Landslide susceptibility mapping in an area of underground mining using the multicriteria decision analysis method

Landslides are geomorphological phenomena that affect anthropogenic and natural features on the Earth’s surface. Many previous studies have identified several factors that have contributed to landslides. Among these factors are physical characteristics, such as slope, aspect, and land cover, of Earth’s surface. Moreover, landslides can be triggered by human activities such as underground mining. This study aims to identify landslide susceptibility areas by analyzing landslide-related factors, including land subsidence triggered by underground mining. The area of interest was Kozlu, Turkey, where underground mining has been in progress for the past 100 years. Thus, to identify landslide risk zones, the multicriteria decision analysis method, together with the analytical hierarchy method, was used. The datasets included were topography, land cover, geological settings, and mining-induced land subsidence. The spatial extent of land subsidence was estimated using a previously published model. A landslide susceptibility map (LSM) was developed using a purposely developed GIS-based software. The results were compared with a terrain deformation map, which was developed in a separate study using the differential synthetic aperture radar interferometry (DInSAR) technique. The results showed a substantial correlation between the LSM and DInSAR map. Furthermore, it was found that ~?88% of the very high and high landslide risk areas coincided with location of the past landslide events. These facts suggest that the algorithm and data sources used were sufficient to produce a sufficiently accurate LSM, which may be used for various purposes such as urban planning.     

Stochastic landslide vulnerability modeling in space and time in a part of the northern Himalayas, India

Little is known about the quantitative vulnerability analysis to landslides as not many attempts have been made to assess it comprehensively. This study assesses the spatio-temporal vulnerability of elements at risk to landslides in a stochastic framework. The study includes buildings, persons inside buildings, and traffic as elements at risk to landslides. Building vulnerability is the expected damage and depends on the position of a building with respect to the landslide hazard at a given time. Population and vehicle vulnerability are the expected death toll in a building and vehicle damage in space and time respectively. The study was carried out in a road corridor in the Indian Himalayas that is highly susceptible to landslides. Results showed that 26% of the buildings fall in the high and very high vulnerability categories. Population vulnerability inside buildings showed a value >0.75 during 0800 to 1000 hours and 1600 to 1800 hours in more buildings that other times of the day. It was also observed in the study region that the vulnerability of vehicle is above 0.6 in half of the road stretches during 0800 hours to 1000 hours and 1600 to 1800 hours due to high traffic density on the road section. From this study, we conclude that the vulnerability of an element at risk to landslide is a space and time event, and can be quantified using stochastic modeling. Therefore, the stochastic vulnerability modeling forms the basis for a quantitative landslide risk analysis and assessment.     

Vegetation recovery patterns assessment at landslides caused by catastrophic earthquake: A case study in central Taiwan

The catastrophic earthquake, 7.3 on the Richter scale, occurred on September 21, 1999 in Central Taiwan. Much of standing vegetation on slopes was eliminated and massive, scattered landslides were induced at the Jou-Jou Mountain area of the Wu-Chi basin in Nantou County. We evaluated three methods for assessing landslide hazard and vegetation recovery conditions. (1) Self-organizing map (SOM) neural network coupled with fuzzy technique was used to quickly extract the landslide. (2) The NDVI-based vegetation recovery index derived from multi-temporal SPOT satellite images was used to evaluate vegetation recovery rate in the denudation sites. (3) The spatial distribution index (SDI) based on land-cover topographic location was employed to analyze vegetation recovery patterns, including the invading, surviving and mixed patterns at the Jou-Jou Mountain area. On September 27, 1999, there were 849.20 ha of landslide area extracted using the self-organizing map and fuzzy technique combined model. After six years of natural vegetation succession, the landslide has gradually restored, and vegetation recovery rate reached up to 86%. On-site observation shows that many native pioneer plants have invaded onto the denudation sites even if disturbed by several typhoons. Two native surviving plants, Arundo formosana Hack and Pinus taiwanensis Hayata, play a vital role in natural vegetation succession in this area, especially for the sites on ridgeline and steep slopes.     

