=Paper=
{{Paper
|id=Vol-3101/Short23
|storemode=property
|title=Geo-informational approach to risk analysis of slope mass movement (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3101/Short23.pdf
|volume=Vol-3101
|authors=Sergii Babichev,Tetiana V. Kril,Stella B. Shekhunova,Waldemar Wojcik
|dblpUrl=https://dblp.org/rec/conf/citrisk/BabichevKSW21
}}
==Geo-informational approach to risk analysis of slope mass movement (short paper)==
https://ceur-ws.org/Vol-3101/Short23.pdf
Geo-Informational Approach to Risk Analysis of Slope Mass
Movement
Sergii Babichev1,2, Tetiana V. Kril3, Stella B. Shekhunova3 and Waldemar Wójcik4
1Jan Evangelista Purkyne University in Usti nad Labem, Ceske mladeze, 8, Usti nad Labem, 40096, Czech Republic
2Kherson State University, Universytetska st. 27, Kherson,73003, Ukraine
3Institute of Geological Sciences, NAS of Ukraine, O. Honchara Str., 55-b, Kyiv, 01054, Ukraine
4Lublin University of Technology, Nadbystrzycka Str., 38 D, Lublin, 20-618, Poland
Abstract
The assessment of the development of dangerous erosion and slope mass movement processes has
been done for the territory around Verhnie Vodiane and Solotvyno area (Transcarpathian region,
Ukraine). The risk analysis has been carried out with reliance on the evaluation of meteorological and
geomorphological factors. Remote sensing data have been processed using a geoinformation system
and a database of indicators such as slope steepness and Normalized Difference Moisture Index (NDMI).
The risk matrix for analyzing slope mass movement was constructed. Risk gradation was assessed as the
percentage of recorded landslide areas with different ranges of meteorological and geomorphological
factors.
The indicator values with extreme risk of dangerous processes were determined.
It is shown that with an increase in the angle of slope steepness, a simultaneous increase in the NDMI
value indicates the possible development of landslides. The risk matrix obtained can be used to predict
the slope prone to mass movement for areas with a steepness of 5°40' to 24° and geological (lithological)
conditions similar to Quaternary deposits of alluvial, deluvial, eluvial-deluvial and deluvial-colluvial
genesis.
Keywords1
Risk analysis, GIS techniques, landslides, slope steepness, moisture index.
1. Introduction
Hazardous exogenous geological processes occurring on the slopes of river valleys, ravines and
gullies, mountain slopes and sea coasts in abnormal weather conditions (showers, snowfalls /
snowmelt, floods, storms) can be destructive, they threaten the lives and activities of people,
agriculture, create the risks of emergencies. The patterns of slope mass movement and erosion
CITRisk’2021: 2nd International Workshop on Computational & Information Technologies for Risk-Informed Systems, September
16–17, 2021, Kherson, Ukraine
EMAIL: sergii.babichev@ujep.cz (S.Babichev); kotkotmag@gmail.com (T.Kril); shekhun@gmail.com (S.Shekhunova);
waldemar.wojcik@pollub.pl (W.Wójcik)
ORCID: 0000-0001-6797-1467 (S.Babichev); 0000-0002-4324-9231 (T.Kril); 0000-0002-5975-3491 (S.Shekhunova); 0000-0002-
0843-8053 (W.Wójcik)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
depend on geological, geomorphological, hydrogeological, meteorological, hydrological, seismic,
neotectonic etc. natural conditions and anthropogenic activity as well.
Nowadays abnormal rains and prolonged droughts are observed in areas where they are not
typical [1]. Climate change, namely the redistribution of heat and moisture, humidity conditions,
affect the soil. Insufficient moisture or waterlogging of the soil leads to water or wind erosion.
Slope soil saturation by water of intense rainfall, snowmelt leads to change in groundwater levels.
An increase in the recharge of the groundwater aquifer and moistening of clay, sandy, sandy loam
soils of the aeration zone are additional factors of the activation of existing landslides and the
formation of new ones. Assessment of the slope stability in the work [2] showed that the value of
the stability coefficient decreases from 1.7 to 0.9, while assessing excessive moisture.
Consequently, moisture accumulation due to infiltration of atmospheric precipitation is one of the
main factors for the loss of slope stability and landslides formation.
