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.

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 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 Estimation of Total Biomass in Aleppo Pine Forest Stands Applying Parametric and Nonparametric Methods to Low-Density Airborne Laser Scanning Data

Forests ◽  
2018 ◽  
Vol 9 (4) ◽  
pp. 158 ◽  
Author(s):  
Darío Domingo ◽  
María Lamelas ◽  
Antonio Montealegre ◽  
Alberto García-Martín ◽  
Juan de la Riva
Keyword(s):  
Laser Scanning ◽  
Nonparametric Methods ◽  
Pine Forest ◽  
Airborne Laser Scanning ◽  
Aleppo Pine ◽  
Total Biomass ◽  
Low Density ◽  
Forest Stands ◽  
Airborne Laser

Using low-density discrete Airborne Laser Scanning data to assess the potential carbon dioxide emission in case of a fire event in a Mediterranean pine forest

2017 ◽  
Vol 54 (5) ◽  
pp. 721-740 ◽  
Author(s):  
Antonio Luis Montealegre-Gracia ◽  
María Teresa Lamelas-Gracia ◽  
Alberto García-Martín ◽  
Juan de la Riva-Fernández ◽  
Francisco Escribano-Bernal
Keyword(s):  
Carbon Dioxide ◽  
Laser Scanning ◽  
Carbon Dioxide Emission ◽  
Pine Forest ◽  
Airborne Laser Scanning ◽  
Low Density ◽  
Fire Event ◽  
Airborne Laser ◽  
Mediterranean Pine ◽  
Potential Carbon

scholarly journals Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

2019 ◽  
Vol 11 (3) ◽  
pp. 261 ◽  
Author(s):  
Darío Domingo ◽  
Rafael Alonso ◽  
María Teresa Lamelas ◽  
Antonio Luis Montealegre ◽  
Francisco Rodríguez ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Growth Models ◽  
Model Performance ◽  
Basal Area ◽  
Stand Density ◽  
Airborne Laser Scanning ◽  
Support Vector ◽  
Low Density ◽  
Airborne Laser ◽  
Forest Attributes

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones.

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.

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