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Frontiers of Earth Science

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Front. Earth Sci.    2018, Vol. 12 Issue (2) : 311-324    https://doi.org/10.1007/s11707-017-0679-3
RESEARCH ARTICLE
Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models
Bilaşco ŞTEFAN1,2, Roşca SANDA1(), Fodorean IOAN1, Vescan IULIU1, Filip SORIN1, Petrea DĂNUŢ1
1. Faculty of Geography, "Babeş-Bolyai" University, 400006 Cluj-Napoca, Romania
2. Romanian Academy Cluj-Napoca Subsidiary Geography Section, 400015 Cluj-Napoca, Romania
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Abstract

Maramureş Land is mostly characterized by agricultural and forestry land use due to its specific configuration of topography and its specific pedoclimatic conditions. Taking into consideration the trend of the last century from the perspective of land management, a decrease in the surface of agricultural lands to the advantage of built-up and grass lands, as well as an accelerated decrease in the forest cover due to uncontrolled and irrational forest exploitation, has become obvious. The field analysis performed on the territory of Maramureş Land has highlighted a high frequency of two geomorphologic processes – landslides and soil erosion – which have a major negative impact on land use due to their rate of occurrence. The main aim of the present study is the GIS modeling of the two geomorphologic processes, determining a state of vulnerability (the USLE model for soil erosion and a quantitative model based on the morphometric characteristics of the territory, derived from the HG. 447/2003) and their integration in a complex model of cumulated vulnerability identification. The modeling of the risk exposure was performed using a quantitative approach based on models and equations of spatial analysis, which were developed with modeled raster data structures and primary vector data, through a matrix highlighting the correspondence between vulnerability and land use classes. The quantitative analysis of the risk was performed by taking into consideration the exposure classes as modeled databases and the land price as a primary alphanumeric database using spatial analysis techniques for each class by means of the attribute table. The spatial results highlight the territories with a high risk to present geomorphologic processes that have a high degree of occurrence and represent a useful tool in the process of spatial planning.
Keywords risk evaluation      spatial analysis      GIS modeling      soil erosion      landslides      spatial planning     
Corresponding Author(s): Roşca SANDA   
Just Accepted Date: 10 November 2017   Online First Date: 26 December 2017    Issue Date: 09 May 2018
 Cite this article:   
Bilaşco ŞTEFAN,Roşca SANDA,Fodorean IOAN, et al. Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models[J]. Front. Earth Sci., 2018, 12(2): 311-324.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0679-3
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I2/311
Fig.1  Geographical position of the study area.
No. Database name Type Structure Attribute Origin Level of modeling
1 DEM Raster GRID Altitude (m) Primary Model level I
(USLE)
2 Cover management Vector
Raster		 Polygon
GRID		 Land use type
Cover management factor		 Primary
Derived
3 Slope length Raster GRID Slope angle (%) Modeled
4 Soil Vector
Raster		 Polygon
GRID		 Type
Soil erodibility factor		 Primary
Derived
5 Slope length Raster GRID Length (m) Modeled
6 Erosion value Raster GRID Soil loss t/ha/year Modeled
7 Hypsometry Raster GRID Altitude (m) Primary Model level I
(LANDSLIDES)
8 Slope Raster GRID Slope angle (%) Modeled
9 Aspect Raster GRID Aspect (8 direction) Modeled
10 Drainage density Raster GRID Density (m/kmp) Modeled
11 Depth Raster GRID Depth (m) Modeled
12 Wetness index Raster GRID Value Modeled
13 Stream power index Raster GRID Value Modeled
14 Profile curvature Raster GRID Value Modeled
15 Plan curvature Raster GRID Value Modeled
16 Landslide probability Raster GRID Value (0 – 0.57) Modeled
17 Cumulative probability Raster
Vector		 GRID
Polygon		 Classes (0–5) Modeled Model level II
(cumulative probability)
18 Land use Vector Polygon Land use type Primary Model level III
(RISK EVALUATION)
19 Vulnerability Vector Polygon Surface of cumulative probability/Land use Modeled
20 Exposure Vector Polygon Surface Modeled
21 Risk Vector Polygon Surface
Price/m2
Reduction coefficient		 Modeled
Tab.1  Database structure
Fig.2  Methodological flow chart of risk evaluation (where SAM – spatial analysis model, SAE – spatial analysis equation and 1,2,3–model ,evel).
Fig.3  Soil erosion vulnerability map.
Fig.4  Landslide vulnerability map.