The aim of the research is to assess the risks of landslide occurrence by analyzing
meteorological and geomorphological factors, i.e., slope instability depending on the moisture
content in the upper soil layers and on the slope steepness.
In general, risk is understood as the danger of possible future losses, or the danger of adverse
consequences of an event, identifying the risk with the danger, a dangerous process. Risk
assessment is carried out at different levels from the national to the object one, including the
concept of multi-risk (integral value), the cascade-like development of hazardous processes and
risk matrices [3-5]. In this paper, by the risk of hazardous geological processes, we mean a set of
meteorological and geological factors with the gradation of the values at which the process can be
realized.
The study area is in the Transcarpathian region (Ukraine), around Verhnie Vodiane and
Solotvyno townships.
The study area belongs to the low-mountain flat of the Solotvynska depression. The area is
characterized by the development of dangerous mass movement processes (landslides) in
Quaternary deposits of alluvial, deluvial, eluvial-deluvial and deluvial-colluvial genesis in
southern part and Neogene deposits in the south-eastern part of Solotvynska depression. Within
the study area, 15 ancient landslides with a total area of 0.5 sq. km have been mapped; landslides
occur on slopes with angles of 4-28°, but most of them are typical on slopes with steepness of 8-
20° [6-8].
2. Related works
The landslide study and measures for slopes stabilization on the territory of the Transcarpathian
region, around Verhnie Vodiane and Solotvyno area were carried out and described in reports and
papers by Barnichka V.Yu., Gabor M.M., Shekhunova S.B., Yakovlev E.O. and others. Database
and landslide inventory map, relief horizontals, the tectonic disturbance map for Transcarpathian
region were published in [7, 8].
The construction of digital model of terrain with using radar images was presented in a number
of papers [9-12 etc.]. The ALOS World 3D, ASTER Global DEM, and SRTM-30 m are Digital
Elevation Models (DEMs) that have a horizontal resolution from 30-m (1 arcsec mesh) to 12-m
(0.4 arcsec mesh) and almost global coverage and are available to the general public free of charge.
Among them the vertical accuracy of the ALOS World 3D was found to be the most accurate,
especially under different land cover types and at various points throughout the world [9, 10]. This
DEM has the lowest mean error, root mean square error and standard deviation. Regional studies
of fluvial landforms and mountain landscapes with use satellite-derived DEM data sets showed
the best representation of ALOS World 3D data for the geomorphological analysis in mountain
areas [11]. All authors notice that it also contains a widespread elevation anomaly. This anomaly
does not affect the research results, since in the present work, not the absolute value of the height
was assessed, but the angle of the slope inclination.
Soil moisture and its estimation as a hydrologic variable and essential climate change factor
were discussed in several works [2, 13, 14]. The knowledge of soil moisture at large scale, with
reasonable temporal and spatial resolution, is required to improve hydrologic and climatic
modeling and prediction. Remote sensing data are used to collect and analyze geospatial data on
the moisture content of the first soil layers from the surface.
The ERDAS IMAGINE image processing method by using the LANDSAT 8 band 3 (Green),
band 4 (Red), band 5 (NIR), band 6 (SWIR), and band 10 (TIR) data for determining various
spectral indices was described in works [15, 16]. The LANDSAT 8 imagery with used of Arc GIS
soft-ware depicts a band combination for visualize land/water differentiation sharply. Calculation
of Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI)
and Normalised Difference Moisture Index (NDMI) allow to identify differently land and water
by moisture content, considering band variations of different multi-spectral images. NDMI index
is often used synonymously with the NDWI index, with using near-infrared radiation and
shortwave infrared reflection combination as one of the two options [16]. Difference between them
is that NDMI manifest water content of leaves, but NDWI – water content of water bodies. In our
research, it was accepted to use the NDMI index as an indicator of abnormal rains, as the higher
distribution of the rainfall leads the higher possibilities of the landslide occurrences.
3. Materials and methods
Remote sensing data processing was performed in ArcGis 9.3. Satellite images were downloaded
from electronic resources EarthExplorer, Vertex, Coordinate projection – WGS 1984. All the
satellite data are re-projected to a Universal Transverse Mercator (UTM) coordinate system,
(datum WGS84, zone 34N). Interferometric radar images (ALOS-PALSAR radar systems) were
used for analyzing the geomorphological factors. The elevation discreteness of the images is 1 m,
spatial resolution – 12.5×12.5 m.
