Coastal Hazard Modeling from Radar Data

The effects of wave force or the effects of the coastal engineering structures induce coastal hazards such as erosion. Coastal engineering structures such as jetties could trap a sediment transport along the coastline. This could induce erosion in the downstream. The aim of this study is to model the effects of shoreline changes to jetties located along coastal water of Chendering, Malaysia. The numerical model will be based on the change of wave spectra extracted from ERS-I data. For this purpose, two-dimensional Fourier Transform was applied on window size of 200 x 200. The quasi-linear model was used to model significant wave height. The significant wave height was used to model the volume of sediment transport and shoreline evaluation along jetties. The result shows that the erosion occurred in the south of Chendering with rate of change of 4 m/month. The prediction shows that the rate of erosion would increase within 10 years. This study shows the location of jetty decreases the rate of sediment transport along the south of Chendering. It can be said that ERS-l data are able to predict shoreline evaluation along the coastal structures. The jetty induced an equilibrium beach profile along jetty. This is due to that jetties-trap sediment in the north of Chendering, which lead to erosion in the south of Chendering

Google the earth: what's next?

Sensing the Earth has proven to be a tremendously valuable tool for understanding the world around us. Over the last half-century, we have built a sophisticated network of satellites, aircraft, and ground-based remote sensing systems to provide raw information from which we derive and improve our knowledge of the Earth and its phenomena. Through remote sensing, our basic scientific knowledge of the Earth and how it functions has expanded rapidly in the last few decades. Applications of this knowledge, from natural hazard prediction to resource management, have already proven their benefit to society many times over. Today maps and satellite imageries have become an integral part of the developmental process and have also triggered new business opportunities. Maps are essential at all stages of infrastructure development, resource planning and the disaster management cycle. Satellite imagery/data can be used for everything from ground truthing and change detection, to more sophisticated analyses, including feature extraction and natural hazard prediction. As imagery has become more accessible and more affordable in recent years, there is also a growing convergence of imagery and geographic information system (GIS) applications. Geospatial scientists and analysts thus, need to be able to easily access imagery and move seamlessly between GIS and image processing applications to derive the most information possible from them. Technologically, the challenge is to design sensors that exhibit high sensitivity to the parameters of interest while minimizing instrument noise and impacts of other natural variables. The scientific challenge is to develop retrieval algorithms that describe the physical measurement process in sufficient detail, yet are simple enough to allow robust inversion of the remotely sensed signals. Considering the exponential growth of data volumes driven by the rapid progress in sensor and computer technologies in recent years, the future of remotely sensed data should ideally be in automated data processing, development of robust and transferable algorithms and processing chains that require little or no human intervention. In meeting the above mentioned challenges, some research works have been done at Universiti Putra Malaysia. These works cover all aspects of the remote sensing process, from instrument design, image processing, image analysis to the retrieval of geophysical parameters and their application in natural resources planning and disaster management. Some of the major research efforts include feature extraction from satellite imagery; spatial decision support system for oil spill detection, monitoring and contingency planning; fish forecasting; UAV-based remote imaging and natural disaster management and early warning systems for floods and landslides. This lecture concludes that through remote sensing, our basic scientific knowledge of the Earth and how it functions have expanded rapidly in the last few decades. Applications of this knowledge, from natural hazard prediction to resource management, have already proven to be beneficial to society many times over. As the demand for even faster, better and more temporally and spatially variable information grows dramatically, this lectures answers the question of what remote sensing will be like in the coming decades and the new capabilities and challenges that will emerg

Three-Dimensional Nepal Earthquake Displacement Using Hybrid Genetic Algorithm Phase Unwrapping from Sentinel-1A Satellite

