Spatio-temporal of landslide potential in upstream areas, Bali tourism destinations: remote sensing and geographic information approach


  • I Wayan Diara Soil Sciences and Environment, Faculty of Agriculture, Udayana University
  • I Ketut Agus Wahyu Wiradharma Soil Sciences and Environment, Faculty of Agriculture, Udayana University
  • R Suyarto Spatial Data Infrastructure Development Center (PPIDS), Udayana University
  • W Wiyanti Spatial Data Infrastructure Development Center (PPIDS), Udayana University
  • Moh Saifulloh Spatial Data Infrastructure Development Center (PPIDS), Udayana University



Geographic Information System, landslides, remote sensing, tourism destinations, upstream


Upstream Bali has tourist destinations with beautiful natural panoramas such as mountains, forest areas, and lakes. Characteristics of the area with steep slopes, high rainfall, and altitude above 1,500 masl. The area is inseparable from the threat of disasters, such as landslides, especially in the Baturiti District. This area often experiences landslides but has not been mapped spatially. Mitigation efforts are needed to minimize the impact of landslides. This study aimed to determine the potential for landslides and their distribution in different periods, namely 2000, 2010, and 2020. The scoring method considers four parameters: rainfall, slope, soil type, and vegetation density, using ArcGIS 10.8 Apps. Parameters extracted from remote sensing data include Landsat with ETM+ and OLI sensors, rainfall from the CHIRPS satellite, and slopes from DEMNAS. Geographic Information System (GIS) data includes soil types. Another role of GIS is to quantify raster data to build a landslide potential prediction model. Baturiti Subdistrict has a low to high potential for landslides, which are administratively distributed in Candikuning, Baturiti, Antapan, Batunya, and Bangli villages. The landslide potential in the high category in 2000, 2010, and 2020 respectively, is 70.12 ha (1%), 597.05 ha (5%), and 39.12 ha (1%). Based on the findings of this study, the leading cause of landslides is high rainfall followed by reduced vegetation density. Other factors include steep slopes (>45%) and soil types of Andosol and Regosol.


Aggarwal, A., Alshehri, M., Kumar, M., Alfarraj, O., Sharma, P. and Pardasani, K.R. 2020. Landslide data analysis using various time-series forecasting models. Computers and Electrical Engineering Volume 88, December 2020, 106858, doi:10.1016/ j.compeleceng.2020.106858.

Aggarwal, A., Rani, A., Sharma, P., Kumar, M., Shankar, A. and Alazab, M. 2022. Prediction of landsliding using univariate forecasting models. Internet Technology Letters 5(1):1-6, doi:10.1002/itl2.209.

Bachri, S., Shrestha, R.P., Yulianto, F., Sumarmi, S., Utomo, K.S.B. and Aldianto, Y.E. 2021. Mapping landform and landslide susceptibility using remote sensing, GIS, and field observation in the southern crossroad, Malang regency, East Java, Indonesia. Geosciences (Switzerland) 11(1):4, doi:10.3390/geosciences11010004.

Bangsawan, L., Satriagasa, M.C. and Bahri, S. 2021. Improved performance of the CHIRPS monthly rainfall estimation extraction from the Google Earth Engine (GEE) platform in South Sulawesi Region. IOP Conference Series: Earth and Environmental Science 893(1), doi:10.1088/1755-1315/893/1/012057.

Bourenane, H. and Bouhadad, Y. 2021. Impact of land use changes on landslides occurrence in an urban area: the case of Constantine City (NE Algeria). Geotechnical and Geological Engineering 39(6):1-21, doi:10.1007/s10706-021-01768-1.

Çellek, S. 2022. Effect of the slope angle and its classification on landslides. Natural Hazards and Earth System Sciences Discussion, doi:10.5194/nhess-2020-87, 2020.

Chen, L., Guo, Z., Yin, K., Pikha Shrestha, D. and Jin, S. 2019. The influence of land use and land cover change on landslide susceptibility: A case study in Zhushan Town, Xuan’en County (Hubei, China). Natural Hazards and Earth System Sciences 19(10):2207-2228, doi:10.5194/nhess-19-2207-2019.

Diara, I.W., Suyarto, R. and Saifulloh, M. 2022. Spatial distribution of landslide susceptibility in new road construction Mengwitani-Singaraja, Bali-Indonesia: based on geospatial data. International Journal of GEOMATE 23(96):95-103,doi:10.21660/2022.96.3320.

Emberson, R., Kirschbaum, D. and Stanley, T. 2021. Global connections between El Nino and landslide impacts. Nature Communications 12(1), Article number: 2262, doi:10.1038/s41467-021-223984.

Fan, L., Lehmann, P. and Or, D. 2016. Effects of soil spatial variability at the hillslope and catchment scales on characteristics of rainfall-induced landslides. Water Resources Research 52(3), doi:10.1002/2015WR017758.

