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         Geoplanning 
Vol 3, No 1, 2016, 15-22                                                                                                                                                           Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi:10.14710/geoplanning.3.1.15-22   

GIS-BASED ANALYSIS FOR ASSESSING LANDSLIDE AND DROUGHT HAZARD IN THE 
CORRIDOR OF MT. MERAPI AND MT. MERBABU NATIONAL PARK, INDONESIA 

H. Marhaentoa 
a
 Gadjah Mada University, Indonesia 

Abstract: A corridor is an area located between two or more protected areas that are 

important to support the sustainability of the protected areas. This study is aimed at 
assessing landslide and drought hazard in the corridor area between Mt. Merapi 
National Park (MMNP) and Mt. Merbabu National Park (MMbNP) as a part of the 
corridor management strategy. The corridor area of MMNP and MMbNP comprises 
four sub-districts in Central Java Province, namely, Sawangan, Selo, Ampel, and 
Cepogo. A spatial analysis of ArcGIS 10.1 software was used to assess landslide hazard 
map and the Thornthwaite & Mather Water Balance approach was used to assess 
drought hazard map. The results have shown that three villages in Cepogo Sub-district 
and all villages in Selo Sub-district are highly prone to landslide hazard. Furthermore, 
two villages in Cepogo Sub-district and four villages in Selo Sub-district are prone to 
drought hazard. This study suggests that these villages should initiate a program called 
conservation village model based on disaster mitigation for mitigating future landslide 
and drought disasters.  

 
Copyright © 2016 GJGP-UNDIP  

This open access article is distributed under a  
Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

 
How to cite (APA 6th Style): 
Marhaento, H. (2016). GIS-based analysis for assessing landslide and drought hazard in the corridor of Mt. Merapi and Mt. Merbabu National Park, 
Indonesia. Geoplanning: Journal of Geomatics and Planning, 3(1), 15-22. doi:10.14710/geoplanning.3.1.15-22 

 

1. INTRODUCTION 

The 5th World Congress on National Parks in Durban, South Africa in 2003 under the theme Benefits 
Beyond Boundaries recommended that the principle of collaborative management between public bodies 
and local communities become a new model in the management of national parks (IUCN, 2005). A 
manifestation of this new paradigm is the arrangement of the buffer zone of protected areas. Conceptually, 
a buffer zone aims to enhance the conservation values of the buffered area (regulation of Indonesian 
Government No.68 of 1998). A buffer zone that connects two or more protected areas is known as a 
corridor area (Beier & Noss, 1998; Indrawan et al., 2012).  

Corridor area can be production forests and plantations, and even cultivated lands owned by 
communities, which, if it is designed properly, will be a valuable conservation tool (Beier & Noss, 1998). 
Management of corridor area so far focused on its function as the expansion of protected areas to connect 
between biomes (Joshi et al., 2013; Wangchuk, 2007). Numerous studies had been done to explain the 
function of the corridor area as wildlife migration path especially those with a broad range of habitat 
(Douglas-Hamilton et al., 2005; Joshi et al., 2011; Silveira et al., 2014). However, the presence of people 
who live in the corridor can be a threat to the survival of wildlife migration process (Kushwaha & Hazarika, 
2004). The expansion of settlements and cultivation of seasonal crops became the most influential factor to 
disturb the existence of the corridor (Joshi et al., 2011). One of the efforts to preserve the corridor area is 
through spatial planning and land management of it. 

