Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran

Watershed degradation due to soil erosion and sedimentation is considered to be one of the major environmental problems in Iran. In order to address the critical conditions of watershed degradation in arid and semiarid regions, a study based on the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was carried out at Golestan watershed, northeast of Iran. The model information layers comprising nine effective factors in erosion and sedimentation at the watershed site were obtained by digitalization and spatial interpolation of the basic information data in a GIS program. These factors are geology, soil, climate, runoff, topography, land cover, land use, channel, and upland erosion. The source data for the model were obtained from available records on rainfall and river discharge and sediment, topography, land use, geology, and soil maps as well as field surveys and laboratory analysis. The results of the MPSIAC model indicated that 60.75 % (194.4 km²) and 54.97 % (175.9 km²) of the total watershed area were classified in the heavy sedimentation and erosion classes, and the total basin sediment yield and erosion were calculated as 4,171.1 and 17,813.4 m³ km^?2 year^?1, respectively. In the sensitivity analysis, it was found that the most sensitive parameters of the model in order of importance were topography (slope), land cover and use, runoff, and channel erosion (R ²?=?0.92–0.94), while geology, climate (rainfall), soil, and upland erosion factors were found to have moderate effect to the model output (R ²?=?0.74–0.59).     

基于GIS的滑坡、泥石流灾害危险性区划关键问题研究

随着GIS技术的引入,滑坡、泥石流灾害危险性区划的效率和准确性得以大大提高。依据工程地质类比原则,在灾害学理论指导下,结合专家打分、层次分析、人工神经网络、信息量、Logistic回归、统计量等模型方法,以MAPGIS软件为平台,利用C++语言开发了滑坡、泥石流灾害危险性区划评价分析系统;并重点探讨了GIS支持下的滑坡、泥石流灾害危险性区划过程中的因子分析、模型选取、模型复合、单元划分、系统集成、结果评价等关键问题,建立了一整套基于GIS的滑坡、泥石流灾害评价方法体系。应用该系统对长江三峡库区和辽宁省鞍山市分别开展了滑坡、泥石流灾害危险性区划研究,取得了较好的效果。     

滑坡灾害区划系统研究

本文系统地介绍了滑坡灾害区划研究的国内外研究现状，提出减灾的关键在于从区域上做好预防研究的观点，并阐述了滑坡灾害与风险术语统一化的重要性，由此提出滑坡灾害区划的核心是灾害、易损性和风险三要素的综合分析。文章进一步提出了灾害区划研究的两种基本途径，破坏概率法和信息分析法，把传统的滑坡单体稳定性分析延伸到以Monte-     

滑坡灾害预测分区的计算机制图

本文在滑坡灾害预测分区的信息模型基础上,重点讨论了灾害预测的计算机制图化的主要过程:因素的数值化,单元边界的确定和彩色图件的绘制。运用中国地质大学计算机系开发的Mapcad系统,在Mv/10000计算机上较好地处理了不规则图幅边界的自然裁剪,不规则单元的输入,以及彩色图件的绘制等问题。     

Earthquake hazard zonation of Sikkim Himalaya using a GIS platform

An earthquake hazard zonation map of Sikkim Himalaya is prepared using eight thematic layers namely Geology (GE), Soil Site Class (SO), Slope (SL), Landslide (LS), Rock Outcrop (RO), Frequency–Wavenumber (F–K) simulated Peak Ground Acceleration (PGA), Predominant Frequency (PF), and Site Response (SR) at predominant frequencies using Geographic Information System (GIS). This necessitates a large scale seismicity analysis for seismic source zone classification and estimation of maximum earthquake magnitude or maximum credible earthquake to be used as a scenario earthquake for a deterministic or quasi-probabilistic seismic scenario generation. The International Seismological Center (ISC) and Global Centroid Moment Tensor (GCMT) catalogues have been used in the present analysis. Combining b-value, fractal correlation dimension (Dc) of the epicenters and the underlying tectonic framework, four seismic source zones are classified in the northeast Indian region. Maximum Earthquake of M _W 8.3 is estimated for the Eastern Himalayan Zone (EHZ) and is used to generate the seismic scenario of the region. The Geohazard map is obtained through the integration of the geological and geomorphological themes namely GE, SO, SL, LS, and RO following a pair-wise comparison in an Analytical Hierarchy Process (AHP). Detail analysis of SR at all the recording stations by receiver function technique is performed using 80 significant events recorded by the Sikkim Strong Motion Array (SSMA). The ground motion synthesis is performed using F–K integration and the corresponding PGA has been estimated using random vibration theory (RVT). Testing for earthquakes of magnitude greater than M _W 5, a few cases presented here, establishes the efficacy and robustness of the F–K simulation algorithm. The geohazard coverage is overlaid and sequentially integrated with PGA, PF, and SR vector layers, in order to evolve the ultimate earthquake hazard microzonation coverage of the territory. Earthquake Hazard Index (EHI) quantitatively classifies the terrain into six hazard levels, while five classes could be identified following the Bureau of Indian Standards (BIS) PGA nomenclature for the seismic zonation of India. EHI is found to vary between 0.15 to 0.83 quantitatively classifying the terrain into six hazard levels as “Low” corresponding to BIS Zone II, “Moderate” corresponding to BIS Zone III, “Moderately High” belonging to BIS Zone IV, “High” corresponding to BIS Zone V(A), “Very High” and “Severe” with new BIS zones to Zone V(B) and V(C) respectively.     

