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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]

Overlay (or heuristic) models have been shown to be a reasonable method to generate landslide susceptibility maps. However, overlay models are subjective and rely on expert judgment. The method developed here employs an overlay model but it tries to estimate certain parameters, such as slope angle or distance to streams, in a more quantitative manner. Each individual factor believed to have an influence on mass wasting processes is scaled to a value between 0 and 1. All different factors are then multiplied, producing the degree of stability, De which lies on a ratio scale, rather than a nominal or interval scale. Thus, a pseudo probability of failure can be obtained. The data required are derived partly from thematic maps and partly from stereo air photos. The air photo stereo pair is used to automatically derive a digital elevation model which is then used to create an orthoimage of an area. The air photos are also used to develop a land use map, an important component in estimating landslide susceptibility. This methodology was tested on a section of the Santa Monica Mountains near Malibu, California. The resulting map both visually and statistically appears to successfully identify problematic areas. Although the methodology was primarily developed to identify shallow slides, the crest of a large, deep-seated landslide was also recognized. The general methodology should be easily adaptable to other regions or could be applied with a different set of factors than those considered in this specific case. KEYWORDS: Landslide susceptibility, GIS, remote sensing, Introduction Landslide susceptibility maps can be an effective planning tool for identifying areas of concern or areas that need further, more specific investigations. For example, when planning a new road [...]