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1.
This study applied, tested and compared a probability model, a frequency ratio and statistical model, a logistic regression to Damre Romel area, Cambodia, using a geographic information system. For landslide susceptibility mapping, landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and a spatial database was constructed from topographic maps, geology and land cover. The factors that influence landslide occurrence, such as slope, aspect, curvature and distance from drainage were calculated from the topographic database. Lithology and distance from lineament were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite imagery. The relationship between the factors and the landslides was calculated using frequency ratio and logistic regression models. The relationships, frequency ratio and logistic regression coefficient were overlaid to make landslide susceptibility map. Then the landslide susceptibility map was compared with known landslide locations and tested. As the result, the frequency ratio model (86.97%) and the logistic regression (86.37%) had high and similar prediction accuracy. The landslide susceptibility map can be used to reduce hazards associated with landslides and to land cover planning.  相似文献   

2.
For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.  相似文献   

3.
Quantitative landslide susceptibility mapping at Pemalang area,Indonesia   总被引：3，自引：0，他引：3  
For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression, and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database. Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used to reduce hazards associated with landslides and to land-use planning.  相似文献   

4.
The purpose of this study is to detect landslide locations from satellite images and use them for landslide susceptibility mapping in the Sagimakri area, Korea using a geographic information system and a data-driven weight of evidence model. The landslide location areas were identified from Korea multipurpose satellite images by means of change detection technique and further verified by extensive field survey. Subsequently, landslide locations were randomly selected in a 70:30 ratio for training and validation of the model, respectively. A spatial database was constructed, which is composed of topography, forest, soil, and land cover, and 14 landslide-related factors were extracted from the database. The relationships between the detected landslide locations and the factors were identified and quantified by weights of evidence model. Tests of conditional independence were performed for the selection of factors, allowing five different combinations of factors to be analyzed. The relationships were used as the contrast values, W + and W ? of factor ratings in the overlay analysis to create landslide susceptibility indexes and maps. The results of the analysis were validated by comparison with known landslide locations that were not used directly in the analysis.  相似文献   

5.
This study is aimed at the evaluation of the hazard of soil erosion and its verification at Boun, Korea, using a Geographic Information System (GIS) and remote sensing. Precipitation, topographic, soil, and land use data were collected, processed, and constructed into a spatial database using GIS and remote sensing data. Areas that had suffered soil erosion were analysed and mapped using the Universal Soil Loss Equation (USLE). The factors that influence soil erosion are rainfall erosivitiy (R) from the precipitation database, soil erodibility (K) from the soil database, slope length and steepness (LS) from the topographic database, and crop and management (C) and conservation supporting practices (P) from the land use database. Land use was classified from Landsat Thematic Mapper satellite images. The soil erosion map verified use of the landslide location data. Landslide locations were identified in the Boun area from interpretation of aerial photographs and field surveys.  相似文献   

6.
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.  相似文献   

7.
Probabilistic landslide susceptibility and factor effect analysis   总被引：18，自引：0，他引：18  
The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the geographic information system (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from 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 were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat Thermatic Mapper (TM) satellite images; and the vegetation index value from SPOT HRV (High-Resolution Visible) satellite images. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors employing the probability–frequency ratio method using the all factors. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, all factors had relatively positive effects, except lithology, on the landslide susceptibility maps in the study area.  相似文献   

8.
The logistic regression and statistical index models are applied and verified for landslide susceptibility mapping in Daguan County, Yunnan Province, China, by means of the geographic information system (GIS). A detailed landslide inventory map was prepared by literatures, aerial photographs, and supported by field works. Fifteen landslide-conditioning factors were considered: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, STI, SPI, and TWI were derived from digital elevation model; NDVI was extracted from Landsat ETM7; rainfall was obtained from local rainfall data; distance to faults, distance to roads, and distance to rivers were created from a 1:25,000 scale topographic map; the lithology was extracted from geological map. Using these factors, the landslide susceptibility maps were prepared by LR and SI models. The accuracy of the results was verified by using existing landslide locations. The statistical index model had a predictive rate of 81.02%, which is more accurate prediction in comparison with logistic regression model (80.29%). The models can be used to land-use planning in the study area.  相似文献   