An expert-based approach to forest road network planning by combining Delphi and spatial multi-criteria evaluation

Changes in forest landscapes resulting from road construction have increased remarkably in the last few years. On the other hand, the sustainable management of forest resources can only be achieved through a well-organized road network. In order to minimize the environmental impacts of forest roads, forest road managers must design the road network efficiently and environmentally as well. Efficient planning methodologies can assist forest road managers in considering the technical, economic, and environmental factors that affect forest road planning. This paper describes a three-stage methodology using the Delphi method for selecting the important criteria, the Analytic Hierarchy Process for obtaining the relative importance of the criteria, and finally, a spatial multi-criteria evaluation in a geographic information system (GIS) environment for identifying the lowest-impact road network alternative. Results of the Delphi method revealed that ground slope, lithology, distance from stream network, distance from faults, landslide susceptibility, erosion susceptibility, geology, and soil texture are the most important criteria for forest road planning in the study area. The suitability map for road planning was then obtained by combining the fuzzy map layers of these criteria with respect to their weights. Nine road network alternatives were designed using PEGGER, an ArcView GIS extension, and finally, their values were extracted from the suitability map. Results showed that the methodology was useful for identifying road that met environmental and cost considerations. Based on this work, we suggest future work in forest road planning using multi-criteria evaluation and decision making be considered in other regions and that the road planning criteria identified in this study may be useful.     

Effects of land-use changes on landslides in a landslide-prone area (Ardesen, Rize, NE Turkey)

Various natural hazards such as landslides, avalanches, floods and debris flows can result in enormous property damages and human casualties in Eastern Black Sea region of Turkey. Mountainous topographic character and high frequency of heavy rain are the main factors for landslide occurrence in Ardesen, Rize. For this reason, the main target of the present study is to evaluate the landslide hazards using a sequence of historical aerial photographs in Ardesen (Rize), Turkey, by Photogrammetry and Geographical Information System (GIS). Landslide locations in the study area were identified by interpretation of aerial photographs dated in 1973 and 2002, and by field surveys. In the study, the selected factors conditioning landslides are lithology, slope gradient, slope aspect, vegetation cover, land class, climate, rainfall and proximity to roads. These factors were considered as effective on the occurrence of landslides. The areas under landslide threat were analyzed and mapped considering the landslide conditioning factors. Some of the conditioning factors were investigated and estimated by employing visual interpretation of aerial photos and topographic data. The results showed that the slope, lithology, terrain roughness, proximity to roads, and the cover type played important roles on landslide occurrence. The results also showed that degree of landslides was affected by the number of houses constructed in the region. As a consequence, the method employed in the study provides important benefits for landslide hazard mitigation efforts, because a combination of both photogrammetric techniques and GIS is presented.     

Landslides susceptibility change over time according to terrain conditions in a mountain area of the tropic region

Susceptibility to landslides in mountain areas results from the interaction of various factors related to relief formation and soil development. The assessment of landslide susceptibility has generally taken into account individual events, or it has been aimed at establishing relationships between landslide-inventory maps and maps of environmental factors, without considering that such relationships can change in space and time. In this work, temporal and space changes in landslides were analysed in six different combinations of date and geomorphological conditions, including two different geological units, in a mountainous area in the north-centre of Venezuela, in northern South America. Landslide inventories from different years were compared with a number of environmental factors by means of logistic regression analysis. The resulting equations predicted landslide susceptibility from a range of geomorphometric parameters and a vegetation index, with diverse accuracy, in the study area. The variation of the obtained models and their prediction accuracy between geological units and dates suggests that the complexity of the landslide processes and their explanatory factors changed over space and time in the studied area. This calls into question the use of a single model to evaluate landslide susceptibility over large regions.     

A GIS-based spatial multi-index model for flood risk assessment in the Yangtze River Basin,China

This paper developed a GIS-based spatial multi-index model for large basin-scale flood risk assessment. In terms of the risk definition proposed by the IPCC, the flood risk in the Yangtze River Basin (YRB) was classified into indexes of hazard, vulnerability, and exposure. The model systematically accounts for various flood risk indicators related to the economic, social and ecological environment of the YRB. Using the robust data space analysis and processing capabilities of ArcGIS, these flood risk indicators were superimposed and analyzed to generate an integrated flood risk spatial distribution map for the YRB. The modeling results were verified reasonably well using observation data from the YRB floods in 1998, 2008, and 2016. We found that 24.90% of the study area was at very high and high risk in 1998, and the risk in these areas decreased to 15.95% and 17.61% in 2008 and 2016, respectively. We believe that the GIS-based spatial multi-index model can be applied to other areas where basin-scale flood risk assessment is desired and contribute to further scientific research on flood forecasting and mitigation.     