Analytic network process model for landslide hazard zonation

Various controlling factors such as lithology, slope angle, slope aspect, landuse, channel proximity etc. are generally considered for the landslide hazard assessment. Although outer dependence of these parameters to a landslide is inevitably taken into account, inter-dependence among the factors is seldom addressed. Analytic Network Process (ANP) is the multi-criteria decision making (MCDM) tool which takes into account such a complex relationship among parameters. In this research, an ANP model for landslide susceptibility is proposed, priority weights for each parameter controlling the landslide were determined, and a hazard map was prepared of an area in a fragile mountainous terrain in the eastern part of Nepal. The data used in the example were derived from published sources, aerial photographs and a topographic map. However, the procedures developed can readily incorporate additional information from more detailed investigations.     

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect.     

The nature and provenance of Golestan loess deposits in northeast Iran

Mineralogical, textural, geochemical, and weathering characteristics of loess deposits in Golestan province of Iran suggest that they are mostly derived from felsic igneous rocks and are related to Quaternary palaeoclimate. Whole‐rock analyses indicate heavy minerals such as zircon, tourmaline and phyllosillicate minerals (e.g. muscovite, chlorite, illite) exert a significant control on the chemical composition. The loess samples display uniform chemical composition, indicative of similar alteration history. Chemical index of alteration suggests a weak to moderate degree of weathering in a felsic source area. Scanning electron micrographs of quartz grains reveal abundant silt‐sized quartz particles, a result of glacial grinding during the Late Pleistocene in Central Asia. Subsequently, these silt particles were transported from Central Asia to their depositional site by wind and paraglacial processes. Local topography of northeast Iran (Alborz Mountains) acted as a major barrier, entrapping the airborne particles on the plains of Golestan province. Copyright © 2012 John Wiley & Sons, Ltd.     

Study of regional monsoonal effects on landslide hazard zonation in Cameron Highlands, Malaysia

In general, landslides in Malaysia mostly occurred during northeast and southwest periods, two monsoonal systems that bring heavy rain. As the consequence, most landslide occurrences were induced by rainfall. This paper reports the effect of monsoonal-related geospatial data in landslide hazard modeling in Cameron Highlands, Malaysia, using Geographic Information System (GIS). Land surface temperature (LST) data was selected as the monsoonal rainfall footprints on the land surface. Four LST maps were derived from Landsat 7 thermal band acquired at peaks of dry and rainy seasons in 2001. The landslide factors chosen from topography map were slope, slope aspect, curvature, elevation, land use, proximity to road, and river/lake; while from geology map were lithology and proximity to lineament. Landslide characteristics were extracted by crossing between the landslide sites of Cameron Highlands and landslide factors. Using which, the weighting system was derived. Each landslide factors were divided into five subcategories. The highest weight values were assigned to those having the highest number of landslide occurrences. Weighted overlay was used as GIS operator to generate landslide hazard maps. GIS analysis was performed in two modes: (1) static mode, using all factors except LST data; (2) dynamic mode, using all factors including multi-temporal LST data. The effect of addition of LST maps was evaluated. The final landslide hazard maps were divided into five categories: very high risk, high risk, moderate, low risk, and very low risk. From verification process using landslide map, the landslide model can predict back about 13–16% very high risk sites and 70–93% of very high risk and high risk combined together. It was observed however that inclusion of LST maps does not necessarily increase the accuracy of the landslide model to predict landslide sites.     

