Recent methods for detailed and accurate biomass and carbon stock estimation of forests have been driven by advances in remote sensing technology. The conventional approach to biomass estimation heavily relies on the tree species and site-specific allometric equations, which are based on destructive methods. This paper introduces a non-destructive, laser-based approach (terrestrial laser scanner) for individual tree aboveground biomass estimation in the Royal Belum forest reserve, Perak, Malaysia. The study area is in the state park, and it is believed to be one of the oldest rainforests in the world. The point clouds generated for 35 forest plots, using the terrestrial laser scanner, were geo-rectified and cleaned to produce separate point clouds for individual trees. The volumes of tree trunks were estimated based on a cylinder model fitted to the point clouds. The biomasses of tree trunks were calculated by multiplying the volume and the species wood density. The biomasses of branches and leaves were also estimated based on the estimated volume and density values. Branch and leaf volumes were estimated based on the fitted point clouds using an alpha-shape approach. The estimated individual biomass and the total above ground biomass were compared with the aboveground biomass (AGB) value estimated using existing allometric equations and individual tree census data collected in the field. The results show that the combination of a simple single-tree stem reconstruction and wood density can be used to estimate stem biomass comparable to the results usually obtained through existing allometric equations. However, there are several issues associated with the data and method used for branch and leaf biomass estimations, which need further improvement.

Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN.

A massive forest fire in Indonesia in 1997 affected the whole Asian region by producing a large smoke plume, with Malaysia bearing the brunt due to the wind direction and weather conditions and because of its proximity to the source. The five primary fire produced pollutants were carbon monoxide (CO), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ) and particulate matter less than 10 mm (PM10). The first four of these are, of course, invisible to conventional satellite-flown multispectral scanners operating in the visible and near infrared regions of the electromagnetic spectrum. The fifth, PM10, is present in the haze and therefore makes an observable contribution to the signal received by the Advanced Very High Resolution Radiometer (AVHRR). The haze in AVHRR channels 1 and 2 data for the fires of September 1997 has been used to study the concentration of PM10 directly. It has also been used to study the concentration indirectly-as a tracer or surrogate-for the four remaining materials, the gases CO, SO 2 , NO 2 and O 3 . Data from ground observations have been used to calibrate the results and the distributions of the fire pollutants over Peninsular Malaysia have been plotted.

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