9.
Gimbarzevsky (1988) collected an exceptional landsliding inventory for Haida Gwaii, British Columbia that included over 8,000 landsliding vectors covering an area of approximately 10,000 km2. This database was never published in the referred literature, despite its regional significance. It was collected prior to widespread application of GIS technologies in landsliding studies, limiting the analyses undertaken at the time. Gimbarzevsky identified landslides using 1:50,000 aerial photographs, and transferred the information to NTS map sheets. In our study, we digitized the landslide vectors from these original map sheets and connected each landslide to a digital elevation model. Lengths of landslide vectors are compared to the landsliding inventory for Haida Gwaii analyzed in Rood (1984), Martin Y et al. BC Can J Earth Sci 39:289–305 (2002); the latter inventory is based on larger-scale aerial photographs (~1:12,000). Rood’s database contains a more complete record of smaller landslides, while the inventory of Gimbarzevsky provides improved statistical representation of less frequent, medium to large landslides. It is suggested that combined landslide delineation at different scales could provide a more complete landslide record. Discriminant analysis was undertaken to assess which of nine predictor variables, chosen on the basis of mechanical theory, best predict failed versus unfailed locations. Seven of the nine variables were found to be statistically significant in discriminating amongst failed and unfailed locations. Results show that 81.7% of original grouped cases were correctly classified.  相似文献   

10.
Statistical analysis of landslide susceptibility at Yongin, Korea   总被引：35，自引：1，他引：35  
The aim of this study is to evaluate the susceptibility of landslides at Yongin, Korea, using a geographic information system (GIS). Landslide locations were identified in the Yongin area from interpretation of aerial photographs, field surveys, and maps of the topography, soil type, timber cover, and geology. These data were collected and constructed into a spatial database using GIS. The factors that influence landslide occurrence, such as slope, aspect, and curvature of topography, were calculated from the topographic database. Texture, material, drainage, and effective soil thickness were extracted from the soil database, and type, age, diameter, and density of timber were extracted from the forest database. Lithology was extracted from the geological database, and land use was classified from the Landsat TM satellite image. Landslide susceptibility was analyzed using the landslide occurrence factors by probability and logistic regression methods. The results of the analysis were verified using the landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide location. The GIS was used to analyze the vast amount of data efficiently, and statistical programs were used to maintain specificity and accuracy. The results can be used to reduce associated hazards, and to plan land use and construction.  相似文献   

11.
We apply the logic of clinical epidemiological studies to quantify the accuracy of mapping sinkholes by ALSM in the 750 km2 Pinellas County. By such studies, a new diagnostic procedure is tested by comparing the diagnoses in a clinical trial to diagnoses on the same patients from a more reliable, but more elaborate and expensive procedure (“gold standard” in epidemiological context). A relatively undeveloped, 65 km2 focus area where we have aerial photographs that are effectively contemporaneous with the ALSM flights serves as the “clinical trial”. The xy-locations in the focus area are the “patients” in the trial. The “diagnostic test” for having “sinkhole disease” is inclusion in a database of sinkhole polygons delimited by ALSM contours (“ALSM-alone”), as detailed in Part 1. The standard of comparison (“gold standard” would be an overstatement in the absence of geophysical testing) is inclusion in a database of sinkhole outlines derived by best judgment of conjunctive interpretation of ALSM and aerial photography. GIS intersections that indicate the sensitivity and specificity of the test (ALSM-alone) are 43 and 98.3%, respectively, and, in the focus area where the prevalence of “sinkhole disease” is 4.7%, the positive and negative predictive values are 55.5 and 97.2%, respectively. Over much of the rest of the county, where only the test can be applied, the prevalence of sinkholes is sufficiently small that it cannot be determined to be any different from zero given the paucity of interpreted sinkholes (positive diagnoses) and the low specificity of the test method. The conclusion, therefore, is that contemporaneous aerial photography is essential to compile an ALSM-derived database that aims to state that the given xy-points lay inside or outside of topographic depressions in the covered karst of west-central Florida.  相似文献   

12.
This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.  相似文献   

13.
Landslide susceptibility zonation mapping assists researchers greatly to understand the spatial distribution of slope failure probability in a region. Being extremely useful in reducing landslide hazards, such maps could simply be produced using both qualitative and quantitative methods. In the present study, a multivariate statistical method called ‘logistic regression’ was used to assess landslide susceptibility in Hashtchin region, situated in west of Alborz Mountainsnorthwest of Iran. In this study, two independent variables, categorical (predictor) and continuous, were drawn on together in the model. To identify the region’s landslides use was made of aerial photographs, field studies and topographic maps. To prepare the database of factors affecting the region’s landslides and to determine landslide zones, geographic information system (GIS) was used. Using such information, landslide susceptibility modeling was accomplished. The data related to factors causing landslides were extracted as independent variables in each cell (in 50 m×50 m cells). Then, the whole data were input into the SPSS, Version 18. The prepared database was later analyzed using logistic regression, the forward stepwise method and based on maximum likelihood estimation. Regression equation was determined using obtained constants and coefficients and the landslide susceptibility of the area in grid-cells (pixels) was computed between 0 and 0.9954. The Receiver Operating Characteristic (ROC) curve was used to assess the accuracy of the logistic regression model. The predicting ability of the model was 84.1% given the area under ROC curve. Finally, the degree of success of landslide susceptibility zonation mapping was estimated to be 79%.  相似文献   

