Slope failure susceptibility zonation using integrated remote sensing and GIS techniques: A case study over Jhingurdah open pit coal mine,Singrauli coalfield,India

The susceptibility of slopes in open pit coal mines to various modes of failure (i.e., plane, wedge, circular and toppling failure) could be envisaged by virtue of processing and analysis of pertinent satellite data. The aim of the present study was to integrate thematic maps generated using remote sensing image processing techniques, in order to finally produce slope failure hazard zonation maps in and around Singrauli coalfield, India. The various failure-inducing factors, variables and parameters can be extracted from different satellite data and imageries. The data acquired by different sensors such as TM, ETM+, etc., of LANDSAT series and CARTOSAT of ISRO Bhuvan was used in this study. All these data were subsequently used to create different thematic maps such as slope map, lithological map, land use/land cover map, principal component analysis map, digital elevation model (DEM), etc. An advanced analysis for extraction of lineament attributes was also undertaken.     

Forecasting areas vulnerable to forest conversion using artificial neural network and GIS (case study: northern Ilam forests,Ilam province,Iran)

Forest conversion due to illegal logging and agricultural expansion is a major problem that is hampering biodiversity conservation efforts in the Zagros region. Yet, areas vulnerable to forest conversion are unknown. This study aims to predict the spatial distribution of deforestation in western Iran. Landsat images dated 1988, 2001, and 2007 are classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. Meanwhile, in order to examine deforestation factors’ investigation, deforestation maps with physiographic and human spatial variables are entered into the model. Areas vulnerable to forest changes in the Zagros forest region are predicted by a multilayer perceptron neural network (MLPNN) with a Markov chain model. The results show that about 19,294 ha forest areas are deforested in the last 19 years. The predictive performance of the model appears successful, which is validated using the actual land cover map of the same year from Landsat data. The validated map is found to be 94 % accurate. The validation is also tested using the relative operating characteristic approach which yielded a value of 0.96. The model is then further extended to predict forest cover losses for 2020. The MLPNN approach was found to have a great potential to predict land use/land cover changes because it permits developing complex, nonlinear models.     

基于GIS的岩溶区土地利用的地形梯度分布研究——以酉阳县板溪乡为例

基于 RS 和 GIS 技术,采用地形位指数、分布指数和土地利用程度综合指数,探讨了2002、2011两年重庆市酉阳县板溪乡不同地形梯度土地利用结构的变化特征和定量分析了土地利用程度。结果显示：建设用地、耕地和水域对地形条件的选择性较强;建设用地主要聚集在海拔〈850 m、坡度〈15°176;的区域,耕地主要聚集在海拔〈1050 m 和坡度〈25°176;的区域,5和7级地形位为制约水域分布的地形条件;2011年,灌木林地在海拔650-1250 m、坡度〉15°176;的区域集中增加。总体上,该研究区的土地利用程度较高,但仍然存在耕地分布不合理的现象。     

Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

The purpose of this study is to evaluate and compare the results of applying the statistical index and the logistic regression methods for estimating landslide susceptibility in the Hoa Binh province of Vietnam. In order to do this, first, a landslide inventory map was constructed mainly based on investigated landslide locations from three projects conducted over the last 10 years. In addition, some recent landslide locations were identified from SPOT satellite images, fieldwork, and literature. Secondly, ten influencing factors for landslide occurrence were utilized. The slope gradient map, the slope curvature map, and the slope aspect map were derived from a digital elevation model (DEM) with resolution 20 × 20 m. The DEM was generated from topographic maps at a scale of 1:25,000. The lithology map and the distance to faults map were extracted from Geological and Mineral Resources maps. The soil type and the land use maps were extracted from National Pedology maps and National Land Use Status maps, respectively. Distance to rivers and distance to roads were computed based on river and road networks from topographic maps. In addition, a rainfall map was included in the models. Actual landslide locations were used to verify and to compare the results of landslide susceptibility maps. The accuracy of the results was evaluated by ROC analysis. The area under the curve (AUC) for the statistical index model was 0.946 and for the logistic regression model, 0.950, indicating an almost equal predicting capacity.     