Territorial Administrative Units Vulnerability classes/km2
Low Medium Medium–High High Very high
ESV LV ESV LV ESV LV ESV LV ESV LV
Remeţi 59.7 329 1.89 46.65 0.36 13 0.12 0.04 0.29 0
Campulung la Tisa 29.69 3.67 1.65 27.42 0.22 0.92 0.08 0 0.09 0
Săpânţa 128.1 5.44 4.61 73.29 0.74 55.59 0.3 0.18 0.44 0
Sarasău 16.52 2.26 1.14 15.39 0.19 0.79 0.06 0 0.1 0
Bocicoiu Mare 23.51 4.13 1.34 19.97 0.23 1.52 0.08 0.01 0.14 0
Repedea 105.44 0.02 4.12 17.3 0.94 90.87 0.27 3.16 0.36 0
Poienile de sub Munte 252.83 0 14.11 46.09 3.84 221.53 1.49 7 2.16 0
Sighetu Marmaţiei 120.16 7.11 6.07 95.64 1 25.92 0.34 0 0.62 0
Rona de Jos 20.81 0.65 0.69 17.89 0.08 3.12 0.03 0 0.05 0
Rona de Sus 57.61 0.71 2.1 38.13 0.31 21.52 0.11 0 0.22 0
Bistra 112.22 0.73 6.82 29.28 1.39 87.49 0.48 4.2 0.64 0
Vadu Izei 15.47 1.41 0.47 11.17 0.1 3.5 0.03 0 0.02 0
Oncesti 17.61 1.63 1.43 14.54 0.21 3.23 0.07 0 0.08 0
Petrova 37.15 2.64 2.04 29.63 0.29 7.4 0.1 0.02 0.11 0
Giuleşti 68.4 3.54 5.78 55.99 0.95 16.38 0.31 0 0.47 0
Vişeu de Sus 395.05 0.47 14.64 76.72 4.41 331.24 1.55 10.5 2.31 0
Bârsana 62.99 2.4 4.71 47.16 0.72 19.3 0.22 0 0.22 0
Ruscova 37.48 0.23 1.04 17.43 0.13 20.75 0.06 0.36 0.07 0
Ocna Şugatag 79.85 0.86 4.93 58.62 0.92 27.1 0.34 0.02 0.56 0
Călineşti 58.1 2.39 3.24 50.16 0.48 9.66 0.16 0 0.22 0
Strâmtura 79.15 0.82 6.55 57.05 1.25 29.89 0.39 0.06 0.47 0
Deseşti 128.01 0.28 5.26 70.9 0.9 63.48 0.3 0.23 0.44 0
Leordina 26.48 1.01 1.53 16.27 0.3 11.26 0.09 0 0.14 0
Vişeu de Jos 47.06 0.25 3.97 18.39 0.85 33.63 0.27 0.3 0.42 0
Budeşti 73.93 0.02 5.13 42.32 0.85 38.17 0.25 0.11 0.46 0
Rozavlea 37.57 1.72 2.26 24.98 0.39 13.8 0.12 0 0.16 0
Bogdan Vodă 29.65 0.78 2.05 21.05 0.39 10.55 0.13 0 0.15 0
Şieu 18.42 0.75 1.49 15.7 0.17 3.76 0.06 0 0.06 0
Borşa 354.13 0 28.51 107.62 9.73 285.42 3.7 8.64 5.3 0
Poienile Izei 14.4 0 0.73 9.01 0.11 6.3 0.03 0 0.04 0
Ieud 68.28 0.5 2.89 41.51 0.39 29.39 0.12 0.45 0.17 0
Saliştea de Sus 60.81 0.06 3.64 30.99 0.43 34.09 0.14 0.09 0.21 0
Botiza 66.76 0.01 2.89 29.75 0.44 40.01 0.13 0.63 0.18 0
Moisei 98.29 0 7.06 30.38 1.43 75.06 0.52 2.66 0.81 0
Dragomireşti 90.91 0.25 3.07 34.29 0.65 60.09 0.27 0.66 0.38 0
Săcel 70.45 0 6.27 31.7 1.18 46.84 0.4 0.35 0.59 0
Tab.2  Comparative analysis using a quantitative basis for the extent of vulnerability classes to soil erosion (ESV) and landslides (LV) in the UAT
Land type Price Territorial Administrative Units (TAU.)
RON/m2 EURO/m2
Build-up area 159 35.333 Sighetu Marmaţiei
100 22.222 Borşa, Vişeul de Sus
42 9.333 Moisei, Vişeul de Jos, Ruscova, Petrova, Leordina
36 8.000 Săliştea de Sus, Dragomireşti, Bistra, Repedea, Poienile de Sub Munte
24 5.333 Deseşti, Giuleşti, Vadu Izei, Onceşti, Bârsana, Bocicoiu Mare, Rona de Sus, Rona de Jos, Ocna Şugatag, Budeşti, Călineşti, Câmpulung la Tisa, Sarasău, Săpânţa, Remeţi
Out-of-town land
Arable 3.18 0.707 Sighetu Marmaţiei
3.13 0.696 Vişeul de Sus, Borşa
1.30 0.289 Dragomireşti, Săliştea de Sus
0.52 0.116 Moisei, Vişeul de Jos, Ruscova, Petrova, Leordina
0.45 0.100 Bistra, Repedea, Poienile de Sub Munte, Deseşti, Giuleşti, Vadu Izei, Onceşti, Bârsana, Bocicoiu Mare, Rona de Sus, Rona de Jos, Ocna Şugatag, Budeşti, Călineşti, Câmpulung la Tisa, Sarasău, Săpânţa, Remeţi
Orchards and vineyards 2.78 0.618 Sighetu Marmaţiei
2.50 0.556 Vişeul de Sus, Borşa
1.14 0.253 Dragomireşti, Săliştea de Sus
0.42 0.093 Moisei, Vişeul de Jos, Ruscova, Petrova, Leordina
0.36 0.080 Bistra, Repedea, Poienile de Sub Munte, Deseşti, Giuleşti, Vadu Izei, Onceşti, Bârsana, Bocicoiu Mare, Rona de Sus, Rona de Jos, Ocna Şugatag, Budeşti, Călineşti, Câmpulung la Tisa, Sarasău, Săpânţa, Remeţi
Forest 2.39 0.531 Sighetu Marmaţiei
1.35 0.300 Vişeul de Sus, Borşa
1.04 0.231 Moisei, Vişeul de Jos, Ruscova, Petrova, Leordina, Bistra, Repedea, Poienile de Sub Munte
0.98 0.218 Dragomireşti, Săliştea de Sus
0.27 0.060 Deseşti, Giuleşti, Vadu Izei, OnceVti, Bârsana, Bocicoiu Mare, Rona de Sus, Rona de Jos, Ocna Şugatag, Budeşti, Călineşti, Câmpulung la Tisa, Sarasău, Săpânţa, Remeţi
Pastures 1.67 0.371 Vişeul de Sus, Borşa
1.59 0.353 Sighetu Marmaţiei
0.65 0.144 Dragomireşti, Săliştea de Sus
0.21 0.047 Moisei, Vişeul de Jos, Ruscova, Petrova, Leordina
0.18 0.040 Bistra, Repedea, Poienile de Sub Munte, Deseşti, Giuleşti, Vadu Izei, Onceşti, Bârsana, Bocicoiu Mare, Rona de Sus, Rona de Jos, Ocna Şugatag, Budeşti, Călineşti, Câmpulung la Tisa, Sarasău, Săpânţa, Remeţi
Other categories / non-productive 0.88 0.196 Sighetu Marmaţiei
0.42 0.093 Vişeul de Sus, Borşa
0.33 0.073 Dragomireşti, Săliştea de Sus
0.10 0.022 Moisei, Vişeul de Jos, Ruscova, Petrova, Leordina
0.09 0.020 Bistra, Repedea, Poienile de Sub Munte, Deseşti, Giuleşti, Vadu Izei, Onceşti, Bârsana, Bocicoiu Mare, Rona de Sus, Rona de Jos, Ocna Şugatag, Budeşti, Călineşti, Câmpulung la Tisa, Sarasău, Săpânţa, Remeţi
Tab.3  Buy-sell value/land categories/TAU (Territorial Administrative Units)
Land use classes Cumulated probability classes
P.VH P.H P.MH P.M P.L
Watercourses 5 5 5 5 5
Orchards 2 4 4 5 5
Swamps 5 5 5 5 5
Coniferous forests 2 2 3 3 4
Broad-leaved forests 2 3 3 3 4
Mixed forests 2 2 3 3 4
Secondary pastures 1 2 3 3 3
Urban and rural area 1 1 1 1 2
Non-irrigated arable land 1 1 1 2 2
Agricultural land and natural vegetation 1 2 2 3 4
Industrial and trading units 1 1 1 4 4
Complex cultures 1 1 2 3 3
Deforested areas 1 1 2 2 3
Scarce vegetation areas 1 2 2 4 5
Waste dumps 1 2 3 4 5
Natural pastures 1 1 2 3 3
Rocky fields 5 5 5 5 5
Subalpine vegetation 1 1 2 2 3
Recreation areas 1 1 1 4 4
Mining areas 2 3 4 4 5
Tab.4  Matrix used for identifying the classes of risk exposure
Fig.5  Map of the cumulated risk classes.
Fig.6  Land market value in Maramureş Land.
Fig.7  Map of the risk calculated as financial loss.
Fig.8  Economic loss at the UAT level.
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