Jerry Davis

A Hybrid Physical and Maximum-Entropy Landslide Susceptibility Model

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.

Physical and maximum entropy models applied to inventories of hillslope sediment sources

PurposeThe purpose of our study was to identify major hillslope sediment sources in a partially urbanized coastal watershed supporting salmonid habitat and to evaluate the use of physical and maximum entropy models in predicting sites of greatest concern. Questions include when and where increased runoff from trail and unpaved road surfaces has influenced patterns of landslides and gullies to a greater degree than what would be expected from background processes and controls, such as precipitation intensity, vegetation, soils, and slope characteristics.Materials and methodsSan Pedro Creek Watershed, USA, provides habitat for Oncorhynchus mykiss despite 33% of the watershed being urbanized. The watershed drains steep hillslopes with a median slope of 21°, with the steepest slopes on the 578-m North Peak of Montara Mountain. We inventoried hillslope sediment sources based on field surveys and aerial photographic interpretation in 1941, 1955, 1975, 1983, and 1997. We interpreted causative factors using precipitation records, geologic and soil mapping, digital elevation derivatives, land cover, and road/trail network changes and applied a physical landslide susceptibility model (Stability Index Approach to Terrain Stability Hazard Mapping (SINMAP)) for hillslope stability and a maximum entropy model for assessing gully and landslide centroids.Results and discussionMaps of landslide and gullies reveal an association with land use changes over time. Agricultural land uses led to the development of extensive gullies in parts of the watershed, and some of these continue to contribute significant sediment to the stream system; others were built-over in residential developments. The most significant remaining gullies result from impervious runoff from roads built into steep hillslopes. Although the best single predictor of landslide susceptibility is physically modelled hillslope stability (SINMAP), slope equally contributed to multivariate MAXENT models (area under the receiver operator characteristic curve (AUC) = 0.74 in 1941, 0.65 in 1975, and 0.79 in 1983). Other covariates in the maximum entropy models include plan curvature, trail distance in 1975, geology in 1983 (favoring colluvium), and vegetation.ConclusionsCombining physical hillslope stability with a maximum entropy model appears promising, although overall slope angle also contributed equally. Landslides are episodic and linked to major precipitation/runoff events, such as ENSO events in 1962, 1972, and 1982, but road and trail development from 1955 to 1975 also contributed equally. As by count most gullies relate to earlier agricultural practices, they represent ongoing sediment sources.

Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

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.

Two-dimensional discrete Fourier transform analysis of karst and coral reef morphologies

Fourier transforms have been used in the analysis of landscapes that exhibit the influence of cyclic structures or other morphogenetic controls. Two-dimensional Fourier transforms have been most successful when modeling features with a high frequency over the sample space. This research focuses on applications of 2D discrete Fourier transforms for karst and spur and groove coral reefs, using ArcGIS geoprocessing tools extended with Python NumPy numerical methods. Ten-meter digital elevation data from Puerto Rico and Kentucky holokarst landscapes and five-meter bathymetry from more unidirectional spur and groove coral reefs at Midway Atoll were analyzed. Our method identifies the dominant contributing waves in frequency space, and analyzed power contributions by 5° and 15° azimuth bins. A limiting factor in this analysis is the spatial extent of consistent morphology in the landscape. In contrast to time-domain Fourier analysis, dominant landform frequencies can thus be of low magnitude, creating an imprecise estimate of wave morphometry and direction since this is derived from the combination of inverted x and y frequency values, and the limited frequency grain inherent in the discrete model degrades precision in the solution. Simulated karst and spur & groove landscapes were used to evaluate the grain of waveform orientation solutions.