IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v118y2023i2d10.1007_s11069-023-06063-1.html
   My bibliography  Save this article

Soil micromorphology for modeling spatial on landslide susceptibility mapping: a case study in Kelara Subwatershed, Jeneponto Regency of South Sulawesi, Indonesia

Author

Listed:
  • Asmita Ahmad

    (Hasanuddin University)

  • Meutia Farida

    (Hasanuddin University)

  • Nirmala Juita

    (Hasanuddin University)

  • Muh Jayadi

    (Hasanuddin University)

Abstract

Comprehensively, the results of categorizing the susceptibility levels demonstrate distinct results, where landslides are more common in areas with a relatively high to moderate susceptibility class in comparison with those with a high susceptibility class. For this research, the soil parameter test method was conducted by utilizing a split-plot design with land use as the main plot, slope as a subplot, and soil physics (permeability, bulk density, and porosity) as a sub-subplot with three replications. Spatial modeling through regression analysis by incorporating ordinary least squares. The interaction between the type of land use, slope, and physical properties of the soil on the occurrence of landslides at the study site demonstrates a strong relationship with a significant p-value = 0.043. Increased land use by the community has accelerated the formation of soil micromorphology in the form of plane voids, cross-striated and grano-striated, thereby catalyzing internal shifts (micro-shifts) in the soil body. The landslide susceptibility map at the study site has been categorized into seven spatial susceptibility classes: extremely low, very low, low, moderate, high, very high, and extremely high. Spatial modeling with OLS illustrates that the independent factors in the form of lithology, rainfall, slope, land cover/land use, and population only get an R2 value of 30.8%. Adding landslide independent parameter data in the form of soil organic carbon factor, texture, erodibility, and soil micromorphology produces a spatial model of landslide susceptibility with an increase in the accuracy value of R2 by 66.66%. The spatial model demonstrates a high level of consistency with very significant soil micromorphology at a p-value of

Suggested Citation

  • Asmita Ahmad & Meutia Farida & Nirmala Juita & Muh Jayadi, 2023. "Soil micromorphology for modeling spatial on landslide susceptibility mapping: a case study in Kelara Subwatershed, Jeneponto Regency of South Sulawesi, Indonesia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(2), pages 1445-1462, September.
  • Handle: RePEc:spr:nathaz:v:118:y:2023:i:2:d:10.1007_s11069-023-06063-1
    DOI: 10.1007/s11069-023-06063-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06063-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11069-023-06063-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nussaïbah B. Raja & Ihsan Çiçek & Necla Türkoğlu & Olgu Aydin & Akiyuki Kawasaki, 2017. "Landslide susceptibility mapping of the Sera River Basin using logistic regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(3), pages 1323-1346, February.
    2. Emmanouil Psomiadis & Andreas Papazachariou & Konstantinos X. Soulis & Despoina-Simoni Alexiou & Ioannis Charalampopoulos, 2020. "Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data," Land, MDPI, vol. 9(5), pages 1-26, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sandipta Debanshi & Swades Pal, 2020. "Assessing gully erosion susceptibility in Mayurakshi river basin of eastern India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 883-914, February.
    2. Jie Liu & Zhen Wu & Huiwen Zhang, 2021. "Analysis of Changes in Landslide Susceptibility according to Land Use over 38 Years in Lixian County, China," Sustainability, MDPI, vol. 13(19), pages 1-23, September.
    3. Di Wang & Mengmeng Hao & Shuai Chen & Ze Meng & Dong Jiang & Fangyu Ding, 2021. "Assessment of landslide susceptibility and risk factors in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 3045-3059, September.
    4. Christos Polykretis & Manolis G. Grillakis & Athanasios V. Argyriou & Nikos Papadopoulos & Dimitrios D. Alexakis, 2021. "Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece," Land, MDPI, vol. 10(9), pages 1-23, September.
    5. Mária Barančoková & Matej Šošovička & Peter Barančok & Peter Barančok, 2021. "Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone," Land, MDPI, vol. 10(12), pages 1-28, December.
    6. Weidong Wang & Zhuolei He & Zheng Han & Yange Li & Jie Dou & Jianling Huang, 2020. "Mapping the susceptibility to landslides based on the deep belief network: a case study in Sichuan Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 3239-3261, September.
    7. Shahab S. Band & Saeid Janizadeh & Sunil Saha & Kaustuv Mukherjee & Saeid Khosrobeigi Bozchaloei & Artemi Cerdà & Manouchehr Shokri & Amirhosein Mosavi, 2020. "Evaluating the Efficiency of Different Regression, Decision Tree, and Bayesian Machine Learning Algorithms in Spatial Piping Erosion Susceptibility Using ALOS/PALSAR Data," Land, MDPI, vol. 9(10), pages 1-23, September.
    8. Richard Mind’je & Lanhai Li & Jean Baptiste Nsengiyumva & Christophe Mupenzi & Enan Muhire Nyesheja & Patient Mindje Kayumba & Aboubakar Gasirabo & Egide Hakorimana, 2020. "Landslide susceptibility and influencing factors analysis in Rwanda," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 7985-8012, December.
    9. Okoli Jude Emeka & Haslinda Nahazanan & Bahareh Kalantar & Zailani Khuzaimah & Ojogbane Success Sani, 2021. "Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement," Land, MDPI, vol. 10(10), pages 1-23, September.
    10. Quynh Duy Bui & Hang Ha & Dong Thanh Khuc & Dinh Quoc Nguyen & Jason von Meding & Lam Phuong Nguyen & Chinh Luu, 2023. "Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2283-2309, March.
    11. Ahmed Cemiloglu & Licai Zhu & Agab Bakheet Mohammednour & Mohammad Azarafza & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm," Land, MDPI, vol. 12(7), pages 1-20, July.
    12. Sudatta Wadadar & Bhabani Prasad Mukhopadhyay, 2022. "GIS-based landslide susceptibility zonation and comparative analysis using analytical hierarchy process and conventional weighting-based multivariate statistical methods in the Lachung River Basin, No," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 113(2), pages 1199-1236, September.
    13. Athanasios V. Argyriou & Christos Polykretis & Richard M. Teeuw & Nikos Papadopoulos, 2022. "Geoinformatic Analysis of Rainfall-Triggered Landslides in Crete (Greece) Based on Spatial Detection and Hazard Mapping," Sustainability, MDPI, vol. 14(7), pages 1-25, March.
    14. Hassan Abedi Gheshlaghi & Bakhtiar Feizizadeh, 2021. "GIS-based ensemble modelling of fuzzy system and bivariate statistics as a tool to improve the accuracy of landslide susceptibility mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(2), pages 1981-2014, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:118:y:2023:i:2:d:10.1007_s11069-023-06063-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.