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Article

Utilizing the Analytic Hierarchy Process to Establish Weighted Values for Evaluating the Stability of Slope Revegetation based on Hydroseeding Applications in South Korea

by
Sung-Ho Kil
1,
Dong Kun Lee
2,*,
Jun-Hyun Kim
1,
Ming-Han Li
1 and
Galen Newman
1
1
Department of Landscape Architecture and Urban Planning, College of Architecture, Texas A&M University, College Station, TX 77843-3137, USA
2
Department of Landscape Architecture, Graduate School of Environmental Studies, Seoul National University, #82, 1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2016, 8(1), 58; https://doi.org/10.3390/su8010058
Submission received: 13 August 2015 / Revised: 12 December 2015 / Accepted: 29 December 2015 / Published: 8 January 2016
(This article belongs to the Section Environmental Sustainability and Applications)
Browse Figures
Versions Notes

Abstract

:
The aim of this study was to identify the major variables identified as important for considering the stabilization of slope revegetation based on hydroseeding applications and evaluate weights of each variable using the analytic hierarchy process (AHP) with both environmental experts and civil engineers. Twenty-five variables were selected by the experts’ survey from a total of 65 from the existing literature, with each variable considered as an important factor for slope stabilization in South Korea. The final results from the AHP method showed that variables associated with the driving force of water resources showed higher values in all expert groups such as rain intensity, seepage water and drainage condition. Other important variables were related to plant growth such as vegetation community, vegetation coverage and quality of soil ameliorant produced in an artificial factory such as tensile strength, permeability coefficient, soil texture and organic matter. The five highest-ranked variables that satisfied both environmental experts and civil engineers were rain intensity, seepage water, slope angle, drainage condition and ground layer. The findings of this research could be helpful for developing a more accurate rating system to evaluate the stability of slope revegetation.

1. Introduction

Slope revegetation—the use of vegetation and construction to protect a barren slope damaged by road and building construction—has been widely accepted and used for decades as a means of achieving stability and ecological rehabilitation of rugged terrain. However, revegetated slopes can fail due to various environmental factors such as high rain intensity and steep slopes. Many revegetation applications seek to stabilize barren slopes as a result of urban development and/or road construction. Among the many slope revegetation options available, hydroseeding has been widely applied on large and steep slopes in temperate climates [1,2,3]. Many slope stability analyses conducted by multi-disciplinary experts show that hydroseeding has become the representative application for damaged slopes in South Korea. Once revegetated, however, slopes can still undergo soil erosion and even natural slope failure (e.g., landslides). Although slope failure can occur as a result of natural geomorphological processes over time [4], revegetated slopes typically fail because most hydroseeding applications are involved with the imported soil and vegetation from an entirely new site rather than reinforcing the natural interactions of native soil and vegetation from neighboring environments [5,6,7].
Studies on slope stability have primarily been conducted from two perspectives: civil and soil conservation engineering. Studies conducted from the civil engineering perspective have mainly evaluated the stability of cut slopes on solid bedrock. These studies mostly evaluated stability in relation to the use of concrete structures to stabilize a damaged slope [8,9,10] and on numerical and statistical analyses to assess the risks of steep slope failure [11,12]. Studies from the perspective of soil conservation engineering have investigated rock or soil movement by using field monitoring systems [13,14,15]. They have also involved field surveys and laboratory soil testing [16,17,18,19], analytical methods [20], and physically based models for rainfall-induced shallow landslides, including SINMAP [21], dSLAM [22], and SHETRAN [23]. These scientific achievements seek to individually understand the causes and effects of slope failure through interpretation and standardization of particular soil and vegetation variables such as soil porosity, soil organic matter and vegetation cover [24,25]. However, many of these approaches have not been directly associated with measuring the stabilization of revegetated slopes.
A multitude of variables are required to analyze the overall stability of revegetated slopes, but integrating all variables of interest together generally results in an ineffective method that is too costly [26]. Not only are scientific approaches needed to investigate slope failure, but simple methods are also required to evaluate stability of revegetated slopes. One of the more popular methods is a sociological approach based on the Multi-Criteria Decision Making (MCDM) approach, a valuable method in making important decisions that cannot be easily decided [27].
MCDM methods can be broadly classified into the following four types: Analytical Hierarchy Process (AHP), Novel Approach to Imprecise Assessment and Decision Environments (NAIADE), Multi-Attribute Utility Theory (MAUT), and Multi-Objective Programming (MOP) [28]. Among these, AHP—a standard method to calculate weights—has been widely used in the decision-making approaches of fields such as landscape/architectural design, urban planning and the evaluation of strategic policies [29,30,31,32,33]. Introduced by Saaty [34], AHP is used to derive ratio scales from both discrete and continuous paired comparisons [35]. It is a reliable tool to reinforce logical and reasonable decision-making processes, and determine the importance of criteria and sub-criteria [27,36]. Analyzing the relative weights of variables by AHP can help to evaluate the overall stability of slope revegetation.
The knowledge gained from this method could then be used to develop a facilitated rating system such as that of rockfall hazard [37,38,39]. Furthermore, a comprehensive review of the stability of slope revegetation has not yet been conducted in South Korea. This study focused on the selection of appropriate variables through the use of AHP to represent numerous variables for slope failure and soil erosion of revegetation and evaluating whether the weighted values for the selected variables differed between environmental experts and civil engineers.

