Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP)

In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ??M??) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919.     

IRS-LISS-III and PAN data analysis for landslide susceptibility mapping using heuristic approach in active tectonic region of Himalaya

Structurally disturbed zones of Himalaya are among the worst landslide affected regions in the world. Although landslides are induced/triggered either by torrential rain during monsoon or by seismic activity in the region, the inherent terrain conditions characterize the prevailing basic conditions susceptible to landslides. Using remotely sensed data and Geographic Information System (GIS), geological and terrain factors can be integrated for preparation of factor maps and demarcation of areas susceptible to landslides. Moderate to high resolution data products available from Indian Remote Sensing satellites have been utilized for deriving geological and terrain factor maps, which were integrated using knowledge driven heuristic approach in Integrated Land and Water Information System (ILWIS) GIS. The resultant map shows division of the area into landslide susceptibility classes ranked in terms of hazard potential in one of the structurally disturbed zones in western Himalaya around Rishikesh.     

Remote Sensing and GIS Based Landslide Susceptibility Assessment using Binary Logistic Regression Model: A Case Study in the Ganeshganga Watershed, Himalayas

A comprehensive Landslide Susceptibility Zonation (LSZ) map is sought for adopting any landslide preventive and mitigation measures. In the present study, LSZ map of landslide prone Ganeshganga watershed (known for Patalganga Landslide) has been generated using a binary logistic regression (BLR) model. Relevant thematic layers pertaining to the causative factors for landslide occurrences, such as slope, aspect, relative relief, lithology, tectonic structures, lineaments, land use and land cover, distance to drainage, drainage density and anthropogenic factors like distance to road, have been generated using remote sensing images, field survey, ancillary data and GIS techniques. The coefficients of the causative factors retained by the BLR model along with the constant have been used to construct the landslide susceptibility map of the study area, which has further been categorized into four landslide susceptibility zones from high to very low. The resultant landslide susceptibility map was validated using receiver operator characteristic (ROC) curve analysis showing an accuracy of 95.2 % for an independent set of test samples. The result also showed a strong agreement between distribution of existing landslides and predicted landslide susceptibility zones.     

Delineation and parameterization of banikdih watershed using remote sensing and avswat model

Delineation of Banikdih Agricultural watershed in Eastern India was carried out and various watershed parameters were extracted using Geographic Information System (GIS) and Remote Sensing. Digital Elevation Model (DEM) was developed with a contour interval of 10 m in the scale of 1:25000 using ARC/INFO modules. Sub watershed, drainage, slope, aspect, flow direction, soil series, soil texture, and soil class maps were independently generated and they were properly registered and integrated for analysis. The watershed was digitally delineated using AVSWAT model that couples hydrological model and GIS with appropriate threshold value of cell size. Subsequently, stream characteristics through the interface were generated. Indian Remote Sensing Satellite IRS-1D LISS-III data pertaining to the period of October 29, 1998 and October 23, 2000 was used to develop land use/land cover thematic map using ERDAS- 8.4 version image processing software. Eight major land use/land cover classes namely water body, lowland paddy, upland paddy, fallow land, upland crop (non-paddy crops), settlement, open mixed forest, and wasteland were segregated through digital image processing techniques using maximum likelihood algorithm. The information generated would be of immense help in hydrological modeling of watershed for prediction of runoff and sediment yield, thereby providing necessary inputs for developing suitable developmental management plans with sound scientific basis.     

遥感和GIS技术应用于伊拉克南部Basrah省土地利用/覆盖变化及城市扩张研究(英文)

In recent years, land use/cover dynamic change has become a key subject that needs to be dealt with in the study of global environmental change. In this paper, remote sensing and geographic information systems (GIS) are integrated to monitor, map, and quantify the land use/cover change in the southern part of Iraq (Basrah Province was taken as a case) by using a 1:250 000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation, sand, urban area, unused land, and water bodies. Supervised classification and normalized difference build-up index (NDBI) were used respectively to retrieve its urban boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. Results showed that the urban area had increased by the rate of 1.2% per year, with area expansion from 3 299.1 km² in 1990 to 3 794.9 km² in 2003. Large vegetation area in the north and southeast were converted into urban construction land. The land use/cover changes of Basrah Province were mainly caused by rapid development of the urban economy and population immigration from the countryside. In addition, the former government policy of “returning farmland to transportation and huge expansion in military camps” was the major driving force for vegetation land change. The paper concludes that remote sensing and GIS can be used to create LULC maps. It also notes that the maps generated can be used to delineate the changes that take place over time. Supported by the Al-Basrah University, Iraq, the Geo-information Science and Technology Program (No. IRT 0438)China).     

