Engineering Geology 222 (2017) 236–249
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Engineering Geology
journal homepage: www.elsevier.com/locate/enggeo
Dip-slope mapping of sedimentary terrain using polygon auto-tracing and
airborne LiDAR topographic data
MARK
Chih-Hsiang Yeha,b, Ming-Lang Lina, Yu-Chang Chanb,⁎, Kuo-Jen Changc, Yu-Chung Hsiehd
a
Department of Civil Engineering, National Taiwan University, Taipei, Taiwan
Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan
c
Department of Civil Engineering, National Taipei University of Technology, Taipei, Taiwan
d
Central Geological Survey, Ministry of Economic Affairs, Taipei, Taiwan
b
A R T I C L E I N F O
A B S T R A C T
Keywords:
Dip-slope mapping
Polygon auto-tracing
Daylight analysis
Python
LiDAR DEM
Dip-slope mapping is a fundamental task for landslide investigation and mitigation. However, most dip-slope
mapping methods involve visual interpretations and manual processes that are inevitably subjective and time
consuming. The advent of high-resolution digital elevation models (DEM) and increases in computing power
have provided opportunities to improve the dip-slope mapping process. This study proposes a polygon autotracing method for generating dip-slope maps based on airborne Light Detection and Ranging (LiDAR) data and a
customized spatial analysis toolset developed in Python. This method requires the input of strata boundaries
produced for sedimentary terrain based on 2 m resolution LiDAR DEMs. The method begins by deriving the
raster layer of the dip direction of the bedding, and it then executes a series of raster calculations among the
three raster layers slope, aspect, and dip direction to extract the dip-slope raster cells. Using the clustering
pattern of the dip-slope raster cells, we implement the Point-Density analysis tool to determine the dip-slope
areas. The ArcGIS ModelBuilder platform is used to lay out an automated workflow for the proposed polygon
auto-tracing method using the customized toolset. For demonstration purposes, we successfully mapped 298 dip
slopes in the study area, which frequently experiences dip-slope landslides and is located in the sedimentary
terrain of northern Taiwan. The dip-slope mapping results were compared and validated against two
government-funded visually interpreted dip-slope maps. Our dip-slope mapping results were also used in a
daylight analysis along major freeways to identify potential locations of daylighted dip slopes.
1. Introduction
Remote-sensing data, including aerial photographs, satellite imagery, and shaded relief images produced using Light Detection and
Ranging (LiDAR) and Synthetic Aperture Radar (SAR) have been widely
used for large-area mapping in topographical, geological and naturalhazard research (CrÓSta and Moore, 1989; Dueholm et al., 1993; Dong
and Leblon, 2004; Chang et al., 2005; Chen et al., 2006; Colesanti and
Wasowski, 2006; Chan et al., 2007; Bedini, 2009; Chang et al., 2010;
Jaboyedoff et al., 2010; Lan et al., 2010; Dong et al., 2014; Yeh et al.,
2014; Hsieh et al., 2016). With advances in remote-sensing technology,
many mapping efforts have been shifted towards automated or semiautomated detection and mapping (Jiménez-Perálvarez et al., 2008;
Grebby et al., 2010; Hölbling et al., 2012; Palenzuela et al., 2014). For
dip-slope mapping, however, visual interpretation of aerial images and
field investigations have remained the major methodologies for identifying dip slopes (Ayalew and Yamagishi, 2005; Gupta, 2013; Lillesand
et al., 2014). These methodologies can be challenging, time consuming
and ineffective when investigating large areas and areas with terrain
Large landslides have frequently occurred around the world and
have resulted in severe casualties and damages (Aleotti and
Chowdhury, 1999; Hung, 2000; Keefer, 2002; Nadim et al., 2006;
Chigira, 2009; Borgatti and Soldati, 2010; Lee and Fei, 2015). Dip-slope
landslides in particular have received the public's attention because
they regularly occur within populated regions (Shou and Wang, 2003;
Tang et al., 2009; Lo et al., 2011; Barla and Paronuzzi, 2013; Wang
et al., 2013; Lo et al., 2015). In general, a slope can be defined as a dip
slope when the strike of the slope face and the strike of the bedding are
approximately parallel and the intersection angle between both is
within 20° (Hoek and Bray, 1981; Goodman, 1989; Wyllie and Mah,
2004). Dip-slope mapping has been an essential component of landslide
disaster prevention and mitigation, provides useful information on
potential locations of dip-slope failures and helps in the design of
infrastructure engineering projects for stability.
⁎
Corresponding author at: Institute of Earth Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei 11529, Taiwan.
E-mail address: yuchang@earth.sinica.edu.tw (Y.-C. Chan).
http://dx.doi.org/10.1016/j.enggeo.2017.04.009
Received 28 November 2016; Received in revised form 17 February 2017; Accepted 4 April 2017
Available online 13 April 2017
0013-7952/ © 2017 Elsevier B.V. All rights reserved.
Engineering Geology 222 (2017) 236–249
C.-H. Yeh et al.
that is poorly accessible and covered with dense vegetation. Historically, such difficulties may have resulted from a lack of high-resolution
and high-precision stratigraphic bedding data. The identification of dip
slopes is highly dependent on the strike and dip of the bedding, and
traditional geological maps provide simplified and imprecise bedding
information that usually results in error-prone identifications of dip
slopes (Gad and Kusky, 2007; Roy et al., 2009; Grebby et al., 2010). The
features or objects of dip slopes in remote sensing data do not have
unique shapes or spectral, spatial and morphometric properties for
exact and unique discrimination (Borghuis et al., 2007; Stumpf and
Kerle, 2011; Martha et al., 2013). Thus, intensive visual interpretations
are required to minimize mistakes in dip-slope mapping.