2. Methods

2.1. Variable Selection and Survey Method

The process of variable selection was conducted in two steps: (1) collecting appropriate variables from the literature, and (2) extracting major variables from this list using an expert survey (Figure 1). In the first step, the variables relevant to stabilization failure of slope revegetation were collected from previous studies in multiple fields including civil engineering, soil erosion control engineering, and slope revegetation. Variables with overlapping meanings or repeated occurrences were integrated into a single category. For example, slope inclination, inclination, and slope angle have similar meanings. Therefore, “slope angle” was selected as the variable. As shown in Table 1, a total of 65 variables were collected from previous literature. Brainstorming with expert groups via emails helped to both select and label these variables. The 65 variables also included a short description to explain the content (See Appendix Table A1). Based on the review of research studying weights of factors affecting slope stability [40], the 65 variables were divided into seven main categories: topography, geography, climate, soil physics, soil chemistry, vegetation and construction. The numbers of variables for the seven categories by the experts were as follows: 11 for topography, eight for geology, three for climate, 12 for soil physics, 11 for soil chemistry, 13 for vegetation, and seven for construction.
Sustainability 08 00058 g001 1024
Figure 1. Variable and data collection process with multiple surveys.
Figure 1. Variable and data collection process with multiple surveys.
Sustainability 08 00058 g001
Table
Table 1. Sixty-five variables related to slope stability from the existing literature.
Table 1. Sixty-five variables related to slope stability from the existing literature.
CategoriesMajor VariablesReferences
TopographySlope angle, Slope height, Slope location, Slope type, Slope length, Altitude, Aspect, Curvature, Catchment basin, Stream power index (SPI), Topographic wet index (TWI)[39,40,41,42,43,44,45,46,47]
GeologyGround layer, Rock type, Joint condition, Joint orientation, Weathering characteristics, Weathered condition, Tension crack, Seepage water[39,40,41,42,43,48,49,50]
ClimateRain intensity, Daily rainfall, Accumulated rainfall[39,40,44,47,51]
Soil physicsPorosity, Bulk density, Gravel contents, Grain size, Soil hardness, Water content, Soil texture, Permeability coefficient, Tensile strength, Shear strength, Specific gravity[22,40,43,46,47,51,52,53,54,55,56,57,58]
Soil chemistrySoil acidity (pH), Cation Exchange Capacity (CEC), Electronic conductivity (EC), Available phosphate, Soil organic matter, C/N, Salt concentration, Total nitrogen (T-N), Exchangeable calcium, Exchangeable magnesium, Exchangeable potassium, Exchangeable sodium[43,55,59]
VegetationForest stand, Tree height, Species diversity, Dominant plant species, Number of trees, Number of herbs, Vegetation coverage, Vegetation density, Germination percentage, Vegetation community, Timber age class, Timber diameter class, Root reinforcement[40,46,54,55,58,60,61,62]
ConstructionSoil depth, Land use, Drainage system, Elapsed year, Scale of failure, Collapse history, Reinforced facility for slope protection[39,45,46,57,62]
The second step was to establish the key variables for slope stability by hydroseeding-based revegetation. The number of the key variables related to stabilizing slopes using the hydroseeding method was reduced to 25 of the total 65 variables. These 25 variables were selected using a survey distributed to experts based on the multiple response method, which allowed respondents to choose two or more answers to a question.
As shown in Table 2, the numbers of variables for the seven categories eventually utilized for the AHP analysis were as follows: five for topography, two for geology, two for climate, six for soil physics, three for soil chemistry, four for vegetation, and three for construction.
Table
Table 2. Twenty-five variables extracted for Analytical Hierarchy Process (AHP) analysis from the first survey.
Table 2. Twenty-five variables extracted for Analytical Hierarchy Process (AHP) analysis from the first survey.
CategoriesExtracted Variables
TopographySlope angle, Aspect, Slope length, Slope height, Slope type
GeographyGround condition, Seepage water
ClimateRain intensity, Accumulated rainfall
Soil physicsPorosity, Soil hardness, Water contents, Soil texture, Tensile strength, Hydraulic conductivity
Soil chemistrySoil acidity (pH), Salt concentration, Organic Matter
VegetationVegetation community, Vegetation coverage rate, Number of trees, Number of herbs
ConstructionElapsed year, Drainage condition, Soil depth
Variables dealing with structural stability were either excluded or integrated via a survey of experts and brainstorming because the evaluation of structural stability is generally performed before slope revegetation. Revegetating construction is performed after assessing stability through geotechnical investigation by civil engineers who review applicable techniques of revegetation to minimize impediment of structural stability after revegetating.
Participants in the brainstorming process had wide experience in regards to slope revegetation: one soil expert, one environmental planner and one vegetation expert who served in their professional fields for 10 years or more, two officers in charge of road construction in National Highway Planning and Construction Division in the Ministry of Land, Infrastructure and Transport (MOLIT) in the South Korean government and two civil engineers who have worked on a considerable number of construction projects. During the brainstorming prior to the survey, continual correspondence occurred back and forth in order to select final variables. Respondents for the survey were selected from revegetation-experienced experts who had published one or more articles on the subject, had hands-on experience in revegetation, or had previously led a revegetation project (Table 3). Most had more than 10 years of experience in their respective fields. There were fewer civil engineers among the respondents. Most of the respondents were landscape architects or environmental engineers because they usually deal with the processes of slope vegetation. In addition, many of them were also in academia in the field of ecological restoration and/or civil engineering, were employees in high-ranked companies having a considerable amount of annual turnover in the field of construction and design of slope revegetation, or were in the department of road construction in National Highway Planning and Construction Division in MOLIT in the South Korean government.
Table
Table 3. Classification of respondents
Table 3. Classification of respondents
ClassificationFirst SurveySecond Survey
MajorEnvironment2823
Civil engineering915
CareerAbove 10 years2527
6 to 9 years108
Below 5 years23
OrganizationEducational institution1011
Government310
Private company2417
Total 3738
A questionnaire survey based on the multiple response method was distributed during the period of 1–13 April 2013 to the experts through interview and e-mail. The final 25 variables were suggested by more than half the respondents were first identified from the responses of 37 experts. The weights of the major variables were set by 38 experts using AHP through the second survey. For each variable, a pairwise comparison matrix was created to calculate its weighted value by AHP.

