Forest road quality control using ALS data

2015 ◽  
Vol 45 (11) ◽  
pp. 1636-1642 ◽  
Author(s):  
Katalin Kiss ◽  
Jukka Malinen ◽  
Timo Tokola
Keyword(s):  
Laser Scanning ◽  
Road Surface ◽  
Airborne Laser Scanning ◽  
Forest Roads ◽  
Forest Road ◽  
Topographic Position ◽  
The Road ◽  
Topographic Position Index ◽  
Airborne Laser ◽  
Position Index

Good road conditions are necessary for the smooth transportation of forest machines and products. High-density airborne laser scanning data were used here to determine the quality of road surfaces and ditching systems. Forest roads in Kiihtelysvaara, Finland, were assessed in August 2013. Eight categories (structural condition, seasonal damage, drying, bridges, surface wear, visibility, coppicing, and flatness) have been inventoried and divided into three quality classes: poor, satisfactory, and good. The topographic position index, standardize elevation index, and hydrology tools were used on digital elevation models with different resolutions to test which categories could be derived. The road surface quality was most clearly related to surface wearing and flatness, and the topographic position index described the road surface best at resolutions of 0.20 m and 0.25 m; however, the standardized elevation index was superior at a 0.50 m resolution. The ditching system plays an important role in the drying of roads, and the hydrological tools and land facet analysis were most suitable for identifying the location of ditches and assessing their quality at 0.20 m and 0.25 m resolutions, respectively. The road surface was classified in all resolutions at least 66% correctly, whereas the ditches were classified in all resolutions at least 60% correctly. The results confirm that airborne laser scanning data can be used for obtaining quality information on forest roads.

scholarly journals COMPARISON OF HIGH AND LOW DENSITY AIRBORNE LIDAR DATA FOR FOREST ROAD QUALITY ASSESSMENT

2016 ◽  
Vol III-8 ◽  
pp. 167-172
Author(s):  
K. Kiss ◽  
J. Malinen ◽  
T. Tokola
Keyword(s):  
Laser Scanning ◽  
High Density ◽  
Road Surface ◽  
Airborne Laser Scanning ◽  
Low Density ◽  
Surface Wear ◽  
Forest Roads ◽  
Density Data ◽  
The Road ◽  
Airborne Laser

Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.

scholarly journals COMPARISON OF HIGH AND LOW DENSITY AIRBORNE LIDAR DATA FOR FOREST ROAD QUALITY ASSESSMENT

2016 ◽  
Vol III-8 ◽  
pp. 167-172 ◽  
Author(s):  
K. Kiss ◽  
J. Malinen ◽  
T. Tokola
Keyword(s):  
Laser Scanning ◽  
High Density ◽  
Road Surface ◽  
Airborne Laser Scanning ◽  
Low Density ◽  
Surface Wear ◽  
Forest Roads ◽  
Density Data ◽  
The Road ◽  
Airborne Laser

Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31–92%) than on low-density data (25–40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.

scholarly journals Forest Road Status Assessment Using Airborne Laser Scanning

2019 ◽  
Vol 66 (4) ◽  
pp. 501-508 ◽  
Author(s):  
Katalin Waga ◽  
Piotr Tompalski ◽  
Nicholas C Coops ◽  
Joanne C White ◽  
Michael A Wulder ◽  
...  
Keyword(s):  
British Columbia ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Height Distribution ◽  
Forest Roads ◽  
The Road ◽  
Airborne Laser ◽  
Independent Field ◽  
The Status ◽  
The Impact

Abstract Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.

scholarly journals Mapping Drainage Structures Using Airborne Laser Scanning by Incorporating Road Centerline Information

2021 ◽  
Vol 13 (3) ◽  
pp. 463
Author(s):  
Chi-Kuei Wang ◽  
Nadeem Fareed
Keyword(s):  
Laser Scanning ◽  
Google Earth ◽  
Airborne Laser Scanning ◽  
Rural Site ◽  
Urban Site ◽  
Positional Accuracy ◽  
The Road ◽  
Airborne Laser ◽  
Public Data ◽  
Manual Methods

