Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data

The aim of this study is to evaluate the hazard of landslides at Penang, Malaysia, using a Geographical Information System (GIS) and remote sensing. 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 Thematic Mapper (TM) satellite images; and the vegetation index value from Système Probatoire de l'Observation de la Terre (SPOT) satellite images. Landslide hazardous areas were analysed and mapped using the landslide‐occurrence factors by logistic regression model. The results of the analysis were verified using the landslide location data and compared with probabilistic model. The validation results showed that the logistic regression model is better in prediction than probabilistic model.     

Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley,Malaysia

The purpose of the present paper is to manifest the results of the neuro-fuzzy model using remote sensing data and GIS for landslide susceptibility analysis in a part of the Klang Valley areas i Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. SPOT 5 satellite imagery was used to map vegetation index. Maps of topography, lineaments, NDVI and land cover were constructed from the spatial datasets. Seven landslide conditioning factors such as altitude, slope angle, plan curvature, distance from drainage, soil type, distance from faults and NDVI were extracted from the spatial database. These factors were analyzed using a neuro-fuzzy model (adaptive neuro-fuzzy inference system, ANFIS) to construct the landslide susceptibility maps. During the model development works, total 5 landslide susceptibility models were obtained by using ANFIS results. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the ROC curves for all landslide susceptibility models were drawn and the area under curve values was calculated. Landslide locations were used to validate results of the landslide susceptibility map and the verification results showed 98% accuracy for the model 5 employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed sufficient agreement between the obtained susceptibility map and the existing data on landslide areas. Qualitatively, the model yields reasonable results which can be used for preliminary landuse planning purposes. As a conclusion, the ANFIS is a very useful tool for regional landslide susceptibility assessments.     

Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model

This paper presents the application of remote sensing techniques, digital image analysis and Geographic Information System tools to delineate the degree of landslide hazard and risk areas in the Balik Pulau area in Penang Island, Malaysia. Its causes were analysed through various thematic attribute data layers for the study area. Firstly, landslide locations were identified in the study area from the interpretation of aerial photographs, satellite imageries, field surveys, reports and previous landslide inventories. Topographic, geologic, soil and satellite images were collected and processed using Geographic Information System and image processing tools. There are 12 landslide-inducing parameters considered for the landslide hazard analyses. These parameters are: topographic slope, topographic aspect, plan curvature, distance to drainage and distance to roads, all derived from the topographic database; geology and distance to faults, derived from the geological database; landuse/landcover, derived from Landsat satellite images; soil, derived from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value, derived from SPOT satellite images. In addition, hazard analyses were performed using landslide-occurrence factors with the aid of a statistically based frequency ratio model. Further, landslide risk analysis was carried out using hazard map and socio-economic factors using a geospatial model. This landslide risk map could be used to estimate the risk to population, property and existing infrastructure like transportation networks. Finally, to check the accuracy of the success-rate prediction, the hazard map was validated using the area under curve method. The prediction accuracy of the hazard map was 89%. Based on these results the authors conclude that frequency ratio models can be used to mitigate hazards related to landslides and can aid in land-use planning.     

Extraction of landslide-related factors from ASTER imagery and its application to landslide susceptibility mapping

The aim of this study is to extract landslide-related factors from remote-sensing data, such as Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery, and to examine their applicability to landslide susceptibility near Boun, Korea, using a geographic information system (GIS). Landslide was mapped from interpretation of aerial photographs and field surveying. Factors that influence landslide occurrence were extracted from ASTER imagery. The slope, aspect and curvature were calculated from the digital elevation model (DEM) with 25.77 m root mean square error (RMSE), which was derived from ASTER imagery. Lineaments, land-cover and normalized difference vegetation index (NDVI) layers were also estimated from ASTER imagery. Landslide-susceptible areas were analysed and mapped using the occurrence factors by a frequency ratio and logistic regression model. Validation results were 84.78% in frequency ratio and 84.20% in logistic regression prediction accuracy for the susceptibility map with respect to ground-truth data.     

气象台站环境的卫星遥感调查与评估-以安徽代表气象站为例

气象观测环境的保护与代表性在天气预报和气候变化研究中具有重要意义。利用多时相的高分辨率陆地卫星资料,结合GIS技术,提取了气象台站3 km缓冲区内的土地利用类型,并将其分为四大类:耕地、植被、水体和城建用地。选取了安徽省6个气象站作为代表,分析了其周边土地类型的时空变化特征,并应用于气象站点环境代表性的评价研究。结果表明,基于卫星遥感的气象站点缓冲区内土地利用类型时空演变能很好地应用于气象站的环境代表性调查与评估,且效果良好。遥感结果显示安徽省国家气候基准站的环境代表性较好,而受到城市化影响严重的合肥国家基本站环境代表性较差。此外,上述卫星遥感的气象站环境分析方法也可以有效地应用于城市热岛研究中参考站点的选取与判别。     

Mapping land use/cover distribution on a mountainous tropical island using remote sensing and GIS