Landslide susceptibility mapping based on frequency ratio and logistic regression models

The aim of this study is to apply and compare a probability model, frequency ratio and statistical model, and a logistic regression to Sajaroud area, Northern Iran using geographic information system. Landslide locations of the study area were detected from interpretation of aerial photographs and field surveys. Landslide-related factors such as elevation, slope gradient, slope aspect, slope curvature, rainfall, distance to fault, distance to drainage, distance to road, land use, and geology were calculated from the topographic and geology map and LANDSAT ETM satellite imagery. The spatial relationships between the landslide location and each landslide-related factor were analyzed and then landslide susceptibility maps were produced using the frequency ratio and forward stepwise logistic regression methods. Finally, the maps were tested and compared using known landslide locations, and success rates were calculated. Predicted accuracy values for frequency ratio (79.48%) and logistic regression models showed that the map obtained from frequency ratio model is more accurate than the logistic regression (77.4%) model. The models used in this study have shown a great deal of importance for watershed management and land use planning.     

Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey)

The northeast part of Turkey is prone to landslides because of the climatic conditions, as well as geologic and geomorphologic characteristics of the region. Especially, frequent landslides in the Rize province often result in significant damage to people and property. Therefore, in order to mitigate the damage from landslides and help the planners in selecting suitable locations for implementing development projects, especially in large areas, it is necessary to scientifically assess susceptible areas. In this study, the frequency ratio method and the analytical hierarchy process (AHP) were used to produce susceptibility maps. Especially, AHP gives best results because of allowing better structuring of various components, including both objective and subjective aspects and comparing them by a logical and thorough method, which involves a matrix-based pairwise comparison of the contribution of different factors for landslide. For this purpose, lithology, slope angle, slope aspect, land cover, distance to stream, drainage density, and distance to road were considered as landslide causal factors for the study area. The processing of multi-geodata sets was carried out in a raster GIS environment. Lithology was derived from the geological database and additional field studies; slope angle, slope aspect, distance to stream, distance to road and drainage density were invented from digital elevation models; land cover was produced from remote sensing imagery. In the end of study, the results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.     

Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China

The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.     

甘肃迭部扎尕那地区山地土壤过程的垂直分带性研究

为了研究迭部扎尕那地区山地土壤过程的垂直分带性, 对不同海拔分布的土壤类型进行垂直分带取样, 并针对不同植被类型和特殊地理因素进行横向取样, 分析土壤的化学成分及其结构. 应用GIS软件数字化研究区地形图, 提取海拔、 坡度、 坡向等地形因子, 结合土地利用资料得到土地利用类型图. 通过采样点数据与数字化地形因子图与土地利用类型图的叠加, 结合属性数据的统计分析, 讨论影响土壤过程垂直分带性各要素的垂直性规律和分布. 结果表明: 有机碳含量和全氮含量呈极好的线性正相关, 并随海拔的升高而增加. 此外, 在同一海拔范围内, 土壤有机碳含量随着土地利用类型和坡向的变化呈规律性变化. 其中, 不同土地利用类型下的土壤有机碳含量依次为有林地>高寒草甸>高覆盖度草地>农村居民点>平原耕地>山地耕地; 不同坡向上, 土壤有机碳含量依次为阴坡>半阴坡>半阳坡>阳坡.     

Geospatial tools for assessing land degradation in Budgam district,Kashmir Himalaya,India

Land degradation reduces the ability of the land to perform many biophysical and chemical functions. The main aim of this study was to determine the status of land degradation in the Budgam area of Kashmir Himalaya using remote sensing and geographic information system. The satellite data together with other geospatial datasets were used to quantify different categories of land degradation. The results were validated in the field and an accuracy of 85% was observed. Land use/land cover of the study area was determined in order to know the effect of land use on the rate of land degradation. Normalized differential vegetation index (NDVI) and slope of the area were determined using LANDSAT-enhanced thematic mapper plus (ETM+) data, advanced space borne thermal emission and reflection radiometer, and digital elevation model along with other secondary data were analysed to create various thematic maps, viz., land use/land cover, geology, NDVI and slopes used in modelling land degradation in the Kashmir Himalayan region. The vegetation condition, elevation and land use/land cover information of the area were integrated to assess the land degradation scenario in the area using the ArcGIS ‘Spatial Analyst Module’. The results reveal that about 13.19% of the study area has undergone moderate to high degradation, whereas about 44.12% of the area has undergone slight degradation.     

Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand

The purpose of this study is to produce a landslide susceptibility map for the lower Mae Chaem watershed, northern Thailand using a Geographic Information System (GIS) and remotely sensed images. For this purpose, past landslide locations were identified from satellite images and aerial photographs accompanied by the field surveys to create a landslide inventory map. Ten landslide-inducing factors were used in the susceptibility analysis: elevation, slope angle, slope aspect, lithology, distance from lineament, distance from drainage, precipitation, soil texture, land use/land cover (LULC), and NDVI. The first eight factors were prepared from their associated database while LULC and NDVI maps were generated from Landsat-5 TM images. Landslide susceptibility was analyzed and mapped using the frequency ratio (FR) model that determines the level of correlation between locations of past landslides and the chosen factors and describes it in terms of frequency ratio index. Finally, the output map was validated using the area under the curve (AUC) method where the success rate of 80.06% and the prediction rate of 84.82% were achieved. The obtained map can be used to reduce landslide hazard and assist with proper planning of LULC in the future.     

猫跳河流域土地覆被变化及其驱动力定量研究

应用RS、GIS以及GPS集成技术,以黔中喀斯特地区的猫跳河流域为对象,研究了这一地区从1973—2002年间土地利用／覆被的变化过程,并利用典型相关分析法研究了形成这种变化的驱动因素。研究表明,从1973—1990年,猫跳河流域耕地、建设用地、有林地、水域以及裸岩地呈增长趋势,而灌木林和灌草地趋于减少,流域生态环境质量下降。进入20世纪90年代后,有林地、灌草地、建设用地、水域持续增加,灌草地总量变化不大,耕地总量和裸岩地减少,流域生态环境质量趋于好转。定量分析表明,1990年以前,驱动建设用地变化的因素是总人口的变化,1990年以后则是农业产值;1990年以前驱动耕地变化的是农业人口的增长,1990年以后则和粮食产量的提高有关。      

Spatio-temporal land cover changes in a semi-arid watershed: Central India

Land use/land cover change is a global phenomenon which reflects natural resources degradation and/or utilization. Remote sensing and GIS have been widely used to monitor such changes at watershed level. The present study evaluates the LU/LC change during 1989 - 2001 in a semi-arid watershed of central India. Geocoded satellite data of 1989 and 2001 on 1:50,000 scale, were visually interpreted to prepare thematic maps which were later digitized using ArcGIS softwares. The analysis shows that vast tracts of cultivated land have become uncultivated and at some places even converted to wasteland. However, the land under dense forest and open forest has decreased due to expansion of built-up land and other anthropogenic activities. Increase in area of uncultivated land, wasteland and decrease in cultivated land and open scrub is also supported by rainfall analysis, which shows a declining trend and a fall of 186.93 mm in average annual rainfall for 1986-2003 period. The change detection map prepared using land use/land cover of 1989 and 2001 as inputs shows that out of the total geographical area of the watershed, 25.78% of the watershed area has seen a change from one land use category to another, however rest 74.22% has remained unchanged.     