The purpose of this study is to propose a polygon auto-tracing
method by developing a customized spatial analysis toolset using
LiDAR data inputs to provide objective, reliable and efficient dip-slope
mapping that only requires limited visual interpretation in the sedimentary dip-slope terrain. In addition to LiDAR data, LiDAR-derived
strata boundaries in the sedimentary terrain of interest are required for
data preparation in the beginning of this study. The strata boundaries
used for deriving finer bedding data can be produced using the 3Dmapping method developed by Yeh et al. (2014), and this process is
briefly explained in Section 3.2. The ESRI ArcGIS ModelBuilder platform was used to build a workflow for automatically implementing the
mapping method. We developed a customized toolset, including four
geoprocessing tools for particular geoprocessing tasks, and integrated
these tools into the workflow. The customized toolset was programmed
in this study using Python language with ArcPy (ESRI, 2016). Finally,
we generated a dip-slope map by the proposed polygon auto-tracing
method and compared the map with two commonly used dip-slope
maps. Several high-risk dip slopes along major freeways were selected
for further daylight analysis, which will provide useful information on
the locations for potential dip-slope failures in the future.
Fig. 2. Field photograph showing cuesta and dense vegetation. Note the construction of
freeways (the freeway no. 3 and the expressway no. 62) passing through the study area;
this infrastructure has the potential to generate engineering problems related to slope
instability.
categorized into three rock types: sandstone, shale, and sandstone-shale
alternation. The area exhibits a large-scale cuesta topography inclined
towards the southeast and presents dip angles of approximately 10–30°
and heavy vegetation coverage on the ground (Fig. 2). Such topographic conditions increase the difficulties of traditional field and
photogeological mapping because the terrain is poorly accessible and
conceals many geologic attributes. Thus far, the only large-scale
published map associated with the regional geology and potential
disaster risks is a 1:25,000-scaled map (CGS, 2002a). Although this
scale may be common for geological research and applications, it is
insufficient and impractical for detailed geological mapping and many
engineering applications.
Because of the dip-slope topography and sandstone-shale stratigraphy, the study area is regarded as a geologically sensitive area prone to
dip-slope landslides. The study area is located between two densely
populated cities, Taipei and Keelung, and has been developed for
transport infrastructure and large-scale communities, which has resulted in many dip-slope landslides that have caused severe casualties
and damages (Hung, 2002; Wang et al., 2013; Lo et al., 2015). For these
landslide cases, the dominant failure mechanism is plane failure, which
commonly occurs along daylighted slopes. Thus, producing a reliable
dip-slope map is indispensable for preventing or mitigating dip-slope
failure disasters in the common geologic terrain.
2. Study area
The study area covers approximately 46 km2 and is located on the
north bank of the Keelung River in northern Taiwan (Fig. 1). This
region is a part of the fold-and-thrust belt generated by the collision of
the Eurasian tectonic plate and the Philippine Sea plate (Seno, 1977;
Suppe, 1980; Teng, 1990), and the stratigraphy is mainly composed of
Miocene sedimentary rocks divided into four units (from oldest to
youngest): Mushan formation (abbreviated as Ms. Fm.), Taliao formation (Tl Fm.), Shihti formation (St Fm.), and Nankang formation (Nk
Fm.) (Teng et al., 2001; CGS, 2005). Generally, the lithology can be
Fig. 1. Geological map of the northern Taipei area adapted from the regional geological map created by the Central Geological Survey of Taiwan (CGS, 2005). The study area is located on
the north bank of the Keelung River and indicated by the black box. The study area is 46 km2 and used to demonstrate our dip-slope mapping method.
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vertical error varied according to the different terrain types, such as
forest and bare surface. The mean error, the root mean square (RMS)
error, and the standard deviation (STD) for the LiDAR data used in this
study were 0.037, 0.168, and 0.165 m, respectively.
3.2. LiDAR-derived strata boundary
The strata boundary, which is the boundary between two different
lithological beds, is an essential type of input data generated in the form
of a polyline layer, and it was used here to derive the raster layer of the
dip direction of the bedding. According to Yeh et al. (2014), strata
boundaries can be traced manually using a LiDAR DEM and 3Dvisualized interpretation of sedimentary areas with clear features of
differential erosion. Although this study uses the derived strata
boundaries from the study of Yeh et al. (2014), the results were slightly
improved by recent advances in 3D imaging technology and increased
computer efficiency. Next, we provide a brief review of our method of
tracing and mapping strata boundaries on the LiDAR-based 3D interactive environments based on the methods proposed in Yeh et al.
(2014).