2.2. AHP Analysis

The AHP method is a mathematical method for analyzing and organizing complex decisions using ratio scale measurement [32]. It has been applied in studies with small sample sizes to solicit and determine the hierarchical analysis, typically based on experts’ opinion. This study used a limited number of experts with thorough experience, but few in South Korea have had extensive experience with slope revegetation. Several studies reported findings from AHP with small numbers of experts: five respondents [63], five participants [64], seven participants [65], 18 participants [66] and 25 respondents [67].
The evaluation method of AHP requires a small number of key variables that jointly explain much of the variance in the stability of revegetated slopes [68,69]. The variables should be weighted relatively to how important each is for the structural stability of a particular revegetated slope [70]. Ideally, there should be a broad consensus among the experts on variables that determine the stability of a revegetated slope.
The AHP analysis involved the following steps: (1) identifying environmental experts and civil engineers; (2) calculating local and global weights for each category and variable through geometric average for acceptable consistency ratio related to a value less than or equal to 0.1 for each group; (3) calculating integrated weights by considering both groups.
In the first step, the experts consisted of two groups: environmental experts and civil engineers. The former included landscape architects, environmental engineers, and forest specialists. The latter included geotechnical and professional engineers for civil engineering structures and road development.
In the second step, weights were estimated for the variables obtained from the initial survey. A scale of relative importance based on a pairwise comparison of questionnaires is shown in Table 4. The survey consisted of pairwise comparisons of the individual variables on the same hierarchy within a group of variables. Each variable also included a short description to explain the content. Experts selected a value on the scale 1–9 proposed by Saaty and Vargas [71]. There were 60 pairwise comparisons determined by the experts in the survey.
The weights were classified into two types: local weights and global weights. The value of a local weight was the AHP result of each category or variable. The sum of local weights of the total categories or variables on the same hierarchy was 1.00. The value of global weight equaled the value of the local weight within each category multiplied by the value of local weight within each variable. The sum of global weights was also 1.00. For example, the value of the global weight for slope angle was equal to the local weight of the topography category multiplied by the local weight of the slope angle variable. The ranking was arranged according to the order of the global weight.
Table
Table 4. Pairwise comparison scale for AHP preferences [34].
Table 4. Pairwise comparison scale for AHP preferences [34].
Intensity of importanceDefinitionExplanation
1Equal importanceTwo categories or variables contribute equally to the objective
3Moderate importanceExperience and judgment slightly favor one category or variable over another
5Strong importanceExperience and judgment strongly favor one category or variable over another
7Very strong or demonstrated importanceAn category or variable is favored very strongly over another; its dominance demonstrated in practice
9Extreme importanceThe evidence favoring one category or variable over another is of the highest possible order of affirmation
Reciprocals of aboveIf activity i has one of the above nonzero numbers assigned to it when compared with activity j, then j has the reciprocal value when compared with iA reasonable assumption
RationalRatios arising from the scaleIf consistency were to be forced by obtaining n numerical values to span the matrix
The AHP verifies a consistency ratio (CR) to measure the consistency of experts’ judgments arranged in pairwise comparisons from the result of survey. A CR value above 0.10 indicate that the respondent is considered to give reasonable answers [71]. In addition to the CR, the consistency index (CI) and random index (RI) were measured. The CI evaluates the consistency of matrix of order n to determine how much inconsistency is in a matrix. RI is the average CI depending on the order n of the matrix. The RI generally utilize the value given by Saaty [34] as following Table 5 [27,33,64]. The formulas for CR and CI are shown below:
CR = C I R I
CI = ( λ max − n ) ( n − 1 )
where λmax is the maximum eigenvalue of the matrix, n is the matrix size, and RI is the average CI for a number of randomly generated matrices according to Table 5. For each variable, the reasons for why the two groups may have differed were considered.
Table
Table 5. Random consistency index for corresponding number of categories and variables [34].
Table 5. Random consistency index for corresponding number of categories and variables [34].
n1234567
RI000.580.91.121.241.32
RI, Random index.
In the third step, the integrated weights for categories or variables satisfying the CR value above 0.10 were calculated by an AHP analysis including the result of pairwise comparisons selected by all experts based on results from the second step.

3. Results

3.1. Tendencies for Weighted Factors

3.1.1. Weighted Values of Environmental Experts

Environmental experts weighted the variables as shown in Table 6. Compared with local weights in each category, the values for soil physics (0.183) and vegetation (0.176) indicated relatively higher levels of importance. Among the variables for topography, the value for slope angle (0.509) showed the highest importance. The variable of the greatest importance in geography and climate category were seepage water (0.576) and rain intensity (0.769), respectively. The values for tensile strength (0.210) and permeability coefficient (0.218) were higher than those for other variables in the soil physics category. Salt concentration (0.458) was the main soil chemistry variable. The values for vegetation community (0.277) and vegetation coverage rate (0.388) were higher than those for other variables in the vegetation category. The variable of drainage condition (0.613) showed the highest value among the variables in the construction category.
Table
Table 6. Weighted values and rankings considered by environmental experts.
Table 6. Weighted values and rankings considered by environmental experts.
CategoriesLocal WeightVariablesLocal WeightGlobal WeightRank
Topography0.142Slope angle0.5090.07233
Aspect0.0990.014125
Slope length0.1090.015524
Slope height0.1420.020221
Slope type0.1410.020022
Geography0.113Ground layer0.4240.04797
Seepage water0.5760.06515
Climate0.152Rain intensity0.7690.11691
Accumulated rainfall0.2310.035112
Soil physics0.183Porosity0.1290.023618
Soil hardness0.1280.023419
Water content0.1210.022120
Soil texture0.1950.035711
Tensile strength0.2100.038410
Permeability coefficient0.2180.03998
Soil chemistry0.087Soil acidity0.2220.019323
Salt concentration0.4580.03989
Organic matter0.3200.027815
Vegetation0.176Vegetation community0.2770.04886
Vegetation coverage rate0.3880.06834
Number of trees0.1460.025717
Number of herbs0.1890.033313
Construction0.147Elapsed year0.1990.029314
Drainage condition0.6130.09012
Soil depth0.1880.027616
The variables with the five highest-ranked final weights among global weights were rain intensity (0.1169), drainage condition (0.0901), slope angle (0.0723), vegetation coverage rate (0.0683), and seepage water (0.0651).

