GIS Modeling for Landslide Hazard and Risk Assessment: A Case Study at Kulon Progo mountaineous area, Yogyakarta, Yogyakarta, Indonesia by M. Pramono Hadi
[email protected]
Research Center for Disaster, Gadjah Mada University
[email protected]
Abstract
This case study of the GIS modeling for for landslide hazard and risk assessment assessment is aimed to be able to design a course module on urban disaster management. Practice has shown that the use of local case study is much more efficient in the learning experience of the student following such a module. Among the universities at Asia countries, under the project of CASITA, will exchange these case studies through the virtual platform (website). This module is expressed our willingness to implement course module on Urban Disaster Management in our academic curr iculum. iculum. The area of Kulon Progo was chosen as project area of case study because of reachable from campus, ca mpus, and many of landslides evident occur in this area. The input of the model contain some variables such as slope map which is produced from contour map, land cover index which is derived from Landsat data, the available soil map including the shear stress factors of each type of soil, and geologic map of the area. Look up table of each component should be performed in order to calculate the total score for final hazard map. To produce the vulnerability map it should be compromise with land use, population density/distribution and rainfall character characteristic istic as an agent agent to trigger trigger the landslide. landslide. The model validation can be done by comparing the empirical output map (hazard & vulnerability map) with the field evident of landslides and the amount of inhabitant affected.
Introduction:
In order to promote and accelerate t he landslide risk mitigation efforts in the Kulon Progo District, the citizen and local government officers became aware of the vulnerability of their area to landslide. The people should have knowledge knowledge about the landslide processes and landslide hazard map. Objective:
1) To be acquainted acquainted with the most most prone area related related with landslides at Kulon Progo 2) To be familiar with the input data that will be be used in this exercise on landslides hazard mapping using GIS and RS data. Methods: H:\Work_related\casita\A_casita\R_Gadjah Mada University\Modeling for Landslide Hazard and Risk Assessment _ case study FINAL.doc
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1. All procedure in this exercises should be elaborate using ILWIS GIS software. 2. The data collected were a) slope steepness, b) soil, c) geology, d) Landsat TM for calculating NDVI e) the number of inhabitant per village, f) landuse g) rainfall data for some stations. These all data were prepared in form of digital format. The evidence of slumps, rock falls, creeps and land slides have been collected a nd mapped on the field. 3. The output of modeling is base on composite value of the input. All the input were scored base the look up table. The formulation for performing the hazard map is as: Hazard = (Sc_SLP + Sc_DR + Sc_DP + Sc_STB + Sc_TX + Sc_WE + Sc_STR + Sc_VI) ……………(1)
minimum value is 8, and maximum values is 40 Hazard Sc_SLP Sc_DR Sc_DP Sc_STB Sc_TX Sc_WE Sc_STR SC_VI
: hazard map : Slope map (scored) : Soil Drainage map (scored) : Soil depth map (scored) : Soil stability map (scored) : Soil texture map (scored) : Rock weathering map (scored) : Geological structure map : Vegetation index map
The vulnerability map is calculated using the formula as; Vulner = Hazard * LU * RAIN * DENS ………………………………….……. (2)
Where Vulner Hazard LU RAIN DENS
: vulnerability map : hazard map : land use map (scored value) : rainfall base on daily rainfall 2yr probability. : population density base on dasimetri value (population div village area)
Furthermore, this Vulnerability map can be classified into 3 catogories such as Low, Medium dan High.