By manipulating 3D navigation tools, we can arbitrarily change the
viewing direction to observe and examine the terrain for tracing
identified lineaments, such as strata boundaries, in 3D space. Fig. 4
shows a comparison of a 2D top-view image and a 3D image. Fig. 4a
and b show a traditional top-view aerial photograph and a top-view
LiDAR image, which do not effectively displaying side views of the
terrain, thus increasing the difficulty of tracing accurate strata boundaries using visual interpretations. Fig. 4c and d show two images of a 3D
flythrough with different viewing angles. The figures demonstrate that
the LiDAR DEM developed using 3D flythrough data can reveal vivid
topographic effects for geological interpretation, especially for tracing
strata boundaries. All of the strata boundaries in this study were traced
using visual interpretations and are consistent with the basic bedding
characteristics, such as the overall bedding continuity and parallelism.
Fig. 5a shows the results of strata-boundary tracing in a 3D perspective.
Fig. 5b is a projected 2D strata-boundary map that was generated by
removing the vertical component of the data points. The study area
includes 19 well-defined strata-boundary traces. We also used information on field outcrops from available geological maps to identify the
sedimentary strata distribution (see the red lines in Fig. 5).
Fig. 3. Research flowchart presenting the implemented methodological approach for
assessing the effectiveness of using airborne LiDAR data for dip-slope mapping. The
dashed arrow indicates the proposed method for deriving strata boundaries described in
Yeh et al. (2014).
3. Data preparation
The research flowchart presented in Fig. 3 consists of two parts: data
preparation and polygon auto-tracing method development. This section discusses the data preparation, which includes two types of data
input: LiDAR DEM and LiDAR-derived strata boundary. The development of the polygon auto-tracing method will be discussed in following
section. Our dip-slope mapping method is processed using the ESRI
ArcGIS system; therefore, all of the data are compatible with or
converted to ArcGIS data types, such as raster, polyline, polygon and
layers.
3.1. LiDAR DEM
4. Developing the polygon auto-tracing method
In recent years, the laser altimetry of LiDAR has become a common
technique for acquiring high-resolution DEMs. Because LiDAR data
have the capacity to show subtle features on the ground, these data can
also be used to characterize geologic lineaments (Ganas et al., 2005;
Chan et al., 2007; Chang et al., 2010; De Pascale et al., 2014). For the
study area, the LiDAR data were obtained in April 2006 by the Central
Geological Survey of Taiwan, which used the Optech ALTM 30/70
model equipped with a single-channel airborne scanner, which has a
200 kHz sampling capability. The scanner was mounted on a helicopter
with a flight speed of 219 km/h. The flight altitude was approximately
1800 m above sea level, and the ground-helicopter distance ranged
from 1000 to 1500 m. As for other technical specifications of the
survey, the operating pulse repetition rate was 71 kHz, the scan rate
was 38 Hz, and the field of view (FOV) was ± 20°. There were three
flight lines covering the entire study area, for which each swath width
was approximately 730 m. The overlap between adjacent swaths was
approximately 40% (290 m). The vertical misfit among swaths ranged
from − 13 to 6 cm and had a standard deviation of approximately
17 cm.
The survey conducted for this study collected approximately 130
million data points, which is a sufficient amount to generate a DEM
with a grid size of 2 m from the original classified point cloud. The
average density of the point clouds for the DEM is 0.78 points/m2. A
comparison of the kinematic GPS measurements indicated that the
The polygon auto-tracing method proposed in this study consists of
four major processes: deriving spatial raster layers, extracting dip-slope
cells, performing Point-Density analyses, and determining dip-slope
areas. This section discusses how these processes were developed and
subsequently automated and combined into a workflow for dip-slope
mapping.
4.1. Deriving spatial raster layers
The topographic gradient, the slope face direction, and the dip
direction of the bedding, hereafter referred to as slope, aspect, and dip
direction, are three basic data types in our dip-slope mapping method.
These basic data types are transformed into raster layers for geoprocessing in GIS platforms. In this study, the size of raster cell in slope and
aspect layers is 2 m, and the size of raster cell in dip-direction layer is
10 m. In general, both slope and aspect raster layers can be derived
simply and precisely from a high-resolution DEM by using built-in
ArcGIS tools. The raster layer of the dip direction is commonly acquired
from strike and dip point measurements using the Kriging method
(Meentemeyer and Moody, 2000). The Kriging method is used to derive
the dip direction; however, it may result in geological simplification
and generate relatively inaccurate information. To mitigate this problem, we applied the LiDAR-based “Interpolation-derived Strike and
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Fig. 4. Comparisons of 2D top-view and 3D images. (a) Aerial photograph and (b) LiDAR image. Both are images of the same 1 × 1 km region from the study area. (c) and (d) 3D
perspective images with different views demonstrating how to trace strata boundaries. The different viewing directions are indicated in (b). The strata-boundary traces were delineated
manually by experienced geologists on the GIS platform in 3D environments.
not include the two terms x2 and y2.
Dip of Bedding” method developed by Yeh et al. (2014) to acquire a
finer and more accurate raster layer of the dip direction. The LiDARbased method consists of three major steps.
z = c1 + c2 x + c3 y + c4 xy + c5 y 2 + c6 x 2
(1)
where x and y are the longitude and latitude in the TWD97 TM2
coordinate system, respectively; z is the elevation of a given datum,
i.e., the TWD97 vertical datum in this study; and c1 to c6 are
estimated coefficients.
