Lithological mapping with multispectral data–setup and application of a spectral database for rocks in the Balakot area, Northern Pakistan

In the frame of landslide susceptibility assessment, a spectral library was created to support the identification of materials confined to a particular region using remote sensing images. This library, called Pakistan spectral library(pklib) version 0.1, contains the analysis data of sixty rock samples taken in the Balakot region in Northern Pakistan.The spectral library is implemented as SQLite database. Structure and naming are inspired by the convention system of the ASTER Spectral Library. Usability, application and benefit of the pklib were evaluated and depicted taking two approaches, the multivariate and the spectral based. The spectral information were used to create indices. The indices were applied to Landsat and ASTER data tosupportthespatial delineation of outcropping rock sequences instratigraphic formations. The application of the indices introduced in this paper helps to identify spots where specific lithological characteristics occur. Especially in areas with sparse or missing detailed geological mapping, the spectral discrimination via remote sensing data can speed up the survey. The library can be used not only to support the improvement of factor maps for landslide susceptibility analysis, but also to provide a geoscientific basisto further analyze the lithological spotin numerous regions in the Hindu Kush.

Spectral indices derived,non-parametric Decision Tree Classification approach to lithological mapping in the Lake Magadi area,Kenya

Here,we demonstrate the application of Decision Tree Classification(DTC)method for lithological mapping from multi-spectral satellite imagery.The area of investigation is the Lake Magadi in the East African Rift Valley in Kenya.The work involves the collection of rock and soil samples in the field,their analyses using reflectance and emittance spectroscopy,and the processing and interpretation of Advanced Spaceborne Thermal Emission and Reflection Radiometer data through the DTC method.The latter method is strictly non-parametric,flexible and simple which does not require assumptions regarding the distributions of the input data.It has been successfully used in a wide range of classification problems.The DTC method successfully mapped the chert and trachyte series rocks,including clay minerals and evaporites of the area with higher overall accuracy(86%).Higher classification accuracies of the developed decision tree suggest its ability to adapt to noise and nonlinear relations often observed on the surface materials in space-borne spectral image data without making assumptions on the distribution of input data.Moreover,the present work found the DTC method useful in mapping lithological variations in the vast rugged terrain accurately,which are inherently equipped with different sources of noises even when subjected to considerable radiance and atmospheric correction.

Leucogranite mapping via convolutional recurrent neural networks and geochemical survey data in the Himalayan orogen

Geochemical survey data analysis is recognized as an implemented and feasible way for lithological mapping to assist mineral exploration.With respect to available approaches,recent methodological advances have focused on deep learning algorithms which provide access to learn and extract information directly from geochemical survey data through multi-level networks and outputting end-to-end classification.Accordingly,this study developed a lithological mapping framework with the joint application of a convolutional neural network(CNN)and a long short-term memory(LSTM).The CNN-LSTM model is dominant in correlation extraction from CNN layers and coupling interaction learning from LSTM layers.This hybrid approach was demonstrated by mapping leucogranites in the Himalayan orogen based on stream sediment geochemical survey data,where the targeted leucogranite was expected to be potential resources of rare metals such as Li,Be,and W mineralization.Three comparative case studies were carried out from both visual and quantitative perspectives to illustrate the superiority of the proposed model.A guided spatial distribution map of leucogranites in the Himalayan orogen,divided into high-,moderate-,and low-potential areas,was delineated by the success rate curve,which further improves the efficiency for identifying unmapped leucogranites through geological mapping.In light of these results,this study provides an alternative solution for lithologic mapping using geochemical survey data at a regional scale and reduces the risk for decision making associated with mineral exploration.

Mapping salt diapirs and salt diapir-affected areas using MLP neural network model and ASTER data

This study employs visible-near infrared and short wave infrared datasets of Advanced Spaceborne Thermal Emission and Reflection Radiometer(ASTER)to map salt diapirs and salt diapir-affected areas using Multi-Layer Perceptron(MLP)in the Zagros Folded Belt,Iran,and introduces the role of earth observation technology and a type of digital earth processing in lithological mapping and geo-environmental impact assessment.MLP neural network model with several learning rates between 0.01 and 0.1 was carried out on ASTER L1B data,and the results were compared using confusion matrices.The most appropriate classification image for L1B input to MLP was produced by learning rate of 0.01 with Kappa coefficient of 0.90 and overall accuracy of 92.54%.The MLP result of input data set mapped lithological units of salt diapirs and demonstrated affected areas at the southern and western parts of the Konarsiah and Jahani diapirs,respectively.Field observations and X-ray diffraction analyses of field samples confirmed the dominant mineral phases identified remotely.It is concluded that MLP is an efficient approach for mapping salt diapirs and salt-affected areas.

Evaluating pixel-based vs.object-based image analysis approaches for lithological discrimination using VNIR data of WorldView-3

The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared(VNIR)bands of WorldView-3(WV-3)satellite imagery.The study area is Hormuz Island,southern Iran,a salt dome composed of dominant sedimentary and igneous rocks.When performing the object-based image analysis(OBLA)approach,the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine(SVM)algorithm.However,in the pixelbased image analysis(PBIA),the spectra of lithological end-members,extracted from imagery,were used through the spectral angle mapper(SAM)method.Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively.Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54%which was 19.33%greater than the accuracy of PBIA.OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders.This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery.It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.