In contrast to that of the other types of region, spatial planning of a corridor area has rarely got 
attention (Pouzols & Moilanen, 2014). Moreover, McRae et al., (2012) stated that there is a lack of spatial 
planning in a corridor area that focuses on protecting biological diversity in all levels (genetic, species, and 
landscapes). One of the obstacles is less of understanding between protected area managers and regional 

Article Info: 
Received: 10 January 2016 
In revised form: 12 March 2016 
Accepted: 25 April 2016 
Available Online: 30 April 2016 
 

Keywords:  
Corridor area, hazard analysis, GIS, 
landslide, drought 
 

Corresponding Author: 
Hero Marhaento 
Gadjah Mada University, 
Yogyakarta, Indonesia 
Email: marhaento@ugm.ac.id  

OPEN ACCESS 

http://dx.doi.org/10.14710/geoplanning.3.1.15-22
mailto:marhaento@ugm.ac.id


 
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governments (Anshari, 2006). In addition, people who live in corridor area are also lack of understanding 
about other functions of protected area, especially as a water and soil regulator to prevent natural 
disasters (Qutni, 2004). 

The aspects of disaster vulnerability in the corridor management strategy are still new and challenging. 
We believe that putting the element of disaster management in corridor areas is one of the solutions to 
reach an understanding between protected area managers, local communities, and local governments. 
Issues on disaster mitigation are more attractive than biodiversity protection so that it may gain more 
support from local community and local government. In addition, it is obligatory for local governments to 
protect communities from future disaster as stated in the Indonesia Law No. 24 of 2007 on Disaster 
Management. 

This study aims to analyze the two potential hazards in the corridor area of Mt. Merapi National Park 
(MMNP) and Mt. Merbabu National Park (MMbNP), namely landslides and drought. The corridor area of 
MMNP and MMbNP comprises administratively the Sawangan sub-district (Magelang district), Selo sub-
district, Cepogo sub-district and Ampel sub-district (Boyolali district) (see Figure 1). According to INFRONT 
(2008), these research areas are classified landslides prone caused by non-conservative tillage practice on 
the land use management. Moreover, agricultural productivity in the research sites is threatened to 
decrease due to hydrometeorological conditions (Putri, 2008). The results of this study will be useful as an 
advice for the conservation manager and local government to manage the corridor area based on disaster 
mitigation of landslides and drought hazard. 

Figure 1. Map of the study area (Geospatial Information Agency of Indonesia, 2015) 

 

2. DATA AND METHODS  

2.1 Research Material 

All the materials used in the study were obtained from various sources, both institutional and public 
domains. The institutional data was acquired by examining the data and reports from relevant institutions, 
while the public domain data was acquired by online access from the web page of the data provider. Table 
1 describes the materials used in this study. 

Table 1. Research material (authors, 2015) 

Name Scale/resolution  Source  

Administration map 
Land Use Map 
Altitude Map 
Slope Map 

1:25.000 Indonesia Topographic Map NLP: 1408-244, 1408-333, 
1408-522, 1408-611; Geospatial Information Agency 

Canopy Density Map 15 meter Citra ASTER VNIR on 07 August 2009 
Rainfall data Monthly, 1997 – 2007 Selo rain station, Cepogo rain station, Sawangan rain 

station and Ampel rain station 
Air temperature data Monthly, 1997 – 2007 Mt Merapi observation station, Center for Volcanology 

and Geological Hazard Mitigation (PVMBG) 
Soil Map 1:50,000 Boyolali Development Planning Agency (Bappeda) 



 
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Note:  
1. The slope map is obtained by analyzing the Digital Elevation Model (DEM) map from the contour map of Indonesia Topographic map 
2. Canopy density map is obtained by analyzing the Normalized Difference Vegetation Index (NDVI) image of ASTER VNIR and classified into 3 

classes: high (NDVI ≥ 0.6), moderate (0.2 ≤ NDVI < 0.6) and low (NDVI < 0.2) 
3. We performed all spatial analysis using ArcGIS 10.1 software. 