14.
As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.  相似文献   

15.
用光学遥感数据和地理信息系统(GIS)分析了马来西亚Selangor地区的滑坡灾害。通过遥感图像解译和野外调查,在研究区内确定出滑坡发生区。通过GIS和图像处理,建立了一个集地形、地质和遥感图像等多种信息的空间数据库。滑坡发生的因素主要为:地形坡度、地形方位、地形曲率及与排水设备距离;岩性及与线性构造距离;TM图像解译得到的植被覆盖情况;Landsat图像解译得到的植被指数;降水量。通过建立人工神经网络模型对这些因素进行分析后得到滑坡灾害图:由反向传播训练方法确定每个因素的权重值,然后用该权重值计算出滑坡灾害指数,最后用GIS工具生成滑坡灾害图。用遥感解译和野外观测确定出的滑坡位置资料验证了滑坡灾害图,准确率为82.92%。结果表明推测的滑坡灾害图与滑坡实际发生区域足够吻合。  相似文献   

16.
Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia   总被引：15，自引：0，他引：15  
This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.  相似文献   

17.
The 1999 Chi-Chi earthquake triggered the catastrophic Tsaoling landslide in central Taiwan. We mapped the landslide area and estimated the landslide volume, using high-resolution digital elevation model from airborne LiDAR (Light Detection and Ranging), satellite images, aerial photographs and topographic maps. The comparison between cut and fill volumes, about 0.126 and 0.150 km3, respectively, suggests a volume increase of 19% due to decompaction during landsliding. In April 2002, the cut and fill volumes were about 0.137 and 0.116 km3, respectively. These estimates suggest that 2.5 years after the event, the volume of landslide debris removed by river erosion was nearly 0.045 km3. Such a large value highlights the importance of landslide processes for erosion and long-term denudation in the Taiwan mountain belt. To cite this article: R.-F. Chen et al., C. R. Geoscience 337 (2005).  相似文献   

18.
The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.  相似文献   

19.
The aim of this study is to produce landslide susceptibility mapping by probabilistic likelihood ratio (PLR) and spatial multi-criteria evaluation (SMCE) models based on geographic information system (GIS) in the north of Tehran metropolitan, Iran. The landslide locations in the study area were identified by interpretation of aerial photographs, satellite images, and field surveys. In order to generate the necessary factors for the SMCE approach, remote sensing and GIS integrated techniques were applied in the study area. Conditioning factors such as slope degree, slope aspect, altitude, plan curvature, profile curvature, surface area ratio, topographic position index, topographic wetness index, stream power index, slope length, lithology, land use, normalized difference vegetation index, distance from faults, distance from rivers, distance from roads, and drainage density are used for landslide susceptibility mapping. Of 528 landslide locations, 70 % were used in landslide susceptibility mapping, and the remaining 30 % were used for validation of the maps. Using the above conditioning factors, landslide susceptibility was calculated using SMCE and PLR models, and the results were plotted in ILWIS-GIS. Finally, the two landslide susceptibility maps were validated using receiver operating characteristic curves and seed cell area index methods. The validation results showed that area under the curve for SMCE and PLR models is 76.16 and 80.98 %, respectively. The results obtained in this study also showed that the probabilistic likelihood ratio model performed slightly better than the spatial multi-criteria evaluation. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.  相似文献   

20.
The heat of mixing for the binary solid solution diopside–Ca-Tschermak was investigated at T = 980 K by lead borate solution calorimetry. A new statistical technique was applied to overcome the problem of using experimental data of various precisions. A two-parameter Margules model was fitted to the calorimetric data leading to W CaTs–DiH = 31.3 ± 3.4 kJ mol−1 and W H Di–CaTs = 2.4 ± 4.3 kJ mol−1. The results are in good agreement with calorimetric data given in the literature. They agree also with enthalpy data that were extracted from phase equilibrium experiments. With configurational entropy values taken from the literature, the volume and the vibrational entropy, presented in Part I of this work, and the enthalpy data of this study, the activity–composition relationships of the diopside–Ca-Tschermak binary were calculated.  相似文献   

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