3.1.2. Weighted Values of Civil Engineers

Civil engineers weighted the variables as shown in Table 7. Compared with local weights in categories, the values for topography (0.172) and geography (0.219) indicated relatively higher importance. As was the case for the environmental experts, the value for slope angle (0.374) showed the highest importance among the variables in the topography category. The values for seepage water (0.615) and accumulated rainfall (0.520) indicated that they were the variables of the greatest importance for the categories of geography and climate, respectively. The values for tensile strength (0.262), soil texture (0.208), and water content (0.160) were higher than those for other variables in the soil physics category. Organic matter (0.443) was the main soil chemistry variable. The values for vegetation community (0.356) and vegetation coverage rate (0.237) were higher than those for the other variables in the vegetation category. The elapsed year (0.360) and drainage condition (0.467) were the main variables in the construction category.
Table
Table 7. Weighted values and rankings considered by civil engineers.
Table 7. Weighted values and rankings considered by civil engineers.
CategoriesLocal WeightVariablesLocal WeightGlobal WeightRank
Topography0.172Slope angle0.3740.06435
Aspect0.1100.018922
Slope length0.1120.019321
Slope height0.1910.032913
Slope type0.2130.03669
Geography0.219Ground layer0.3850.08432
Seepage water0.6150.13471
Climate0.147Rain intensity0.4800.07064
Accumulated rainfall0.5200.07643
Soil physics0.129Porosity0.1220.015724
Soil hardness0.0890.011525
Water content0.1600.020619
Soil texture0.2080.026817
Tensile strength0.2620.033811
Permeability coefficient0.1580.020420
Soil chemistry0.087Soil acidity0.3160.027516
Salt concentration0.2410.021018
Organic matter0.4430.03858
Vegetation0.154Vegetation community0.3560.05486
Vegetation coverage rate0.2370.036510
Number of trees0.1990.030615
Number of herbs0.2080.032014
Construction0.092Elapsed year0.3600.033112
Drainage condition0.4670.04307
Soil depth0.1730.015923
The variables with the five highest-ranked weights among global weights were seepage water (0.1347), ground layer (0.0843), accumulated rainfall (0.0764), rain intensity (0.0706), and slope angle (0.0644). Geographical and climatic variables dominated the global weights. In addition, variables related to water resources ranked high among the global weights.

3.2. Weighted Values of Comprehensive Experts.

Both groups of experts weighted the variables as shown in Table 8. Among the categories, the local weights of most categories ranged from 0.150 to 0.170, except for soil chemistry (0.088) and construction (0.125). As the results of the environmental experts and civil engineers, the value for slope angle (0.465) showed the highest importance among the variables in topography category. The values for seepage water (0.591) and rain intensity (0.668) indicated that they were the variables of the greatest importance for the categories of geography and climate, respectively. The values for tensile strength (0.232) and soil texture (0.202) were higher than those for the other variables in soil physics category. Organic matter (0.375) and salt concentration (0.361) were the main variables in soil chemistry category. The values for vegetation community (0.305) and vegetation coverage rate (0.333) were higher than those for the other variables in the vegetation category. The drainage condition (0.564) was the main variable in the construction category.
Table
Table 8. Weighted values and rankings considered by both groups of experts.
Table 8. Weighted values and rankings considered by both groups of experts.
CategoriesLocal WeightVariablesLocal WeightGlobal WeightRank
Topography0.154Slope angle0.4650.07163
Aspect0.1040.016025
Slope length0.1110.017124
Slope height0.1580.024318
Slope type0.1620.024917
Geography0.147Ground layer0.4090.06015
Seepage water0.5910.08692
Climate0.152Rain intensity0.6680.10151
Accumulated rainfall0.3320.05058
Soil physics0.163Porosity0.1270.020722
Soil hardness0.1110.018123
Water content0.1370.022321
Soil texture0.2020.032912
Tensile strength0.2320.03789
Permeability coefficient0.1910.031114
Soil chemistry0.088Soil acidity0.2640.023219
Salt concentration0.3610.031813
Organic matter0.3750.033011
Vegetation0.170Vegetation community0.3050.05197
Vegetation coverage rate0.3330.05666
Number of trees0.1640.027916
Number of herbs0.1980.033710
Construction0.125Elapsed year0.2500.031315
Drainage condition0.5640.07054
Soil depth0.1850.023120
The variables with the five highest-ranked weights were rain intensity (0.1015), seepage water (0.0869), slope angle (0.0716), drainage condition (0.0705), and ground layer (0.0601). The global weights were similar to those of the two groups analyzed independently.

