scholarly journals Rill Erosion in Unpaved and Rock-Paved Roads after Wildfire in a Mediterranean Forest

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 79
Author(s):  
Manuel Esteban Lucas-Borja ◽  
Demetrio Antonio Zema
Keyword(s):  
Linear Models ◽  
Bare Soil ◽  
Road Surface ◽  
Rill Erosion ◽  
Forest Roads ◽  
Unpaved Roads ◽  
The Road ◽  
Burned Areas ◽  
La Mancha ◽  
Input Variables

Forest roads are often subject to intense runoff and erosion, and the rates can be increased by other disturbance factors, such as wildfires. Since scarce literature exists on the effects of wildfires on rill erosion of forest roads, this study presents the first results of a wider research, evaluating rill erosion in four different types of roads on a forest in Hellìn (Castilla-La Mancha, Central-Eastern Spain): unpaved roads made of native materials (soil found at the study site) and rock-paved roads, both built in unburned areas as well as unpaved and rock-paved roads, in fire-affected areas. In general, the unpaved roads are more subject to rill erosion compared to the rock-paved roads. In particular, the road of burned areas shows an erodibility that is higher by more than 200% compared to the unpaved and unburned roads, and even by about 400% compared to rock-paved roads (in both burned and unburned areas). A modeling approach based on distance linear models and distance-based redundancy analysis has identified the slope of road surface and upstream hillslope as well as the percent bare soil over the road surface as important input variables to predict rill erosion in future modeling experiences. All these variables may be easily measured by quick field surveys. Although the analytical approach of this study is limited to the geometric characteristics of erosion features, the results and the methods developed are useful to support the activity of land managers to better understand the magnitude of road erosion and to develop efficient measures for its control and mitigation.

scholarly journals Application of Mixed Linear Models in the Analysis of Road Surface Features

2015 ◽  
Vol 54 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Jurgita Židanavičiūtė ◽  
Audrius Vaitkus
Keyword(s):  
Linear Models ◽  
Road Surface ◽  
Linear Regression Models ◽  
Mixed Linear Models ◽  
Time Intervals ◽  
Surface Strength ◽  
The Road ◽  
Unequally Spaced ◽  
Units Of Analysis ◽  
The Impact

The data were collected by researchers at the Road Research Institute, in a study investigating the impact of differentfactors on road surface strength. In this statistical analysis, we apply linear mixed models (LMMs) to clustered longitudinal data, inwhich the units of analysis (points in the road) are nested within clusters (sample of four different road segments), and repeatedmeasures of road strength in these different points are collected over time with unequally spaced time intervals. The data arebalanced – each cluster has the same number of units, which are measured at the same number of time points. Because of correlateddata and different clusters in which data could be correlated, linear regression models are not appropriate here, and therefore linearmixed models are applied.

scholarly journals NEURAL FUZZY NETWORK FOR ASSESSING TECHNOLOGICAL SOLUTIONS FOR FOREST ROADS

2020 ◽  
Vol 10 (3) ◽  
pp. 95-103
Author(s):  
Vladimir Pobedinskiy ◽  
Sergey Buldakov ◽  
Andrey Berstenev ◽  
Elena Anastas
Keyword(s):  
Neural Network ◽  
Intelligent Systems ◽  
Information Technologies ◽  
Rapid Assessment ◽  
Road Construction ◽  
Road Surface ◽  
Forest Roads ◽  
The Road ◽  
Logging Roads ◽  
Fuzzy Network

The article is devoted to the problem of improving road construction technologies, in particular, technological solutions for logging roads. As you know, in road construction, the choice and justification of technological solutions for the road surface is one of the first stages of design, the efficiency of which affects further project as a whole, timing and costs of construction. The solution to such a problem is extremely difficult and, first of all, due to the many interrelated parameters, factors, as well as the uncertainties of data in the problem. The task becomes much more complicated when it is also necessary to take into account the economic indicators of road construction project. But it is in this form that it is of the greatest interest, since these characteristics are often the most important in practice. For these reasons, the problem remains completely unsolved. Therefore, requires further research, as noted, taking into account the uncertainties in the problem. Intelligent systems based on the theory of fuzzy sets, neural networks and their hybrid solutions are proposed for this class of problems, as a result of modern achievements in the field of mathematics and information technologies. Thus, the purpose of this research was to develop a neural network for evaluating technological solutions for logging roads. The result of the research was the development of an adaptive neuro-fuzzy network such as ANFIS, which allows calculating the cost of the road surface depending on the main technological and initial financial parameters. The neural network can be recommended for the design of forest roads, as well as for rapid assessment of the effectiveness of various technological solutions during competitive (tender) selection.

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 Road Surface Classification with Images Captured From Low-cost Camera - Road Traversing Knowledge (RTK) Dataset

2019 ◽  
Vol 26 (3) ◽  
pp. 50-64 ◽  
Author(s):  
Thiago Rateke ◽  
Karla Aparecida Justen ◽  
Aldo Von Wangenheim
Keyword(s):  
Low Cost ◽  
Autonomous Vehicle ◽  
Road Surface ◽  
Navigation Systems ◽  
Road Maintenance ◽  
Unpaved Roads ◽  
The Road ◽  
Road Pavement ◽  
Surface Patterns ◽  
Autonomous Vehicle Navigation

The type of road pavement directly influences the way vehicles are driven. It’s common to find papers that deal with path detection but don’t take into account major changes in road surface patterns. The quality of the road surface has a direct impact on the comfort and especially on the safety of road users. In emerging countries it’s common to find unpaved roads or roads with no maintenance. Unpaved or damaged roads also impact in higher fuel costs and vehicle maintenance. This kind of analysis can be useful for both road maintenance departments as well as for autonomous vehicle navigation systems to verify potential critical points. For the experiments accomplishment upon the surface types and quality classification, we present a new dataset, collected with a low-cost camera. This dataset has examples of good and bad asphalt (with potholes and other damages) other types of pavement and also many examples of unpaved roads (with and without potholes). We also provide several frames from our dataset manually sorted in surface types for tests accuracy verification. Our road type and quality classifier was done through a simple Convolutional Neural Network with few steps and presents promising results in different datasets.

