLS will provide fluvial researchers with better estimates of the physical properties of riverscape topography and vegetation.

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 EFFECTIVE DETECTION OF SUB-SURFACE ARCHEOLOGICAL FEATURES FROM LASER SCANNING POINT CLOUDS AND IMAGERY DATA

2017 ◽  
Vol XLII-2/W5 ◽  
pp. 245-251 ◽  
Author(s):  
A. Fryskowska ◽  
M. Kedzierski ◽  
P. Walczykowski ◽  
D. Wierzbicki ◽  
P. Delis ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Archaeological Site ◽  
Airborne Laser Scanning ◽  
Alternative Methods ◽  
Archaeological Heritage ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Aerial Vehicle ◽  
Imagery Data

The archaeological heritage is non-renewable, and any invasive research or other actions leading to the intervention of mechanical or chemical into the ground lead to the destruction of the archaeological site in whole or in part. For this reason, modern archeology is looking for alternative methods of non-destructive and non-invasive methods of new objects identification. The concept of aerial archeology is relation between the presence of the archaeological site in the particular localization, and the phenomena that in the same place can be observed on the terrain surface form airborne platform. One of the most appreciated, moreover, extremely precise, methods of such measurements is airborne laser scanning. In research airborne laser scanning point cloud with a density of 5 points/sq. m was used. Additionally unmanned aerial vehicle imagery data was acquired. Test area is located in central Europe. The preliminary verification of potentially microstructures localization was the creation of digital terrain and surface models. These models gave an information about the differences in elevation, as well as regular shapes and sizes that can be related to the former settlement/sub-surface feature.<br><br> The paper presents the results of the detection of potentially sub-surface microstructure fields in the forestry area.

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.

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2019 ◽  
pp. 1175-1196
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

scholarly journals DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

2018 ◽  
Vol XLII-2 ◽  
pp. 407-410 ◽  
Author(s):  
E. Hadas ◽  
G. Jozkow ◽  
A. Walicka ◽  
A. Borkowski
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Apple Orchard ◽  
Lidar Data ◽  
Local Minima ◽  
Alpha Shape ◽  
Aerial Vehicle ◽  
Individual Trees

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&amp;thinsp;points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&amp;thinsp;% of trees in the test area. 6&amp;thinsp;% of trees in orchard were not separated from each other and 2&amp;thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&amp;thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

scholarly journals Extraction of linear structures from digital terrain models using deep learning

2021 ◽  
Vol 2 ◽  
pp. 1-14
Author(s):  
Ramish Satari ◽  
Bashir Kazimi ◽  
Monika Sester
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Laser Scanning ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Linear Structures ◽  
Digital Terrain Models ◽  
Deep Convolutional Neural Networks ◽  
Digital Terrain ◽  
Terrain Models

Abstract. This paper explores the role deep convolutional neural networks play in automated extraction of linear structures using semantic segmentation techniques in Digital Terrain Models (DTMs). DTM is a regularly gridded raster created from laser scanning point clouds and represents elevations of the bare earth surface with respect to a reference. Recent advances in Deep Learning (DL) have made it possible to explore the use of semantic segmentation for detection of terrain structures in DTMs. This research examines two novel and practical deep convolutional neural network architectures i.e. an encoder-decoder network named as SegNet and the recent state-of-the-art high-resolution network (HRNet). This paper initially focuses on the pixel-wise binary classification in order to validate the applicability of the proposed approaches. The networks are trained to distinguish between points belonging to linear structures and those belonging to background. In the second step, multi-class segmentation is carried out on the same DTM dataset. The model is trained to not only detect a linear feature, but also to categorize it as one of the classes: hollow ways, roads, forest paths, historical paths, and streams. Results of the experiment in addition to the quantitative and qualitative analysis show the applicability of deep neural networks for detection of terrain structures in DTMs. From the deep learning models utilized, HRNet gives better results.

scholarly journals Laser scanning applications in fluvial studies

2011 ◽  
Vol 35 (6) ◽  
pp. 782-809 ◽  
Author(s):  
Johanna Hohenthal ◽  
Petteri Alho ◽  
Juha Hyyppä ◽  
Hannu Hyyppä
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Lidar Data ◽  
Digital Terrain ◽  
Terrain Models ◽  
Airborne Laser ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Set Up ◽  
Terrain Elevation

During recent decades, the use of high-resolution light detection and ranging altimetry (LiDAR) data in fluvial studies has rapidly increased. Airborne laser scanning (ALS) can be used to extensively map riverine topography. Although airborne blue/green LiDAR can also be utilized for the mapping of river bathymetry, the accuracy levels achieved are not as good as those of terrain elevation measurements. Moreover, airborne bathymetric LiDAR is not yet suitable for mapping shallow water areas. More detailed topographical data may be obtained by fixed-position terrestrial laser scanning (TLS) or mobile terrestrial laser scanning (MLS). One of the newest applications of MLS approaches involves a boat/cart-based mobile mapping system (BoMMS/CartMMS). This set-up includes laser scanning and imaging from a boat moving along a river course and may be used to expand the spatial extent of terrestrial scanning. Detailed digital terrain models (DTMs) derived from LiDAR data can be used to improve the recognition of fluvial landforms, the geometric data of hydraulic modelling, and the estimation of flood inundation extents and fluvial processes.

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2015 ◽  
Vol 6 (3) ◽  
pp. 58-77 ◽  
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

Alternative Methods for Developing and Assessing the Accuracy of UAV-Derived DEMs

2019 ◽  
pp. 249-270
Author(s):  
Dion J. Wiseman ◽  
Jurjen van der Sluijs
Keyword(s):  
High Resolution ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Alternative Methods ◽  
Data Sets ◽  
Digital Terrain ◽  
Terrain Models ◽  
Dense Point ◽  
Data Points ◽  
Derived Data

Digital terrain models are invaluable datasets that are frequently used for visualizing, modeling, and analyzing Earth surface processes. Accurate models covering local scale landscape features are often very expensive and have poor temporal resolution. This research investigates the utility of UAV acquired imagery for generating high resolution terrain models and provides a detailed accuracy assessment according to recommended protocols. High resolution UAV imagery was acquired over a localized dune complex in southwestern Manitoba, Canada and two alternative workflows were evaluated for extracting point clouds. UAV-derived data points were then compared to reference data sets acquired using mapping grade GPS receivers and a total station. Results indicated that the UAV imagery was capable of producing dense point clouds and high resolution terrain models with mean errors as low as -0.15 m and RMSE values of 0.42 m depending on the resolution of the image dataset and workflow employed.

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