4. Discussion

4.1. Variable Extraction

The 25 selected variables could play a major role in stability evaluation of a revegetated slope. These variables could be used to create a rating system, such as the slope mass rating (SMR) system proposed by Romana et al. [72], and to integrate the dynamics of major variables for the stability of slope revegetation. The main variables selected for this research could be effectively used to substantially reduce time and costs.

4.2. Comparison of Weighted Values between Environmental Experts and Civil Engineers

Environmental experts and civil engineers had different opinions on some variables (Figure 2). For the categories, environmental experts indicated soil physics (0.183) and vegetation (0.176) as the more important categories, whereas civil engineers considered topography (0.172) and geography (0.219) as more important. Although structural stability is generally checked to revegetate a damaged slope, civil engineers considered topographical and geographical variables as key values. Environmental experts might be interested in how to grow healthy plants and form soil profiles. Weighted values considered by comprehensive experts focused on soil physics (0.163) and vegetation (0.170). The value for vegetation was higher than that for soil. Among the three groups, the value for soil chemistry was lowest for environmental experts, followed by civil engineers and comprehensive experts.
Sustainability 08 00058 g002a 1024Sustainability 08 00058 g002b 1024
Figure 2. Local weights for each category and variable, (a) values of each category, (b–h) values of each variable among three groups: civil engineers, environmental experts and comprehensive experts.
Figure 2. Local weights for each category and variable, (a) values of each category, (b–h) values of each variable among three groups: civil engineers, environmental experts and comprehensive experts.
Sustainability 08 00058 g002aSustainability 08 00058 g002b
For variables, the slope angle, slope length, and slope type were more important than other topographical variables in all three groups. The value for slope angle had the highest score. Slope angle is one of the major variables in surface stability because it has a direct effect on how soil particles respond to erosional strength [73]. A steep slope is a sufficient condition that causes failure and a factor that makes it difficult to establish vegetation coverage [74]. This, presumably, is why slope angle was selected by the three groups as a main variable.
Of the two geographical variables, seepage water was more critical than ground layer in all three groups. Seepage affects the stability of slope revegetation. Seepage flow often ensues when the pores between soils or the holes of a crack in a bedrock become saturated with water and intersect with a restrictive layer—any soil stratum or layer with low permeability, including unfractured bedrock, which restricts the vertical movement of water [73,75].
Tensile strength, permeability coefficient, and soil texture were the main identified variables among the six variables for soil physics. Soil texture is one of the fundamental variables of soil physics. Soil containing a large volume of sand has a high level of permeability but low nutrition holding capacity [76]. The nutrition holding capacity affects soil organic matter. Soil that contains sufficient soil organic matter forms a stable granular structure, in which water conducts more rapidly than in an unstable structure easily damaged by humidity [77]. These three variables in soil physics category may have been selected because these characteristics would be expected to be considered by experts.
The value for soil chemistry was the lowest among the categories. The chemical properties of soil measure the nutrient conditions necessary for plant growth [76]. The selected variables in this study were soil acidity (pH), soil organic matter, and salt concentration.
Vegetation community and vegetation coverage were the key vegetation variables. Regardless of the number of vegetation species, experts focused on vegetation community with high-coverage plants and high species diversity. Vegetation coverage is, apparently, a good protector of soil particle detachment because it intercepts raindrops [74,78,79]. However, variables in root zone such as tensile root force and root shear strength should also be considered to develop evaluation of slope stability with numerical modeling and stability analyses by considering vegetation influencing the factor of safety, defined as the ratio between resistive and driving forces by gravity [80,81,82].
Rain intensity, seepage water, and drainage condition affecting water resources generally indicated high global-weight values in all groups. Among experts, the erosion and failure caused by the driving force of water resources was recognized from important studies. The second key issue was the importance of variables related to plant growth. The representative variables were slope angle, vegetation community, and vegetation coverage, which showed somewhat high values in all three groups. Among experts, vegetation variables were considered to play an important role in erosion control and failure protection. The third was the quality of the soil ameliorant input to slope revegetation. The tensile strength, permeability coefficient, soil texture, and organic matter were relatively critical variables. These variables could become important factors when they reach certain levels in soil produced in a special factory for slope revegetation.
Interestingly, several experts considered slope length and aspect as less important variables. Solar radiation is as crucial as a microclimate factor in a cut slope [74]. The aspect in a cut slope determines the incident angle of solar radiation [83,84]. Aspect is closely related to the sunshine duration of mountainous slopes. In addition, the longer the slope length, the less the pace of vegetation coverage and the longer the time required to stabilization of revegetation [85,86]. Therefore, these variables could be considered to stabilize a damaged slope sufficiently despite their lower values.
These identified variables may be applied as a model or framework for variable selection in various future studies. Furthermore, various analyses such as correlation analysis and numerical analysis, conducted through measurements of each variable in on-the-spot surveys, will be helpful to understand the stability of slope revegetation and to develop a detailed rating system such as that of rockfall hazard [37,38,39].