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 The Influence of Vegetation Succession on Bearing Capacity of Forest Roads Made of Unbound Aggregates

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1137
Author(s):  
Sylwester M. Grajewski ◽  
Andrzej Czerniak ◽  
Ewa E. Kurowska
Keyword(s):  
Bearing Capacity ◽  
Plant Cover ◽  
Deformation Modulus ◽  
Vegetation Succession ◽  
Road Surface ◽  
Plant Succession ◽  
Functional Feature ◽  
Forest Roads ◽  
The Road ◽  
Unbound Aggregates

The aim of the research was to verify a common opinion concerning a positive influence of plants on the bearing capacity and durability of forest roads made of unbound aggregates. The surface bearing capacity is defined as the ability to transfer traffic loads without any excessive deformations which would hinder regular use of the surface and shorten its durability. It is a significant functional feature of any road. The article analyzed the influence of road surface plant succession on its bearing parameters. The research was conducted on sections of experimental road constructed using macadam technology and reinforced partly with a biaxial geogrid. Measurements were taken with a lightweight Zorn ZFG 3000 GPS type deflectometer with a 300 mm pressure plate radius and 10 kg drop weight which allowed to measure dynamic deformation modulus (Evd) and s/v parameter regarded as an indicator of compaction accuracy of the studied layer. Evd values and s/v parameters, which were obtained by measuring the road pavement covered in vegetation and after having it mechanically removed (mowed), were submitted to the analysis; next, they were compared with the results of an analysis done on areas naturally deprived of the plant cover and located in the immediate vicinity of the measuring points. The conducted research has indicated unfavorable influence of vegetation succession on the bearing parameters of the analyzed sections. The greatest drop in the mean Evd value was 39%, and s/v parameter deteriorated as much as 9%. Hence, a regular mowing of the road surface (including the maneuvering, storage and passing areas) should be taken as standard and mandatory procedures of forest road maintenance.

Tire-Road Interactions — Harmony or Conflict?

1989 ◽  
Vol 17 (1) ◽  
pp. 66-84
Author(s):  
A. R. Williams
Keyword(s):  
Rolling Resistance ◽  
Major Effect ◽  
Road Surface ◽  
Interactive Effects ◽  
Central Theme ◽  
Water Drainage ◽  
The Road ◽  
Truck Tires ◽  
Road Surfaces ◽  
Tire Wear

Abstract This is a summary of work by the author and his colleagues, as well as by others reported in the literature, that demonstrate a need for considering a vehicle, its tires, and the road surface as a system. The central theme is interaction at the footprint, especially that of truck tires. Individual and interactive effects of road and tires are considered under the major topics of road aggregate (macroscopic and microscopic properties), development of a novel road surface, safety, noise, rolling resistance, riding comfort, water drainage by both road and tire, development of tire tread compounds and a proving ground, and influence of tire wear on wet traction. A general conclusion is that road surfaces have both the major effect and the greater potential for improvement.

scholarly journals Relationship between Cycling Infrastructure and Transportation Cycling in a Small Urban Area

2021 ◽  
Vol 1 (1) ◽  
pp. 99-112
Author(s):  
Richard Larouche ◽  
Nimesh Patel ◽  
Jennifer L. Copeland
Keyword(s):  
Generalized Linear Models ◽  
Qualitative Data ◽  
Linear Models ◽  
The Other ◽  
Small City ◽  
The Road ◽  
The Past ◽  
Low Prevalence ◽  
The Relationship

The role of infrastructure in encouraging transportation cycling in smaller cities with a low prevalence of cycling remains unclear. To investigate the relationship between the presence of infrastructure and transportation cycling in a small city (Lethbridge, AB, Canada), we interviewed 246 adults along a recently-constructed bicycle boulevard and two comparison streets with no recent changes in cycling infrastructure. One comparison street had a separate multi-use path and the other had no cycling infrastructure. Questions addressed time spent cycling in the past week and 2 years prior and potential socio-demographic and psychosocial correlates of cycling, including safety concerns. Finally, we asked participants what could be done to make cycling safer and more attractive. We examined predictors of cycling using gender-stratified generalized linear models. Women interviewed along the street with a separate path reported cycling more than women on the other streets. A more favorable attitude towards cycling and greater habit strength were associated with more cycling in both men and women. Qualitative data revealed generally positive views about the bicycle boulevard, a need for education about sharing the road and for better cycling infrastructure in general. Our results suggest that, even in smaller cities, cycling infrastructure may encourage cycling, especially among women.

scholarly journals CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1402
Author(s):  
Taehee Lee ◽  
Yeohwan Yoon ◽  
Chanjun Chun ◽  
Seungki Ryu
Keyword(s):  
Surface Crack ◽  
Autonomous Vehicles ◽  
Crack Detection ◽  
Model Performance ◽  
Semantic Segmentation ◽  
Road Surface ◽  
Image Brightness ◽  
The Road ◽  
Detection Model ◽  
Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

Sign in / Sign up

Export Citation Format

Share Document