Ayalew, L., Yamagishi, H. 2005.
The application of GIS-based logistic regression for landslide
susceptibility mapping in the Kakuda-Yahiko Mountains, Central
Japan.
Geomorphology, 65, 15-31.
The authors develop a GIS
based logistic regression model to assess landslide susceptibility in the Kakuda-Yahiko Mountains of Central Japan. Using
logistic regression, the presence of landslides is correlated with independent
physical parameters including lithology, elevation, lineaments, bed rock-slope
relationship, slope gradient, aspect and proximity to roads. Remote sensing edge detection techniques were
used to isolate lineaments, and a 100m buffer was established to assess the
influence of lineaments on landslide activity.
A 10m DEM was used to derive slope and aspect and landslide density was
calculated for classified relief zones and slope. Road networks were buffered to assess the
relationship with landslide distribution.
Logistic regression was used to test and weight the relationship between
landslide distribution and each independent physical parameter. Based on this analysis, lineaments were
deemed insignificant and road networks were identified as the most influential
in landslide activity. Coefficients for
each parameter were developed and a landslide probability value was assessed
and mapped.
Curtis, J.A., Flint, L.E.,
Alpers, C.N., Yarnell, S.M. 2005. Conceptual model of sediment processes in the
upper Yuba River watershed, Sierra Nevada, CA.
Geomorphology, 68, 149-166.
Curtis et al. develop a GIS
based model to identify erosion potential based on field derived measurements
of hillslope erosion and channel storage sediment yields. Hillslope erosion and sediment source was
calculated using various layers including: soil erodability, road networks,
bedrock geology, vegetation type and percent cover, slope and elevation,
potential evapotranspiration and locations of existing mass failures. The model spatially distributes major
processes controlling sediment dynamics and is useful for identifying the
spatial distribution of sediment sources.
This article is useful for understanding the integration of field
derived sediment yield information with a GIS to calculate potential hillslope
erosion.
Dai, F.C., Lee, C.F.
2002. Landslide characteristics
and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42, 213-228.
The authors develop a GIS
based logistic multiple regression model for assessing slope instability on Lantau Island. The authors develop an
algorithm to model slope instability using a GIS. Historic landslides were digitized from
aerial photographs and incorporated into a GIS.
Physical parameters including slope and aspect derived from a 20m DEM,
land-use, terrain morphology, and geology were assessed. In addition, the distance between landslide
distributions and drainage networks was established. Vector datasets were rasterized to the DEM
spatial resolution to calculate landslide susceptibility as a function of the
described physical parameters. Using a
GIS overlay, landslide distributions and frequency were correlated with slope
gradient, elevation, aspect, geology, and land use. Logistic multiple regression modeling was
used to assess the weight of each physical parameter in contributing to
landslide susceptibility. Based on these
coefficients, the authors developed an algorithm for use in a GIS.
Gorsevski, P.V., Gessler, P.E., Boll, J., Elliot,
W.J., Foltz, R.B. 2006. Spatially and temporally distributed modeling
of landslide susceptibility.
Geomorphology 80, 178-198.
Gorsevski et al. develop a
dynamic landslide susceptibility model capable of predicting landslide activity
over space and time. Spatial and
temporal variation is integrated into the model using the Soil Moisture Routing
(SMR) model. The SMR model calculates
water balance per raster cell as a function of slope, soil depth, root strength,
and vegetation surcharge. The model
dynamically integrates weather data to model temporal change in hydrologic
conditions. The model incorporates
documented landslides, Landsat TM vegetation cover, soils data and weather
information and dynamically simulates soil moisture. The authors compare their model outputs
against established landslide susceptibility models including: FSmet and
SHALSTAB. This article is useful for
providing a thorough framework for developing a landslide susceptibility model
within a GIS for a forested watershed.
Gritzner, M.L., Marcus, W.A., Aspinall, R., Custer,
S.G. 2001. Assessing landslide potential using GIS, soil
wetness modeling and topographic attributes, Payette River, Idaho. Geomorphology, 37, 149-165.
Gritzner et al. evaluate the
effectiveness of the DYNWET model for use in GIS modeling of landslide
susceptibility. The DYNWET model is used
to predict regions of surface saturation.