Application of frequency ratio,weights of evidence and evidential belief function models in landslide susceptibility mapping

The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.     

Urban land use mapping and zoning of Bombay Metropolitan Region using remote sensing data

Bombay Metropolitan Region covering an area of about 4,360 sq. km. was selected for urban land use studies and for urban land use zoning. Urban land use mapping was carried out using SPOT multispectral linear array imagery on 1∶25,000 scale employing visual analysis tehcniques. Fifteen maps were prepared depicting the spatial distribution of various urban classes in the Greater Bombay and New Bombay regions. Sixteen urban land use maps were also prepared using Landsat TM data showing the distribution of land use pattern on 1∶50,000 scale for the entire metropolitan region. Urban land use zoning was carried out based upon suitability index on 1∶250,000 scale. This map provides information on the areas to be used for construction and areas to be kept under green belt in the metropolitan region. This study is a joint venture of Space Applications Centre with Bombay Metropolitan Development authority.     

Landslide Mapping and Assessment by Integrating Landsat-8, PALSAR-2 and GIS Techniques: A Case Study from Kelantan State,Peninsular Malaysia

Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments.     

Landslide susceptibility assessment using evidential belief function,certainty factor and frequency ratio model at Baxie River basin,NW China

This study evaluates and compares landslide susceptibility maps of the Baxie River basin, Gansu Province, China, using three models: evidential belief function (EBF), certainty factor (CF) and frequency ratio (FR). First, a landslide inventory map is constructed from satellite image interpretation and extensive field data. Second, the study area is partitioned into 17,142 slope units, and modelled using nine landslide influence parameters: elevation, slope angle, slope aspect, relief amplitude, cutting depth, gully density, lithology, normalized difference vegetation index and distance to roads. Finally, landslide susceptibility maps are presented based on EBF, CF and FR models and validated using area under curve (AUC) analysis. The success rates of the EBF, CF and FR models are 0.8038, 0.7924 and 0.8088, respectively, while the prediction rates of the three models are 0.8056, 0.7922 and 0.7989, respectively. The result of this study can be reliably used in land use management and planning.     

Macro Landslide Hazard Zonation Mapping - Case Study from Bodi – Bodimettu Ghats Section,Theni District,Tamil Nadu - India

Landslide is a common natural hazard that usually occurs in mountainous areas. Rapid urban development and high traffic intensity movements have been hampered to a great extent by phenomenon of landslides. In Ghat section, vertical cuttings and steep slopes are induced slope failures. An assessment of landslide hazards is therefore a prerequisite for sustainable development of the hilly region. In the present study, Macro Landslide Hazard Zonation was carried out in the Bodi – Bodimettu ghats section, Western Ghats, Theni district. The slope spreads over an area of about 10.09 sq km encompassing Puliuttu Ar. sub-watershed. The study was made with help of different types of data including Survey of India topographic map, geology map, important inherent factors like lithology, structure, slope morphometry, relative relief, land use/land cover and hydrogeological conditions using Bureau of Indian Standard (BSI 14496 (Part 2):1998) and related thematic maps. Based on the thematic layers, landslide hazard evaluation factor (LHEF) and total estimated hazard (TEHD) were calculated and the macro hazard zonation map was prepared. Landslide Hazard Zonation (LHZ) of the terrain shows that out of 17 facets, facets 1 to 5 and 8 falls under Moderate Hazard zone category and facets 6, 7 and 9 to 17 under the High Hazard zone category. The field study with further analysis for hazard concluded that about 68% of the total area falls in the high hazard zone.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

Approaches for delineating landslide hazard areas using different training sites in an advanced artificial neural network model