Wide-area drainage structure (DS) mapping is of great concern, as many DSs are reaching the end of their design life and information on their location is usually absent. Recently, airborne laser scanning (ALS) has been proven useful for DS mapping through manual methods using ALS-derived digital elevation models (DEMs) and hillshade images. However, manual methods are slow and labor-intensive. To overcome these limitations, this paper proposes an automated DS mapping algorithm (DSMA) using classified ALS point clouds and road centerline information. The DSMA begins with removing ALS ground points within the buffer of the road centerlines; the size of the buffer varies according to different road classes. An ALS-modified DEM (ALS-mDEM) is then generated from the remaining ground points. A drainage network (DN) is derived from the ALS-mDEM. Candidate DSs are then obtained by intersecting the DN with the road centerlines. Finally, a refinement buffer of 15 m is placed around each candidate DS to prevent duplicate DS from being generated in close proximity. A total area of 50 km2, including an urban site and a rural site, in Vermont, USA, was used to assess the DSMA. Based on the road functional classification scheme of the Federal Highway Administration (FHWA), the centerline information regarding FHWA roads was obtained from a public data portal. The centerline information on non-FHWA roads, i.e., private roads and streets, was derived from the impervious surface data of a land cover dataset. A benchmark DS dataset was gathered from the transport agency of Vermont and was further augmented using Google Earth Street View images by the authors. The one-to-one correspondence between the benchmark DS and mapped DS for these two sites was then established. The positional accuracy was assessed by computing the Euclidian distance between the benchmark DS and mapped DS. The mean positional accuracy for the urban site and rural site were 13.5 m and 15.8 m, respectively. F1-scores were calculated to assess the prediction accuracy. For FHWA roads, the F1-scores were 0.87 and 0.94 for the urban site and rural site, respectively. For non-FHWA roads, the F1-scores were 0.72 and 0.74 for the urban site and rural site, respectively.

scholarly journals Locally invariant analysis of forest road quality using two different pulse density airborne laser scanning datasets

Silva Fennica ◽  
2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Katalin Waga ◽  
Jukka Malinen ◽  
Timo Tokola
Keyword(s):  
Laser Scanning ◽  
Spline Interpolation ◽  
Structural Condition ◽  
Poor Quality ◽  
Airborne Laser Scanning ◽  
Road Maintenance ◽  
Topographic Analysis ◽  
Forest Road ◽  
Airborne Laser ◽  
Pulse Density

Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology to determine how airborne laser scanning derived variables with the use of reference surface can determine forest road quality. The concept of a reference DEM (Digital Elevation Model) was used to guarantee locally invariant topographic analysis of road roughness. Structural condition, surface wear and flatness were assessed at two test sites in Eastern Finland, calculating surface indices with and without the reference DEM. The high pulse density dataset (12 pulses m) gave better classification results (77% accuracy of the correctly classified road sections) than the low pulse density dataset (1 pulse m). The use of a reference DEM increased the precision of the road quality classification with the low pulse density dataset when the classification was performed in two-steps. Four interpolation techniques (Inverse Weighted Distance, Kriging, Natural Neighbour and Spline) were compared, and spline interpolation provided the best classification. The work shows that applying a spline reference DEM it is possible to identify 66% of the poor quality road sections and 78% of the good ones. Locating these roads is essential for road maintenance.–2–2

scholarly journals New Advances in Automated Urban Modelling from Airborne Laser Scanning Data

2011 ◽  
Vol 5 (3) ◽  
pp. 196-208 ◽  
Author(s):  
D. F. Laefer ◽  
T. Hinks ◽  
H. Carr ◽  
L. Truong-Hong
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Urban Modelling

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals Airborne laser scanning reveals large tree trunks on forest floor

2021 ◽  
Vol 491 ◽  
pp. 119225
Author(s):  
Einari Heinaro ◽  
Topi Tanhuanpää ◽  
Tuomas Yrttimaa ◽  
Markus Holopainen ◽  
Mikko Vastaranta
Keyword(s):  
Forest Floor ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Large Tree ◽  
Airborne Laser
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