The authors assess the relationship between the DYNWET indicator and
documented landslides and evaluate the influence of topographic variables on
landslide events. Using GIS, documented
landslide locations were digitized as point locations and rasterized to compare
with the DYNWET output and topographic variables. Basic topographic variables including slope,
aspect, elevation, and slope curvature were calculated. Soil saturation was calculated using the
DYNWET model. A Bayesian model was
integrated with a GIS and input variables were tested to assess landslide
susceptibility. Chi-square analysis was
performed to identify the most influential variables. The authors determine that elevation is most
strongly linked with landslides. In
addition, it was determined that the soil saturation index does not improve
landslide predictions.
Guzzetti, F., Reichenbach, P., Cardinali, M., Galli,
M., Ardizzone, F. 2005. Probabilistic landslide hazard assessment at
the basin scale. Geomorphology, 72,
272-299.
Guzzetti et al. present a
model to represent the spatial and temporal dynamics of landslides. The model predicts where landslides will
occur, the physical extent of landslides and the frequency of landslides. A multi-temporal assessment of landslide
activity was created using aerial photographs.
Using GIS, the authors partition landslide susceptibility regions based
on environmental conditions including morphology, lithology, and land use. The model assesses frequency of occurrence
and calculates the probability of a particular landslide size.
Kheir, R.B., Cerdan, O., Abdallah, C. 2006.
Regional soil erosion risk mapping in Lebanon. Geomophology, 82, 347-359.
Kheir et al. assess soil
erosion risk through use of basic GIS overlay using multiple thematic variables
characterizing slope, land cover, drainage density, geology, annual rainfall. The authors develop three base maps to classify
landscape sensitivity, runoff potential and soil erodibility. Landscape sensitivity is assessed with an
overlay of slope classes derived from a 50m DEM, drainage density and landcover
density. Runoff potential assesses
hydrologic conditions as a function of annual precipitation and soil water retention
and infiltration. Erodibility is
classified based on the quantity of material available. The authors derive potential soil erosion by
overlaying the landscape sensitivity map and the runoff potential map. To determine erosion risk, regions of potential
erosion are linked to regions of erodibility.
The model was derived for use in Lebanon and may not be transferable to environments with dissimilar
environmental characteristics; however the methodology provides a relatively
simple approach for assessing soil erosion risk.
Lee, S.
2004. Application of likelihood
ratio and logistic regression models to landslide susceptibility mapping using
GIS. Environmental Management, 34,
223-232.
Lee uses the likelihood
ratio and logistic regression models coupled with a GIS to identifying regions
susceptible to mass failure. Using
satellite imagery, landslides were identified and mapped. Landslides were converted from vector to
raster for incorporation into the GIS model.
Vector based thematic maps consisting of topography, soil, forest cover,
and geology were incorporated for analysis.
Land use data was derived from classified Landsat TM imagery. Lastly, a DEM was used to calculate slope,
aspect and slope curvature. An overlay
between documented landslides and the thematic layers was conducted to analyze
the spatial relationship between the features.
Using the likelihood ratio and logistic regression models, that spatial
relationship between documented landslide and the thematic layers was quantified. Based on these relationships, a formula was
generated to define the probability for landslide occurrence.
Sun, G., McNulty,
S.G. 1998. Modeling soil erosion and transport on forest
landscape. In: Winning
solutions for risky problems: Proceedings of conference 29; 1998 February
16-20; Reno, NV. Steamboat
Springs, CO:
International Erosion Control Association: 189-198.
The authors present a model
to predict sediment erosion and transportation in response to various forest
management activities based on a coupling of a GIS and the Universal Soil Loss
Equation (USLE). USLE is used to predict
soil erosion per grid cell within a 30m DEM using a rainfall runoff factor,
soil erosivity, slope length and steepness, forest cover and soil conservation
practices. Arc/Info GRID utilities and
empirical models developed from field observation are used to model sediment
routing from hillslope to channel. The
use of a GIS allows the USLE model to be applied per pixel area to allow
managers to assess areas with the greatest potential for soil erosion.
Wilson, C.J.,
Carey, J.W., Beeson, P.C., Gard, M.O., Lane, L.J. 2001.
A GIS-based hillslope erosion and sediment delivery model and its
application in the Cerro Grande burn area.
Hydrological Processes 15, 2995-3010.
Wilson et al. present a
GIS-based model to predict hillslope erosion and sediment yield from a burned
catchment following simulated rain events.
The model generates flow pathways to model runoff depth and volume and
incorporates the HEM model to simulate hillslope erosion and sediment yield as
a function of gradient, soil type and erodibility, and vegetation cover. The model is useful for simulating sediment
routing within channel networks and models hillslope erosion as a function of
hillslope profile and erosion resistant features. Sediment routing within channel networks and
diverse topographic terrain are represented by the model.