Muhammad Zulkarnain Abdul Rahman

Flood loss assessment in the Kota Tinggi

Malaysia is free from several destructive and widespread natural disasters but frequently affected by floods, which caused massive flood damage. In 2006 and 2007, an extreme rainfall occured in many parts of Peninsular Malaysia, which caused severe flooding in several major cities. Kota Tinggi was chosen as study area as it is one the seriously affected area in Johor state. The aim of this study is to estimate potential flood damage to physical elements in Kota Tinggi. The flood damage map contains both qualitative and quantitative information which corresponds to the consequences of flooding. This study only focuses on physical elements. Three different damage functions were adopted to calculate the potential flood damage and flood depth is considered as the main parameter. The adopted functions are United States, the Netherlands and Malaysia. The estimated flood damage for housing using United States, the Netherlands and Malaysia was RM 350/m2 RM 200/m2 and RM 100/m2 respectively. These results successfully showed the average flood damage of physical element. Such important information needed by local authority and government for urban spatial planning and aiming to reduce flood risk.

Extraction of Digital Terrain Model (DTM) over vegetated area in tropical rainforest using LiDAR

This paper presents investigations on the combination effect of landcover types, ground filtering approach and interpolation methods on Digital Terrain Model (DTM) generated from airborne LiDAR over vegetated area in tropical environment. The study area is separated into three landcover types i.e. oil palm, mangrove and mixed forest. The LiDAR data is filtered based on: 1) Adaptive TIN (ATIN), 2) Progressive morphology (Morph), and 3) Elevation Threshold with Expand Window (ETEW). The DTMs are generated by interpolating the ground points using Ordinary Kriging and Inverse Distance Weighted (IDW) methods. The quality of DTMs is evaluated based on the combination of quantitative and qualitative approaches. The results show that combination of ATIN and Ordinary Kriging has produced DTMs with higher quality compared to other combination of filtering and interpolation technique. The smallest value of RMSE obtained for terrain covered by oil palm (0.21m) followed by mixed forest (0.25m) and mangrove (0.32m).

Albedo and land surface temperature shift in hydrocarbon seepage potential area, case study in Miri Sarawak Malaysia

The presence of hydrocarbon seepage is generally associated with rock or mineral alteration product exposures, and changes of soil properties which manifest with bare development and stress vegetation. This alters the surface thermodynamic properties, changes the energy balance related to the surface reflection, absorption and emission, and leads to shift in albedo and LST. Those phenomena may provide a guide for seepage detection which can be recognized inexpensively by remote sensing method. District of Miri is used for study area. Available topographic maps of Miri and LANDSAT ETM+ were used for boundary construction and determination albedo and LST. Three land use classification methods, namely fixed, supervised and NDVI base classifications were employed for this study. By the intensive land use classification and corresponding statistical comparison was found a clearly shift on albedo and land surface temperature between internal and external seepage potential area. The shift shows a regular pattern related to vegetation density or NDVI value. In the low vegetation density or low NDVI value, albedo of internal area turned to lower value than external area. Conversely in the high vegetation density or high NDVI value, albedo of internal area turned to higher value than external area. Land surface temperature of internal seepage potential was generally shifted to higher value than external area in all of land use classes. In dense vegetation area tend to shift the temperature more than poor vegetation area.

Integration of high density airborne LiDAR and high spatial resolution image for landcover classification

This paper discusses landcover classification using high density airborne LiDAR data and multispectral imagery. The study area is located at the Duursche Waarden floodplain, the Netherlands. The density of the FLI-MAP 400 LiDAR system is between 50 and 100 points per m2. Other than height and intensity, the LiDAR system also measures spectral information (Red, Green, and Blue). Several features are created for height, intensity, Red, Green, and Blue. The landcover classification process is divided into Support Vector Machine (SVM) and Maximum Likelihood (ML) classifiers. Each classifier is used on three different datasets: 1) FLI-MAP 400-generated multispectral images, 2) LiDAR-derived features, and 3) a combination of the multispectral images and the LiDAR-derived features. The results show that the SVM method produces better classification results than the ML method. Landcover classification based on the combination of LiDAR-derived features and multispectral images produces better results than classification based on either dataset only.