The land use/cover distribution on Langkawi Island, Malaysia was mapped using remote sensing and a Geographic Information System (GIS). A Landsat Thematic Mapper (TM) satellite image taken in March 1995 was processed, geocorrected and analysed using IDRISI, raster-based GIS software. An unsupervised classification was performed based on spectral data from a composite image of the bands TM3, TM4 and TM5. Using this output, field data together with available secondary data consisting of topography, land use and soil maps were used to perform a maximum likelihood supervised classification. The overall accuracy of the output image was 90% and individual class accuracy ranged from 74% for rubber to 100% for paddy fields. The classified areas on the image were mainly confined to the mountainous and hilly regions on the island. A shaded relief map, simulating sunshine conditions, showed that the unclassified areas are located in the shadowed slopes, i.e. the slopes facing west. Consequently, the imagery was subdivided on the basis of slope aspect and a stratified classification was performed. As a result of this procedure, the overall accuracy increased to 92% and the individual class accuracy for the inland forest class increased by 9% to 90% . Using IDRISI, individual class areas as well as percentages were calculated. The kappa coefficient for the classified image was 0.90. Qualitative analysis indicates that topography is the main control on the spatial distribution of land use/cover types on the island. As Langkawi Island has been developing rapidly over the last decade, successful planning will require reliable information about land use/cover distribution and change. This study illustrates that remote sensing and GIS techniques are capable of providing such information.     

基于RS与GIS的大尺度水土流失分级制图

以长春市双阳区鹿乡镇为例，选用SPOT5的遥感数据、DWG全要素地形图，运用遥感软件提取植被指数并进行密度分割，确定植被覆盖度；用地理信息系统软件提取地形坡度信息，并对地形坡度进行分级。然后根据植被覆盖度、地形坡度和土地利用类型几个水土流失强度因子，确定水土流失强度的分级指标，生成水土流失等级强度图。为水土流失监测、治理提供依据。
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Evaluating temporal changes using Multi-Spectral Scanner and Thematic Mapper data on the landscape of a natural reserve: The New Jersey Pine Barrens,a case study

Natural disturbance suppression and anthropogenic perturbations have altered the composition and structure of the New Jersey Pinelands National Reserve (NJPNR). The combination of satellite remote sensing imagery and GIS provided the means to map and monitor land cover change at landscape level scales in the NJPNR. The Pinelands has experienced a change in landcover, with the mixed deciduous forest replacing the pine forest community.     

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity — true positive fraction values calculated for different threshold values, versus model specificity — true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.     

Detection of landslides using web-based aerial photographs and landslide susceptibility mapping using geospatial analysis

The purpose of this study is to detect landslide locations using web-based digital aerial photographs and to map landslide susceptibility using landslide locations in Jinbu, Korea. The landslide susceptibility map was generated and validated using frequency ratio, weight of evidence, logistic regression and artificial neural network models with a geographic information system (GIS). The landslide locations were identified in the study area from interpretation of digital aerial photographs that were provided on an Internet portal (http://map.daum.net) and checked by field survey. A spatial database of the topography, soil, forest, geology and land use was constructed and landslide-related factors were extracted. Using these factors, landslide susceptibility was analysed using four models. Seventy percent of the landslides were used in landslide susceptibility mapping and the remaining 30% were used for validation. The validation result showed that the frequency ratio, weight of evidence, logistic regression and artificial neural network models had 84.94%, 82.82%, 87.72% and 81.44% accuracies, respectively, representing an overall satisfactory agreement of more than 80%, with the logistic regression model giving the best result. The maps generated could be used to estimate the risk to population, property and existing infrastructure such as the transportation network.     

基于多特征面向对象区域滑坡现象识别

高空间分辨率卫星遥感数据的发展为滑坡灾害数据获取和更新提供了新的途径。以西北黄土高原为研究区,提出了一种基于多特征面向对象区域滑坡现象的识别方法,基于单期高空间分辨率遥感数据,利用集合和特征组合进行区域滑坡现象识别,实验结果表明:该方法是识别滑坡现象有效的技术方法之一,对开展滑坡监测、影像理解和地学分析具有重要的研究意义。     

Soil moisture estimation using MODIS and ground measurements in eastern China

Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.     

Land cover inventory in the Netherlands using remote sensing; application in a soil and groundwater vulnerability assessment system

Abstract

Information on actual land cover is necessary for various applications, such as soil and groundwater protection studies and hydrological studies. Therefore, a decision to produce a national land cover data base of the Netherlands, using satellite images, was made. The first version of the data base is now available for the whole of the Netherlands. Prior to the supervised classification the area was stratified in more or less homogeneous areas. Because cost, time and logistics required for a random sampling of the entire country were prohibitive, a mixed quantitative/qualitative classification accuracy assessment procedure was proposed. Classification performances were quantitatively assessed by comparing the classification results with digitized ground reference maps using a Geographical Information System (GIS). This offers a flexible method for locating the incorrectly labelled pixels and determining the possible reasons thereof. Classification accuracy of the land cover types which do not change much in time was also qualitatively assessed, using aerial photographs and topographical maps. The land cover data derived from remote sensing images can be readily combined with other digitized geographical data bases (e.g. soil maps).

