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Unmanned Aerial Vehicle Technology for Quantitative Morphometry and Geomorphic Processes – Study Case in Rotational Landslide Deposited Areas

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Języki publikacji
EN
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EN
The increasing use of drone technology to produce high-resolution digital imagery and elevation models has been associated with a growing interest in developing quantitative morphometric analysis (QMA). QMA analysis is an invaluable part of creating detailed topographic models in landslide scars that are still highly unstable and prone to erosion. This paper presents the results of a research that aims to create a topographic model in a landslide scarred area where the slope configuration is still varied. The study area was located in the landscape of the Cretaceous-Tertiary volcanic transition where many landslides have occurred. Three landslides were selected on the basis of different soil material characteristics that affect the topographic condition of the landslide scar. Aerial photography was recorded using a UAV with a flying height of 80 m, with an orthomosaic resolution of 1 cm. In detail, three morphometric variables (slope, plan curvature, topographic position index) were selected and calculated with the output evaluated based on visual-spatial interpretation. The results showed that morphometric variables performed well in modeling land surface topography. Steep slopes and surfaces with convex curvature are abundant at the ledges and landslide heads that allow water runoff to disperse as the initiation of gully erosion. The multidimensional gully erosion network is concentrated at relatively low elevations and surfaces with concave curvature. The undulating micro-relief of the land surface as a result of the process of material disposition builds up on each other to a gentle slope. Finally, the topographic model of the landslide surface can be used as a base material in implementation of both physical and vegetative land conservation strategies.
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Twórcy
  • Department of Agrotechnology, Faculty of Agriculture, Stiper Agricultural University, Jl. Nangka II, Maguwoharjo, Special Region of Yogyakarta, 55283 Indonesia
Bibliografia
  • 1. Amatulli, G., McInerney, D., Sethi, T., Strobl, P., Domisch, S. 2020. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Scientific Data, 7(1), 162. https://doi.org/10.1038/s41597-020-0479-6
  • 2. Castro, J., Asta, M.P., Galve, J.P., Azañón, J.M. 2020. Formation of clay-rich layers at the slip surface of slope instabilities: The role of groundwater. Water, 12(9), 2639. https://doi.org/10.3390/w12092639
  • 3. Choubin, B., Borji, M., Hosseini, F.S., Mosavi, A., Dineva, A.A. 2020. Mass wasting susceptibility assessment of snow avalanches using machine learning models. Scientific Reports, 10(1), 18363. https://doi.org/10.1038/s41598-020-75476-w
  • 4. Gao, J., Maro, J. 2010. Topographic controls on evolution of shallow landslides in pastoral Wairarapa, New Zealand, 1979-2003. Geomorphology, 114, 373–381. https://doi.org/10.1016/j.geomorph.2009.08.002
  • 5. Garosi, Y., Sheklabadi, M., Conoscenti, C., Pourghasemi, H.R., Van Oost, K. 2019. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of The Total Environment, 664, 1117–1132. https://doi.org/10.1016/j.scitotenv.2019.02.093
  • 6. Giordan, D., Hayakawa, Y., Nex, F., Remondino, F., Tarolli, P. 2018. Review article: The use of remotely piloted aircraft systems (RPASs) for natural hazards monitoring and management. Natural Hazards and Earth System Sciences, 18(4), 1079–1096. https://doi.org/10.5194/nhess-18-1079-2018
  • 7. Gu, Y., Wylie, B.K. 2016. Using satellite vegetation and compound topographic indices to map highly erodible cropland buffers for cellulosic biofuel crop developments in eastern Nebraska, USA. Ecological Indicators, 60, 64–70. https://doi.org/10.1016/j.ecolind.2015.06.019
  • 8. Kavzoglu, T., Kutlug Sahin, E., Colkesen, I. 2015. Selecting optimal conditioning factors in shallow translational landslide susceptibility mapping using genetic algorithm. Engineering Geology, 192, 101– 112. https://doi.org/10.1016/j.enggeo.2015.04.004
  • 9. Kotsi, E., Vassilakis, E., Diakakis, M., Mavroulis, S., Konsolaki, A., Filis, C., Lozios, S., Lekkas, E. 2023. Using UAS-aided photogrammetry to monitor and quantify the geomorphic effects of extreme weather events in tectonically active mass waste-prone areas: The case of Medicane Ianos. Applied Sciences, 13(2), 812. https://doi.org/10.3390/app13020812
  • 10. Kuradusenge, M., Kumaran, S., Zennaro, M. 2020. Rainfall-induced landslide prediction using machine learning models: The case of Ngororero District, Rwanda. International Journal of Environmental Research and Public Health, 17(11), 4147. https://doi.org/10.3390/ijerph17114147
  • 11. Noviyanto, A., Sartohadi, J., Purwanto, B.H. 2020. The distribution of soil morphological characteristics for landslide-impacted Sumbing Volcano, Central Java—Indonesia. Geoenvironmental Disasters, 7(1), 25. https://doi.org/10.1186/s40677-020-00158-8
  • 12. Nseka, D., Kakembo, V., Bamutaze, Y., Mugagga, F. 2019. Analysis of topographic parameters underpinning landslide occurrence in Kigezi highlands of southwestern Uganda. Natural Hazards, 99(2), 973– 989. https://doi.org/10.1007/s11069-019-03787-x
  • 13. Ohlmacher, G.C. 2007. Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Engineering Geology, 91(2–4), 117– 134. https://doi.org/10.1016/j.enggeo.2007.01.005
  • 14. Samodra, G., Ramadhan, M.F., Sartohadi, J., Setiawan, M.A., Christanto, N., Sukmawijaya, A. 2020. Characterization of displacement and internal structure of landslides from multitemporal UAV and ERT imaging. Landslides, 17(10), 2455–2468. https://doi.org/10.1007/s10346-020-01428-0
  • 15. Sestras, P., Bilașco, Ștefan, Roșca, S., Dudic, B., Hysa, A., Spalević, V. 2021. Geodetic and UAV monitoring in the sustainable management of shallow landslides and erosion of a susceptible urban environment. Remote Sensing, 13(3), 385. https://doi.org/10.3390/rs13030385
  • 16. Sofia, G. 2020. Combining geomorphometry, feature extraction techniques and Earth-surface processes research: The way forward. Geomorphology, 355, 107055. https://doi.org/10.1016/j.geomorph.2020.107055
  • 17. Van Eynde, E., Dondeyne, S., Isabirye, M., Deckers, J., Poesen, J. 2017. Impact of landslides on soil characteristics: Implications for estimating their age. CATENA, 157, 173–179. https://doi.org/10.1016/j.catena.2017.05.003
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fae64644-4649-4f5c-82b7-d0541e27756c
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