Malaysia is in the process of modernizing its oil palm plantation management, by implementing geo-information technologies which include Remote Sensing (RS), Geographic Information System (GIS), and Spatial Decision Support System (DSS). Agencies with large oil palm plantations such as the Federal Land Development Authority (FELDA), Federal Land Consolidation and Rehabilitation Authority (FELCRA), Guthrie Sdn. Bhd., and Golden Hope Sdn. Bhd. have already incorporated GIS in their plantation management, with limited use of RS and DSS. In 2005, FELCRA, Universiti Putra Malaysia (UPM) and Espatial Resources Sdn. Bhd. (ESR) collaborated in a research project to explore the potentials of geo-informatics for oil palm plantation management. The research was conducted in FELCRA located in Seberang Perak Oil Palm Scheme. In that research, a tool integrating RS, GIS and Analytical Hierarchy Process (AHP) was developed to support decision making for replanting of the existing old palms. RS was used to extract productive stand per hectare; AHP was used to compute the criteria weights for the development of a suitable model; and GIS was used for spatial modelling so as to generate the decision support layer for replanting. This paper highlights the approach adopted in developing the tool with special emphasis on the AHP computation.
The growth of residential and commercial areas threatens vegetation and ecosystems. Thus, an urgent urban management
issue involves determining the state and the quantity of urban tree species to protect the environment, as well as controlling
their growth and decline. This study focused on the detection of urban tree species by considering three types of tree
species, namely, Mesua ferrea L., Samanea saman, and Casuarina sumatrana. New rule sets were developed to detect these
three species. In this regard, two pixel-based classification methods were applied and compared; namely, the method of
maximum likelihood classification and support vector machines. These methods were then compared with object-based
image analysis (OBIA) classification. OBIA was used to develop rule sets by extracting spatial, spectral, textural and color
attributes, among others. Finally, the new rule sets were implemented into WorldView-2 imagery. The results indicated
that the OBIA based on the rule sets displayed a significant potential to detect different tree species with high accuracy.
Temporal distribution of forecasted wind speed is important to assess wind capacity for wind-related
technology purposes. Regional wind energy estimation needs the development of wind pattern to monitor
and forecast temporal wind behaviour. Temporal wind in Malaysia mainly depends on monsoonal factor
that circulates yearly and each monsoon derives distinct character of wind. This paper aims to develop a
model of wind speed pattern from historical wind speed data. Then, the model was used to forecast 5-years
seasonal wind speed and identify temporal distribution. Wind speed model development and forecast
was performed by identifying the best combination of wind speed seasonal component using Seasonal
Auto-regressive and Moving Average (SARIMA) model. Thus, three distribution models, Lognormal,
Weibull and Gamma models, were exploited to further observe consistency using Kolmogorov-Smirnov
goodness-of-fit test. The best fit model to represent seasonal wind distribution in each monsoon season
at Pulau Langkawi, Malaysia, is Log-normal distribution (0.04679-0.108).