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Topo map Digitized/Scanned Interpolation
Soil map
drainage
weathering rate
soil depth DEM
Land Use map
Rainfall
Population & Adm
interpolation
Stucture isohyet
NDVI
Stability
Slope map
Landsat TM
Geology map
scoring
texture
scoring Mapcalc
Classification & Scoring
Sc_SLP
Sc_we
Sc_str
Sc_vi
scoring
Sc_dr Sc_dp Sc_stb Sc_tx
Mapcalc
Landslides hazard map
Vulnerability map for landslides
Validate the map Slump Rockfall Slide Creep
Counter-measures (Mitigation)
Figure 1. Flow diagram of the landslide hazard modeling
Processing the input data: 1. Create Slope map from contour To input the Contour map can be digitized, scanned, converted from other format • Contour interpolation can be directly executed by typing one of the following • expressions on the command line of the Main window: OUTMAP = MapInterpolContour(InputSegmentMap, Georeference) OUTMAP = MapInterpolContour(InputRasterMap)
where: OUTMAP
is the name of your output raster map.
MapInterpolContour
is the command to start the Contour interpolation operation.
InputSegmentMap
is the name of your input segment map (value domain). use map C13
InputRasterMap
is the name of an input raster map (value domain).
Georeference
•
is the name of an existing georeference that should be used for the output raster map. OR you can create new_name, use UNKNOWN Coordinate system, and use pixel size 30 m, Min, max X, Y give default value. In this case study we use coordinate as X 394000-416000 and Y 9129000-9146000
Contour interpolation is an operation which first rasterize conto ur lines of a segment map with a value domain, and then calculat es values for pixels that are not covered by segments by means of a linear interpolation. Furthermore, on the command line, this operation can also use an input raster map.
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•
•
•
•
•
When using Contour interpolation on a segment map containing height (contour) information, the resulting raster map is a Digita l Elevation Model (DEM). Input map requirements: o The input segment map should be a value map. The Contour interpolation operation creates an intermediate raster map with rasterized segments. For all pixel values with undefined values in this intermediate map, an output value is calculated. o When using Contour interpolation from the command line, also a raster map (with rasterized segments) can be used as input. Domain and georeference of output map: o The output map uses the same value domain as the input map. The value range and precision can be adjusted for t he output map. The georeference for the output map has to be selec ted or created; you can usually select an existing georeference corners. To calculate height differences in X-direction: start the Filter operation, select the Digital Elevation Model as the input map and select linear filter dfdx. Call the output map for example DX. To calculate height differences in Y-direction: start the Filter operation again, select the Digital Elevation Model as input map and select linear filter dfdy. Call the output map for example DY. To calculate a slope map in percentages from these maps DX and DY, type on the command line of the Main window: SLOPEPCT = 100 * HYP(DX,DY)/ PIXSIZE …………………………(3)
HYP is an internal Mapcalc/Tabcalc function. PIXSIZE returns the pixel size of a raster map SLOPEPCT is the output map name of the slope map in percentages Do classify the slope map use Mapcalc, create clfy table, use criteria below (Table 1) Table 1. Class and Score for Slope map Criteria Description Flat Flat to modetare Moderate Steep Very steep
Score Steepness ( o) 0 -- ≤8 o 8 - ≤15 o 15 - ≤25 o 25 - ≤45 o >45 o
Class Very good Good Fair Bad Very bad
1 2 3 4 5
Van Zuidam and Cancelado (1985)
Base on Table 1, group domain SLP_CLS for classification table is created. The SLOPEPCT map can be simplified into 5 class. The algorithm is as; SLP_CLASS = CLFY(SLOPEPCT, SLP_CLS) For presentation purposes, it is better to generalist t he this slope map using filtering method. The filter of MAJORITY is used to do this procedure. The result is SLOPE_MJR map.