Assessment of groundwater vulnerability in the coastal region of Oman using DRASTIC index method in GIS environment

A study was carried out to develop a vulnerability map for Barka region in the North Batina of Oman using DRASTIC vulnerability index method in GIS environment. DRASTIC layers were created using data from published reports and the seven DRASTIC layers were processed by the ArcGIS geographic information system. Finally, DRASTIC maps were created for 1995 and 2004 to understand the long-term changes in the vulnerability index. DRASTIC vulnerability maps were evaluated using groundwater quality data such as chemical and biological parameters. DRASTIC vulnerability maps of 1995 and 2004 indicate that the northern part of Barka is more vulnerable to pollution than southern part and the central part of Barka also shows high relative vulnerability which is mostly related to the high conductivity values. Moreover, the changes in water level due to high abstraction rate of groundwater reflect in the vulnerability maps and low vulnerability area is increased in the southern part during 2004 compared to 1995. Moreover, regional distribution maps of nitrate, chloride and total and fecal coliforms are well correlated with DRASTIC vulnerability maps. In contrast to this, even though DRASTIC method predicted the central part of the study region is highly vulnerable, both chemical and biological parameters show lower concentrations in this region compared to coastal belt, which is mainly due to agricultural and urban development. In Barka, urban development and agricultural activities are very high in coastal region compared to southern and central part of the study area. Hence, this study concluded that DRASTIC method is also applicable in coastal region having ubiquitous contamination sources.     

Tailoring groundwater quality monitoring to vulnerability: a GIS procedure for network design

Monitoring networks aiming to assess the state of groundwater quality and detect or predict changes could increase in efficiency when fitted to vulnerability and pollution risk assessment. The main purpose of this paper is to describe a methodology aiming at integrating aquifers vulnerability and actual levels of groundwater pollution in the monitoring network design. In this study carried out in a pilot area in central Italy, several factors such as hydrogeological setting, groundwater vulnerability, and natural and anthropogenic contamination levels were analyzed and used in designing a network tailored to the monitoring objectives, namely, surveying the evolution of groundwater quality relating to natural conditions as well as to polluting processes active in the area. Due to the absence of an aquifer vulnerability map for the whole area, a proxi evaluation of it was performed through a geographic information system (GIS) methodology, leading to the so called “susceptibility to groundwater quality degradation”. The latter was used as a basis for the network density assessment, while water points were ranked by several factors including discharge, actual contamination levels, maintenance conditions, and accessibility for periodical sampling in order to select the most appropriate to the network. Two different GIS procedures were implemented which combine vulnerability conditions and water points suitability, producing two slightly different networks of 50 monitoring points selected out of the 121 candidate wells and springs. The results are compared with a “manual” selection of the points. The applied GIS procedures resulted capable to select the requested number of water points from the initial set, evaluating the most confident ones and an appropriate density. Moreover, it is worth underlining that the second procedure (point distance analysis [PDA]) is technically faster and simpler to be performed than the first one (GRID?+?PDA).     

三江源畜牧业示范区土壤重金属含量特征及评价

为了明确三江源智慧生态畜牧业示范区内土壤重金属元素含量特征及潜在风险,2015年7月在三江源智慧生态畜牧业示范区11个示范村镇进行土壤样品采集,带回实验室分析Pb、Cd、Hg、Cr、Cu、Zn、Ni含量。对数据进行统计分析并采用内梅罗综合污染指数和生态危害指数进行风险等级评价。结果显示:部分采样点重金属元素含量高于青海省背景值;Pb、Cr、Cu、Zn、Ni主要受土壤母质的影响,Cd受自然和人为因素的双重影响,Hg主要受人为因素影响;内梅罗综合污染指数显示11个点位的指数都小于1,该地未出现污染状况;生态危害指数显示除GMY点位处于轻微风险等级,其余采样点为中等风险等级。总体上示范区内土壤未出现强污染和面源污染状况,但需要加强点源污染的风险防范。     

Environmental vulnerability assessment using Grey Analytic Hierarchy Process based model

