Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the classification results can be achieved by using the proposed approach.

The study aims to investigate the use of panchromatic (PAN) satellite image data for flood hazard assessment with anaid of various digital image processing techniques. Two SPOT PAN satellite images covering part of the Nile River inEgypt were used to delineate the flood extent during the years 1997 and 1998 (before and after a high flood). Threeclassification techniques, including the contextual classifier, maximum likelihood classifier and minimum distanceclassifier, were applied to the following: 1) the original PAN image data, 2) the original PAN image data and grey-levelco-occurrence matrix texture created from the PAN data, and 3) the enhanced PAN image data using an edgesharpeningfilter. The classification results were assessed with reference to the results derived from manualdigitization and random checkpoints. Generally, the results showed improvement of the calculation of flood area whenan edge-sharpening filter was used. In addition, the maximum likelihood classifier yielded the best classificationaccuracy (up to 97%) compared to the other two classifiers. The research demonstrates the benefits of using PANsatellite imagery as a potential data source for flood hazard assessment.

Light Detection and Ranging (LiDAR) systems are used intensively in terrain surface modelling based on the range data determined by the LiDAR sensors. LiDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which has high separability in the reflected energy from different targets. This characteristic is investigated to implement the LiDAR intensity data in land-cover classification. The goal of this paper is to investigate and evaluates the use of LiDAR data only (range and intensity data) to extract land cover information. Different bands generated from the LiDAR data (Normal Heights, Intensity Texture, Surfaces Slopes, and PCA) are combined with the original data to study the influence of including these layers on the classification accuracy. The Maximum likelihood classifier is used to conduct the classification process for the LiDAR Data as one of the best classification techniques from literature. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to test the different band combinations to extract four information classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas. The results show that an overall accuracy of more than 70% can be achieved using the intensity data, and other auxiliary data generated from the range and intensity data. Bands of the Principle Component Analysis (PCA) are also created from the LiDAR original and auxiliary data. Similar overall accuracy of the results can be achieved using the four bands extracted from the Principal Component Analysis (PCA).

Radiometric correction (RC) of the airborne Light Detection And Ranging (LiDAR) intensity data has been studied in the last few years. The physical model of the RC relies on the use of the laser range equation to convert the intensity values into the spectral reflectance of the reflected objects. A number of recent studies investigated the effects of the LiDAR system parameters (i.e. range, incidence angle, beam divergence, aperture size, automatic gain control, etc.) on the results of the RC process. Nevertheless, the condition of the object surface (slope and aspect) plays a crucial role in modelling the recorded intensity data. The variation of the object surface slope and aspect affects the direction as well as the magnitude of the reflected laser pulse which makes significant influence on the bidirectional reflectance distribution function. In this paper, the effects of the angle of reflection, which is the angle between the surface normal and the incidence laser pulse, on the RC results of the airborne LiDAR intensity data is investigated. A practical approach is proposed to compute the angle of reflection using the digital surface model (DSM) derived from the LiDAR data. Then, a comparison between the results of the intensity data after RC using the scan angle and RC using the angle of reflection is carried out. The comparison is done by converting the intensity data into equivalent image data and evaluating the classification results of the intensity image data. Preliminary findings show that: 1) the variance-to-mean ratio of the land cover features are significantly reduced while using the angle of reflection in the RC process; 2) 4% of accuracy improvement can be achieved using the intensity data corrected with the scan angle. The accuracy improvement increases to 8% when using the intensity data corrected with the angle of reflection. The research work practically justifies the use of the reflection angle in the RC process of airborne LiDAR intensity data.