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2. NDVI ( Normalized Difference Vegetation Index )
This index represent the density of land cover, which will affect the amount of water (from rainfall) to trigger the landslide. It is also possible to use coefficient of runoff to r eplace this index. To calculate NDVI values, you can use the MapCalc function NDVI(a,b). This function requires 2 satellite bands (one with visible or red values and the other near-infra red values). The function performs the calculation: (b - a) / (a + b) When using the NDVI( a,b) function, replace a with the band with visible or red reflectance (Landsat Band 3), and b with the band with near-infrared reflectance (Landsat band 4). Before this, the both band of Landsat must be georeferenced first. VegInd = NDVI (PROGO03, PROGO04) …………………………..….. (4)
PROGO03 is Landsat Band 3 of the area, and PROGO04 for band 4. Output map VegInd contains NDVI values, range from -1 to 1: Vegetated areas will generally yield high values because of their relativel y high • near-infrared reflectance and low visible reflectance. In contrast, water, clouds, and snow have larger visible reflectance t han near• infrared reflectance. Thus, these features yield negative index values. Rock and bare soil areas have similar re flectances in the two bands and result in • vegetation indices near zero. Table 2. Scoring for NDVI No. 1 2 3 4
cover density Dense Fairly dense Bare land & water unclassified
NDVI
Score
> 0.4 0.2 - 0.4 (-0.2) - 0.2 < -0.2
1 2 3 3
(Hadi, 2003)
Base on Table 2, a domain group for classification purposes is creat ed. The name of domain group is NDVI_CLS. Then classify the map using an algorithm as; VEG_CLS = CLFY(VEGIND, NDVI_CLS) Resample the map in order match with other map using coordinate mentioned above. Then do generalization of the map using filtering procedure. The output map is VEG_MJ.
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3. Raster map for Soil
This map contain information such as drainage character istic of the unit, the soil depth, stability of soil, and texture. Table 3. Scoring for soil texture No. 1 2 3 4 5
Class
Score
Loam Loamy clay, Loamy silt Loamy sand Heavy clay Sandy clay
1 2 3 4 5
ILACO (1981), Fletcher & Gibb (1990)
Table 4. Scoring for depth of soil No. 1 2 3 4 5
Class
soil depth (cm)
Score
0 – 30 30 – 60 60 – 90 90 – 150 > 150
1 2 3 4 5
Very shallow Shallow Moderate Deep Very deep
FAO Guidelines for Soils Profile Description (1968, in Worosuprojo & Jamulya , 1991)
Table 5. Laboratory test for Direct Shear and stability of soil material at study area No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
0,036
Shear Pressure (kN/cm2) 0,25
Shear resistance (kN/cm2) 0,09
42o47’51”
0,605
0,25
Loam Silty Loam
35 o41’53” 38o27’55”
0,062 0,11
Silty Loam Loamy sand Loamy clay Silty loam
18 o26’06” 48 o06’27” 36 o15’14” 03o48’51”
Silty clay-loam Sandy loam Clay Clay Clay Clay Silty clay-loam Silty clay-loam Clay Clay loam Silty loam Silty clay-loam Clay Silty clay-loam clay
Soil group
Texture
shear inclination
Complex of Troporthents Eutropepts Complex of Troporthents Eutropepts Hapludalfs Eutropepts Associations of Hapludalf Eutropepts Tropafluents Troporthents Ass. of Tropafluents Eutropepts Associations of Tropaquepts Eutropepts Ass. of Hapludalfs troporthents Ass. of Hapludalfs Troporthents Ass. of Troporthents Pelluderts Ass. of troporthents Dystropepts Tropopsamments Ass. of Eutropepts Pelluderts Epiaquepts Dystropepts Ass. of Eutropepts Dystropepts Hapludalfs Ass. of Pelludert Epiaquepts Ass. of Cromusderts Eutropepts Complex of Eutoprpts Pelluderts Troportents Endoaquepts Ass. of Epiaquepts Endoaquepts
Clay
13o02’19”
Loam
Cohesion (kN/cm2)
Safety factor
Criteria
0,38
not stable
0,836
3,35
Stable
0,25 0,25
0,242 0,309
0,97 1,23
not stable Stable
0,012 0,019 0,032 0,02
0,25 0,25 0,25 0,25
0,095 0,297 0,215 0,037
0,38 1,19 0,86 0,15
not stable Stable not stable not stable
19 o56’56” 37 o52’30” 37 o20’58” 19 o34’23” 04 o39’30” 32 o47’58” 50 o42’38” 35 o50’16” 34 o25’06” 45 o56’11” 46 o02’11” 45 o00’00” 34o36’05”
0,022 0,026 0,675 0,038 0,002 0,016 0,11 0,035 0,15 0,053 0,397 0,332 0,065