 
2.2 Landslide Hazard Analysis 

Landslide hazard analysis was conducted by the spatial analysis of the factors that influence the 
occurrence of landslides in the area of research, namely: slope, land use, soil depth, soil type and rainfall. 
Selection of these factors was based on field observations, previous studies from Marhaento & Sudibyakto 
(2007), Subekti & Hadmoko (2013), and interviews with the local communities. Subsequently, all these 
factors were given a score for each class according to the level of importance on landslide occurrences (see 
Table 2). We used overlay analysis using ArcGIS 10.1 software to combine all spatial information. The sum 
of scores from each class of each land unit was then used to determine the landslides hazard level. The 
results of the final score were proportionally classified into 5 classes, namely Very Low (VL), Low (L), 
Medium (M), High (H) and Very High (VH). 

Table 2. Scores of each landslides triggering factor in the study area  
(Marhaento & Sudibyakto, 2007; Subekti & Hadmoko, 2013) 

Factor Class  Score  

Land use 
HD : High Density (NDVI ≥ 0.6) 
MD: Medium Density (0.2 ≤ NDVI < 0.6) 
LD : Low Density ( NDVI < 0.2) 

HD Mixed-plantation, MD Mixed-plantation 10 
Pasture and Shrub 20 
LD Mixed-plantation 30 

Settlements, Rice Field 40 
Moor 50 

Mean annual rainfall (mm) < 2000 10 
2000 - < 2500 20 
2500 - < 3000 30 
≥ 3000 40 

Slope class (%) 0 < 8 10 
8 – < 15 20 
15 – < 25 30 
25 – < 45 40 
≥ 45 50 

Soil depth (cm) < 90 10 
90 – < 150 20 
150 – < 300 30 
≥ 300 40 

Soil Type Grey andosol complex and litosols, brown andosols, brown latosols 30 

Grey regosol complex and latosols, litosols, brown litosols 40 

 
2.3 Drought Hazard Analysis 

Drought hazard analysis was carried out by measuring the water balance and aridity index using the 
Thornthwaite and Matter Water Balance (TMWB) method (Thornthwaite & Mather, 1957). Aridity index is a 
ratio between soil moisture deficiency and water demand for potential evapotranspiration to occur. The 
analysis calculates water balance in monthly basis and averages it in annual basis. There are three possible 
water balance conditions. First, a balance condition occurs when soil moisture meets water demand for 
potential evapotranspiration. Second, a surplus condition occurs when soil moisture exceeds 
evapotranspiration needs and then contributes to runoff. Third, a deficit condition occurs when soil 
moisture is not sufficient for fulfil evapotranspiration demand. 

TMWB aridity index (AI) is calculated using the following equation: 

N

D
AI

*100


;
 

where the water deficiency D is calculated as the sum of the monthly differences between precipitation 
and potential evapotranspiration for those months when the normal precipitation is less than the normal 
evapotranspiration; and N stands for the sum of monthly values of potential evapotranspiration for the 
deficient months. Based on the results of AI calculation and considering the agro-climate zones according to 



 
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Oldeman, we determined the drought hazard level based on the criteria on Table 3. We performed AI 
calculation in each Land Mapping Unit (LMU) that is formed by soil type map and elevation map. We note 
that drought hazard analysis in the present study continued the work of Putri (2008). 

Table 3. Criteria for drought hazard classes (Thornthwaite & Mather, 1957)  

No. Hazard Class Criteria 

1.  
2. 
3. 

Not Risk  (NR) 
Risk (R) 
Highly Risk (HR) 

Ia < 16,7 % 
16,7 % < Ia < 33,3 % 
Ia ≥ 33,3 % 

 
Three parameters are required to calculate D and N, namely monthly air temperature average, monthly 
rainfall and storage capacity. Storage capacity parameter is determined by soil-texture and root-depth. In 
order to give an insight about the procedure to calculate the AI, steps to determine AI are as follows: 
 

1. Calculate monthly mean temperature (T) 
2. Calculate heat index value (i) monthly for each month, according to the formula 

  

3. Calculate annual heat index (I) and constant values (a) with the formula: 





12

1j

ijI  

 a = (675.10-9 *I3) - (771.10-7 *I2) + (1792.10-5 *I) + 0.49239 
4. Calculate mean monthly potential evapotranspiration (PET) (mm / month), with the formula PET = 