Fiorucci, F., Ardizzone, F., Mondini, A.C., Viero, A. and Guzzetti, F. 2019. Visual interpretation of stereoscopic NDVI satellite images to map rainfall-induced landslides. Landslides 16(1):165-174, doi:10.1007/s10346-018-1069-y.

Froude, M.J. and Petley, D.N. 2018. Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences 18(8):2161-2181, doi:10.5194/nhess-18-2161-2018.

Gonzalez-Ollauri, A. and Mickovski, S.B. 2017. Hydrological effect of vegetation against rainfall-induced landslides. Journal of Hydrology 549:374-387, doi:10.1016/j.jhydrol.2017.04.014.

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R. 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18-27, doi:10.1016/j.rse.2017.06.031.

Ismail, Y. 2021. Creating sustainability of natural tourism destinations. Geojournal of Tourism and Geosites 39(4):1331-1335, doi:10.30892/gtg.394spl02-775.

Jeong, S., Lee, K., Kim, J. and Kim, Y. 2017. Analysis of rainfall-induced landslide on unsaturated soil slopes. Sustainability (Switzerland) 9(7):1280, doi:10.3390/su9071280.

Jia, G., Tang, Q. and Xu, X. 2020. Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings. Landslides 17(2):1-17, doi:10.1007/s10346-019-01277-6.

Joyce, K.E., Belliss, S.E., Samsonov, S.V., McNeill, S.J. and Glassey, P.J. 2009. A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Progress in Physical Geography 33(2):187-207, doi:10.1177/0309133309339563.

Kartini, N.L., Saifulloh, M., Trigunasih, N.M. and Narka, I.W. 2023. Assessment of soil degradation based on soil properties and spatial analysis in dryland farming. Journal of Ecological Engineering 24(4):368-375, doi:10.12911/22998993/161080.

Kawamura, S., Kawajiri, S., Hirose, W. and Watanabe, T. 2019) Slope failures/landslides over a wide area in the 2018 Hokkaido Eastern Iburi earthquake. Soils and Foundations 59(6):2376-2395, doi:10.1016/ j.sandf.2019.08.009.

Khoiri, M., Jaelani, L.M. and Widodo, A. 2018. Landslides hazard mapping using remote sensing data in Ponorogo Regency, East Java. Internet Journal of Society for Social Management Systems 11(2):101-108.

Kumar, V., Gupta, V. and Sundriyal, Y.P. 2019. Spatial interrelationship of landslides, litho-tectonics, and climate regime, Satluj valley, Northwest Himalaya. Geological Journal 54(1):537-551, doi:10.1002/gj.3204.

Lee, S. and Choi, J. 2004. Landslide susceptibility mapping using GIS and the weight-of-evidence model. International Journal of Geographical Information Science 18(8):789-814, doi:10.1080/ 13658810410001702003.

Liu, Y., Deng, Z. and Wang, X. 2021. The effects of rainfall, soil type and slope on the processes and mechanisms of rainfall-induced shallow landslides. Applied Sciences (Switzerland) 11(24):11652, doi:10.3390/app112411652.

Liu, Z., Qiu, H., Zhu, Y., Liu, Y., Yang, D., Ma, S., Zhang, J., Wang, Y., Wang, L. and Tang, B. 2022. Efficient identification and monitoring of landslides by time-series InSAR combining single-and multi-look phases. Remote Sensing 14(4):1026, doi:10.3390/rs14041026.

Mehdi, A., Mobin, E., Mohammad, A., Elyasi, A.H. and Zahra, N. 2021. Application assessment of GRACE and CHIRPS data in the Google Earth Engine to investigate their relation with groundwater resource changes (Northwestern region of Iran). Journal of Groundwater Science and Engineering 9(2):102-113, doi:10.19637/j.cnki.2305-7068.2021.02.002.

Moreiras, S.M. 2005. Climatic effect of ENSO associated with landslide occurrence in the Central Andes, Mendoza Province, Argentina. Landslides 2(1):53-59, doi:10.1007/s10346-005-0046-4.

Priyono, K.D., Jumadi, Saputra, A. and Fikriyah, V.N. 2020. Risk analysis of landslide impacts on settlements in Karanganyar, Central Java, Indonesia. International Journal of GEOMATE 19(73):100-107, doi:10.21660/2020.73.34128.

Psomiadis, E., Charizopoulos, N., Efthimiou, N., Soulis, K.X. and Charalampopoulos, I. 2020. Earth observation and GIS-based analysis for landslide susceptibility and risk assessment. ISPRS International Journal of Geo-Information 9(9):552, doi:10.3390/ijgi9090552.

Qu, F., Qiu, H., Sun, H. and Tang, M. 2021. Post-failure landslide changes detection and analysis using optical satellite Sentinel-2 images. Landslides 18(1):447-455, doi:10.1007/s10346-020-01498-0.

