Asian Research Thesis Index

Abstract

Landslides are frequent hazard in the mountainous region of Pakistan with significant threat to surrounding infrastructure and communities. Landslide inventories and susceptibility maps are prerequisite for landslide hazard mitigation; however, these are rarely available for most of the mountainous areas in northern Pakistan. Traditionally, landslide inventory is manually developed through visual interpretation of remote sensing image and aerial photographs. Recently, Object-Based Image Analysis (OBIA) image classification technique is effectively applied to high resolution remote sensing data for rapid and precise landslide detection and temporal change analysis. However, due to unavailability of timely and optimal remote sensing data, OBIA methods are rarely applied for quick post-disaster landslide inventory and susceptibility assessment. Aim of this study is to utilize manual and semi-automated techniques for landslide inventories and susceptibility assessment. The selected study areas include Hunza-Nagar valley of Gilgit Baltistan and Muzaffarabad region of Azad Kashmir in northern Pakistan.For Hunza Nagar valley, landslide inventory was developed using visual interpretation of the SPOT-5 multi-spectral data in 3D environment. The acquired landslide inventory is correlated with seven landslide causative factors through Weight of Evidence and Frequency Ratio techniques, to developed landside susceptibility map for the study area. The produced landslides susceptibility maps are validated by the success rate and area under curves criteria. The prediction powers of the statistical models are also validated with the prediction rate curve. It is observed that Weight of Evidence modeling is suited for landslide susceptibility mapping in the study area. The developed landslide inventory and susceptibility map can be used for landslide disaster mitigation strategies. However, it was observed that manual development of landslide inventory over a large area is laborious and time consuming.OBIA technique is used for rapid and precise detection of landslides in the Muzaffarabad and Balakot surrounding regions in northern Pakistan. Transferability and efficiency of the three existing OBIA landslide detection methods are assessed for landslide detection. OBIA technique is modified and applied to the SPOT-5, SPOT-6 and ALOS PALSAR DEM data for landslides detection in the Muzaffarabad and Balakot region. The SPOT-6 multi-spectral data with ALOS PALSAR DEM derivatives e.g. slope, aspect, hill-shade,    vi   elevation and streams are used for semi-automated landslide detection. The spectral, contextual, textural, spatial and morphological characteristics of landslide in remote sensing image are assessed for landslide identification. Moreover, a semi-automated method is developed to use the recently launched and freely available Sentinel-2 MSI data for near-real time landslide monitoring and temporal change detection. NDVI, terrain slope and mean brightness were found useful for landslide recognition in the area. The resultant semi-automated landslide inventories are validated through confusion matrix. The temporal change analysis shows that landslides area and numbers are increased from the year 2016 to 2018 in the study area. The developed semi-automated method using the Sentinel-2 time series data depicts that the Sentenil-2 data could be used for rapid post-disaster landslide detection, monitoring and spatio-temporal change analysis in the landslide prone areas over a regional scale. OBIA based landslide inventory for the year 2018 used for landslide susceptibility assessment in the study area. The proposed OBIA method can help to develop landslide inventory relatively quickly, and hereafter has the capability to be used for landslide susceptibility, hazard, vulnerability and risk assessment. Moreover, the results from this study can help in disaster risk mitigation and quick inventory and susceptibility assessment in the aftermath of a landslide triggering events like earthquake or rainfall.

Add/Update Thesis