1.6 (I* T* 10)
a
 

5. Determine latitude correction factor (f) according to site study, which in the present study is at 
latitude 70 so that the value of f is: 

7
0
S Month 

1 2 3 4 5 6 7 8 9 10 11 12 
f 1.07 0.96 1.04 1.00 1.02 0.98 1.02 1.03 1.00 1.05 1.04 1.07 

 
6. Calculate corrected value of potential evapotranspiration (ET) (mm / month), with the formula ET = 

PET * f 
7. Calculate monthly rainfall data (P) 
8. Calculate difference between rainfall and potential evapotranspiration in monthly basis (P - ET) 
9. Calculate potential accumulation of water lost (APWL). If the result of the calculation no.8 is 

positive, then the value APWL is zero, whereas if the result of the calculation no.8 is negative, then 
APWL is calculated based on accumulative of negative values until reach positive. 

10. Calculate storage capacity (Sto) that taking into account soil texture and root-depth. 
11. Calculate soil water consumption (St) 

St is in optimum condition when APWL is positive. If APWL is negative, St is calculated using the 
formula: 
St = Sto * eAPWL / Sto 

e = 2, 718 
12. Calculate change in soil moisture (Δ St) in monthly basis.  

Change in soil moisture (mm / month) is the difference between usage of soil moisture in a month 
with usage of soil moisture in a previous month 
St Δ = Sti – Sti-1 

13. Calculate the actual evapotranspiration (EA) in wet months (P > ET) with the formula EA = ET, while 
in dry months (P <ET) we use formula, EA = P + |-Δ St │ 

14. Calculate water deficiency (D) 
Water deficiency is calculated in months when P < ET using formula D = ET – EA. 
 



 
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3. RESULTS AND DISCUSSION 

3.1 Landslide Hazard Analysis 

The study area has nine land use classes, the three dominant classes of which are moor with the area of 
3,552.8 ha (47.6%), settlement area of 1.158,2 ha (15.5%), and high density mixed-plantation area of 868.3 
ha (11,6%). The moor areas consist of vegetables such as Cabbage (Brassica oleracea), Prei (Allium Porum), 
Carrot (Daucus carota), Onion (Allium cepa), Jipang (Sechium edule), Fennel (Pimpinela anisium) and Beans 
(Phaseolus vulgaris). The mixed-plantation areas consist of perennial trees such as: Sengon (Paraserianthes 
falcataria), Suren (Toona sureni), Leucaena (Leucaena leucocephala), Jackfruit (Artocarpus sp), Gmelina 
(Gmelina arborea), Acacia (Acacia auriculiformis), Cinnamon (Cinamomum sp) and Teak (Tectona grandis). 

Based on the Digital Elevation Model (DEM), medium slope and steep slope are dominant with the area 
of 2,664.2 ha (35.7%) and 1,783.5 ha (23.9%) respectively. The dominant soil type is Andosol with the area 
of 3,513.9 ha (47.1%) and complex Regosol and Latosol type with the area of 2,840.3 ha (38.1%). Soil with 
the depth of less than 90 cm and between 90-150 cm are dominant with the area of 2,632.4 ha (35.3%) and 
4,823.4 ha (64.7%) respectively. 

Using Krigging analysis in the ArcGIS software to analyze monthly rainfall data from years 1997 - 2007 
of four rainfall stations, we found that 6,540.9 ha (87.7%) area has a mean annual rainfall of 2500 - 3000 
mm/year and 914.9 ha (12.3%) area has a mean annual rainfall above 3.000 mm, which mainly occurred in 
the Cepogo sub-district. After overlaying all thematic maps and classifying the scoring results, we found 
that medium hazard class (M) is dominant in the study area with the area of 3,131.5 hectares (42.0%), 
followed by low class (L) with an area of 2,060.1 ha (27.6 %) and high class (H) with the area of 1,713.0 ha 
(23.0%). Table 4 shows the detail results of the hazard analysis. 

