Polygon-based Landslide Inventory for Bandung Basin Using Google Earth


Sukristiyanti Sukristiyanti(1*), Ketut Wikantika(2), Imam A. Sadisun(3), Lissa F. Yayusman(4), Jevon A. Telaumbanua(5)

(1) Remote Sensing and GIS Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB) and Research Centre for Geotechnology, Indonesian Institute of Sciences (LIPI)
(2) Remote Sensing and GIS Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB) and 3Center for Remote Sensing, Bandung Institute of Technology (ITB)
(3) Applied Geology Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB)
(4) Center for Remote Sensing, Bandung Institute of Technology (ITB)
(5) Applied Geology Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB)
(*) Corresponding Author


A landslide inventory representing landslide locations is used as a key factor in landslide susceptibility assessment. This paper explores Google Earth (GE) for generating a polygon-based landslide inventory in Bandung Basin. How far GE can identify landslides and their boundaries, source areas, and types were discussed here. Visual interpretation of GE images supported by path tool in GE, official landslide reports, previous research papers, and media was performed. The result is a polygon-based landslide inventory consisting of 194 landslide areas and 194 landslide source areas during 1993-2020. The limitations of GE in preparing the landslide inventory are (1) not covering the timing of the landslide occurrences, (2) tricky to identify small landslides (<100 m2) in anthropogenically transformed areas, and (3) not able to distinguish between earth and debris of landslide material.


landslide inventory; Google Earth; polygon-based; Bandung Basin

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Anbalagan, R. (1992). Landslide hazard evaluation and zonation mapping in mountainous terrain. Engineering Geology, 32(4), 269–277. https://doi.org/10.1016/0013-7952(92)90053-2

Cao, W., Tong, X. H., Liu, S. C., & Wang, D. (2016). Landslides extraction from diverse remote sensing data sources using semantic reasoning scheme. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 41(July), 25–31. https://doi.org/10.5194/isprsarchives-XLI-B8-25-2016

Cepeda, J., Smebye, H., Vangelsten, B., Nadim, F., & Muslim, D. (2010). Landslide risk in Indonesia. October, October, 20. http://www.preventionweb.net/english/hyogo/gar/2011/en/bgdocs/Cepeda_et_al._2010.pdf

Chen, W., Pourghasemi, H. R., & Naghibi, S. A. (2017). A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China. Bulletin of Engineering Geology and the Environment, 77(2), 647–664. https://doi.org/10.1007/s10064-017-1010-y

Dąbek, P. B., Patrzałek, C., Ćmielewski, B., & Żmuda, R. (2018). The use of terrestrial laser scanning in monitoring and analyses of erosion phenomena in natural and anthropogenically transformed areas. Cogent Geoscience, 4(1), 1437684. https://doi.org/10.1080/23312041.2018.1437684

Dou, J., Chang, K. T., Chen, S., Yunus, A. P., Liu, J. K., Xia, H., & Zhu, Z. (2015). Automatic case-based reasoning approach for landslide detection: Integration of object-oriented image analysis and a genetic algorithm. Remote Sensing, 7(4), 4318–4342. https://doi.org/10.3390/rs70404318

Figueroa, F., & Sánchez-Cordero, V. (2008). Effectiveness of natural protected areas to prevent land use and land cover change in Mexico. Biodiversity and Conservation, 17(13), 3223–3240. https://doi.org/10.1007/s10531-008-9423-3

Guzzetti, F., Carrara, A., Cardinali, M., & Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology, 31(1–4), 181–216. https://doi.org/10.1016/S0169-555X(99)00078-1

Hong, H., Shahabi, H., Shirzadi, A., Chen, W., Chapi, K., Ahmad, B. Bin, Roodposhti, M. S., Yari Hesar, A., Tian, Y., & Tien Bui, D. (2019). Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. In Natural Hazards (Vol. 96, Issue 1). Springer Netherlands. https://doi.org/10.1007/s11069-018-3536-0

Hungr, O., Leroueil, S., & Picarelli, L. (2014). The Varnes classification of landslide types, an update. Landslides, 11(2), 167–194. https://doi.org/10.1007/s10346-013-0436-y

