NOISE REDUCTION IN CONTOUR LINES AND SLOPE MAPS FROM MEDIUM/HIGH-DENSITY LIDAR DATA

The use of medium/high-density LIDAR (Light Detection And Ranging) data for land modelling and DTM (Digital TerrainModel) is becoming more widespread. This level of detail is difficult to achieve with other means or materials. However,the horizontal and vertical geometric accuracy of the LIDAR points obtained, although high, is not homogeneous.Horizontally you can reach precisions around 30-50 cm, while the vertical precision is rarely greater than 10-15 cm. Theresult of LIDAR flights, are clouds of points very close to each other (30-60 cm) with significant elevation variations, evenif the terrain is flat. And this makes the triangulated models TIN (Triangulated Irregular Network) obtained from such LIDARdata especially chaotic. Since contour lines are generated directly from such triangulated models, their appearance showsexcessive noise, with excessively broken and rapidly closed on themselves. Getting smoothed contour liness, withoutdecreasing accuracy, is a challenge for terrain model software. In addition, triangulated models obtained from LIDAR dataare the basis for future slope maps of the land. And for the same reason explained in the previous paragraph, these slopemaps generated from high or medium density LIDAR point clouds are especially heterogeneous. Achieving uniformity andgreater adjustment to reality by reducing the natural noise of LIDAR data is another added challenge. In this paper, theproblem of excessive noise from LIDAR data of high (around 8 points/m2) and medium density (around 2 points/m2) in thegeneration of contour lines and terrain slope maps is raised and solutions are proposed to reduce this noise. All this, in thearea of specific software for the management of TIN models and GIS (Geographic Information System) and adapting thealternatives proposed by these programmes.

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POSSIBILITIES FOR USING LIDAR AND PHOTOGRAMMETRIC DATA OBTAINED WITH AN UNMANNED AERIAL VEHICLE FOR LEVEE MONITORING

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

10.5194/isprsarchives-xli-b1-773-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 773-780 ◽

Cited By ~ 4

Author(s):

K. Bakuła ◽

W. Ostrowski ◽

M. Szender ◽

W. Plutecki ◽

A. Salach ◽

...

Keyword(s):

Laser Scanner ◽

Digital Camera ◽

Digital Terrain Model ◽

Point Clouds ◽

Lidar Data ◽

Unmanned Aerial System ◽

Navigation Systems ◽

Control Points ◽

Terrain Model ◽

Increase In Accuracy

This paper presents the possibilities for using an unmanned aerial system for evaluation of the condition of levees. The unmanned aerial system is equipped with two types of sensor. One is an ultra-light laser scanner, integrated with a GNSS receiver and an INS system; the other sensor is a digital camera that acquires data with stereoscopic coverage. Sensors have been mounted on the multirotor, unmanned platform the Hawk Moth, constructed by MSP company. LiDAR data and images of levees the length of several hundred metres were acquired during testing of the platform. Flights were performed in several variants. Control points measured with the use of the GNSS technique were considered as reference data. The obtained results are presented in this paper; the methodology of processing the acquired LiDAR data, which increase in accuracy when low accuracy of the navigation systems occurs as a result of systematic errors, is also discussed. The Iterative Closest Point (ICP) algorithm, as well as measurements of control points, were used to georeference the LiDAR data. Final accuracy in the order of centimetres was obtained for generation of the digital terrain model. The final products of the proposed UAV data processing are digital elevation models, an orthophotomap and colour point clouds. The authors conclude that such a platform offers wide possibilities for low-budget flights to deliver the data, which may compete with typical direct surveying measurements performed during monitoring of such objects. However, the biggest advantage is the density and continuity of data, which allows for detection of changes in objects being monitored.

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Road Ditch Line Mapping with Mobile LiDAR

10.5703/1288284317354 ◽

2021 ◽

Author(s):

Ayman Habib ◽

◽

Darcy M. Bullock ◽

Yi-Chun Lin ◽

Raja Manish

Keyword(s):

Flow Direction ◽

Digital Terrain Model ◽

Point Clouds ◽

Lidar Data ◽

Design Standards ◽

Cross Sectional ◽

Terrain Model ◽

State Highway ◽

3D Point Clouds ◽

Longitudinal Profiles

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires mapping of the ditch profile to identify areas requiring excavation of long-term sediment accumulation. High-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) provide an opportunity for effective monitoring of roadside ditches and performing hydrological analyses. This study evaluated the applicability of mobile LiDAR for mapping roadside ditches for slope and drainage analyses. The performance of alternative MLMS units was performed. These MLMS included an unmanned ground vehicle, an unmanned aerial vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system. Point cloud from all the MLMS units were in agreement in the vertical direction within the ±3 cm range for solid surfaces, such as paved roads, and ±7 cm range for surfaces with vegetation. The portable backpack system that could be carried by a surveyor or mounted on a vehicle and was the most flexible MLMS. The report concludes that due to flexibility and cost effectiveness of the portable backpack system, it is the preferred platform for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulders, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground filtering approach is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from LiDAR data, and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data was found to be very close to highway cross slope design standards of 2% on driving lanes, 4% on shoulder, as well as 6-by-1 slope for ditch lines. Potential flooded regions are identified by detecting areas with no LiDAR return and a recall score of 54% and 92% was achieved by the medium-grade wheel-based and vehicle-mounted portable systems, respectively.

