Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.     

Comparison of A1-absorption data with theoretically computed values based on the International Reference Ionosphere (IRI)

Total absorption of hf radio waves at vertical incidence is calculated using the IRI electron density N(h) profiles at a low latitude for low and high solar activities and the calculated values of absorption are compared with the observed values. It is found that the IRI model holds good in this respect only for equinoxial months in years of low solar acitvity; however, it yields much higher values of absorption than observed during years of high solar activity (all seasons). It is suggested that seasonal anomalous variations of gas composition and bottomside thickness of the E-layer may be given due weight in revising the IRI.     

Evaluation of GPM sampling error over major basins in Indian subcontinent using bootstrap technique

The study investigates the evaluation and comparison of sampling error for the Global Precipitation Measurement (GPM) mission orbital data products by implementing a bootstrap technique over the two major basins in the Indian subcontinent i.e the Ganga and the Mahanadi basin. The relative sampling error evaluated over both the Ganges and Mahanadi basins showed commendable results thus giving the confidence to adopt the bootstrap technique to evaluate the sampling error. The region over India with large seasonal rainfall seems to have less sampling uncertainty and vice versa with some regions showing exceptions which might be due to the difference in precipitation variability and space-time correlation length. The scale dependence was verified for four grid sizes along with seasonal time scale. Results indicate that the relative sampling error estimates are inversely proportional to the scale of the grid size. The comparative study of evaluation of sampling uncertainty to different precipitation types resulted to have maximum sampling error in GPM Microwave Imager (GMI) in comparison to Dual Precipitation Radar (DPR) convective and DPR total precipitation. Thus, the comparable results of sampling uncertainty between the major basins in the Indian sub-continent provides the user a decision making criteria before utilizing the GPM orbital products in any applications.     

Mapping hydrothermal alteration zones and lineaments associated with orogenic gold mineralization using ASTER data: A case study from the Sanandaj-Sirjan Zone,Iran

The Sanandaj-Sirjan Zone (SSZ) is considered as an important region for gold exploration in the western sector of Iran. Its mountainous topography and unpaved routes make its study challenging for researchers and raise the costs for mining companies strating new exploration plans. Gold mineralization mainly occurs as irregular to lenticular sulfide-bearing quartz veins along shear zones in deformed mafic to intermediate metavolcanic and metasedimentary rocks. In this investigation, ASTER data are used for mapping hydrothermal alteration minerals and to better discriminate geological structural features associated with orogenic gold occurrences in the area. Image transformation techniques such as specialized band ratioing and Principal Component Analysis are used to delineate lithological units and alteration minerals. Supervised classification techniques, namely Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) are applied to detect subtle differences between indicator alteration minerals associated with ground-truth gold locations in the area. The directional filtering technique is applied to help in tracing along the strike the different linear structures. Results demonstrate that the integration of image transformation techniques and supervised classification of ASTER data with fieldwork and geochemical exploration studies has a great efficiency in targeting new prospects of gold mineralization in the SSZ. The approach used in this research provides a fast, cost-efficient means to start a comprehensive geological and geochemical exploration programs in the study area and elsewhere in similar regions.     

Field-based landcover classification using TerraSAR-X texture analysis

The present study aims to evaluate the field-based approach for the classification of landcover using high-resolution SAR data. TerraSAR-X (TSX) strip mode imagery, coupled with digital ortho-photos (DOPs) with 20 cm spatial resolution was used for landcover classification and parcel mapping respectively. Different filtering and analysis techniques were applied to extract textural information from the TSX image in order to assess the enhancement of the classification accuracy. Several attributes of parcels were derived from the available TSX images in order to define the most suitable parameters discriminating between different landcover types. Then, these attributes were further statistically analysed in order to define separability and thresholds between different landcover types. The results showed that textural analysis resulted in high classification accuracy. Hence, this paper confirms that integrated landcover classification using the textural information of TerraSAR-X has a high potential for landcover mapping.     

Eigenstructure assignment using output feedback

An eigenspace, achievable by gain output feedback, is proposed by Andry et al. (1983). One example contradicting the above conclusion is given by Kang and Lee (1992). The contradiction is due to the limitation of the result presented by Andry. This note derives the complete specification of the eigenspace which can be achieved by gain output feedback