(2) Calculating the bedding data: after obtaining the surface equation
of the fitted bedding, we can calculate the bedding data, including
the dip direction, at any point on the fitted bedding. The vector of
the dip direction can be calculated by a two-variable gradient
function:
(1) Fitting the bedding surface: a strata-boundary polyline consists of
many vertices composed of the observed and recorded points
during bedding identification in 3D environments. The distributed
data points of a strata-boundary polyline ideally lie within the same
bedding surface. These points can be used to fit a bedding surface
by quadratic regression, and the bedding surfaces in the sedimentary area must be fitted by quadratic regression one by one. Here,
we changed the regression equation to Eq. (1) to provide for better
fitting. The original regression equation in Yeh et al. (2014) does
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Fig. 5. Distribution map of the strata boundaries shown in 3D and 2D images modified from Yeh et al. (2014). (a) 3D perspective shows strata boundaries that present continuity and
parallelism, which meet basic bedding characteristics. (b) 2D top-view map obtained using a projection from the 3D perspective. The arrow indicates the viewing direction of (a). The
strata-boundary traces were delineated manually by experienced geologists on the GIS platform in 3D environments. (For interpretation of the references to color in this figure, the reader
is referred to the web version of this article.)
∂z ⇀ ∂z⇀
⇀
vdip direction = ∇z =
i +
j
∂x
∂y
raster cells that satisfy the definition of the dip slope. The extracted
cells, which are referred to as dip-slope cells, are distributed over the
study area and exhibit clustering patterns, and they are helpful for
determining dip-slope areas (see Sections 4.3 and 4.4).
The schematic diagram in Fig. 7 explains the detailed steps of
extracting dip-slope cells. We first execute a raster subtraction between
the aspect raster layer and dip-direction raster layer to acquire the
temporary layer of angle difference (Layer T1) and then execute the
logical operation to extract those raster cells whose angle differences
are within the range of ± 30° and generate the second temporary layer
(Layer T2). The above setting of ± 30° includes the intersection angle
from the dip-slope definition ( ± 20°) and the tolerance angle for the
dip-direction estimate ( ± 10°). Next, we take slope raster layer and
extract the raster cells with slope values greater than ± 10° and
generate the third temporary layer (Layer T3). Finally, we take Layers
T1 and T3 and execute the “AND” logical operation to generate the dipslope raster layer. Each of the cells in the dip-slope raster layer stores a
Boolean value, which is used to determine the distribution of the dipslope cells as shown in Fig. 8a.
(2)
⇀
⇀
vdip direction is the dip direction vector and i and j are unit
where ⇀
vectors in the x and y directions, respectively. Using Eq. (2), we can
derive the raster layer of the fitted bedding with the dip direction
for the geoprocessing in the next step.
(3) Deriving the raster layer of the dip direction: after acquiring the
raster layers of all of the fitted bedding surfaces, we then use the zaxis linear interpolation to generate the raster layer of the dip
direction, which is shown in Fig. 6. An unknown point (point A) on
the topographic surface corresponds perpendicularly to the two
given points on the upper bedding (point B) and lower bedding
(point C). The value of the dip direction of point A can be calculated
based on points B and C using the z-axis linear interpolation. After
calculating the dip direction for each raster cell, we can derive the
raster layer of the dip direction of the ground surface.
4.2. Extracting dip-slope cells
Based on the ArcGIS platform, the extraction of dip-slope cells is
performed by overlapping and calculating among the three raster layers
of slope, aspect and dip direction. The main purpose is to extract the
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Fig. 6. Schematic diagram of the derivation of the raster layer of the dip direction on the topographic surface. Point A is an unknown point of dip direction on the topographic surface.
Points B and C both have the same horizontal coordinates (x, y) as point A and represent the given points on the upper and lower beddings, respectively. The dip-direction vector of point
A can be calculated by a linear interpolation along the z-axis using the given dip directions at points B and C.
slope areas, show uncertain dip-slope boundaries because of the pixelbased data source. Visual interpretation provides a solution for
determining the dip-slope boundaries; however, it is subjective and
may generate inconsistent criteria when interpreting the dip-slope
boundaries. In this study, we adopted a Point-Density analysis because
it can be implemented automatically, thereby reducing the amount of
subjective interpretation and increasing the efficiency of boundary
determinations.
Point-Density analyses are commonly used to calculate the density
of point features around each raster cell (Silverman, 1986). Conceptually, a neighborhood is defined around each raster cell center and the
number of points that fall within the neighborhood is summed, and this
sum is divided by the area of the neighborhood. In this study, PointDensity analyses are used to calculate the density of dip-slope cells
around each raster cell, and the density values are shown with contour
lines in the study area.
For example, the Point-Density contour of dip-slope cells is shown in
a 1 × 1 km demonstration area (Fig. 8a), and a portion of this area is
enlarged to allow the raster cells to be clearly displayed (Fig. 8b). A
random raster cell is selected, and its Point-Density value (hereafter
referred to as PD) is calculated using a circular area around the cell
(Fig. 8b). Twenty-one dip-slope cells fall within the circular neighborhood when the search radius R is set to 40 m, and the PD of the selected
cell can be calculated as shown in the following equation:
PD =
N
N
21
=
=
= 0.0042 (point m2)
A
π × R2
π × 40 2
(3)
where N indicates the number of feature points falling within the
neighborhood, A indicates the area of the circular neighborhood, R is
the search radius, and PD is the Point-Density value. After the PointDensity analysis of all the raster cells, the PD contours can be derived
over the study area.