0,25 0,25 0,25 0,25 0,25 0,25 0,25 0,25 0,25 0,25 0,25 0,25 0,25
0,113 0,220 0,866 0,127 0,022 0,177 0,416 0,215 0,321 0,311 0,656 0,582 0,237
0,45 0,88 3,46 0,51 0,09 0,71 1,66 0,86 1,29 1,25 2,63 2,33 0,95
not stable not stable Stable not stable not stable not stable Stable not stable Stable Stable Stable Stable not stable
08 o25’37” 32 o29’55”
0,001 0,026
0,25 0,25
0,038 0,185
0,15 0,74
not stable not stable
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24 25
Ass. of Pelluderts Eutropepts Pelluderts
Silty loam clay
01 o30’37” 08 o13’09”
0,002 0,033
0,25 0,25
0,008 0,069
0,03 0,28
not stable not stable
Laboratory analysis (PSBA-UGM, 2001)
4. Raster map for geology
These map contain information about the degree of weat hering and geological structure. Table 6. Score for soil-rock weathering No. 1 2 3 4 5
Degree of weathering Slightly weathered Moderate
Description Bedrock have slightly changed in color Bedrock have slightly changed in color & some of the portion have weathered to be soil Moderate-high Bedrock have changed in color & more than half of the portion have weathered to be soil High all portion of rock have been decomposite, mostly weathered, but some of original rock still there very High, completely All the rock have been completely decomposite, and weathered weathered to be soil Fletcher and Gibb (1990)
Score 1 2 3 4 5
Table 7. Scoring for rock structure No. 1 2 3 4 5
The inclination of rock structure (o) o
Horizontal, flat (0–3 ) o vertical, sloping on flat-undulating landform (>3-8 ) o Non structural on steep slope (>20 ), sloping on o undulating landform (>8-14 ) o Sloping on undulating landform (>8-20 ) o Sloping on heavy rock on undulating landform (>20 )
Class
Score
very good good fair
1 2 3
fair-bad very bad
4 5
Misdiyanto (1992, modification by PSBA UGM 2001)
4. Rainfall
The distribution of rainfall were calculated using moving average interpolation from at least 18 rainfall station. The data collected from each rainfall station was maximum daily rainfall for over 20 years period. The coor dinate (in UTM) of the station is also important information in order to plot on the map. Table 8. The rainfall data for some station at the study area NO 1 2 3 4
Rainfall Station JOGOBOYO KALIGESING BANYUASIN KOKAP
X 392000 395700 399700 400596
Y 9129500 9143300 9154800 9133425
P2 78 158 81 102
P5 119 225 114 150
P10 146 270 137 183
P25 180 326 165 223
P50 205 386 186 253
P100 230 409 207 283
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5 6 7 8 9 10 11 12 13 14 15 16 17 18
TAMBAK WATES SAMIGALUH PANJATAN MENDUT WIJILAN KENTENG NGENTAK KALIBAWANG SENDANGPITU MUNTILAN GAMPING MEDARI TURI
405277 406709 408365 409000 410700 413292 413873 418000 418548 422000 422500 425184 427176 428733
9128673 9131422 9151636 9126000 9158800 9138108 9143520 9138500 9151596 9145500 9156500 9137849 9149029 9153942
117 113 129 103 60 104 111 112 112 99 117 109 103 125
157 154 170 155 87 142 143 138 148 118 160 140 130 145
184 182 197 189 104 168 165 154 173 128 189 164 145 156
217 217 231 233 127 200 192 174 203 139 225 199 165 166
241 243 256 265 143 223 212 188 226 146 252 228 181 172
266 268 281 298 160 247 232 202 249 153 279 160 197 177
X, Y is coordinate in UTM (zone 49) P2 … P100 is design daily rainfall for return per iod of 2 yrs … 100 yrs
•
•
•
•
•
use the Rainfall Station data (rainkp.dbf). This table could be created using spread sheet of MS Excels, and to be saved as dbf format. o No nr station o Station station name o X, Y coordinate (UTM of zone 49) P2…P100 attribute for design daily rainfall, expressing the o magnitude of rainfall to trigger the landslide. This value was analyzed using Gumble I probability theory. P2 is for 2 years probability daily rainfall, P5 for 5 years probability, and so. In File menu, use Import, Table to convert the dbf file to Ilwis table (output file rainkp1.tbl) Create point map Table to Point Map , in Operations menu, Table Operation, use coordinate system unknown, use column P2 (P2…P100), output point map rainkp1 Do Point interpolation, moving average, select input rainkp1, Georeference kp, output raster map rainkp1 The isohyet of P2 rainfall ready.