An image of the Landsat 8 satellite mission equipped with multispectral and thematic scanners
(Operational Land Imager, Thematic Mapper and Thermal Infrared Sensor) was used to determine
the values of the NDMI index. Cloudiness of images is less than 10 %, spatial resolution – 30×30
m.
The square-wise assessment method [12] was used to analyze the recorded landslides in the
study area, followed by their comparison with the values of meteorological and geomorphological
factors. A grid with a set of single squares (the side size of 12.5 m) was created for the test site.
The risk analysis of erosion occurrence and mass movement processes, determining the values
of the factor indicators considered were obtained relying on the matrix principle. Risk gradation
was assessed by the percentage of the recorded landslide areas with different range of climatic and
geomorphological factors. Both factors were divided into five ranges. The area (in %) occupied
by each combination of factors was estimated. The conditions for which the area of distribution
was more than 5% were taken into account.
4. Obtained results
4.1. Calculation of the normalized moisture index
The assessment of moisture content according to remote sensing data was carried out
indirectly through the calculation of the Normalized Difference Moisture Index (NDMI),
according to Landsat8 data.
NDMI was used to determine the moisture content in vegetation. It was calculated as the ratio
between the values of near-infrared radiation (NIR – band 5, Landsat 8) and shortwave infrared
reflection (SWIR – band 6, Landsat 8) [16]:
(𝑁𝑁𝑁𝑁𝑁𝑁−𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆)
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 = (𝑁𝑁𝑁𝑁𝑁𝑁+𝑆𝑆𝑊𝑊𝑊𝑊𝑊𝑊). (1)
The index can take values from -1 to +1. Positive values characterize the areas with varying
degrees of moisture content. The distribution of NDMI index values is shown in Figure 1.
The NDWI classification has been divided into five classes. The classes of low values of
NDMI are in between -0.63 to 0.12, moderate value is in between 0.12 to 0.16, the high value of
NDMI is in between 0.16 to 0.24 and the extreme value is more than 0.24.
The index of moisture content could be related to the rainfall amount, which is as known one
of the triggering factors for landslides. The total average annual precipitation of the research
territory in the lowland is 600-800 mm, and in the mountains – 1000-1500 mm. There are years of
high water, when 1600-2400 mm falls within the year with an average long-term rate of 1000-
1200 mm.
If the rainfall amounts increase, that means the value of the NDMI will be also increasing.
Areas with the high or extreme value of NDMI are potential places of the landslides. Up to 65.5
% of the study area is characterized by such values.
Figure 1: Distribution of NDMI index values
4.2. Geomorphological factor
The study area is located within the Tisza and Apshytsia rivers, in the southeast of the
Transcarpathia. It is characterized by low-mountain relief and tectonically belongs to the
Solotvynska depression [6, 8]. An altitude terrain ranging from 30 to 570 m above sea level. The
slopes are characterized by steepness of 10-20° in the lower and middle parts, and steepness of up
to 30-40° in the upper ones [7].
Besides the humidification regime, the geology (type of soil, lithology, thickness of layers),
relief and slope steepness influence the development of hazardous geological processes (erosion,
landslides). Loose sandy-clay soils in areas with the slopes of up to 5°40' (10 %) practically do not
experience movements in bulk and at speeds that can pose a threat to objects/facilities and
people [17]. At surface inclinations from 5°40' to 11°20' (10-20 %), deformations and
displacements of clay soils at moisture-induced changes of their consistence from firm to plastic
and fluid-plastic are possible. Such changes occur due to humidification from the surface by
atmospheric precipitation, man-made leaks or as a result of rising groundwater levels.