Fig. 7. Schematic diagram showing the concepts of extracting raster cells for the dipslope layer. The blue layers are the input-data raster layers, including the slope, aspect,
dip direction; the gray layers are the temporary raster layers, including T1, T2 and T3; the
red layer is the output-data raster, which is the dip-slope layer; θdiff is the differential
angle between the aspect and dip direction; and θslp is the slope gradient. (For
interpretation of the references to color in this figure legend, the reader is referred to
the web version of this article.)
4.4. Determining dip-slope areas
This section describes the method of selecting the best contour from
the PD contours for fitting the clustering pattern. The best contour
PDdipslope indicates the dip-slope boundaries used to identify the dipslope areas. Although the resolution of dip-slope cells is equal to 10 m,
the PDdipslope value can fall between 0.002 and 0.004 point/m2. To
explain how the PDdipslope value was determined, we carefully traced all
4.3. Performing point-density analyses
In Fig. 8a, the dip-slope cells are distributed over the study area and
exhibit a pattern of many clusters. These clusters, which indicate dip241
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Fig. 8. Determination of the dip-slope boundary by the Point-Density analysis. (a) Distribution of dip-slope cells and point-density contours in 1 × 1 km demonstration area. (b) Detailed
view showing the Point-Density analysis in cells. The red dashed circle indicates a neighborhood around the cell for the Point-Density calculation. In this case, there are 21 feature points
(dip-slope cells marked by blue dots) within the area of the red dashed circle. See the text for additional details. (For interpretation of the references to color in this figure legend, the
reader is referred to the web version of this article.)
1. Input data (double-line ellipse): Two types of input data, the LiDAR
DEM and strata boundaries, are required for the polygon autotracing method. The LiDAR DEM is a raster data type, and the strata
boundaries are a set of polyline data.
2. Output data (bold-line ellipse): The output is the dip-slope map,
which is a set of polygon data representing the calculated dip-slope
areas.
3. Temporary data (regular-line ellipse): The temporary data are used
for delivering or transferring data between two connected geoprocessing tools. The data types include raster, polyline and polygon.
4. Built-in tools (regular-line rectangle): Built-in tools are the geoprocessing tools provided in ArcGIS. This study used four built-in
tools—Aspect, Slope, Raster Calculator and Point-Density—to construct our automated workflow. Aspect and Slope generate the
aspect raster and slope raster from the given LiDAR DEM; Raster
Calculator performs algebraic and/or logical calculations between
two or three raster layers; Point-Density calculates the magnitude
per unit area from point features that fall within a neighborhood
around each cell.
5. Customized tools (yellow bold-line rectangle): This study developed
four customized geoprocessing tools in the Python language using
ArcPy: Bedding Surface Fitting, Bedding Dip Direction, Dip-slope
Calculation, and dip-slope map.
of the dip-slope boundaries in a 1 × 1 km demonstration area using
visual interpretation of the LiDAR-based 3D environments (Fig. 9a). In
the absence of a reference dip-slope boundary, it is still possible to input
a reasonable PD contour in the process although the potential mapping
errors may be higher. We then selected the three most-possible PD
values as the PDdipslope and generated their PD contours for similarity
comparisons (Fig. 9b). After performing a comparison of the visually
interpreted boundaries, a value of 0.003 was selected for the PDdipslope
because its contour pattern was most similar to the carefully interpreted
dip-slope boundaries. The search radius R is also an important parameter that affects the determination of PDdipslope because the size of R
affects the degree of smoothness of the PD contours, with large R values
generating a smooth contour that might not capture the details of a dipslope boundary and small R values generating a zigzag contour that
may induce many small and closed contours that can increase the
difficulty of appropriately identifying the number and size of dip slopes.
Although the resolution of the dip-slope cells is equal to 10 m, the value
of R can be empirically set between 30 and 50 m. We selected 40 m as
the most appropriate value of R based on the similarity comparison of
three different R values as shown in Fig. 9c.
4.5. Constructing the automated workflow
(a) Bedding Surface Fitting: This tool can fit bedding surfaces by
surface-regression calculations, and it uses the observed points on
a strata boundary to fit a polynomial to the curved bedding surface.
The input is the strata-boundary polylines, and the output is the
raster layers of fitted bedding surfaces. The parameter P1 can be set
as 1, 2 (default value) and 3, which represent linear, quadratic and
cubic regression models, respectively.
(b) Bedding Dip Direction: The tool helps generate the dip-direction
raster of the ground surface. The input is the raster layers of fitted
bedding surfaces, and the output is the raster of dip direction of the
ground. Details on the construction of this tool are described in
This study uses the ArcGIS ModelBuilder application as the basic
platform for constructing an automated workflow for the polygon autotracing method. ModelBuilder is a visual programming language that
can be used to combine geoprocessing tools, map layers, datasets, and
other ArcGIS data types for the construction of geoprocessing workflows. For the dip-slope mapping calculations in this study, we
developed four customized geoprocessing tools using the Python
language (version 2.7.6) with ArcPy, a Python program library that
supports ArcGIS geoprocessing. Fig. 10 shows the automated workflow
of this study, which was constructed using ModelBuilder. The components of the workflow are explained as follows.
