Impact of Landscape Factors on Automobile Road Deformation Patterns—A Case Study of the Almaty Mountain Road

Ainur Kairanbayeva, Gulnara Nurpeissova (), Zhumabek Zhantayev, Roman Shults, Dina Panyukova, Saniya Kiyalbay and Kerey Panyukov
Additional contact information
Ainur Kairanbayeva: Institute of Ionosphere, National Center of Space Research and Technology, Almaty 050020, Kazakhstan
Gulnara Nurpeissova: L.B. Goncharov Kazakh Auto Road Institute (KazADI), Almaty 050061, Kazakhstan
Zhumabek Zhantayev: Institute of Ionosphere, National Center of Space Research and Technology, Almaty 050020, Kazakhstan
Roman Shults: Department of Geomatics, Faculty of Civil Engineering, Czech Technical University in Prague, 166 29 Prague, Czech Republic
Dina Panyukova: L.B. Goncharov Kazakh Auto Road Institute (KazADI), Almaty 050061, Kazakhstan
Saniya Kiyalbay: L.B. Goncharov Kazakh Auto Road Institute (KazADI), Almaty 050061, Kazakhstan
Kerey Panyukov: Engineering Institute, Caspian University, Almaty 050000, Kazakhstan

Sustainability, 2022, vol. 14, issue 22, 1-20

Abstract: The geography of Kazakhstan is characterized by a diverse landscape and a small population. Therefore, certain automobile roads pass through unpopulated mountain regions, where physical road diagnostics are rare or almost absent, while landscape factors continue to affect the road. However, modern geo-information approaches and remote sensing could effectively provide the road diagnostics necessary to make timely control decisions regarding a road’s design, construction, and maintenance. To justify this assumption, we researched the deformation of a mountain road near Almaty city. Open access satellite images of and meteorological archival data for the region were processed. The resulting data were compared to validate the road’s deformation triggers. Extreme weather conditions’ impacts could be identified via road destruction (nearly 40 m longitudinal cracks, 15 m short transversal cracks, and two crack networks along a 50 m road section). The remotely sensed parameters (vertical displacement velocity, slope exposure, dissections, topographic wetness index, aspect, solar radiation, SAVI, and snow melting) show the complexity of triggers of extensive road deformations. The article focuses only on open access data from remote sensing images and meteorological archives. All the resulting data are available and open for all interested parties to use.

Keywords: road deformation; open data; remote sensing; meteorological data; data digitization; data analytics; impact assessment; multiple criteria evaluation; decision-making support (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/22/15466/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/22/15466/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:22:p:15466-:d:979736

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().