Surface steepness was estimated by means of the ArcGIS “Slope”. For each cell of the digital
relief model (obtained by processing of satellite radar images), the “Slope” tool calculates the
maximum degree of change in the value of height z between the cell and its neighbors [18]. The
“Slope” determines the degree of surface change in the horizontal (dz/dx) and vertical (dz/dy)
directions relative to the central cell. The calculation of the slope (deg) is performed automatically
by the expression:
180
𝛼𝛼 = arctan (�((𝑑𝑑𝑑𝑑/𝑑𝑑𝑑𝑑)2 + (𝑑𝑑𝑑𝑑/𝑑𝑑𝑑𝑑)2 )) ∙ . (2)
𝜋𝜋
The results of slope steepness values in the study area are presented in Figure 2.
Figure 2: Slope steepness values on study area
The slope steepness classification has been divided into five classes according to the plucked out
limit values. The most critical for the formation of landslides are the classes with values from 5°40'
to 11°20', which is 24.5 % of the area and the class of values from 11°20' to 24° – 34.6 %. In
general, 12.4 % of the study area has an angle of inclination of more than 18°, which, according
to the work [2], is a critical value for slopes consisting of fluvial-glacial soils.
4.3. Risk matrix of landslide development
Using the geographic information system for the areas with landslides (see Fig. 1-2), data were
selected from remote images of various missions. The percentage of areas with different sets of
values for the “Slope steepness” indicator and NDMI index were calculated. The risk analysis
carried out on the basis of meteorological – atmospheric precipitation (NDMI) and
geomorphological (Slope steepness) factors are presented in Figure 3.
Figure 3: Risk matrix of landslide in terms of meteorological and geomorphological factors, сell
areas (%)
The conditions considered as the most dangerous (Extreme Risk) are those with the values of
parameters in terms of meteorological and geomorphological factors, whose area is more than 5 %.
They are as follows:
• Slope steepness from 5°40' to 11°20' and NDMI is from 0.12 to 0.24;
• Slope steepness from 11°20' to 24° and NDMI is from 0.16 to 0.24 and more.
The low percentage of areas for dangerous values of slopes and humidity is explained by the
lack of the sets of such conditions in the study area.
5. Conclusions
Abnormal rains and prolonged droughts activate hazardous exogenous geological processes
nowadays. Risk analysis of slope mass movement were proposed relaying on analysis
meteorological and geomorphological factors with geo-informational approach. The assessment
of landslide occurrence has been done for the territory Verhnie Vodiane and Solotvyno townships
(Transcarpathian region, Ukraine) for the first time.
The square-wise assessment method, statistical methods and the matrix principle were used for
the test site. Geomorphological and remote sensing data have been processed using ArcGis 9.3.
As the result, the most dangerous values (Extreme Risk) of parameters meteorological and
geomorphological factors are slope steepness from 5°40' to 11°20' and NDMI is from 0.12 to 0.24;
slope steepness from 11°20' to 24° and NDMI is from 0.16 to 0.24 and more.
The climatic factor directly affects erosion processes due to the amount and the mode of
precipitation. The geomorphological factor largely determines the development of mass
movement processes on the slopes and the intensity of erosion, since the relief affects the speed
and strength of water flows, their concentration in individual areas and linear natural boundaries.
Comparison of the respective values of these factors, taking into account the geological
structure, will allow the identification of areas with a high risk of the occurrence of hazardous
exogenous geological processes (landslides).
6. Acknowledgements
The research was carried out with the financial support of the EU (project No. 783232
ImProDiReT) and co-financing under the state budget program KPKVK 6541230 – “Support for
the development of priority research areas”.
References
[1] National report on the state of the environment in Ukraine in 2017, Kyiv. Ministry of Ecology
and Natural Resources of Ukraine. URL:
http://komekolog.rada.gov.ua/uploads/documents/36493.pdf
[2] E.D.Kuzmenko, A.P.Nikitash, E.A.Yakovlev, Yu.V.Heru,. Excess moistening as a factor of
landslide activation on the slopes of the Kiev water reservoir, GEOINFORMATIKA, 2017,
1 (61). URL: http://nbuv.gov.ua/j-pdf/geoinf_2017_1_8.pdf
[3] S.Safaie (Ed.), National Disaster Risk Assessment. UNISDR, 2017. URL:
http://www.indiaenvironmentportal.org.in/files/file/Words%20into%20Action%20guideline
s.pdf
[4] U.Nations, UNISDR terminology on disaster risk reduction. United Nations Office for
Disaster Risk Reduction, Report, 2009. URL:
https://www.preventionweb.net/files/7817_UNISDRTerminologyEnglish.pdf
[5] H.Akcin, A GIS-based building risk assessment for the subsidence due to under city coal
mining activities in Zonguldak, Turkey, Arabian Journal of Geosciences, 2021, 14: 376.
doi:10.1007/s12517-021-06702-6.