5. Conclusions

Every case study to investigate slope stability needs to be analyzed and resolved independently through numerical and statistical methods with on-the-spot investigation. A single case study can be helpful to understand cause and effect of slope failure though detailed analysis. However, when many provisional diagnoses of slope stability are needed, single case studies can be costly and time consuming. In order to overcome these limitations, simple and easy-to-use methods such as Slope Mass Rating (SMR) [87] and the Rockfall Hazard Rating System (RHRS) [37,38,39] have been developed for evaluation of slope stability. However, a clear evaluation method related to slope revegetation has not yet been developed. This study aimed to investigate weights of major variables to develop a rating system for stability of slope revegetation using various experts. The selected variables, identified via an expert survey, have direct and indirect effects on the stability of a revegetated slope. In our results, variables related to water resources, plant growth, and soil quality were highly ranked. These were rain intensity, seepage water, drainage condition, slope angle, vegetation community, vegetation coverage, tensile strength, permeability coefficient, soil texture, and organic matter. The five highest-ranked variables that satisfied both groups were rain intensity, seepage water, slope angle, drainage condition, and ground layer. Because ground layer was highly ranked by civil engineers, it was eventually selected.
This study did not include some of the potential variables for diastrophisms such as earthquakes, and were limited to hydroseeding applications of slope revegetation. Therefore, these results might be restricted to studies related to slope failure issues such as landslide and large-scale soil erosion. Furthermore, the use of these variables should be accompanied by scientific results including numerical and statistical analyses to develop the rating system for evaluating the stability of slope revegetation.

Acknowledgments

This research was supported by “Development of Climate Change Adaptation and Management Technique, and Supportive System (Korea Ministry of Environment, Project number: 416-111-014)” and “Development of Economic Assessment Technique for Climate Change Impact and Adaptation Considering Uncertainties (Korea Ministry of Environment, Project number: 2014001310010)”. The funding organization had no involvement in study design; collection, analysis and interpretation of data; the writing of the report; or the decision to submit the article for publication.

Author Contributions

Sung-Ho Kil designed the research, analyzed data, and led to write this manuscript; Dong-Kun Lee made a substantial contribution to the interpretation of the results; Jun-Hyun Kim reviewed the literature and contributed to improving the discussion about AHP analysis. Ming-Han Li and Galen Newman edited the manuscript. All authors read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix

Table
Table A1. Variable explanation used for this study.
Table A1. Variable explanation used for this study.
TopographyUnitExplanation
Slope angle°Angle of inclination
Slope heightmStraight-line distance from the center of the bottom of a slope to the top of adjacent forests
Slope location-Location of a revegetated slope
Slope type-Various types of curved or straightened slope after constructing (Straight (口), Concave (凹), Convex (凸), Compound (凹凸))
Slope widthmStraight-line distance of lower section in a slope
Altitude mAltitude above sea level
Aspect°Compass direction facing of a slope
Curvature Torsion degree of a curved-slope
Catchment basinm2An area of land having capacity of water resources
Stream power index (SPI)mMeasurement of erosive power connected to flowing water in a certain catchment area
Topographic wet index (TWI)mUpslope contributing area per width orthogonal to local topographic gradient as a steady state wetness index (frequently used to analyze soil moisture conditions in a certain catchment area)
GeologyUnitExplanation
Ground layer-Various types of underlying rock or soil after constructing such assoil, weathered rock and blasted rock
Rock type-Various types of rock such as granite, gneiss and sandstone
Joint condition-Condition of discontinuities measured from roughness, separation and weathering of joint wall
Joint orientation°Dip direction measured with geological compass
Weathering characteristics-Weathering peculiarity on the bedrock
Weathered condition-Weathering degree on the bedrock
Tension crack-A discrete rock fracture forming perpendicularly to the direction of maximum extension
Seepage water-Sporadic seepage flow when the pores between soils or the hole of a crack in a bedrock become saturated with water and intersect a restrictive layer
ClimateUnitExplanation
Rain intensitymm h−1Hourly rainfall
Daily rainfallmm day−1Rainfall in a day
Accumulated rainfallmmCumulative rainfall in a couple of days
Soil physicsUnitExplanation
Porosity%Capacity of air-filled voids in dry soil
Bulk densityg cm−3Measurement as the dry weight of soil divided by its volume
Gravel contents%Mineral particles larger than 2 mm in diameter
Grain sizemmParticle size referring to the diameter of individual soil
Soil hardnessmmPhysical treatment of soil as a result of trampling or by mechanical equipment. (soil compaction)
Water content%Water quantity contained in a soil
Soil texture-Systematic arrangement of soils classified into relative ratios of sand, silt, and clay (soil classification)
Permeability coefficientm s−1Permeability about how much water in a soil can move though pore fractures (Darcy’s law (Calculation by constant head method using the flux per hour, length of soil column, and hydraulic head))
Tensile strengthkg m−2Maximum stress that a soil can withstand while it is stretched before it breaks
Shear strengthkg m−2Maximum resistance of a structural member or material to shearing stress
Specific gravity%Ratio of the density of a soil
Soil chemistryUnitExplanation
Soil acidity (pH)-Soil alkalinity (1:5 solution of soil : water)
Cation Exchange Capacity (CEC)cmol kg−1The number of positive cations that a soil can hold
Electronic conductivity (EC)dS kg−1Measuring ability of a soil to accommodate the transport of an electrical charge. (generally charged according to the degree of salination)
