Manfred F. Buchroithner

Planimetric and volumetric glacier changes in the Khumbu Himal, Nepal, since 1962 using Corona, Landsat TM and ASTER data

Multitemporal space imagery from 1962 (Corona KH-4), 1992 (Landsat TM), 2001 and 2005 (Terra ASTER) was used to investigate the glacier changes in the Khumbu Himal, Nepal. The ice coverage in the investigation area decreased by about 5% between 1962 and 2005, with the highest retreat rates occurring between 1992 and 2001. The debris coverage increased concomitantly with the decrease in total glacier area. The clean-ice area decreased by >10%. Digital terrain model (DTM) generation from the early Corona KH-4 stereo data in this high-relief terrain is time-consuming, and the results still contain some elevation errors. However, these are minor in the snow-free areas with gentle slopes. Thus comparison of the surfaces of the debris-covered glacier tongues based on the Corona DTM and an ASTER DTM is feasible and shows the downwasting of the debris-covered glaciers. The highest downwasting rates, more than 20 m (>0.5 m a -1 ), can be found near the transition zone between the active and the stagnant glacier parts of the debris-covered glacier tongues. The downwasting is lower, but still evident, in the active ice areas and at the snout with thick debris cover. All investigated debris- covered glaciers in the study area show similar behaviour. The estimated volume loss for the investigated debris-covered glacier tongues is 0.19 km 3 .

Landslide Susceptibility Mapping by Neuro-Fuzzy Approach in a Landslide-Prone Area (Cameron Highlands, Malaysia)

This paper presents the results of the neuro-fuzzy model using remote-sensing data and geographic information system for landslide susceptibility analysis in a part of the Cameron Highlands areas in Malaysia. Landslide locations in the study area were identified by interpreting aerial photographs and satellite images, supported by extensive field surveys. Landsat TM satellite imagery was used to map the vegetation index. Maps of the topography, lineaments, Normalized Difference Vegetation Index (NDVI), and land cover were constructed from the spatial data sets. Eight landslide conditioning factors such as altitude, slope gradient, curvature, distance from the drainage, distance from the road, lithology, distance from the faults, and NDVI were extracted from the spatial database. These factors were analyzed using a neuro-fuzzy model adaptive neuro-fuzzy inference system to produce the landslide susceptibility maps. During the model development works, a total of five landslide susceptibility models were constructed. For verification, the results of the analyses were then compared with the field-verified landslide locations. Additionally, the receiver operating characteristic curves for all landslide susceptibility models were drawn, and the area under curve values were calculated. Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed a 97% accuracy for model 5, employing all parameters produced in the present study as the landslide conditioning factors. The validation results showed a sufficient agreement between the obtained susceptibility map and the existing data on the landslide areas. Qualitatively, the model yields reasonable results, which can be used for preliminary land-use planning purposes.

A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses

Abstract Landslide-susceptibility mapping is one of the most critical issues in Malaysia. These landslides can be systematically assessed and mapped through a traditional mapping framework that uses geoinformation technologies (GIT). The main purpose of this paper is to investigate the possible application of an artificial neural network model and its cross-application of weights at three study areas in Malaysia, Penang Island, Cameron Highland and Selangor. Landslide locations were identified in the study areas from the interpretation of aerial photographs, field surveys and inventory reports. A landslide-related spatial database was constructed from topographic, soil, geology, and land-cover maps. For the calculation of the relative weight and importance of each factor to a particular landslide occurrence, an artificial neural network (ANN) method was applied. Landslide susceptibility was analyzed using the landslide occurrence factors provided by the artificial neural network model. Then, the landslide-susceptibility analysis results were validated and cross-validated using the landslide locations as study areas. Different training sites were randomly selected to train the neural network, and nine sets of landslide-susceptibility maps were prepared. The paper then illustrates the verification of those maps using an “area under the curve” (AUC) method. The verification results show that the case of the weight using the same test area showed slightly higher accuracy than the weight used for the cross-applied area. Among the three studied areas, the verification results showed similar accuracy trends while using the weight for the study area itself. Cameron showed the best accuracy and Penang showed the worst accuracy. Generally, the verification results showed satisfactory agreement between the susceptibility map and the existing data on the landslide location.

Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area

A study demonstrating the application of the weights-of-evidence model (a Bayesian probability model) to landslide susceptibility mapping using geographical remote sensing (GIS) in a tropical hilly area of Malaysia is presented. In the first stage, a landslide related spatial database was created. Seven landslide conditioning factors were considered for the susceptibility analysis. Using landslide location and a spatial database containing information such as topography, soil, lithology, land cover and lineament, the weights-of-evidence model was applied to calculate each relevant factors rating for the Cameron Highlands area in Malaysia. The topographic database including information on slope angle, slope aspect, plan curvature and distance from drainage was developed from a digital elevation model (DEM); the lithology and the distance from the lineament were derived from the geological database; soil texture was derived from the soil database; land cover and normalized difference vegetation index (NDVI) information were extracted from Landsat Thematic Mapper (TM) satellite imagery. Tests of conditional independence were performed for the selection of landslide conditioning factors, allowing nine combinations in total. Finally, landslide susceptibility maps were constructed using the ratings of each landslide conditioning factor. The resultant susceptibility maps were validated using the receiver operating characteristics (ROCs) based area under curve (AUC) method. Landslide locations were used to validate the results of the landslide susceptibility map and the verification results showed 97% accuracy for model 5, which employed a combination of parameters. Plan curvature, distance from drainage, distance from lineament, lithology and land cover performed better than other combinations of landslide conditioning factors.

Soil erosion assessment and its correlation with landslide events using remote sensing data and GIS: a case study at Penang Island, Malaysia

In this paper, an attempt has been made to assess, prognosis and observe dynamism of soil erosion by universal soil loss equation (USLE) method at Penang Island, Malaysia. Multi-source (map-, space- and ground-based) datasets were used to obtain both static and dynamic factors of USLE, and an integrated analysis was carried out in raster format of GIS. A landslide location map was generated on the basis of image elements interpretation from aerial photos, satellite data and field observations and was used to validate soil erosion intensity in the study area. Further, a statistical-based frequency ratio analysis was carried out in the study area for correlation purposes. The results of the statistical correlation showed a satisfactory agreement between the prepared USLE-based soil erosion map and landslide events/locations, and are directly proportional to each other. Prognosis analysis on soil erosion helps the user agencies/decision makers to design proper conservation planning program to reduce soil erosion. Temporal statistics on soil erosion in these dynamic and rapid developments in Penang Island indicate the co-existence and balance of ecosystem.

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.

Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model

This paper deals with landslide hazard analysis using Geographic Information System (GIS) and remote sensing data for Cameron Highland, Malaysia. Landslide locations were identified in the study area from interpretation 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 the landslide hazards. 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 hazard was analyzed using landslide-occurrence factors employing the logistic regression model. The results of the analysis were verified using the landslide location data and compared with logistic regression model. The accuracy of hazard map observed was 85.73%. The qualitative landslide hazard analysis was carried out using the logistic regression model by doing map overlay analysis in GIS environment. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.

Estimation of Urban Green Volume Based on Single-Pulse LiDAR Data

Estimating urban green volume is getting more and more important within the frame of an ecologically orientated city planning and environmentally sustainable development. The first and the last pulse of airborne Light Detection and Ranging (LiDAR) data provide the basis for the estimation of green volume, but these optimal data are not always available, particularly for urban areas. That is why this paper deals with the question whether LiDAR data (last pulse only) that have not been taken during the vegetation period allow a sufficient estimation of the green volume. This paper sets up on previous results where LiDAR data have been compared to photogrammetrically determined vegetation height measurements. The subtraction of the laser-based Digital Terrain Model and Digital Surface Model in vegetated areas leads to a vast underestimation of green volume of up to 85%, which is mainly due to the standing deciduous trees with an underestimation of 90%. Starting from the existence of different laser response characteristics of various vegetation types, the relative point density and the normalized height of classified nonground points were analyzed in depth. The results show a good separation of different vegetation types. Furthermore, a pragmatic approach of reconstruction of the underestimated vegetation (mainly deciduous trees) is carried out by generating cylinders for the classified nonground points to compensate the volume loss. The point density of nonground points and the normalized height of the laser responses were used to regulate the adaptive cylinder construction based on fuzzy logic techniques. Using reference data, the accuracy could be estimated. In spite of the suboptimal LiDAR data, this paper leads to a sufficiently exact and efficient estimation of green volume compared to the costly conventional methods like field investigations. The method makes a contribution in the field of data improvement and is applicable to similar LiDAR data of other areas.