 

Table 4. Landslide hazard classes in the study area (Data Processing, 2015) 

Landslides vulnerability Class Size (Ha) % 

Very Low (VL) 523.5 7.0 

Low (L) 2,060.1 27.6 

Medium (M) 3,131.5 42.0 

High (H) 1,713.0 23.0 

Very High (VH) 27.6 0.4 

Total 7,455.8 100.0 

 

We found that the area with high level of landslides hazard was the eastern part of Selo and all villages 
in the Cepogo sub-district. Apparently, high rainfall occurrence and steep slope that dominant in these 
areas was the main cause of the high level of landslide hazard. In addition, we also found that landslides 
frequently occur on along the main road that connect Selo sub-district with Cepogo sub-districts. Figure 2 
shows the spatial distribution of landslide hazard in the study area.  

Figure 2. Landslide hazard map of the study area (analysis, 2015) 

 



 
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3.2 Drought Hazard Analysis 

Table 5 shows the calculation of Aridity Index (AI) in each Land Mapping Unit. The results show that the 
entire area has AI value below 16.7%, which categorized as Not Risk (NR)—according Thornthwaite and 
Mather (1957). However, there are some areas that have a high value of AI, such as Cepogo village and 
Kembangkuning village at Cepogo sub-district, and Jeruk Village, Senden village, Tarubatang villages, and 
Selo villages at Selo sub-district. Figure 3 shows the spatial distribution of drought hazard in the study area. 

Table 5. Aridity Index in each land mapping unit (Data Processing, 2015) 

No Land Mapping Unit 
Elevation 
(m dpl) 

AI (%) 

1 Ampel_ brown andosol 1000 – 1500 m 1267 4.0 

2 Ampel_ brown andosol > 1500 m 1664 3.3 

3 Ampel_ grey andosol complex and lithosol >1500 m 1664 1.9 

4 Ampel_ grey andosol complex and lithosol 1000 – 1500 m 1664 2.0 

5 Ampel_brown latosol 1000 – 1500 m 1245 2.8 

6 Cepogo_brown andosol < 1000 m 964 11.1 

7 Cepogo_brown andosol coklat 1000 – 1500 m 1102 7.1 

8 Cepogo_ grey complex regosol and lathosol < 1000 m 834 3.5 

9 Cepogo_ grey complex regosol and lathosol 1000 – 1500 m 1085 3.9 

10 Cepogo_brown Latosol < 1000 m  744 3.1 

11 Cepogo_brown Latosol 1000 – 1500 m 1110 4.1 

12 Cepogo_litosol coklat < 1000 m 812 3.2 

13 Cepogo_brown litosol 1000 – 1500 m 1039 3.8 

14 Selo_brown andosol < 1000 m 996 2.8 

15 Selo_ brown andosol 1000 – 1500 m 1326 2.6 

16 Selo_ brown andosol > 1500 m 1950 1.4 

17 Selo_ grey andosol complex and lithosol 1000 – 1500 m 1475 1.2 

18 Selo_ grey andosol complex and lithosol > 1500 m 1934 0.8 

19 Selo_ grey regosol complex and latosol < 1000 m 948 2.3 

20 Selo_ grey regosol complex and latosol 1000 – 1500 m 1276 1.6 

21 Selo_ grey regosol complex and latosol > 1500 m 1704 1.2 

22 Selo_brown latosol 1000 – 1500 m 1262 1.6 

23 Selo_brown litosol < 1000 m 966 2.4 

24 Selo_brown litosol 1000 – 1500 m 1032 4.4 

Note: Land Mapping Unit consist of sub-district name, soil type and elevation class 
 

Figure 3. Drought hazard map of the study area (Analysis, 2015) 

 



