Samsudin Ahmad

Geometric and radiometric evaluation of Razaksat medium-sized aperture camera data

Razaksat, a high-resolution Malaysian remote-sensing satellite, was launched on 14 July 2009. It carries a medium-sized aperture camera (MAC) with one panchromatic and four multispectral bands, of 2.5 and 5 m spatial resolution, respectively. The satellite was placed in a near-equatorial orbit with a low inclination angle of 9° to enable an optimum 14 overpasses per day over the equatorial region (i.e. 9° N to 9° S) as compared to only three daily passes over Malaysia for near-polar orbiting satellites. This article reports on evaluation of the panchromatic and multispectral images of MAC: (i) a geometric evaluation of the panchromatic and multispectral MAC images; (ii) a radiometric evaluation, focusing particularly on the noise level and sharpness of the MAC images; (iii) an evaluation of the MAC panchromatic data for updating planimetric topographic features; and (iv) a classification of MAC multispectral data for land-cover mapping. The noise level within the image set was found to increase with the intensity, while the sharpness of edges tested on the images in all non-homogeneous targets was relatively marginal. However, the outcome of the analysis showed the utility and potential of high-resolution panchromatic and multispectral bands of the Razaksat as stipulated in the system mission for terrain mapping.

An approach for quantification of submerged seagrass biomass in shallow turbid coastal waters

In this paper, we introduced a new approach for absolute seagrass biomass determination using remote sensing technique. Two-step data processing of satellite remote sensing data was performed, namely: (i) detection and mapping of seagrass occurrence; and (ii) determination and mapping of seagrass biomass. The newly launched Landsat-8 Operational Land Imager band 2, 3 and 4 were used and applied in Merambong Shoals located in Straits of Johore, Malaysia that have various density of seagrass coverage in the region. The results indicated good agreement with in-situ verifications. The seagrass occurrence areas were classified at 90 percent overall classification accuracy (khat statistics=0.87) whilst the seagrass aboveground biomass was succesfully modelled agianst the in-situ observations (R2=0.9, p<;0.0001, n=1200). It is then extended to fully map the above seagrass biomass for the large coverage with good accuracy (RMSE+1.75g/m2).

Remote Sensing of shallow sea floor for digital earth environment

Understanding the sea floor biodiversity requires spatial information that can be acquired from remote sensing satellite data. Species volume, spatial patterns and species coverage are some of the information that can be derived. Current approaches for mapping sea bottom type have evolved from field observation, visual interpretation from aerial photography, mapping from remote sensing satellite data along with field survey and hydrograhic chart. Remote sensing offers most versatile technique to map sea bottom type up to a certain scale. This paper reviews the technical characteristics of signal and light interference within marine features, space and remote sensing satellite. In addition, related image processing techniques that are applicable to remote sensing satellite data for sea bottom type digital mapping is also presented. The sea bottom type can be differentiated by classification method using appropriate spectral bands of satellite data. In order to verify the existence of particular sea bottom type, field observations need to be carried out with proper technique and equipment.