Juliev, M., Mergili, M., Mondal, I., Nurtaev, B., Pulatov, A., & Hübl, J. (2019). Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Science of the Total Environment, 653, 801–814. https://doi.org/10.1016/j.scitotenv.2018.10.431

Kalantar, B., Pradhan, B., Amir Naghibi, S., Motevalli, A., & Mansor, S. (2018). Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk, 9(1), 49–69. https://doi.org/10.1080/19475705.2017.1407368

Kartiko, R. D., Brahmantyo, B., & I.A.Sadisun. (2006). Slope and Lithological Controls on Landslide Distribution in West. International Symposium on Geotechnical Hazards: Prevention, Mitigation and Engineering Response, Utomo, Tohari, Murdohardono, Sadisun, April 2006., April, 177–184. https://doi.org/10.13140/2.1.2208.5442

Kavzoglu, T., Colkesen, I., & Sahin, E. K. (2019). Landslides: Theory, Practice and Modelling (S. P. Pradhan, V. Vishal, & T. N. Singh (eds.); Vol. 50). https://doi.org/10.1007/978-3-319-77377-3

Keyport, R. N., Oommen, T., Martha, T. R., Sajinkumar, K. S., & Gierke, J. S. (2018). A comparative analysis of pixel- and object-based detection of landslides from very high-resolution images. International Journal of Applied Earth Observation and Geoinformation, 64(September 2017), 1–11. https://doi.org/10.1016/j.jag.2017.08.015

Lavigne, F., Wassmer, P., Gomez, C., Davies, T. A., Sri Hadmoko, D., Iskandarsyah, T. Y. W. M., Gaillard, J., Fort, M., Texier, P., Boun Heng, M., & Pratomo, I. (2014). The 21 February 2005, catastrophic waste avalanche at Leuwigajah dumpsite, Bandung, Indonesia. Geoenvironmental Disasters, 1(1), 1–12. https://doi.org/10.1186/s40677-014-0010-5

Lee, S., & Talib, J. A. (2005). Probabilistic landslide susceptibility and factor effect analysis. Environmental Geology, 47(7), 982–990. https://doi.org/10.1007/s00254-005-1228-z

Li, Z., Shi, W., Myint, S. W., Lu, P., & Wang, Q. (2016). Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sensing of Environment, 175(March), 215–230. https://doi.org/10.1016/j.rse.2016.01.003

Liu, J., & Duan, Z. (2018). Quantitative assessment of landslide susceptibility comparing statistical index, index of entropy, and weights of evidence in the Shangnan Area, China. Entropy, 20(11), 9–11. https://doi.org/10.3390/e20110868

Ma, S., Xu, C., Xu, X., He, X., Qian, H., Jiao, Q., Gao, W., Yang, H., Cui, Y., Zhang, P., Li, K., Mo, H., Liu, J., & Liu, X. (2020). Characteristics and causes of the landslide on July 23, 2019 in Shuicheng, Guizhou Province, China. Landslides, 17(6), 1441–1452. https://doi.org/10.1007/s10346-020-01374-x

Mallick, J., Singh, R. K., AlAwadh, M. A., Islam, S., Khan, R. A., & Qureshi, M. N. (2018). GIS-based landslide susceptibility evaluation using fuzzy-AHP multi-criteria decision-making techniques in the Abha Watershed, Saudi Arabia. Environmental Earth Sciences, 77(7), 1–25. https://doi.org/10.1007/s12665-018-7451-1

Marjanovic, M. (2013). Advanced methods for landslide assessment using GIS [Palacký University Olomouc]. https://theses.cz/id/erd35p/00182008-728806523.pdf

Moosavi, V., Talebi, A., & Shirmohammadi, B. (2014). Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method. Geomorphology, 204, 646–656. https://doi.org/10.1016/j.geomorph.2013.09.012

Mora, S., & Vahrson, W.-G. (1994). Macrozonation Methodology for Landslide Hazard Determination. Environmental & Engineering Geoscience, xxxi(1), 49–58. https://doi.org/10.2113/gseegeosci.xxxi.1.49