 
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3.3 Discussion 

The new paradigm in management of protected area to emphasize collaborative management between 
public bodies and local communities brings consequence that the buffer zone of protected area should be 
managed. The importance of a buffer zone is even more when it connects more than one protected areas 
as a corridor area (Joshi et al., 2011). In the present study, hazard analysis is used as one component of 
strategy to manage the corridor area between the Mt. Merbabu National Park (MMbNP) and the Mt. 
Merapi National Park (MMNP). Numerous studies on the management of protected and corridor areas 
have focused on aspects of the protection of biodiversity (Douglas-Hamilton et al., 2005; Kushwaha & 
Hazarika, 2004; Marhaento & Kurnia, 2015; Silveira et al., 2014). In fact, the role of corridor area as a 
protector from potential disasters for its surroundings is also significant and thus requires more attention.  

Landslides and land drought are two kinds of natural disasters that can disrupt productivity of land and 
have a major impact on socio-economic of local communities—in the study area, they are mostly farmers. 
We found that approximately 23% of the area has a severe impact of landslide from high to very high 
grade. These areas are mainly in Cepogo village, Genting village, and Sukabumi village at the Cepogo sub-
district and in all villages at Selo sub-district that is adjacent with MMNP and MMbNP. The landslides 
frequently occur in along the main road connecting Selo sub-district with Cepogo sub-districts. It shows that 
the cutting slope, which is often performed in the road-construction process, is as important factor in the 
landslide occurences. Marhaento & Sudibyakto (2007), Priyono (2008), Subekti & Hadmoko (2013), and 
Nirwansyah et al. (2015) also delivered the high contribution of cutting slopes in the landslide. 

Using Thornthwaite-Mather Water Balance (TWMB) method, we found that all areas have an Aridity 
Index (AI) value below 16.7%, or it includes in Not Risk criteria. It indicates that the water balance in the 
corridor area is sufficient to support crop productivity (Thornthwaite & Mather, 1957). However, some 
areas have a quite high AI value close to 16.7%, i.e. Cepogo village and Kembangkuning village at Cepogo 
sub-district and Jeruk village, Senden village, Tarubatang villages, and Selo villages at Selo sub-district.  

The areas detected to be prone to future landslides and drought would then become a prime target in 
the management strategy of MMbNP and MMNP corridor area. We suggest the scheme of Rural 
Conservation (DK) according to Regulation of The Forestry Minister No.P-16 / Menhut-II / 2011 be 
implemented on villages with high level of hazards. Furthermore, these selected villages should be 
designated as Model Desa Konservasi Berbasis Mitigasi Bencana or Rural Conservation Model Based on 
Disaster Mitigation Effort (MDK-BMB). The selected villages in this scheme are pointed as a model to apply 
strategy on community empowerment with full respect to soil and land conservation tillage and disaster 
mitigation.  

 

4. CONCLUSION 

The results showed that several villages in the corridor area of Mt. Merapi National Park (MMNP) and 
Mt. Merbabu National Park (MMbNP) are prone to landslides and drought. The villages are Cepogo, 
Genting, and Sukabumi villages at Cepogo sub-district and all villages at Selo sub-district that are located 
adjacent to the area of MMNP and MMbNP. The villages that are prone to drought hazard are Cepogo 
village and Kembangkuning village at Cepogo sub-district and Jeruk Village, Senden village, Tarubatang 
village, and Selo village at Selo sub-district. We suggest these villages to be included in the national 
program called Rural Conservation Model Based on Disaster Mitigation Effort (MDK-BMB), the 
implementation of which should be collaborative between the local governments and the conservation 
area managers.  

 

5. ACKNOWLEDGMENTS 

The author acknowledges Faculty of Forestry, University of Gadjah Mada (UGM) for its support and fund 
for the research. The author would like to thank Totok Wahyu Wibowo from the Faculty of Geography, 
UGM for his help in processing the spatial data, as well as Prima Nugroho and Yanuar Endro Wicaksono for 
their assistance during the field work. 



 
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