Evaluation of a Coupled Snow and Energy Balance Model for Zhadang Glacier, Tibetan Plateau, Using Glaciological Measurements and Time-Lapse Photography

Abstract We present a new open-source, collaborative “COupled Snowpack and Ice surface energy and MAss balance model” (COSIMA) that is evaluated for Zhadang glacier, Tibetan Plateau. The model is calibrated, run, and validated based on in situ measurements and atmospheric model data from the High Asia Refined analysis (HAR) over the period April 2009 to June 2012. Results for the model runs forced by both in situ measurements and HAR agree well with observations of various atmospheric, glaciological, surface, and subsurface parameters on the glacier. A time-lapse camera system next to the glacier provides a 3-year image time series of the mean transient snow line altitude and the snow cover pattern, which is used for the spatial and temporal validation of the model. The model output corresponds very well to the observed temporal and spatial snow cover variability. The model is then run for a 10-year period of October 2001 to September 2011 forced with HAR data. In general, the radiation components dominate the overall energy turnover (65%), followed by the turbulent fluxes (31%). The generally dry atmosphere on the Tibetan Plateau causes sublimation to be responsible for 26% of the total mass loss. A proportion of 11% of the surface and subsurface melt refreezes within the snowpack.

Terrigenous Mass Movements

Landslide inventories in the tropical dense forested areas are routinely compiled by means of a terrain model interpretation (e.g. using stereoradargrammetry; stereo-aerial photographs; stereo-optical imagery), aided with field investigations. However, construction of the landslide inventories from aerial photographs and field based studies are excessively time consuming which involves relatively high cost. Moreover, these techniques are less effective when applied to dense tropical forest where landslide scars are difficult to map from the aerial photographs. This chapter attempts an automatic procedure for detection of rotational shallow landslides from airborne based light detection and ranging (LiDAR) derived high resolution digital elevation model (DEM) in a tropical forest in Cameron Highlands, Malaysia. For the extraction of landslides from DEM, we used various geomorphic indicators such as surface roughness index, vegetation index and breaklines. The entire landslide extraction process was implemented in ArcGIS platform and custom Python scripts was used for the implementation and model construction. For modeling purpose, the Python Imaging Library (PIL) was used. The terrain zone classification was tested for various DEM resolutions of 1.5 m, 2 m, 3 m, 4 m, 5 m and 8 m. For testing purposes, the resolutions with the best results were used for further processing. To automate the classification of the terrain zones, a rule based region growing threshold was defined depending on the resolution of the DEM. Finally, a statistical description was applied to rank the extracted terrain zones according to their compliance with the landslide signature. Subsequently, the landslide probability U. Mann B. Pradhan N. Prechtel M. F. Buchroithner Institute for Cartography, Faculty of Forestry, Geo and Hydro Science, Dresden University of Technology, 01062 Dresden, Germany B. Pradhan (&) Institute of Advanced Technology, Spatial and Numerical Modeling Laboratory, University Putra Malaysia, 43400 UPM, Serdang, Malaysia e-mail: biswajeet@lycos.com; biswajeet24@gmail.com B. Pradhan and M. Buchroithner (eds.), Terrigenous Mass Movements, DOI: 10.1007/978-3-642-25495-6_1, Springer-Verlag Berlin Heidelberg 2012 1 index (LPI) was calculated by performing zonal operation using each of the geomorphic parameters. Hence, the LIDAR-derived DEM provides adequate landslide factor maps to identify the landslide occurred areas, which could be used for further landslide assessment and site-planning purposes in the tropical regions.