The current paper presents landslide hazard analysis around the Cameron area, Malaysia, using advanced artificial neural networks with the help of Geographic Information System (GIS) and remote sensing techniques. Landslide locations were determined in the study area by interpretation of aerial photographs and from field investigations. Topographical and geological data as well as satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten factors were selected for landslide hazard including: 1) factors related to topography as slope, aspect, and curvature; 2) factors related to geology as lithology and distance from lineament; 3) factors related to drainage as distance from drainage; and 4) factors extracted from TM satellite images as land cover and the vegetation index value. An advanced artificial neural network model has been used to analyze these factors in order to establish the landslide hazard map. The back-propagation training method has been used for the selection of the five different random training sites in order to calculate the factor’s weight and then the landslide hazard indices were computed for each of the five hazard maps. Finally, the landslide hazard maps (five cases) were prepared using GIS tools. Results of the landslides hazard maps have been verified using landslide test locations that were not used during the training phase of the neural network. Our findings of verification results show an accuracy of 69%, 75%, 70%, 83% and 86% for training sites 1, 2, 3, 4 and 5 respectively. GIS data was used to efficiently analyze the large volume of data, and the artificial neural network proved to be an effective tool for landslide hazard analysis. The verification results showed sufficient agreement between the presumptive hazard map and the existing data on landslide areas.     

土地利用基础图件更新技术体系研究与软件系统构建

全面、现势的土地利用基础图件是一个国家摸清其土地资源家底,进行土地资源合理利用、规划和科学管理的基础和前提条件。本文从数据源、处理手段与采用的技术方法对土地利用基础图件更新的技术体系进行了研究,基于图件更新技术流程的三个主要环节对图件更新方法进行了系统分析和分类总结,继而提出了一套集RS/GIS/GPS技术于一体的图件更新技术方法与流程;同时依托具有自主知识产权的遥感数据处理平台CASM Im-ageInfo,将整个作业流程一体化,构建了适合我国具体国情的实用和可操作性强的土地利用基础图件更新软件系统,实现了土地利用基础图件的快速更新。     

Use of geo-spatial information and technologies for analyzing morphological changes in the Greater Visakhapatnam coastal region,east coast of India

The objective of this study was to investigate the land use/land cover, landforms, shoreline and coastal regulation zone (CRZ) changes of Greater Visakhapatnam coastal region using Indian Remote Sensing-IRS P6 (Resourcesat-I) satellite data and collateral information. Prominent changes have been observed during the past 30 years through land use/land cover analyzes which clearly indicate that thecoastal regulatory zones have altered in respect of both natural and anthropogenic activities. Various geomorphic units were identified and confirmed with appropriate field work. Significant changes have been recognized in the shoreline map, which denote that the area of erosional shoreline is larger than the accretional and stable prone shoreline. The availability of high resolution data has helped to prepare large-scale maps for implementation of CRZ measures. The results were promising and suggest that the modern geo-spatial information and technological tools are extremely helpful for conducting coastal morphological studies.     

Integrated approach of using remote sensing and GIS to study watershed prioritization and productivity

Soil data obtained from soil resource inventory, land and climate were derived from the remote sensing satellite data (Landsat TM, bands 1 to 7) and were integrated in GIS environment to obtain the soil erosion loss using USLE model for the watershed area. The priorities of different sub-watershed areas for soil conservation measures were identified. Land productivity index was also used as a measure for land evaluation. Different soil and land attribute maps were generated in GIS, and R,K,LS,C and P factor maps were derived. By integrating these soil erosion map was generated. The mapping units, found not suitable for agriculture production, were delineated and mapped as non-arable land. The area suitable for agricultural production was carved out for imparting the productivity analysis; the land suitable for raising agricultural crops was delineated into different mapping units as productivity ratings good, fair, moderate and poor. The analysis performed using remote sensing and GIS helped to generate the attribute maps with more accuracy and the ability of integrating these in GIS environment provided the ease to get the required kind of analysis. Conventional methods of land evaluation procedures in terms of either soil erosion or productivity are found not comparable with the out put generated by using remote sensing and GIS as the limitations in generating the attribute maps and their integration. The results obtained in this case study show the use of different kinds of data derived from different sources in land evaluation appraisals.     

Shallow Landslide Susceptibility Mapping Using Stereo Air Photos and Thematic Maps

Overlay (or heuristic) models have been shown to be a reasonable method to generate landslide susceptibility maps. However, overlay models are subjective and rely on expert judgment. The method developed here employs an overlay model but it tries to estimate certain parameters, such as slope angle or distance to streams, in a more quantitative manner. Each individual factor believed to have an influence on mass wasting processes is scaled to a value between 0 and 1. All different factors are then multiplied, producing the degree of stability, D_f, which lies on a ratio scale, rather than a nominal or interval scale. Thus, a pseudo probability of failure can be obtained. The data required are derived partly from thematic maps and partly from stereo air photos. The air photo stereo pair is used to automatically derive a digital elevation model which is then used to create an orthoimage of an area. The air photos are also used to develop a land use map, an important component in estimating landslide susceptibility. This methodology was tested on a section of the Santa Monica Mountains near Malibu, California. The resulting map both visually and statistically appears to successfully identify problematic areas. Although the methodology was primarily developed to identify shallow slides, the crest of a large, deep-seated landslide was also recognized. The general methodology should be easily adaptable to other regions or could be applied with a different set of factors than those considered in this specific case.     