Identification of suitable sites for groundwater recharge in Awaspi watershed using GIS and remote sensing techniques

Groundwater is a valuable natural resource for drinking, domestic, livestock use, and irrigation, especially in arid and semi-arid regions like the Garmiyan belt in Kurdistan region. The Awaspi watershed is located 50 km east of Kirkuk city, south Kurdistan, Iraq; and covers an area of 2146 km². The paper presents result of a study aimed at: (1) mapping and preparing thematic layers of factors that control groundwater recharge areas, and (2) determination of sites suitable for groundwater recharge. We used available data such as geological map, groundwater depth map, digital elevation model (DEM), Landsat 8 imagery, and tropical rainfall measuring mission (TRMM) data for this study. These data, supplemented by slope features, lithology, land use land cover, rainfall, groundwater depth, drainage density, landform, lineament density, elevation and topographic position index, were utilized to create thematic maps to identify suitable areas of groundwater recharge, using GIS and remote sensing techniques. Analytic hierarchy process (AHP) was applied to weight, rank, and reclassify these maps in the ArcGIS 10.3 environment, to determine the suitable sites for groundwater recharge within the Awaspi watershed. Fifty-five percent of the total area of the watershed was found to be suitable for groundwater recharge; whereas 45% of the area was determined to have poor suitability for groundwater recharge, but can be used for surface water harvesting.     

GIS-based landslide susceptibility mapping for a problematic segment of the natural gas pipeline,Hendek (Turkey)

A segment of natural gas pipeline was damaged due to landsliding near Hendek. Re-routing of the pipeline is planned, but it requires the preparation of a landslide susceptibility map. In this study, the statistical index (Wi) and weighting factor (Wf) methods have been used with GIS to prepare a landslide susceptibility map of the problematic segment of the pipeline. For this purpose, thematic layers including landslide inventory, lithology, slope, aspect, elevation, land use/land cover, distance to stream, and drainage density were used. In the study area, landslides occur in the unconsolidated to semi-consolidated clayey unit and regolith. The Wf method gives better results than the Wi method. Lithology is found to be the most important aspect in the study area. Based on the findings obtained in this study, the unconsolidated to semi-consolidated clayey unit and alluvium should be avoided during re-routing. Agricultural activities should not be allowed in the close vicinity of the pipeline.     

A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, east Nepal

Landslides are one of the major natural disasters that occur in the Himalayan range with recurring frequency, causing enormous loss of life and property every year. Preparation of landslide inventory maps and landslide susceptibility zonation maps are the important tasks to be taken into account initially for safe mitigation measures. The present paper focuses on landslide susceptibility maps of the Ghurmi–Dhad Khola area, east Nepal, using Geographic Information System. For this purpose, the landslide susceptibility maps are prepared by using the heuristic and bivariate statistical methods. The parameters considered for the study are slope angle, slope aspect, elevation, distance from drainage, geology, land cover, rock and soil type, and distance from faults and folds. The landslide susceptibility zonation map produced from the heuristic method shows that 42.59 % of the observed landslide falls under the very high susceptible zone and 33.00 % under the high susceptible zone. Likewise, the landslide susceptibility zonation map produced from the bivariate method depicts that 44.19 % of the observed landslide falls under the very high susceptible zone and 31.59 % under the high susceptible zone. Both the landslide susceptibility zonation maps are identical, and success rates of both the maps are above 80 %. While comparing the landslide susceptibility maps obtained from two different methods, about 78 % of the study area falls in the identical susceptible zones. Special attention should be taken into consideration for the construction works in the areas which have been spatially agreed as very high and high susceptible zones from both techniques. Moreover, these maps can be used for slope management, land use planning, disaster management planning, etc., by the concerned authorities.     

Climatic and anthropogenic drivers of land use/cover change in fragile karst areas of southwest China since the early 1970s: a case study on the Maotiaohe watershed

Land use/cover change and its driving forces has been one of the most important fields in global environmental change research since the 1990s. Karst areas are distributed extensively on the Earth’s surface and are usually characterized by a fragile eco-environment. In southwest China, karst landforms are fully developed and their eco-environment is highly fragile. Over the past decades, irrational land use practice has caused a series of alarming eco-environmental issues including forest clearing, soil erosion, and karst rocky desertification. Therefore, it is of great practical significance to study land use/cover change in this area and its driving forces in order to re-build the damaged eco-environment and achieve sustainable land use. In this paper, the authors conduct a case study on land use/cover change and its natural and human driving forces since the early 1970s in southwest China’s Maotiaohe watershed. The results indicate that the land use/cover pattern in the study area has undergone a very complex change, which is a result of combined action of both natural and anthropological factors. In the 1970s and 1980s, climate change and fast population increase played dominating roles in the change of arable land, shrub land, grassland, and rocky desertification land. Since the early 1990s, economic development has gradually taken the place of population change to become the overwhelming human factor to go along with climate change in driving the land use/cover change, particularly the change in arable land, construction land, and rocky desertification land.     