Environmental management of an area describes a policy for its systematic and sustainable environmental protection. In the present study, regional environmental vulnerability assessment in Hirakud command area of Odisha, India is envisaged based on Grey Analytic Hierarchy Process method (Grey–AHP) using integrated remote sensing (RS) and geographic information system (GIS) techniques. Grey–AHP combines the advantages of classical analytic hierarchy process (AHP) and grey clustering method for accurate estimation of weight coefficients. It is a new method for environmental vulnerability assessment. Environmental vulnerability index (EVI) uses natural, environmental and human impact related factors, e.g., soil, geology, elevation, slope, rainfall, temperature, wind speed, normalized difference vegetation index, drainage density, crop intensity, agricultural DRASTIC value, population density and road density. EVI map has been classified into four environmental vulnerability zones (EVZs) namely: ‘low’, ‘moderate’ ‘high’, and ‘extreme’ encompassing 17.87%, 44.44%, 27.81% and 9.88% of the study area, respectively. EVI map indicates that the northern part of the study area is more vulnerable from an environmental point of view. EVI map shows close correlation with elevation. Effectiveness of the zone classification is evaluated by using grey clustering method. General effectiveness is in between “better” and “common classes”. This analysis demonstrates the potential applicability of the methodology.     

气候变化下的新疆生态环境脆弱性评价

选取了脆弱性、适应能力作为影响新疆生态环境脆弱性的条件层，进一步筛选出海拔、地质环境、沙尘暴、人口密度、降水量、植被类型、生态保护政策7个因子作为成因指标层，并用专家打分法对每个指标赋予权重。将研究区剖分成195个评价单元格，借助G玛强大的数据管理功能，建立与空间数据和属性数据相互关联的生态环境脆弱性数据库；首次采用模糊数学层次分析法作为新疆生态环境脆弱性等级计算的数学模型，计算出新疆生态环境脆弱性等级，并借助CIS进行成果表达。     

Determining Vulnerability Importance in Environmental Impact Assessment: The case of Colombia

The concept of vulnerability has been used to describe the susceptibility of physical, biotic, and social systems to harm or hazard. In this sense, it is a tool that reduces the uncertainties of Environmental Impact Assessment (EIA) since it does not depend exclusively on the value assessments of the evaluator, but rather is based on the environmental state indicators of the site where the projects or activities are being carried out. The concept of vulnerability thus reduces the possibility that evaluators will subjectively interpret results, and be influenced by outside interests and pressures during projects. However, up until now, EIA has been hindered by a lack of effective methods. This research study analyzes the concept of vulnerability, defines Vulnerability Importance and proposes its inclusion in qualitative EIA methodology. The method used to quantify Vulnerability Importance is based on a set of environmental factors and indicators that provide a comprehensive overview of the environmental state. The results obtained in Colombia highlight the usefulness and objectivity of this method since there is a direct relation between this value and the environmental state of the departments analyzed.     

基于综合环境风险的砷污染场地优控筛选方法

污染场地的优控筛选是国家和地区污染场地管理的关键环节,有利于提高污染场地修复的高效性和经济性。研究以污染源强、污染途径脆弱性和受体敏感性作为筛选的主要因素,构建了基于综合环境风险的优控重金属污染场地筛选方法。通过调查某市15个砷渣污染场地,利用综合环境风险指数法分析区域不同污染场地的危害程度,据此推算不同污染场地危害 程度大小次序,并设置不同情景进行敏感性分析,从而筛选出某市优控砷渣污染场地名单。结果表明:污染场地H15和H12在不同权重情景下均处于高危害程度水平,为优控污染场地；H2在多数情景下危害程度较高,确定为备选优控污染场地。     

GIS-based pollution hazard mapping and assessment framework of shallow lakes: southeastern Pampean lakes (Argentina) as a case study

The assessment of water vulnerability and pollution hazard traditionally places particular emphasis on the study on groundwaters more than on surface waters. Consequently, a GIS-based Lake Pollution Hazard Index (LPHI) was proposed for assessing and mapping the potential pollution hazard for shallow lakes due to the interaction between the Potential Pollutant Load and the Lake Vulnerability. It includes easily measurable and commonly used parameters: land cover, terrain slope and direction, and soil media. Three shallow lake ecosystems of the southeastern Pampa Plain (Argentina) were chosen to test the usefulness and applicability of this suggested index. Moreover, anthropogenic and natural medium influence on biophysical parameters in these three ecosystems was examined. The evaluation of the LPHI map shows for La Brava and Los Padres lakes the highest pollution hazard (≈30 % with high to very high category) while Nahuel Rucá Lake seems to be the less hazardous water body (just 9.33 % with high LPHI). The increase in LPHI value is attributed to a different loading of pollutants governed by land cover category and/or the exposure to high slopes and influence of slope direction. Dissolved oxygen and biochemical oxygen demand values indicate a moderately polluted and eutrophized condition of shallow lake waters, mainly related to moderate agricultural activities and/or cattle production. Obtained information by means of LPHI calculation result useful to perform a local diagnosis of the potential pollution hazard to a freshwater ecosystem in order to implement basic guidelines to improve lake sustainability.     