Identification of Water Conservation Sites in a Watershed (WRJ-2) of Nagpur District, Maharashtra using Geographical Information System (GIS) Technique

The paper deals with the application of Remote Sensing and Geographical Information System (GIS) technique for a watershed development program. For this study, the WRJ-2 watershed falling under Narkhed and Katol Tahsils of Nagpur district, Maharashtra, India is investigated. Various thematic maps (i.e. drainage, geology, soil, geomorphology and land use/ land cover) have been prepared using the remote sensing and GIS techniques. Initially, differential weightage values are assigned to all the thematic maps as per their runoff characteristics. Subsequently, the maps are integrated in GIS environment to identify potential sites for water conservation measures like gully plugs, earthen check dams, continuous contour trenches, percolation tanks, cement bandhara, afforestration and farm ponds, etc. The study depicts that the GIS technique facilitates integration of thematic maps and thereby helps in an identification of micro-zones each with unique characters in-terms of hydrogeology, thus amenable to specific water conservation techniques. It is therefore concluded that, the GIS technique is suitable for an identification of water conservation structures.     

The assessment of potential land use in the proposed irrigation command using Remote Sensing and GIS

Standard false colour composites (Std. FCC) on 1:50,000 scale was visually interpreted in conjunction with soil survey to prepare physiographic-soil map. Thirteen mapping units were delineated indicating soil association at family-level. Soil and land resource was evaluated for their land capability and irrigation suitability for its sustained use under irrigation. Land capability and land irrigability maps were generated as attribute map. These maps were integrated to suggest potential land use map. Current land use/land cover map prepared by visual analysis was spatially analysed in relation to potential land use to study potential changes in land use / land cover using GIS. The study reveals that 14.66% area has no limitation and can be brought to intensive agriculture by double cropping.     

3S集成技术在城镇土地利用变更调查中的应用研究

城镇地区由于建筑物密集、土地利用类型变化快、土地利用现状图比例尺较大等特点,土地利用变更调查主要依靠专业部门利用全站仪完成,但全站仪需要通视的特点限制了其工作效率。而采用3S集成技术进行城镇地区土地利用变更调查的技术方法,则可以相应提高工作效率。基于3S集成技术城镇地区土地利用变更调查的应用模式为:通过GPS接收机与笔记本电脑(已配置GIS软件系统)通讯,实现GPS与GIS的集成,并调用RS影像作为底图,既可实现实时的数据采集、存储和编辑。对于城镇地区部分GPS信号弱的地区,采用测边交会的方法则可以完成这些点的数据采集,克服了GPS使用盲区的限制。     

Assessment of Seismically Induced Landslide Hazard for the State of Karnataka Using GIS Technique

Landslide hazards are a major natural disaster that affects most of the hilly regions around the world. In India, significant damages due to earthquake induced landslides have been reported in the Himalayan region and also in the Western Ghat region. Thus there is a requirement of a quantitative macro-level landslide hazard assessment within the Indian subcontinent in order to identify the regions with high hazard. In the present study, the seismic landslide hazard for the entire state of Karnataka, India was assessed using topographic slope map, derived from the Digital Elevation Model (DEM) data. The available ASTER DEM data, resampled to 50 m resolution, was used for deriving the slope map of the entire state. Considering linear source model, deterministic seismic hazard analysis was carried out to estimate peak horizontal acceleration (PHA) at bedrock, for each of the grid points having terrain angle 10° and above. The surface level PHA was estimated using nonlinear site amplification technique, considering B-type NEHRP site class. Based on the surface level PHA and slope angle, the seismic landslide hazard for each grid point was estimated in terms of the static factor of safety required to resist landslide, using Newmark’s analysis. The analysis was carried out at the district level and the landslide hazard map for all the districts in the Karnataka state was developed first. These were then merged together to obtain a quantitative seismic landslide hazard map of the entire state of Karnataka. Spatial variations in the landslide hazard for all districts as well as for the entire state Karnataka is presented in this paper. The present study shows that the Western Ghat region of the Karnataka state is found to have high landslide hazard where the static factor of safety required to resist landslide is very high.