6. Land Use map
The land use map of the area can be classified info 5 class.
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Table 9. Scoring for Land Use No. 1 2 3 4 5
Land use
Score 1
Water bodies (lake, river) Forest Mixed garden Dry land agricultural Paddy field Settlement
2 3 4 4 6
(Worosuprodjo et al.., 1992; modification)
7. Population density map
Table 10. Population distribution for each village ADM#
Village name
Area (Ha)
Population
Area of Village (Ha)
Dasimetri (pop/ha)
Pop.Density (pop/ha)
1
Hargomulyo
1452,27
8774
93,78
93,56
6,04
2
Hargorejo
1620,82
10268
110,05
93,30
6,34
3
Tawangsari
316,35
4950
46,35
106,80
15,65
4
Karangsari
1182,11
9413
113,69
82,80
7,96
5
Kedungsari
548,73
4178
55,88
74,77
7,61
6
Margosari
500,43
5281
70,33
75,09
10,55
7
Pengasih
599,11
8109
86,62
93,62
13,53
8
Sendangsari
1228,34
9408
104,42
90,10
7,66
9
Donomulyo
963,15
5898
38,67
152,52
6,12
10
Wijimulyo
646,04
5720
39,18
145,99
8,85
11
Jatisarono
402,85
5396
40,98
131,67
13,39
12
Kembang
433,71
5099
42,21
120,80
11,76
13
Pendoworejo
997,26
6366
51,77
122,97
6,38
14
Tanjungharjo
523,16
4605
28,07
164,05
8,80
15
Giripurwo
1481,21
8424
112,77
74,70
5,69
16
Sidomulyo
1356,76
5821
72,90
79,85
4,29
17
Jatimulyo
1654,92
7983
80,32
99,39
4,82
18
Hargowilis
1475,68
7511
94,72
79,30
5,09
19
Hargotirto
1452,32
8316
120,89
68,79
5,73
20
Kalirejo
1255,03
5647
97,10
58,16
4,50
21
Banyuroto
708,22
4063
24,61
165,10
5,74
Source: BPS, 2001
Analisis:
1. Create HAZARD map using MapCalc HAZARD = SLOPE_C + SOIL_DR + SOIL_DP + SOIL_STB + SOIL_TX + ROCK_WE + GEOL_STR + VEG_INDX
min value is 8, max = 40 SLOPE_C
: Slope map
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SOIL_DR SOIL_DP SOIL_STB SOIL_TX ROCK_WE GEOL_STR VEG_INDX
: Soil Drainage map : Soil depth map : Soil stability map : Soil texture map : Rock weathering map : Geological structure map : Vegetation index map
Fig. The histogram of hazard map Table 11. Classification of hazard map
No 1 2 3
Hazard class Low Medium High
Score value < 27 27-31 > 31
Area
2. Create Vulnerability map (similar with hazard map, but add information about Land use, Rainfall, Population of each village)
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