Comparison between pixel-based and object-based classifications using radar satellite image in extracting massive flood extent at northern region of Peninsular Malaysia

Year 2010 massive flood hit the northern region of Peninsular Malaysia particularly Perlis and Kedah involved several districts and destroyed many agricultural areas and the infrastructure.This study focuses on the comparison between pixel-based classification and object-based classification of five machine learning algorithms including Parallelepiped (PP), Minimum Distance (MD), Maximum Likelihood (ML), Mahalanobis Distance (MH) and Neural Network (NN) using radar satellite image in extracting that flood extent. TerraSAR-X image was used to map the flood extent of the study area. In object-based approach, there were three simple machine learning algorithms such as PP, MD, MH together with NN performed with high accuracy while in pixel based approach, NN was the highest accuracy of all machine learning algorithms. The best output was chosen to be converted to vector format for mapping the flood extent.The result showed clearly through the map output that Kubang Pasu, Kota Setar and Kangar districts were highly affected by the flood. From the flood extent information, the collaboration of government, private sector, Non Governmental Organization (NGO) and community are needed to play the appropriate role in managing flood damage especially at the highly affected area and thus prevent loss of human live.Besides that, the authority could take action plan for pre-disaster, during and post-disaster caused by flooding

An investigation of ongoing displacements of active faults in the Gobi desert using persistent scatterer interferometric synthetic aperture radar technique to support the permanent disposal of high-level waste in Beishan, China

This research demonstrated the application of PSInSAR method in identifying and characterising the micro-displacements along active faults in Beishan to support the selection of GDF host rock. This research first distinguishes and separates the tectonic induced and non-tectonic induced deformation within three study areas at Suanjingzi, Jiujing and Xinchang. Through the application of coherence change detection, it found the granite outcrop areas characterised by high coherence provide more robust results of tectonic activity. The Quaternary sediments covered areas which are characterised by low coherence usually show higher deformation rates due to the impacts of erosion and deposition. The tectonic induced displacements generally range from -0.4 to 0.4 mma-1 and are dominated by fault bound tectonic movements. As a part of wrench faut zone, Beishan is impacted by a NE-SW trended maximum in situ compressive stress field (σ1). To correlate the visible valleys, gullies, or cracks in Google Earth imagery with the SAR image deformation discontinuities, this study mapped and characterised more than 40 active faults in the three study areas, these include (1) the NE-SW trended sinistral strike-slip faults triggered by extension and (2) the NW-SE/W-E trended reverse faults triggered by maximum compression. The fault activity is characterised by subtle (minor) displacement rate value difference between the two sides of the fault plane. This research successfully improved the understanding of local structural geology and provided moderate guidance for the selection of HLW disposal sites in China. It was indicated that Xinchang has the highest tectonic stability, and this is then followed by Jiujing and Suanjingzi. This kind of displacement rate difference is possible due to the angle difference towards the Sanweishan Fault Zone. To trace and characterise the undiscovered active fault planes, the PSInSAR approach also benefits the prediction of earthquake by improving the positioning of the potential epicentres.Open Acces

Segmentation of coherence maps for flood damage assessment

Multitemporal coherence map has been successfully used for various types of surface characterized by the same level of interferometric correlation. This paper examines the possible benefits of this source of information to detect the damages caused by the flooding of rivers and lakes. As test case the Yangtze river flooding of summer 1998 was chosen. The interferometric coherence map was obtained using ERS tandem mission images during the flood and a pair dating December 1995. The low level of water surface coherence allows to segment the flooded areas in an easy way with respect to the method which exploits the backscatter intensity. The approach proposed is based on fuzzy connectivity concepts of the flooded areas with reference to the original river bed