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Fig. 9. Determination of the dip-slope mapping parameters in the demonstration area. (a) Manually interpreted dip-slope boundaries shown in the 3D perspective with color-coded
elevation. (b) The map shows 0.001, 0.003, and 0.005 contour lines with a 40 m search radius for the purpose of comparing the dip-slope boundaries traced via manual visual
interpretation. (c) Comparison of the three different search radii at one of the dip slopes. The middle figure shows the contour patterns within a 40 m search radius; the 0.003 contour line
appears to be the best match for the manually interpreted dip-slope boundaries. The top figure shows the contour patterns within a 20 m search radius; these patterns include zigzag
contours and a high number of small and closed contours. The bottom figure shows the contour patterns within a 60 m search radius; these contour patterns are smoother, which increases
the difficulty of describing the dip-slope boundary in detail.
5. Application results
Section 4.2.
(c) Dip-slope Calculation: This tool includes two functions: transferring
the Point-Density raster to the polylines of the Point-Density
contours, and selecting the best contour for determining the dipslope areas. Two parameters, R and PDdipslope, are required to apply
this tool. The input is the Point-Density raster, and the output is the
polygon of the preliminary dip-slope areas.
(d) Dip-slope map: The Dip-slope map tool is used to eliminate
unreasonable areas from the preliminary dip-slope areas and for
generating the final result of the dip-slope map. Unreasonable areas
include calculated dip-slope areas located within the 100 m buffer
zone of the central line of the river and dip-slope areas with
undersized (less than 5000 m2) closed curves. The input data are
the preliminary dip-slope areas, and the output is the dip-slope
map. The parameter P, which is an optional input, can be a polyline
data type, such as a river lineament.
5.1. Dip-slope map
We applied the polygon auto-tracing method to generate a dip-slope
map in the study area. Dividing the study area into several regions is an
effective method of reducing errors during the process of deriving the
bedding data. Separating borders were placed at bedding discontinuities or transition areas, such as faults, anticlines, synclines and river
valleys. We selected three valley lineaments to divide the study area
into four regions. Fig. 11 shows the distribution of the extracted dipslope cells and the separating borders. Most of the extracted dip-slope
cells that show clustering patterns are located on the slopes that dip
towards the south. The most appropriate R and PDdipslope in this study
are 40 m and 0.003 point/m2, respectively (see Section 4.4). Based on
these two parameters, the polygon auto-tracing method can be implemented to generate the dip-slope areas as shown in Fig. 11. A total of
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Fig. 10. Flow chart of the automatic processing in ModelBuilder in this study. The flow chart includes four built-in tools and four customized tools. The initial inputs are the LiDAR DEM
(raster data type) and the strata boundaries (polyline data type), and the final output is the dip-slope map (polygon data type).
overlaying our dip-slope map onto these dip-slope maps, which is
shown in Fig. 12.
Fig. 12 shows that the dip slopes in the two previous maps have
mostly been identified in our polygon auto-tracing dip-slope map, and
varying degrees of similarity are observed in the polygon shapes. We
can distinguish three matching situations for the dip-slope areas in the
figure. The first is the well-matching situation, which can be observed
in the larger and relatively unbroken slopes. The second is the
dispersed-matching situation, which can be observed when a large
dip-slope area in the map of one of the previous projects is delineated
into several parts using our polygon auto-tracing method. This matching situation is usually found for slope faces that present lineaments
that are roughly perpendicular to the dip direction, e.g., roads and
human developments. The third is the merged-matching situation,
which can be observed when adjacent dip-slope areas from the previous
two projects are interpreted as one area using the polygon auto-tracing
method. This matching situation usually occurs in slope faces that have
lineaments roughly parallel to the dip direction, e.g., scarps and gullies.
In order to evaluate the quality of the dip-slope maps generated by
298 dip-slope areas were automatically detected in the study area, and
the average area of the detected dip slopes was approximately
36,000 m2.
5.2. Comparison with previous dip-slope maps
In the study area, dip-slope maps were previously generated by two
government-funded projects, and they are hereafter referred to as PJ-1
(CGS, 2002b: Environmental Geological Map Project) and PJ-2 (CGS,
2012: Investigations of Geology, Landslide and Debris Flow in Catchment Areas and Disaster Risk Assessment). The basic data used by the
two projects to produce the dip-slope maps include a 1:2500 topographic map, a 5 m DEM derived from aerial photographs and 1:50,000
or 1:25,000 geological maps. The mapping methods of the two projects
involve manual hand-tracing by experienced geologists based on image
interpretation and field investigations. The hand-tracing results of the
dip-slope areas from PJ-1 and PJ-2 were then digitized into the GIS
system. Based on the dip-slope maps from PJ-1 and PJ-2, we may
further assess the effectiveness of our polygon auto-tracing method by
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Fig. 11. Distribution of dip-slope cells shown in the shaded-relief map of the study area. The map shows many dip-slope cell clusters and a dip-slope area calculated based on a point
density of 0.003 and a search radius of 40 m. The red lines are zoning borders that divide the study area into four zones. (For interpretation of the references to color in this figure legend,
the reader is referred to the web version of this article.)
Fig. 12. Comparison between two dip-slope maps produced by the government-funded projects PJ-1 (CGS, 2002: Environmental Geological Map Project) and PJ-2 (CGS, 2012:
Investigations of Geology, Landslide and Debris Flow in Catchment Areas and Disaster Risk Assessment) and the dip-slope map produced using the polygon auto-tracing method proposed
in this study. See the text for a detailed explanation.
interpreted dip-slope areas (red-dash areas) in Fig. 13. The calculated
results for the average ratios of dip-slope area difference from PJ-1, PJ2 and this study are 0.246, 0.285 and 0.184, respectively, where a
smaller number indicates a better mapping quality (Table 1).