A small-scale landslide susceptibility assessment for the territory of Western Carpathians

This study presented herein compares the bivariate and multivariate landslide susceptibility mapping methods and presents the landslide susceptibility map of the territory of Western Carpathians in small scale. This study also describes pioneer work for the territory of Western Carpathians, overreaching state borders, using verified sophisticated statistical methods. In the susceptibility mapping, digital elevation model was first constructed using a GIS software, and parameter maps affecting the slope stability such as geology, seismicity, precipitation, topographical elevation, slope angle, slope aspect and land cover were considered. In the last stage of the analyses, landslide susceptibility maps were produced using bivariate and multivariate analyses, and they were then compared by means of their validations. The validation of the bivariate analysis data was performed using the results of bivariate analysis for landslide areas of Slovakia containing five classes of susceptibility in scale 1:500,000. The validation area is the area of Western Carpathians within Slovakia. Eighty-two per cent of area does not differ in more than one class. The validation of the multivariate analysis data was performed using the results from the Kysuce region in the northern part of Slovakia in scale 1:10,000. The raster calculator was used to express the difference between each pair of pixels within these two layers. Seventy-seven per cent of the pixels do not differ in more than 25 %, 94 % of the pixels do not differ in more than 50 %. The maximal possible difference is 100 % (one pixel with value 0 and other with value 1, or vice versa). Receiver operating characteristic analysis was also performed, the area under curve value for bivariate model was calculated to be 0.735, while it was 0.823 for multivariate. The results of the validation can be considered as satisfactory.     

Optical satellite imagery for quantifying spatio-temporal dimension of physical exposure in disaster risk assessments

This work addresses the use of remote sensing imagery to quantify the built environment and its spatial and temporal changes. It identifies building footprint map, building location map and built-up area map as information products that can be used to quantify physical exposure, one of the variables required in disaster risk assessments. The paper also reviews urban land use maps and urban classes in land cover maps as potential source for deriving exposure information. The paper focuses on the latest generation of satellite-borne remote sensing imaging systems that deliver high-resolution optical imagery able to resolve buildings and other three-dimensional man-made constructions. This work also reviews the semantics, the spatial unit used to define physical exposure, image processing procedures and change techniques.     

Landslide Susceptibility Mapping Using GIS-based Fuzzy Logic and the Analytical Hierarchical Processes Approach: A Case Study in Constantine (North-East Algeria)

The main purpose of this study was to compare and evaluate the performance of two multicriteria models for landslide susceptibility assessment in Constantine, north-east of Algeria. The landslide susceptibility maps were produced using the analytic hierarchy process (AHP) and Fuzzy AHP (FAHP) via twelve landslides conditioning factors, including the slope gradient, lithology, land cover, distance from drainage network, distance from the roads, distance from faults, topographic wetness index, stream power index, slope curvature, Normalized Difference Vegetation Index, slope aspect and elevation. In this study, the mentioned models were used to derive the weighting value of the conditioning factors. For the validation process of these models, the receiver operating characteristic analysis, and the area under the curve (AUC) were applied by comparing the obtained results to The landslide inventory map which prepared using the archives of scientific publications, reports of local authorities, and field survey as well as analyzing satellite imagery. According to the AUC values, the FAHP model had the highest value (0.908) followed by the AHP model (0.777). As a result, the FAHP model is more consistent and accurate than the AHP in this case study. The outcome of this paper may be useful for landslide susceptibility assessment and land use management.