Introduction: Geophysicists had forewarned for decades that Nepal was exposed to a deadly earthquake, exceptionally despite its geology, urbanization and architecture. Gorkha earthquake is the most horrible natural disaster to crash into Nepal since the 1934 Nepal-Bihar earthquake. Gorkha earthquake occurred on April 25, 2015, at 11:56 NST and killed more than 10,000 people and injured more than 23,000 population. Objective: The main objective of this work is to utilize hybrid genetic algorithm for three-dimensional phase unwrapping of Nepal earthquake displacement using Sentinel-1A satellite. The three-dimensional best-path avoiding singularity loops (3DBPASL) algorithm was implemented to perform 3D Sentinel-1A satellite phase unwrapping. The hybrid genetic algorithm (HGA) was used to achieve 3DBPASL phase matching. Advancely, the errors in phase decorrelation were reduced by optimization of 3DBPASL using HGA. Results: The findings indicate a few cm of ground deformation and vertical northern of Kathmandu. Approximately, an area of 12,000 km2 has been drifted also the northern of Kathmandu. Further, each fringe of colour represents about 2.5 cm of deformation. The large amount of fringes indicates a large deformation pattern with ground motions of 3 m. Conclusion: In conclusion, HGA can be used to produce accurate 3D quake deformation using Sentinel-1A satellite

A rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images

Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features’ classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%)

Pre-flood inundation mapping for flood early warning.

In this study the results of two rainfall-run-off simulations were used as input into a MIKE11GIS and hydrological modelling process for flood inundation mapping based on the flood event (27 September to 8 October 2000) in Malaysia of the Langat River Basin area. Separate inundation maps were generated for the recorded observed rainfall and from a developed quantitative precipitation forecast (QPF), which was based on top of the cloud reflectance and brightness temperature (TB) derive from Advanced Very High Resolution Radiometer (AVHRR) and Geostationary Meteorological Satellite (GMS) satellite data sets. The QPF had rain rates between 3 and 12 mm/h for the 264 h rainfall duration. While the actual recorded rainfall for the same duration was used for the observed. The objective of the study was to compare the similarities of the flood inundation generated from the QPF run-off with that generated from the rainfall-run-off of the actual flood event. The accuracies of the maps were verified using grid point locations of flooded areas taken during the event. The selected sampled point of the verification showed an accuracy of 70% of the QPF on the observed flood map. Sampled points measured flood extent, coverage and depth of flood in the basin area

Spatial Information Technology for Disaster Management

Most major geohazards such as floods, forest fire and oil spills occur suddenly, and require an immediate response. This paper describes the development and establishment a spatial information technology and engineering system that uses GIS and Remote Sensing technologies to detect, monitor and assess geohazards, including floods, forest fire and oil spills. The potential application of Remote sensing and GIS techniques for floods and oil spills is discussed. The oil spill risk management system study was developed for coastal zone of Peninsular Malaysia. The development of GIS database used remotely sensed data from Landsat TM, SPOT Panchromatic and MSS, AVHRR and air-borne images. For the flood studies, Digital Elevation Model (DEM) was created for Klang River Basin from the input data of contour lines. DEMs stored the data for the slope analysis, terrain analysis and also visualizing for the flood simulation. SCS TR-55 Model was used to predict the extent of inundation and depth of flooding. Parameters of rainfall, landuse and hydrological relief were adopted as the main input data. These two case studies will provide the technical guidelines for in-depth study in GIS and remote sensing for disaster managemen

An approach to a pseudo real-time image processing engine for hyperspectral imaging

Hyperspectral imaging provides an alternative way of increasing the accuracy by adding another dimension: the wavelength. Recently, hyperspectral imaging is also finding its way into many more applications, ranging from medical imaging in endoscopy for cancer detection to quality control in the sorting of fruit and vegetables. But effective use of hyperspectral imaging requires an understanding of the nature and limitations of the data and of various strategies for processing and interpreting it. Also, the breakthrough of this technology is limited by its cost, speed and complicated image interpretation. We have therefore initiated work on designing real-time hyperspectral image processing to tackle these problems by using a combination of smart system design, and pseudo-real time image processing software. The main focus of this paper is the development of a camera-based hyperspectral imaging system for stationary remote sensing applications. The system consists of a high performance digital CCD camera, an intelligent processing unit, an imaging spectrograph, an optional focal plane scanner and a laptop computer equipped with a frame grabbing card. In addition, special software has been developed to synchronize between the frame grabber (video capture card), and the digital camera with different image processing techniques for both digital and hyperspectral data