PJ-1, PJ-2 and this study, we used the manually-interpreted dip-slope
map in Fig. 9a as the dip-slope base data for comparison. The dip-slope
map was carefully delineated in a small area using high-resolution
LiDAR data on the GIS platform under 3D environments. We chose the
1 × 1 km2 demonstration area (Fig. 13) and grouped the dip slope
areas into nine regions to calculate the ratios of the dip-slope area
difference, r, for a quantitative comparison of the dip-slope maps. The
ratio r can be calculated by the following equation:
A − Ab
r=
Ab
5.3. Daylight analysis
For a dip slope, if the bedding surface dips at a flatter angle than the
slope face, then it is considered to have daylighted on the slope face
(Wyllie and Mah, 2004). A key point for assessing dip-slope stability is
whether the slope is daylighted or not. Daylighted slopes are commonly
considered to be high-risk landslide slopes and have been historically
investigated in the field. The LiDAR-derived strata boundaries can be
used to effectively locate daylighted slopes and identify potential
(4)
where A is the dip-slope areas from PJ-1 (blue-dash areas), PJ-2
(yellow-dash areas) and this study (black-line areas) as shown in
Fig. 13. Ab is the base data of the dip-slope areas from the manually245
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fully daylighted slope, a partially daylighted slope and a dip slope that
is not daylighted, respectively.
6. Discussion
6.1. Advantages of using LiDAR data for dip-slope mapping
In recent years, LiDAR technology has emerged as an efficient
remote-sensing tool that offers accurate and high-resolution topographic data of forested or non-forested terrain. The quality of the
topographic data has greatly elevated the sophistication of geologic
research and engineering projects. In dip-slope mapping, data obtained
by LiDAR have several advantages over data obtained by other remotesensing techniques:
(1) Virtual removal of vegetation. For terrain covered by dense
vegetation, images obtained by traditional remote-sensing technology, such as aerial photography and satellite imagery, typically fail
to detect finer surface morphology because of the limitations of
optical sensors. However, LiDAR data reveal the bare ground
surface, which helps identify dip slopes using either manual or
automated interpretation.
(2) Vivid 3D visualization of the bare ground. Most traditional remotesensing techniques acquire 2D top-view images. When applied to
3D topography, these 2D images can cause pixel distortion and
displacement on dip slopes, which increases the difficulty of
identifying these features. LiDAR data are best viewed and analyzed
in 3D environments, which enhance hidden geological features,
such as continuous strata boundaries, for easy identification and
tracing.
(3) Convenient derivation of bedding-orientation data. Traditionally,
clinometer measurements at field outcrops represented the only
method of acquiring bedding-orientation data. However, LiDAR
DEM with 3D visualization can acquire bedding-orientation data
via LiDAR-based derivations of the strike-and-dip method, and such
data complement field investigations (Yeh et al., 2014).
Fig. 13. Dip-slope regions shown in numbers in the 1 × 1 km2 demonstration area for a
quantitative comparison of the dip-slope areas generated by PJ-1, PJ-2 and this study.
(For interpretation of the references to color in this figure, the reader is referred to the
web version of this article.)
Table 1
A quantitative comparison of dip slopes generated by PJ-1, PJ-2 and this study.
Region
1
2
3
4
5
6
7
8
9
Average
Ab (m2)
13,001
42,929
70,569
25,842
10,157
19,000
108,681
5243
14,195
PJ-1
PJ-2
This study
A (m2)
r
A (m2)
r
A (m2)
r
–
50,441
94,828
36,581
–
–
128,743
–
15,951
–
0.17
0.34
0.42
–
–
0.18
–
0.12
0.246
–
–
94,828
–
–
9631
125,474
–
11,903
–
–
0.34
–
–
0.49
0.15
–
0.16
0.285
14,753
40,043
92,838
30,544
14,653
19,834
123,053
5756
17,566
0.13
0.07
0.32
0.18
0.44
0.04
0.13
0.10
0.24
0.184
6.2. Comparison with object-oriented analyses
Note: Ab is the areas of the base data; A is the dip-slope areas; r is the ratio of dip-slope
area difference; “—” means no data.
Object-oriented analyses (OOAs) represent a popular method with
the potential to generate automated procedures for mapping geomorphologic features and landslides (Blaschke, 2010; Martha et al., 2010;
Lu et al., 2011; Stumpf and Kerle, 2011; Martha et al., 2013). In dipslope mapping using OOA, the mapping procedure begins with the
image segmentation used to generate slope units (Giles and Franklin,
1998), and then the slope units that represent dip slopes are selected by
adding the bedding data, which are primarily acquired from geological
maps or derived from field outcrops. A general advantage of OOAs is
that the method supports many types of source images, such as aerial
photographs, satellite imagery and LiDAR images. However, the
simultaneous use of different types of images in an OOA may require
the training and validation of a large number of parameters and
thresholds. Dip-slope mapping using the polygon auto-tracing method
presented in this study only requires two parameters, R and PDdipslope,
which are related to the resolution of the source LiDAR data and are
easy to determine.
sliding regions.
In sedimentary areas, river incisions and slope-toe cutting are two
major sources of daylighted slopes. The potential sliding surfaces of
daylighted slopes are usually parallel to the bedding plane (Hung,
2002). After identifying dip-slope areas in the study area, we selected
several dip-slope areas located along the two freeways for use in our
daylight analysis (Fig. 14a). Because the hundred-meter scale for the
daylight analysis is relatively small and the overall bedding surface in
the study area does not change dramatically, the bedding surface at
individual locations can be simplified and approximated by a plane
without compromising the results of the daylight analysis. Figs. 14b–d
present examples that explain the concepts of the daylight analysis in a
3D perspective, and the Fig. 14b shows the daylighted slope at the
kilometer marker 3.1 of the freeway no. 3 before the catastrophic
failure of the slope in 2010. We use the Bedding Surface Fitting tool
with the strata-boundary data to generate the bedding plane, which is
considered the potential sliding plane, and then input the bedding plane
into the LiDAR DEM in 3D environments to intersect the lines that
represent daylight traces. According to the patterns of the daylight
traces, we classify the dip slopes into four types, which are illustrated in
Fig. 14a with different colors. The red areas are fully daylighted slopes,
the yellow areas are partially daylighted slopes; the blue areas are the
trimmed slopes that have not been daylighted, and black areas are
unaffected slopes that are located above the tunnels or below the
viaduct sections of the freeways. Field photographs in Fig. 15 show a
6.3. Limitations based on topographic and geological conditions
The polygon auto-tracing method is effective, time efficient and
easy to use for dip-slope mapping over large areas. However, the
method is only applicable under certain topographic and geological
conditions. The polygon auto-tracing method is best suited for sedimentary terrain for which bedding-plane failures represent the primary
failure mechanism of dip slopes. Other types of terrains, such as
metamorphic terrain and colluvium, are not well suited for the
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Fig. 14. Daylight analysis along a major freeway. (a) Several dip slopes affected by freeway construction were classified into four types: fully daylighted type (red areas), partially
daylighted type (yellow areas), not daylighted type (blue areas), and unaffected type (black areas). (b), (c) and (d) Examples showing the daylight analysis in a 3D perspective. The
daylight traces shown via red lines are used to classify different types of dip slopes. Topographic profiles, A–A′, B–B′ and C–C′ are shown to illustrate the relationship between the surface
and bedding. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
bedding, which increases the difficulty of deriving bedding data,
including the raster layer of the dip direction. Finally, terrains with
many artificial slopes and considerable infrastructure may also lead to
irregular surface patterns that could result in erroneous identifications
of dip-slope areas.
proposed method. In addition, the degree of differential erosion should
be sufficiently high to enable the tracing of strata boundaries from the
LiDAR data. Because the strata boundaries are derived from LiDAR
DEM in 3D environments, they can be difficult to identify in smooth
terrains or within similar lithological beds. Severe folding and faulting
in sedimentary terrains may also result in displaced and distorted
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Fig. 15. Field photographs showing (a) a fully daylighted dip slope, (b) a partially daylighted dip slope, and (c) a dip slope that is not daylighted. The observed dip-slope types in the field
are consistent with the results of the daylight analysis in this study.
7. Conclusions
interpretations of remotely sensed imagery by experienced experts.
However, this method may only be applicable for sedimentary terrain
where accurate and reliable LiDAR-derived strata boundaries can be
obtained.
This study developed a polygon auto-tracing method and used
airborne LiDAR data for dip-slope mapping to mitigate the common
problems of inefficiency and subjectivity that occur during visual
interpretations of dip-slope areas. In addition, we provide a description
of the automated workflow of the polygon auto-tracing method as
generated by the ArcGIS ModelBuilder platform. This workflow consists
of four major processes: deriving spatial raster layers, extracting dipslope cells, performing Point-Density analyses, and determining dipslope areas. These processes exploit several customized geoprocessing
tools developed in the Python language, such as the Bedding Surface
Fitting tool for fitting the bedding surface, the Bedding Dip Direction
tool for deriving the dip direction of the bedding, the Dip-slope
Calculation tool for calculating the dip-slope polygons, and the Dipslope Map tool for mapping the final dip-slope areas. We demonstrated
the processes and tools by successfully mapping 298 dip slopes in the
sedimentary terrain of northern Taiwan, where dip-slope landslides
have frequently occurred. The dip-slope mapping results were compared to and validated with two previously interpreted dip-slope maps
of the study area. We also successfully assessed the slopes along the
freeways in the study area to identify potentially high-risk dip slopes
that are daylighted. The polygon auto-tracing method using highquality LiDAR topographic data can provide an effective method of
mapping dip-slope areas and determining daylighted dip slopes, and it
has the potential to reduce the subjectivity introduced via visual
Acknowledgments
We thank the Central Geological Survey of Taiwan (Project No.
5226902000-04-95-04) for sponsoring the airborne LiDAR survey in the
research area. The research was supported by the Ministry of Science
and Technology, Taiwan, Project Nos. MOST 105-2625-M-002-014 (M.L. Lin) and MOST-105-2116-M-001-019 (Y.-C. Chan), and Institute of
Earth Sciences, Academia Sinica (No. IESAS2129). We also thank the
reviewers and Editor-in-chief Dr. Janusz Wasowski for their helpful and
constructive comments that have improved this manuscript.
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