基于GIS的北京市延庆县地质灾害易发性区域划分

随着灾害科学研究的深入，区域地质灾害已成为其重要的研究领域。文章利用遥感技术及GPS工具获取地质灾害的特征信息，在对地质灾害的成因背景分析基础上，运用GIS空间分析功能和地质灾害危险性评价、评估理论构建了地质灾害发育度模型。以北京市延庆县为实验区，采用ArcEngine＆．NET进行易发性分区程序的编写，计算研究区域内单元网格的发育度值。为了克服调查数据的局限性和人为因素，在计算发育度时引入修正系数，从延庆县DEM数据中提取单元格网内的地形坡度值，根据坡度值区间确定修正系数。将发育度计算结果按照一定规律、原则聚类。进行地质灾害易发性区域划分，取得了与实际情况较为一致的结果。基于“3S”技术及灾害地质条件，采用地质灾害发育度模型，可以较好地用于区域地质灾害易发性区域的划分，并能为防灾、减灾提供重要信息。     

Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India

The Nilgiri massif, South India, is chronically prone to landslides due to deforestation and the resultant direct entry of rainwater and the final increases of pore pressure leading to landslides in the region. In order to understand such landslide causes, the relative effect method, a new technique, has been adopted for the study area. Among various methods, this is a statistical method developed within the framework of the Geographic Information System to map landslide hazard zones in a mountainous environment. To determine the relative effect (RE) of the factors influencing landslides, data layers of geology, land use/land cover, geomorphology, slope, lineament density, drainage density, and soil were analyzed by calculating the ratio of the unit portion in coverage and landslide, this function that is logarithmic. To quantify the magnitude of factors influencing each grid unit, REs were summed and classified into zones of low-, moderate-, and high-landslide hazard zones. It is also appropriate to follow suitable measures to prevent the landslides in the study area by involving all stockholders and with the active participation of local communities.     

Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study was to investigate the application of the weights-of-evidence and certainty factor approaches for producing landslide susceptibility maps of a landslide-prone area (Haraz) in Iran. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. The landslide conditioning factors considered for the study area were slope gradient, slope aspect, altitude, lithology, land use, distance from streams, distance from roads, distance from faults, topographic wetness index, stream power index, stream transport index and plan curvature. For validation of the produced landslide susceptibility maps, the results of the analyses were compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all the landslide susceptibility models were constructed and the areas under the curves were calculated. The landslide locations were used to validate results of the landslide susceptibility maps. The verification results showed that the weights-of-evidence model (79.87%) performed better than certainty factor (72.02%) model with a standard error of 0.0663 and 0.0756, respectively. According to the results of the area under curve evaluation, the map produced by weights-of-evidence exhibits satisfactory properties.     

Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques

Fire in forested areas can be regarded as an environmental disaster which is triggered by either natural forces or anthropogenic activities. Fires are one of the major hazards in forested and grassland areas in the north of Iran. Control of fire is difficult, but it is feasible to map fire risk by geospatial technologies and thereby minimize the frequency of fire occurrences and damages caused by fire. The fire risk models provide a suitable concept to understand characterization of fire risk. Some models are map based, and they combine effectively different forest fire–causing variables with remote sensing data in a GIS environment for identifying and mapping forest fire risk. In this study, Structural Fire Index, Fire Risk Index, and a new index called Hybrid Fire Index were used to delineate fire risk in northeastern Iran that is subjected to frequent forest fire. Vegetation moisture, slope, aspect, elevation, distance from roads, and vicinity to settlements were used as the factors influencing accidental fire starts. These indices were set up by assigning subjective weight values to the classes of the layers based on their sensitivity ratio to fire. Hot spots data derived from MODIS satellite sensor were used to validate the indices. Assessment of the indices with receiver operating characteristic (ROC) curves shows that 76.7 % accuracy of the HFI outperformed the other two indices. According to the Hybrid Fire Index, 57.5 % of the study area is located under high-risk zone, 33 % in medium-risk zone, and the remaining 9.5 % area is located in low-risk zone.     

Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models

This paper presents landslide hazard analysis at Cameron area, Malaysia, using a geographic information system (GIS) and remote sensing data. Landslide locations were identified from interpretation of aerial photographs and field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence are topographic slope, topographic aspect, topographic curvature, and distance to rivers, all from the topographic database; lithology and distance to faults were taken from the geologic database; land cover from TM satellite image; the vegetation index value was taken from Landsat images; and precipitation distribution from meteorological data. Landslide hazard area was analyzed and mapped using the landslide occurrence factors by frequency ratio and bivariate logistic regression models. The results of the analysis were verified using the landslide location data and compared with the probabilistic models. The validation results showed that the frequency ratio model (accuracy is 89.25%) is better in prediction of landslide than bivariate logistic regression (accuracy is 85.73%) model.     