Dissolved phosphatemg/kgAmount guaranteed on the fertilizer label to be available to plants (P2O5)
Soil organic matter%Organic components by the decomposed plant and animal residues
C/N%Carbon-to-nitrogen ratio (A ratio of the amount of carbon to the amount of nitrogen in a soil)
Salt concentration%Soil salinity
T-N%Total nitrogen
Exchangeable calcium (Ca)cmol kg−1Solubility of Ca sources
Exchangeable magnesium (Mg)cmol kg−1Solubility of Mg sources
Exchangeable potassium (K)cmol kg−1Solubility K sources
Exchangeable sodium (Na)cmol kg−1Solubility Na sources
VegetationUnitExplanation
Forest standmLarge area of predominant trees
Tree heightmHeight of a tree
Species diversityNo.The number of different species that are represented in a certain community
Dominant plant speciesNo.A plant group including the most number and the highest coverage of an individual plant in a specific ecosystem
Number of treesNo.Number of tree species by plant nomenclature
Number of herbsNo.Number of herb species by plant nomenclature
Vegetation coverage%Rate of a vegetation area covered in a specific area
Vegetation densityNo.The number of different plants that are represented in plant community
Germination percentage%A percentage of germination in a certain amount of time
Plant community-A group of plant species expressed by a layered form which classified into tree, shrub and herbaceous layer in a defined plant area
Timber age classNo.An average age of a plant group
Timber diameter classNo.An average diameter of a plant group
Root reinforcementm year−1Permanently increment in volume of a root or root system
ConstructionUnitExplanation
Soil depthmDepth of revegetated soil removed from the slope surface down to the ground layer
Land use-Utilization of use including category designated on developing plans
Drainage system-Supporting well-managed drainage system followed by an act, regulation, or notification of drain facilities provided by the government
Elapsed yearyearNumber of years elapsed since revegetation work was completed
Scale of failurem2Damaged slope area when failed
Collapse history-Previous trace of failure
Reinforced facility for slope protection-Physically-based secondary device to prevent a revegetation measure from failure when a slope is steep (45° or more) and ground layer is weathered rock or blasted rock (Its method: fiber mesh, wire mesh and gabion block)

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MDPI and ACS Style

Kil, S.-H.; Lee, D.K.; Kim, J.-H.; Li, M.-H.; Newman, G. Utilizing the Analytic Hierarchy Process to Establish Weighted Values for Evaluating the Stability of Slope Revegetation based on Hydroseeding Applications in South Korea. Sustainability 2016, 8, 58. https://doi.org/10.3390/su8010058

AMA Style

Kil S-H, Lee DK, Kim J-H, Li M-H, Newman G. Utilizing the Analytic Hierarchy Process to Establish Weighted Values for Evaluating the Stability of Slope Revegetation based on Hydroseeding Applications in South Korea. Sustainability. 2016; 8(1):58. https://doi.org/10.3390/su8010058

Chicago/Turabian Style

Kil, Sung-Ho, Dong Kun Lee, Jun-Hyun Kim, Ming-Han Li, and Galen Newman. 2016. "Utilizing the Analytic Hierarchy Process to Establish Weighted Values for Evaluating the Stability of Slope Revegetation based on Hydroseeding Applications in South Korea" Sustainability 8, no. 1: 58. https://doi.org/10.3390/su8010058

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