Naryanto, H. S. (2017). Analisis Kejadian Bencana Tanah Longsor Tanggal 12 Desember 2014 Di Dusun Jemblung, Desa Sampang, Kecamatan Karangkobar, Kabupaten Banjarnegara, Provinsi Jawa Tengah. Jurnal Alami : Jurnal Teknologi Reduksi Risiko Bencana, 1(1), 1. https://doi.org/10.29122/alami.v1i1.122

NiculiţǍ, M. (2016). Automatic landslide length and width estimation based on the geometric processing of the bounding box and the geomorphometric analysis of DEMs. Natural Hazards and Earth System Sciences, 16(8), 2021–2030. https://doi.org/10.5194/nhess-16-2021-2016

Othman, A. A., Gloaguen, R., Andreani, L., & Rahnama, M. (2018). Improving landslide susceptibility mapping using morphometric features in the Mawat area, Kurdistan Region, NE Iraq: Comparison of different statistical models. Geomorphology, 319, 147–160. https://doi.org/10.1016/j.geomorph.2018.07.018

Pham, B. T., Jaafari, A., Prakash, I., & Bui, D. T. (2018). A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 78(4), 2865–2886. https://doi.org/10.1007/s10064-018-1281-y

Rabby, Y. W., & Li, Y. (2019). An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping. Landslides, 16(3), 633–645. https://doi.org/10.1007/s10346-018-1107-9

Rahardjo, P. P., Hosoda, T., & Handoko, A. (2017). Investigation of Landslides and Monitoring of The Subsequent Ground Movement and Geothermal New Pipe Lines Foundation in West Java. F the 19th International Conference on Soil Mechanics and Geotechnical Engineering, 635–638. https://www.issmge.org/uploads/publications/1/45/06-technical-committee-02-tc102-20.pdf

Riaz, M. T., Basharat, M., Hameed, N., Shafique, M., & Luo, J. (2018). A Data-Driven Approach to Landslide-Susceptibility Mapping in Mountainous Terrain: Case Study from the Northwest Himalayas, Pakistan. Natural Hazards Review, 19(4), 05018007. https://doi.org/10.1061/(asce)nh.1527-6996.0000302

Roccati, A., Paliaga, G., Luino, F., Faccini, F., & Turconi, L. (2021). Planning and Risk Assessment.

Roslee, R., Jamaludin, T. A., & Simon, N. (2017). Landslide Vulnerability Assessment (LVAs): A case study from Kota Kinabalu, Sabah, Malaysia. Indonesian Journal on Geoscience, 4(1), 49–59. https://doi.org/10.17014/ijog.4.1.49-59

Sadisun, I. A., Kartiko, R. D., & Adianto, A. Y. (2006). Landslide Frequency Analysis in a Mountainous Area of Weninggalih , West Java , Indonesia – a Technical Note. PROCEEDINGS PIT IAGI RIAU 2006. The 35th IAGI Annual Convention and Exhibition, November, 21–22. https://doi.org/10.13140/2.1.3584.0322

Sadisun, I. A., Kartiko, R. D., & Dinata, I. A. (2019). Pengamatan Lapangan Aliran Bahan Rombakan di Sirnaresmi, Kabupaten Sukabumi. https://doi.org/10.31227/osf.io/m4pg3

Samia, J., Temme, A., Bregt, A., Wallinga, J., Guzzetti, F., Ardizzone, F., & Rossi, M. (2017). Do landslides follow landslides? Insights in path dependency from a multi-temporal landslide inventory. Landslides, 14(2), 547–558. https://doi.org/10.1007/s10346-016-0739-x

Samodra, G., Chen, G., Sartohadi, J., & Kasama, K. (2017). Comparing data-driven landslide susceptibility models based on participatory landslide inventory mapping in Purwosari area, Yogyakarta, Java. Environmental Earth Sciences, 76(4), 1–19. https://doi.org/10.1007/s12665-017-6475-2

Sassa, K. (2007). Landslide science as a new scientific discipline. Progress in Landslide Science, 1978, 3–11. https://doi.org/10.1007/978-3-540-70965-7_1

Schlögel, R., Marchesini, I., Alvioli, M., Reichenbach, P., Rossi, M., & Malet, J. P. (2018). Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology, 301, 10–20. https://doi.org/10.1016/j.geomorph.2017.10.018