Coastal Flood Risk Analysis Using Landsat-7 ETM+ Imagery and SRTM DEM: A Case Study of Izmir, Turkey

The Intergovernmental Panel on Climate Change (IPCC) reports an acceleration of the global mean sea-level rise (MSLR) in the twentieth century in response to global climate change. If this acceleration remains constant, then some coastal areas are most likely to be inundated by the year 2100. The ability to identify the differential vulnerability of coastlines to future inundation hazards as result of global climate change is necessary for timely actions to be taken. Yildiz et al. (Journal of Mapping, 17, 1-75, 2003) reported that the local MSLR in the city of Izmir rose at a rate of 6.8 +/- 0.9 mm year(-1) between 1984 and 2002. In this study, the spatial distribution of the coastal inundation hazards of Izmir region was determined using not only land-use and land-cover (LULC) types derived from the maximum likelihood classification of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) multi-spectral image set but also the classification of the digital elevation model (DEM) acquired by the shuttle radar topography mission (SRTM). Coastal areas with elevations of 2 and 5 m above mean sea-level vulnerable to inundation were found to cover 2.1 and 3.7% of the study region (6,107 km(2)), respectively. Our findings revealed that Menemen plain along Gediz river, and the settlements of Karsiyaka, Alacati, Aliaga, Candarli and Selcuk are at high risk in order of decreasing vulnerability to permanent and episodic inundation by 2100 under the high MSLR scenarios of 20 to 50 mm year(-1).     

Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island,Malaysia

In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.     

典型金属冶炼城市降尘中重金属污染特征及风险评价

2021年1-3月,采集湖南省某典型金属冶炼城市不同功能区的降尘样品,分析测定了17种重金属元素含量,其中15种重金属元素含量超出了湖南背景值,分别为Ag、Fe、Cd、Ti、Sb、Tl、Pb、As、Zn、Cu、Mo、Ni、Cr、Ba、Mn。采用地累积指数法、潜在生态危害指数法和健康风险评价模型对大气降尘中重金属可能造成的生态风险和健康风险进行评价。结果表明:受长期的有色金属冶炼影响,Ag、Cd、Fe、Sb、Ti、Tl 6种重金属达到了极重度污染,Pb、As、Zn、Cu、Mo在中度污染程度以上。工业区的综合生态危害指数远高于工业居民混合区、工业农业混合区、居民区和交通区,达到了极强生态危害级别。健康风险评价结果显示:大气降尘中As和Pb存在非致癌风险,As、Cd和Ni存在致癌风险,且经口摄入是最主要的暴露途径。与青少年和儿童相比,大气降尘中重金属对成人造成的风险较高,且成年女性面临的风险高于成年男性。     

Development of groundwater quality index

Assessing the water quality status for special use is the main objective of any water quality monitoring studies. The water quality index (WQI) is a mathematical instrument used to transform large quantities of water quality data into a single number which represents the water quality level. In fact, developing WQI in an area is a fundamental process in the planning of land use and water resources management. In this study, a simple methodology based on multivariate analysis is developed to create a groundwater quality index (GWQI), with the aim of identifying places with best quality for drinking within the Qazvin province, west central of Iran. The methodology is based on the definition of GWQI using average value of eight cation and anion parameters for 163 wells during a 3-year period. The proportion of observed concentrations to the maximum allowable concentration is calculated as normalized value of each parameter in observing wells. Final indices for each well are calculated considering weight of each parameter. In order to assess the groundwater quality of study area, the derived indices are compared with those of well-known mineral waters. Using developed indices, groundwater iso-index map for study area and the map of areas of which the indices are near to mineral waters was drawn. In the case study, the GWQI map reveals that groundwater quality in two areas is extremely near to mineral water quality. Created index map provides a comprehensive picture of easily interpretable for regional decision makers for better planning and management.     

A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling

Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling.