Point Based Assessment: Selecting the Best Way to Represent Landslide Polygon as Point Frequency in Landslide Investigation Norbert Simon* Doctor, School of Environment and Natural Resources Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia. *Corresponding Author e-mail:
[email protected]
Michael Crozier Professor Emeritus, School of Geography, Environment and Earth Sciences, Victoria University of Wellington, New Zealand e-mail:
[email protected]
Mairead de Roiste Doctor, School of Geography, Environment and Earth Sciences, Victoria University of Wellington, New Zealand e-mail:
[email protected]
Abdul Ghani Rafek Professor, School of Environment and Natural Resources Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia. e-mail:
[email protected]
ABSTRACT This study evaluates two common methods to represent landslide polygons as frequencies for use within GIS analysis. These methods are used to extract information from factor maps, such as a single value for slope angle for the landslide polygon, which is then used in hazard assessment. Understanding the errors associated with generating points to represent landslide polygons should be clearly understood to evaluate the effectiveness of these points. In this paper, the effectiveness of two representation techniques in gathering slope angle information is examined. The first technique evaluated is based on the centroid (a point feature) of each landslide polygon. The value of the landslide factor class beneath the centroid point, e.g. the slope value at that point, selects the landslide factor class. The second method is an area based technique which depends on the area covered by different landslide factor classes, e.g. the most common slope for the polygon. Here, the landslide factor class that occupies most of the landslide polygon is selected as the attribute for each landslide polygon. As demonstrated in this paper, the results favour the area based technique. While this paper explores this technique for slope information, the finding is applicable to other landslide causal factors, such as elevation and rock type.
KEYWORDS:
Centroid point, area based technique, landslide, GIS.
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INTRODUCTION Casual factor information for mapped landslides can be extracted using a number of ways with different sampling strategies (Xu et al., 2012). Some researchers use points to extract information from factor maps for use in their landslide analysis (e.g. Ruette et al., 2011; Dong et al., 1997; Bai et al., 2009; Matthew et al., 2007) The use of proportional area in landslide analysis has been well explored in the literature but representing landslides as points created from landslide polygons is not covered in detail. Among the researchers that covered the use of points in landslide investigation are Galli et al. (2008), Bathrellos et al. (2009) Bai et al. (2009). Some landslide data are unsuited for polygon representation. For example, the landslide area is too small to be shown (Galli et al., 2009). In this paper, the implication of using points in representing landslide polygons that will be used to obtain landslide attribute from a landslide factor map are explored. Suggestions are also offered on how to improve the selection of landslide attributes for a landslide polygon when using point frequency. To achieve this aim, the centroid, a measure of a central point and the area based technique are used to derive landslide attributes and a comparison between the two techniques is undertaken. For illustration purposes, this paper uses a slope angle map to test the use of the techniques.
METHODOLOGY Landslide polygon inventory This study uses an inventory of 299 landslides polygons digitised from 1:25,000 (black and white) and 1:10,000 (colour) aerial photographs. Landslide identification in aerial photographs is based on manuals for landslide identification by van Westen and Soeters (n.d.), Rib and Ta (1978) and Ho et al. (2010). Typical morphological features used to identify landslides in aerial photographs are: concave-convex slopes, step-like morphology, back tilting of slope faces, hummocky relief, crack formation and steepening of the slopes.
Slope angle map The slope angle map is used in this paper to examine the effectiveness of the two techniques: centroid point and area based. The slope angle map was created from a DEM derived from contour lines digitised from topographical maps and has a 30 m x 30 m resolution. The 4x4 cell method in ArcGIS 9.3’s DEM Surface Tool extension was used to generate the slope steepness map (Jenness, 2010). The 4x4 cell method, known as the ‘rook case’, computes slope angle from the nearest four elevation points in a cell – cardinal directions (Jones 1998). Jones (1998) compared several algorithms and found that the 4x4 cell method is the more accurate method for slope angle compared to Horn’s (1981) and Sharpnack and Akin’s (1969) and other 8 cell algorithms. The slope angle map was classified into four classes based on the classification used by DTCP (2009): 35°.
Centroid based landslide points The centroid point technique used in this study was calculated using ArcGIS 9.3 software. ArcGIS’ feature to point tool generates a centroid point determined by locating the polygon’s centre of gravity. For this technique, the landslide polygons are converted to centroid points before being
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intersected with landslide factor maps. Figure 1 illustrates an example of how a landslide polygon is transformed into a centroid point and then intersected with a slope angle map to extract slope angle information for that landslide. The slope angle class is selected based on the slope angle value directly underneath the centroid point (using the extract to point tool in ArcGIS).
Figure 1: Workflow of centroid point technique Area based technique
This technique intersects the landslide polygon with the landslide factor map, e.g. slope angle. The workflow for the area based technique used in this study is shown in Figure 2. First, the slope angle is converted from a raster grid into a vector. Then, the landslide polygon map is intersected with the slope angle map. This intersection results in several polygons which represent the different slope angle classes inside each landslide polygon (Figure 3).The area of each of the slope polygons within each landslide polygons is calculated. The area for the different slope polygons for each individual landslide is compared and the slope angle class with the largest area is used as the value to represent the whole landslide polygon.
Figure 2: Workflow for the area based technique
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Figure 3: Landslide polygon with multiple polygons representing different slope angle classes
RESULT AND DISCUSSION Several landslide polygons were used to compare the effectiveness of the centroid point and area based techniques to extract information from a landslide factor map. The discussion is illustrated with four cases.
Case 1: Two slope angle classes within a single landslide polygon The first case demonstrates the reliability of the centroid point and area based techniques in choosing the correct slope angle class for a landslide polygon which contains two slope angle classes. The landslide polygon with its centroid point overlaid on the slope angle map is shown in Figure 4.
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Figure 4: Landslide polygon on top of a slope angle map. (a) Landslide polygon with its centroid point on top of two slope angle classes (b) close up of the landslide polygon with calculated area for both slope angle classes which were inside the landslide polygon. Varying shades of grey represent different slope angle classes. In this illustrative example in Figure 4b above, the 15-25° slope angle class had an area of 398 m and the 25-35° class an area of 556 m2. Here the prominent slope angle class is 25-35°, because it occupies most of the polygon. However, if the centroid point of the landslide polygon is used to represent the polygon to extract the slope angle class, the 15-25° slope angle class will be the selected (Figure 5). This is clearly problematic as 15-25° slope angle class has both a smaller area and is located at a lower elevation than the 25-35 ° class. This contravenes landslide theory where landslides move downslope under the influence of gravity (Cruden, 1991; Varnes, 1978). In this case the centroid point is unsuitable to extract landslide attribute directly from a landslide factor map. Additionally, the slope angle selected by the centroid point is unlikely to be the initiation point for the landslide. 2
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Figure 5: Closer examination of the centroid point inside the landslide polygon
Case 2: Two slope angle classes with large area differences In Figure 6, the landslide polygon contains two slope angle classes with large area differences. In this example, the centroid is located in the class with significantly smaller coverage, the
