Search Results (5)

Landslide trails are important elements of landslide inventory maps, providing valuable information for landslide risk and hazard assessment. Compared with traditional manual mapping, skeletonization methods offer a more cost-efficient way to map landslide trails, by automatically generating centerlines from landslide polygons. However, a challenge to existing skeletonization methods is that expert knowledge and manual intervention are required to obtain a branchless skeleton, which limits the applicability of these methods. To address this problem, a new workflow for landslide trail extraction (LTE) is proposed in this study. To avoid generating redundant branches and to improve the degree of automation, two endpoints, i.e., the crown point and the toe point, of the trail were determined first, with reference to the digital elevation model. Thus, a fire extinguishing model (FEM) is proposed to generate skeletons without redundant branches. Finally, the effectiveness of the proposed method is verified, by extracting landslide trails from landslide polygons of various shapes and sizes, in two study areas. Experimental results show that, compared with the traditional grassfire model-based skeletonization method, the proposed FEM is capable of obtaining landslide trails without spurious branches. More importantly, compared with the baseline method in our previous work, the proposed LTE workflow can avoid problems including incompleteness, low centrality, and direction errors. This method requires no parameter tuning and yields excellent performance, and is thus highly valuable for practical landslide mapping. Full article

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25–0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F${}_1$-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method suitable to efficiently and successfully capture the temporal trends and spatial heterogeneity of degradation in alpine grasslands. Additionally, U-Net is a powerful and robust tool to map erosion sites in a predictive manner utilising large amounts of new aerial imagery. Full article

The prediction of landslides and other events associated with slope movement is a very serious issue in many national parks around the world. This article deals with the territory of the Malá Studená Dolina (Little Cold Valley, High Tatras National Park—Slovakia), where there are extensive talus cones, through which seasonally heavy hiking trails lead. In the last few years particularly, there have been frequent falls and landslides in the mountainous environment, which also caused several fatal injuries in 2018. For the above reasons, efforts are being made to develop a methodology for monitoring the changes of the talus cones in this specific alpine area, to determine the size, speed, and character of the morphological changes of the soil. Non-contact methods of mass data collection (laser scanning with Leica P40 and aerial photogrammetry with unmanned aerial system (UAS) DJI Phantom 4 Pro) have been used. The results of these measurements were compared and the overall suitability of both methods for measurement in such terrain evaluated. The standard deviation of the difference of surface determination (represented by the point cloud) is about 0.03 m. As such accuracy is sufficient for the purpose of monitoring talus cones and the use of UAS is easier and associated with lower risk of damage of expensive equipment, we conclude that this method is more suitable for mapping and for repeated monitoring of such terrain. The properties of the outputs of the individual measurement methods, the degree of measurement difficulty and specific measurement conditions in the mountainous terrain, as well as the economy of the individual methods, are discussed in detail. Full article

Urban parks and open spaces offer a unique setting that can play a vital role in improving health and quality of life in cities and towns, making cities more attractive places to live and work, and connecting residents to nature. Degradation of park facilities caused by natural processes or recreational activities requires continuous monitoring for efficient maintenance and management. Identification and continuous monitoring of areas prone to natural hazards such as landslides within an urban park are particularly important for public safety. Traditional techniques for identification and monitoring of such areas involving field surveys, being costly and time-consuming, cannot be used on a regular basis. This research explored the integrated use of aerial photographs and point cloud LiDAR data for identification of areas prone to landslide and soil erosion zones in an urban park and a conservation area known as Wakamow Valley, Moose Jaw, Saskatchewan, Canada. This study used the point cloud LiDAR of 2014 to develop a Digital Elevation Model (DEM) of the area. The accuracy of the DEM was validated through a series of well-distributed ground control points collected through a survey grade handheld GPS device. The areas prone to potential landslides and soil erosion were identified using slope analysis techniques. A typical criterion of areas having a slope greater than 35° was used for classification of potential hazardous zones. Geospatial information including land-cover, land-use, and trail system was extracted from a 2014 aerial photograph to create a base map. It has been estimated that 5.3 km along the banks of the Moose Jaw River and 8 km along the cliff of the canyon-shaped Wakamow Valley are under a possible threat of soil erosion and landslides. This portion of the valley was classified as high-risk for possible landslides and soil erosion. Full article

The clear need for accurate landslide susceptibility mapping has led to multiple approaches. Physical models are easily interpreted and have high predictive capabilities but rely on spatially explicit and accurate parameterization, which is commonly not possible. Statistical methods can include other factors influencing slope stability such as distance to roads, but rely on good landslide inventories. The maximum entropy (MaxEnt) model has been widely and successfully used in species distribution mapping, because data on absence are often uncertain. Similarly, knowledge about the absence of landslides is often limited due to mapping scale or methodology. In this paper a hybrid approach is described that combines the physically-based landslide susceptibility model “Stability INdex MAPping” (SINMAP) with MaxEnt. This method is tested in a coastal watershed in Pacifica, CA, USA, with a well-documented landslide history including 3 inventories of 154 scars on 1941 imagery, 142 in 1975, and 253 in 1983. Results indicate that SINMAP alone overestimated susceptibility due to insufficient data on root cohesion. Models were compared using SINMAP stability index (SI) or slope alone, and SI or slope in combination with other environmental factors: curvature, a 50-m trail buffer, vegetation, and geology. For 1941 and 1975, using slope alone was similar to using SI alone; however in 1983 SI alone creates an Areas Under the receiver operator Curve (AUC) of 0.785, compared with 0.749 for slope alone. In maximum-entropy models created using all environmental factors, the stability index (SI) from SINMAP represented the greatest contributions in all three years (1941: 48.1%; 1975: 35.3; and 1983: 48%), with AUC of 0.795, 0822, and 0.859, respectively; however; using slope instead of SI created similar overall AUC values, likely due to the combined effect with plan curvature indicating focused hydrologic inputs and vegetation identifying the effect of root cohesion. The combined approach––using either stability index or slope––highlights the importance of additional environmental variables in modeling landslide initiation. Full article