Rabby, Y.W., Li, Y., Abedin, J. and Sabrina, S. 2022. Impact of land use/land cover change on landslide susceptibility in Rangamati Municipality of Rangamati District, Bangladesh. ISPRS International Journal of Geo-Information 11(2):89, doi:10.3390/ijgi11020089.

Ramesh, G. 2021. Slope and landslide stabilization: a review. Indian Journal of Structure Engineering 1(2):29-32, doi:10.35940/ijse.a1304.111221.

Reichenbach, P., Busca, C., Mondini, A.C. and Rossi, M. 2014. The influence of land use change on landslide susceptibility zonation: the Briga catchment test site (Messina, Italy). Environmental Management 54(6):1372-1384, doi:10.1007/s00267-014-0357-0.

Saito, H., Uchiyama, S. and Teshirogi, K. 2022. Rapid vegetation recovery at landslide scars detected by multitemporal high-resolution satellite imagery at Aso volcano, Japan. Geomorphology 398:197989, doi:10.1016/j.geomorph.2021.107989.

Senanayake, S., Pradhan, B., Huete, A. and Brennan, J. 2020. Assessing soil erosion hazards using land-use change and landslide frequency ratio method: A case study of Sabaragamuwa province, Sri Lanka. Remote Sensing 12(9):1483, doi:10.3390/RS12091483.

Silalahi, F.E.S., Pamela, Arifianti, Y. and Hidayat, F. 2019. Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia. Geoscience Letters 6(1):1-17, doi:10.1186/s40562-019-0140-4.

Soma, A.S. and Kubota, T. 2018. Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-Loe watershed in South Sulawesi. Forest and Society 2(1):79-91, doi:10.24259/fs.v2i1.3594.

Sujarwo, W. 2019. Bedugul portrait: an ethnoecological study of the relationship between man and the environment. Jurnal Wilayah dan Lingkungan 7(1):52-62, doi:10.14710/jwl.7.1.52-62.

Sunarta, I. N., Adikampana, M., Nugroho, S., Kristianto, Y. and Nuruddin. 2019. Actor relation pattern with nature based “tri ning danu†in the bedugul tourism area of Bali, Indonesia. International Journal of Innovation, Creativity and Change 8(9):332-344.

Sunarta, I.N., Susila, K.D. and Kariasa, I.N. 2018. Landslide hazard analysis and damage assessment for tourism destination at Candikuning Village, Tabanan Regency, Bali, Indonesia. IOP Conference Series: Earth and Environmental Science 123(1), doi:10.1088/1755-1315/123/1/012006.

Tebbens, S.F. 2020. Landslide scaling: a review. Earth and Space Science 7(1):1-12, doi:10.1029/2019EA000662.

Trigunasih, N.M. and Saifulloh, M. 2022. Spatial distribution of landslide potential and soil fertility: a case study in Baturiti District, Tabanan, Bali, Indonesia. Journal of Hunan University Natural Sciences 49(2):229-241, doi:10.55463/issn.1674-2974.49.2.23.

Trigunasih, N.M. and Saifulloh, M. 2023. Investigation of soil erosion in agro-tourism area: guidelines for environmental conservation planning. Geographia Technica 18(1):19-28, doi:10.21163/GT_2023.181.02.

Trimurti, C.P. and Utama, I.G.B.R. 2021. Bali tourism destination structural loyalty model from consumer behavior perspective. Turkish Journal of Computer and Mathematics Education 12(4):494-505, doi:10.17762/turcomat.v12i4.531.

Utama, I.G.B.R., Laba, I.N., Suyasa, N.L.C.P.S. and Junaedi, I.W.R. 2020. Tourism visitor center flowchart as recommendation for Bali tourism destination. Test Engineering and Management 8:18306-18319

Van Beek, L.P.H. and Van Asch, T.W.J. 2004. Regional assessment of the effects of land-use change on landslide hazard by means of physically based modelling. Natural Hazards 31(1):289-304, doi:10.1023/B:NHAZ.0000020267.39691.39.

Wang, G., Chen, X. and Chen, W. 2020. Spatial prediction of landslide susceptibility based on GIS and discriminant functions. ISPRS International Journal of Geo-Information 9(3):144, doi:10.3390/ijgi9030144.

Wang, K., Xu, H., Zhang, S., Wei, F. and Xie, W. 2020. Identification and extraction of geomorphological features of landslides using slope units for landslide analysis. ISPRS International Journal of Geo-Information 9(4):274, doi:10.3390/ijgi9040274.

Zhong, C., Li, C., Gao, P. and Li, H. 2021. Discovering vegetation recovery and landslide activities in the wenchuan earthquake area with Landsat imagery. Sensors 21(15):52453, doi:10.3390/s21155243.








How to Cite

Diara, I. W., Wiradharma, I. K. A. W., Suyarto, R., Wiyanti, W., & Saifulloh, M. (2023). Spatio-temporal of landslide potential in upstream areas, Bali tourism destinations: remote sensing and geographic information approach. Journal of Degraded and Mining Lands Management, 10(4), 4769–4777.



Research Article