The results of the proposed classification and validation procedure are presented for a test site situated in a stratum in the south of the Netherlands. It is shown how the land cover data are applied in a soil and groundwater vulnerability assessment system.     

遥感与GIS在准格尔旗土壤侵蚀监测中的应用

以内蒙古准格尔旗为例,利用TM图像及相关辅助数据,通过GIS软件ARC/INFO、遥感软件ERDAS IMAGINE 8.5,对影像进行格式转换、人机交互解译等处理,得到准格尔旗土壤侵蚀的结果数据。结果表明,准格尔旗风力侵蚀,水力侵蚀总体上仍比较严重,且以水力侵蚀为主。水力侵蚀与风力侵蚀强度侵蚀以上所占比例分别为68.39%和48.45%。     

湖北省兴山县10 年土地利用/覆盖变化遥感监测分析

利用多平台遥感影像和地理辅助信息获取土地利用/覆盖信息是遥感应用的一个重要方面。选择三峡库区湖北省兴山县作为研究区域, 以两期TM 和SPOT 影像为土地利用/覆盖信息提取的遥感信息源, 并在分类过程中结合该地区的地理辅助信息。通过对分类结果分析, 林地和耕地是兴山县两大用地类型, 运用数据运算规则, 获取土地利用/覆盖动态转换矩阵, 对兴山县10 年间土地利用/覆盖变化趋势及引起土地利用/覆盖模式变化的原因进行分析和评价。     

Evaluation of sediment yield by using remote sensing and GIS: A case study from the Phulang Vagu watershed,Nizamabad District (AP), India

The primary objective of this paper is to identify soil erosion zones and to suggest appropriate measures for control of soil erosion using remote sensing, GIS and conventional technique in the Phulang Vagu watershed in the Sriramsagar catchment area of Andhra Pradesh. The digital imagery data of the study area is obtained from the IRS-IC (LISS-III) satellite whereas the toposheets and rainfall data of the study area were obtained from the Survey of India. Satellite images were interpreted to prepare land use/land cover maps by using ERDAS image processing system. Out of 725.983 km 2 of the study area, about 301.435 km 2 is wasteland which is identified as susceptible for soil erosion. Toposheets of the study area were used to prepare drainage and slope maps. Drainage pattern is mainly dendritic with a density of 1.26 km -1 and the stream slope is 0.00614. The arithmetric average method is used to find average annual rainfall. The above parameters were used to calculate the amount of soil erosion from the catchment area. It was found that 882.389 m 3 km -2 year -1 of soil is being eroded from the catchment area which is more than the value adopted in the design of Sriramsagar reservoir. Therefore soil conservation measures such as vegetative cover in the waste land are needed and 12 check dam sites have been proposed by superimposing drainage map and slope map in conjunction with land use/land cover map. With these soil conservation measures, the soil erosion could be kept within the design value of Sriramsagar reservoir.     

Application of a parallel pattern processor to remote sensing

This paper describes application techniques of satellite remote sensing using a newly developed interactive digital image processing system, where a parallel pattern processor performs characteristic roles. The pattern processor can execute several image processing functions at high speed, which are frequently used in remote sensing studies; affine transformation for geometric correction of LANDSAT MSS data to implement a satellite image database, maximum likelihood estimation for land cover classification, and pixelwise operations for change detection, etc.

These application results indicate that the developed interactive image processing system is sufficiently effective to process remote-sensed data.     

森林叶面积指数遥感反演模型构建及区域估算

基于eCognition面向对象分类算法及校正后的TM遥感影像,获取研究区2010年土地利用/覆被数据。同时在ArcGIS平台下,提取遥感影像6个波段反射率及RVI、NDVI、SLAVI、EVI、VII、MSR、NDVIc、BI、GVI和WI等10个植被指数,并辅助于DEM、ASPECT、SLOPE等地形信息,在与植物冠层分析仪(TRAC)实测各森林类型叶面积指数相关性分析的基础上,研究表明:相对多元线性回归方法,偏最小二乘法能够更好地把握各森林类型LAI动态变化,而后结合研究区森林覆被信息进行区域估算。     

A rough set approach to analyze factors affecting landslide incidence

Landslide incidence can be affected by a variety of environmental factors. Past studies have focused on the identification of these environmental factors, but most are based on statistical analysis. In this paper, spatial information techniques were applied to a case study of landslide occurrence in China by combining remote sensing and geographical information systems with an innovative data mining approach (rough set theory) and statistical analyses. Core and reducts of data attributes were obtained by data mining based on rough set theory. Rules for the impact factors, which can contribute to landslide occurrence, were generated from the landslide knowledge database. It was found that all 11 rules can be classified as both exact and approximate rules. In terms of importance, three main rules were then extracted as the key decision-making rules for landslide predictions. Meanwhile, the relationship between landslide occurrence and environmental factors was statistically analyzed to validate the accuracy of rules extracted by the rough set-based method. It was shown that the rough set-based approach is of use in analyzing environmental factors affecting landslide occurrence, and thus facilitates the decision-making process for landslide prediction.