Shirani, K., Pasandi, M., & Arabameri, A. (2018). Landslide susceptibility assessment by Dempster–Shafer and Index of Entropy models, Sarkhoun basin, Southwestern Iran. Natural Hazards, 93(3), 1379–1418. https://doi.org/10.1007/s11069-018-3356-2

Sukristiyanti, S. (2018). Analisis Morfometri Das Di Daerah Rentan Gerakan Tanah. Seminar Nasional Geomatika, 2(April), 307. https://doi.org/10.24895/sng.2017.2-0.425

Tian, Y., Xu, C., Hong, H., Zhou, Q., & Wang, D. (2019). Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event. Geomatics, Natural Hazards and Risk, 10(1), 1–25. https://doi.org/10.1080/19475705.2018.1487471

Tukino, T. (2021). Implementation of Cognitive Behavioral Therapy (Cbt) for the Elderly in Reducing Anxiety Due To Landslides in Margamukti Village-Bandung Regency. Indonesian Journal of Social Work, 4(02). https://doi.org/10.31595/ijsw.v4i02.337

USGS. (2004). Landslide Types and Processes. In Fact Sheet 2004-3072. https://pubs.usgs.gov/fs/2004/3072/fs-2004-3072.html

van Westen, C. J., Castellanos, E., & Kuriakose, S. L. (2008). Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Engineering Geology, 102(3–4), 112–131. https://doi.org/10.1016/j.enggeo.2008.03.010

van Westen, C. J., van Asch, T. W. J., & Soeters, R. (2006). Landslide hazard and risk zonation - Why is it still so difficult? Bulletin of Engineering Geology and the Environment, 65(2), 167–184. https://doi.org/10.1007/s10064-005-0023-0

Wang, D., Hao, M., Chen, S., Meng, Z., Jiang, D., & Ding, F. (2021). Assessment of landslide susceptibility and risk factors in China. Natural Hazards, 0123456789. https://doi.org/10.1007/s11069-021-04812-8

Xu, C. (2015). Preparation of earthquake-triggered landslide inventory maps using remote sensing and GIS technologies: Principles and case studies. Geoscience Frontiers, 6(6), 825–836. https://doi.org/10.1016/j.gsf.2014.03.004

Yu, B., & Chen, F. (2017). A new technique for landslide mapping from a large-scale remote sensed image: A case study of Central Nepal. Computers and Geosciences, 100(December 2016), 115–124. https://doi.org/10.1016/j.cageo.2016.12.007

Yuhendar, A. H., Wusqa, U., Kartiko, R. D., Raya, N. R., & Misbahudin. (2016). Slope stability analysis of landslide in Wayang Windu Geothermal Field, Pangalengan, West Java Province, Indonesia. AIP Conference Proceedings, 1730(November). https://doi.org/10.1063/1.4947412

Zhang, T., Han, L., Han, J., Li, X., Zhang, H., & Wang, H. (2019). Assessment of landslide susceptibility using integrated ensemble fractal dimension with Kernel logistic regression model. Entropy, 21(2). https://doi.org/10.3390/e21020218

Zhu, A. X., Miao, Y., Yang, L., Bai, S., Liu, J., & Hong, H. (2018). Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping. Catena, 171(March), 222–233. https://doi.org/10.1016/j.catena.2018.07.012


‘Foto-foto-bencana-longsor-di-gunung-bonjot-dua-tewas-tertimbun’, jabar.tribunnews.com, 5 March 2018. https://jabar.tribunnews.com/2018/03/05/foto-foto-bencana-longsor-di-gunung-bonjot-dua-tewas-tertimbun

‘Warga Terdampak Longsor Cililin Keluhkan Minim Air Bersihnews’. detik.com, 15 May 2019. news.detik.com/berita-jawa-barat/d-4550422/warga-terdampak-longsor-cililin-keluhkan-minim-air-bersih

‘Berita Gerakan Tanah’, https://vsi.esdm.go.id/index.php/gerakan-tanah/kejadian-gerakan-tanah

DOI: https://doi.org/10.22146/ijg.58014

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