The 2010 Yushu earthquake triggered landslide hazard mapping using GIS and weight of evidence modeling

The Yushu County, Qinghai Province, China, April 14, 2010, earthquake triggered thousands of landslides in a zone between 96°20′32.9″E and 97°10′8.9″E, and 32°52′6.7″N and 33°19′47.9″N. This study examines the use of geographic information system (GIS) technology and Bayesian statistics in creating a suitable landslide hazard-zone map of good predictive power. A total of 2,036 landslides were interpreted from high-resolution aerial photographs and multi-source satellite images pre- and post-earthquake, and verified by selected field checking before a final landslide-inventory map of the study area could be established using GIS software. The 2,036 landslides were randomly partitioned into two subsets: a training dataset, which contains 80 % (1,628 landslides), for training the model; and a testing dataset 20 % (408 landslides). Twelve earthquake triggered landslide associated controlling parameters, such as elevation, slope gradient, slope aspect, slope curvature, topographic position, distance from main surface ruptures, peak ground acceleration, distance from roads, normalized difference vegetation index, distance from drainages, lithology, and distance from all faults were obtained from variety of data sources. Landslide hazard indices were calculated using the weight of evidence model. The landslide hazard map was compared with training data and testing data to obtain the success rate and predictive rate of the model, respectively. The validation results showed satisfactory agreement between the hazard map and the existing landslide distribution data. The success rate is 80.607 %, and the predictive rate is 78.855 %. The resulting landslide hazard map showed five classes of landslide hazard, i.e., very high, high, moderate, low and very low. The landslide hazard evaluation map should be useful for environmental recovery planning and reconstruction work.     

Morphometric,statistical, and hazard analyses using ASTER data and GIS technique of WADI El-Mathula watershed,Qena, Egypt

An attempt to carry out morphometric, statistical, and hazard analyses using ASTER data and GIS technique of Wadi El-Mathula watershed, Central Eastern Desert, Egypt. Morphometric analysis with application of GIS technique is essential to delineate drainage networks; basin geometry, drainage texture, and relief characteristics, through detect forty morphometric parameters of the study watershed and its sub-basins. Extract new drainage network map with DEM, sub-basin boundaries, stream orders, drainage networks, slope, drainage density, flow direction maps with more details is very necessary to analyze different morphometric and hydrologic applications for the study basin. Statistical analysis of morphometric parameters was done through cluster analysis, regression equations, and correlation coefficient matrix. Clusters analyses detect three independents variables which are stream number, basin area, and stream length have a very low linkage distance of 0.001 (at very high similarity of 99.95%) in a cluster with the basin width. Main channel length and basin perimeter (at very high similarity of 99.83%) are in a cluster with basin length. Using the regression equations and graphical correlation matrix indicates the mathematical relationships and helps to predict the behavior between any two variables. Hazard analysis and hazard degree assessment for each sub-basin were performed. The hazardous factors were detected and concluded that most of sub-basins are classified as moderately to highly hazardous. Finally, we recommended that the flood possibilities should be taken in consideration during future development of these areas.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

Assessment of chemical properties and hydro-geochemical coefficients at the Qareh Sou Basin,Golestan Province,Iran

This paper focuses on the Qareh Sou Basin in Golestan Province, Iran. Golestan Province is the third largest cereal producer in Iran and water scarcity and salinity are major problems in this area. This study attempts to facilitate the comprehension of system behavior with respect to water quality issues and hydro-geochemical coefficients within the Qareh Sou Basin. This study was carried out during the year 2010. Various parameters, such as pH, EC, chloride, sulfate, bicarbonate, sodium, potassium, calcium and magnesium have been determined for evaluation purposes. Then, Ca/Mg, Na/Cl, Mg/(Ca + Mg), Ca/HCO₃, (Ca + Mg)–(HCO₃ + SO₄), (Na + K)–Cl, (Ca + Mg + Na + K)–Cl, HCO₃ + SO₄, Ca + Mg and chloro-alkaline indices (CAI) were calculated. Results show that cation exchange probably is an important factor in the hydrochemistry and silicate mineral weathering. Also, CAI-1 plot against CAI-2 demonstrates that most of samples have positive values which suggest normal ion exchange in the system. The carbonic acid is the main agent of calcite, limestone and dolomite weathering which occurs in some stations. According to Chadha’s diagram, the type of water is determined as Ca–Mg–HCO₃.     

Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed, Nepal

The goal of this paper is to evaluate and compare the consistency of GIS-based heuristic and bivariate landslide susceptibility mapping techniques in the Himalayan region, taking the Kulekhani watershed of central Nepal as an example. For this purpose, a heuristic and two statistical bivariate landslide susceptibility mapping methods are applied, and three separate landslide susceptibility zonation maps are produced. The maps are compared using three approaches: landslide density analysis, success rate analysis, and agreed area analysis. A comparison of the values obtained from landslide density analysis and the curves of success rate analysis indicate that the two bivariate methods produce almost identical results, whereas the map produced with the heuristic method differs significantly from the others. On the other hand, the agreed area analysis highlights significant spatial differences in the maps obtained from the three methods. Although the three approaches evaluate the consistency of susceptibility maps, only the agreed area analysis is capable of spatially comparing them. Hence, this approach proves to be more suitable for spatially and quantitatively evaluating the consistency of various landslide susceptibility zonation maps.