[6] EUCPT Risk Assessment Report: Solotvyno salt mine area. Advisory Mission to Ukraine.
Union Civil Protection Mechanism, 2016. URL: https://waterquality.danube-region.eu/wp-
content/uploads/sites/13/sites/13/2019/09/Solotvino_Scoping-Mission_Sept-
2017_Executive-Summary-Final_New-1.pdf.
[7] S.B.Shekhunova, M.V.Aleksieienkova, T.V.Kril, S.M.Stadnichenko, N.P.Siumar, Natural
and man-induced landslides formation factors within the Tysa-Apshytsia interfluve
(Transcarpathia, Ukraine), Second EAGE Workshop on Assessment of Landslide Hazards
and impact on communities (2020), vol.2020, 2020, pp. 1-6. doi:10.3997/2214-
4609.202055018
[8] S.B.Shekhunova, S.P.Siumar, O.P.Lobasov, E.O.Yakovlev, S.Meijer, S.M.Stadnichenko,
GIS tools application for landslides formation factors analysis (Transcarpathian region)
Conference Proceedings, First EAGE Workshop on Assessment of Landslide and Debris
Flows Hazards in the Carpathians, Jun 2019, vol. 2019, 2019, pp. 1-5. doi:10.3997/2214-
4609.201902160
[9] Ç.Bayık, K.Becek, Ç.Mekik, M.Özendi, On the Vertical Accuracy of the ALOS World 3D-
30m Digital Elevation Model, Preprints 2017, 2017080081.
doi:10.20944/preprints201708.0081.v1
[10] J.R.Santillan, M.Makinano-Santillan. Vertical accuracy assessment of 30-m resolution
ALOS, ASTER, and SRTM Global DEMs over northeastern Mindanao, Philippines. The
International Archives of the Photogrammetry, Remote Sensing and Spatial Information
Sciences, Volume XLI-B4, 2016. XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech
Republic. doi:10.5194/isprsarchives-XLI-B4-149-2016
[11] S.J.Boulton, M.Stokes, Which DEM is best for analyzing fluvial landscape development in
mountainous terrains? Geomorphology, vol. 310, pp 168-187.
doi:10.1016/j.geomorph.2018.03.002
[12] T.Kril, S.Shekhunova, Terrain elevation changes by radar satellite images interpretation as a
component of geo-environmental monitoring, 13th International Conference on Monitoring
of Geological Processes and Ecological Condition of the Environment, 12 November 2019.
doi:10.3997/2214-4609.201903176
[13] L.Brocca, F.Melone, T.Toramarco, R.Morbidelli, Spatial-temporal variability of soil
moisture and its estimation across scales. Water Resources Research, 46(2), 2010, W02516.
doi:10.1029/2009WR008016
[14] T.Ochsner, M.Cosh, R.Cuenca, W.Dorigo, C.Draper, Y.Hagimoto, Y.Kerr, K.Larson,
E.Njoku, E.Small, M.Zreda, State of the art in large-scale soil moisture monitoring, Soil
Science Society of America Journal, 77 (6), 2013, pp. 1888-1919.
doi:10.2136/sssaj2013.03.0093
[15] A.KumarTaloor, D.S.Manhas, G.Ch.Kothyari, Retrieval of land surface temperature,
normalized difference moisture index, normalized difference water index of the Ravi basin
using Landsat data, Applied Computing and Geosciences, volume 9 (2021), 100051.
doi:10.1016/j.acags.2020.100051
[16] Index Data Base. A database for remote sensing indices. URL:
https://www.indexdatabase.de/db/i-single.php?id=56
[17] M.G.Demchyshyn, The current dynamics of slopes on the territory of Ukraine (engineering
and geological aspects), Kiev, Naukova Dumka, 1992
[18] ArcGIS for Desktop. URL: https://desktop.arcgis.com/ru/arcmap/10.3/tools/spatial-analyst-
toolbox/slope.htm.