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Temporary roads are often placed in mountainous regions for logging purposes but then never decommissioned and removed. These abandoned forest roads often have unwanted environmental consequences. They can lead to altered hydrological regimes, excess erosion, and mass wasting events. These events can affect sediment budgets in streams, with negative consequences for anadromous fish populations. Maps of these roads are frequently non-existent; therefore, methods need to be created to identify and locate these roads for decommissioning. Abandoned logging roads in the Point Reyes National Seashore in California, an area partially under heavy forest canopy, were mapped using object-based image processing in concert with machine learning. High-resolution Q1 LiDAR point clouds from 2019 were used to create a bare earth model of the region, from which a slope model was derived. This slope model was then subjected to segmentation algorithms to identify and isolate regions of differing slopes. Regions of differing slopes were then used in a convolutional neural network (CNN), and a maximum likelihood classifier was used to delineate the historic road network. The accuracy assessment was conducted using historic aerial photos of the state of the region post-logging, along with ground surveys to verify the presence of logging roads in areas of question. This method was successfully able to identify road networks with a precision of 0.991 and an accuracy of 0.992. It was also found that the CNN was able to identify areas of highest disturbance to the slope gradient. This methodology is a valuable tool for decision makers who need to identify areas of high disturbance in order to mitigate adverse effects. [ABSTRACT FROM AUTHOR]

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads.

The aim of the research was to determine the scale and forms of logging road impacts on the natural environment and residual cultural heritage of Ruthenian Highlanders that characterise the Polish part of the north-eastern Carpathians. To this end, we used LiDAR-derived DEM to detect and map forest roads and skid trails, and to estimate road density, in 60 forest divisions sampled at random from among the 2639 km 2 of forest managed in the above region by the State Forests National Forest Holding. The determined road network was then validated by GPS ground control. Supplementary, detailed LiDAR data were used to detect features of the past cultural landscape, and to determine the interference in that due to contemporary logging roads. Overall skidder traffic was estimated on the basis of a thorough analysis of State Forests documents, including legal acts, guidelines, management plans, detailed databases and maps of planned cuts. LiDAR data proved to be an adequate tool in the mapping of the network of logging roads present under the tree canopy, although it slightly underestimates road density in a densely vegetated, post-landslide topography. Our LiDAR-assessed density of Carpathian logging roads (at 108.5–140.7 m ha −1 , including skid trails) is nevertheless among the highest reported in the literature, and one that far exceeds recommended values. Spatial pattern analysis further revealed the unfavourable phenomenon of multiple parallel roads forming contiguous areas of disturbed soil and constituting dispersal paths for invasive plants. Many examples of cultural remains being run over and destroyed by skidders could also be reported. The observed process of log extraction is one of the least sustainable aspects to forest management in the Carpathians, and our results emphasise very clearly the need for rules to support the preservation of cultural heritage in forested areas, as well as improved tools by which skidding practices can be monitored. [ABSTRACT FROM AUTHOR]

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer's accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads. [ABSTRACT FROM AUTHOR]