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POSSIBILITIES FOR USING LIDAR AND PHOTOGRAMMETRIC DATA OBTAINED WITH AN UNMANNED AERIAL VEHICLE FOR LEVEE MONITORING

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

10.5194/isprs-archives-xli-b1-773-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 773-780 ◽

Cited By ~ 1

Author(s):

K. Bakuła ◽

W. Ostrowski ◽

M. Szender ◽

W. Plutecki ◽

A. Salach ◽

...

Keyword(s):

Laser Scanner ◽

Digital Camera ◽

Digital Terrain Model ◽

Point Clouds ◽

Lidar Data ◽

Unmanned Aerial System ◽

Navigation Systems ◽

Control Points ◽

Terrain Model ◽

Increase In Accuracy

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Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters

Remote Sensing ◽

10.3390/rs11091037 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1037 ◽

Cited By ~ 12

Author(s):

Shangshu Cai ◽

Wuming Zhang ◽

Xinlian Liang ◽

Peng Wan ◽

Jianbo Qi ◽

...

Keyword(s):

Digital Terrain Model ◽

Point Clouds ◽

Parameter Tuning ◽

Airborne Lidar ◽

Lidar Data ◽

Cloth Simulation ◽

Terrain Model ◽

Adaptive Parameter ◽

Digital Terrain ◽

Airborne Lidar Data

Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.

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A Novel Vegetation Point Cloud Density Tree-Segmentation Model for Overlapping Crowns Using UAV LiDAR

Remote Sensing ◽

10.3390/rs13081442 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1442

Author(s):

Kaisen Ma ◽

Yujiu Xiong ◽

Fugen Jiang ◽

Song Chen ◽

Hua Sun

Keyword(s):

Point Cloud ◽

Local Maximum ◽

Window Size ◽

Point Clouds ◽

High Density ◽

Watershed Algorithm ◽

Canopy Area ◽

Individual Trees ◽

Optimal Window ◽

Cloud Density

Detecting and segmenting individual trees in forest ecosystems with high-density and overlapping crowns often results in bias due to the limitations of the commonly used canopy height model (CHM). To address such limitations, this paper proposes a new method to segment individual trees and extract tree structural parameters. The method involves the following key steps: (1) unmanned aerial vehicle (UAV)-scanned, high-density laser point clouds were classified, and a vegetation point cloud density model (VPCDM) was established by analyzing the spatial density distribution of the classified vegetation point cloud in the plane projection; and (2) a local maximum algorithm with an optimal window size was used to detect tree seed points and to extract tree heights, and an improved watershed algorithm was used to extract the tree crowns. The proposed method was tested at three sites with different canopy coverage rates in a pine-dominated forest in northern China. The results showed that (1) the kappa coefficient between the proposed VPCDM and the commonly used CHM was 0.79, indicating that performance of the VPCDM is comparable to that of the CHM; (2) the local maximum algorithm with the optimal window size could be used to segment individual trees and obtain optimal single-tree segmentation accuracy and detection rate results; and (3) compared with the original watershed algorithm, the improved watershed algorithm significantly increased the accuracy of canopy area extraction. In conclusion, the proposed VPCDM may provide an innovative data segmentation model for light detection and ranging (LiDAR)-based high-density point clouds and enhance the accuracy of parameter extraction.

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Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

Forest Ecosystems ◽

10.1186/s40663-019-0212-0 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Wuming Zhang ◽

Shangshu Cai ◽

Xinlian Liang ◽

Jie Shao ◽

Ronghai Hu ◽

...

Keyword(s):

Tree Height ◽

Point Clouds ◽

Airborne Lidar ◽

Canopy Height ◽

Lidar Data ◽

Cloth Simulation ◽

Simulation Based ◽

Maximum Tree Height ◽

Airborne Lidar Data ◽

Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

Remote Sensing ◽

10.3390/rs13132485 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2485

Author(s):

Yi-Chun Lin ◽

Raja Manish ◽

Darcy Bullock ◽

Ayman Habib

Keyword(s):

Flow Direction ◽

Sediment Accumulation ◽

Digital Terrain Model ◽

Vertical Direction ◽

Point Clouds ◽

Lidar Data ◽

Cross Sectional ◽

Premature Failure ◽

3D Point Clouds ◽

Mapping Systems

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

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