Paper
3 November 2008 Spatial patterns of seaweed distribution in Malaysia using GIS
Du Hai Lian, Jillian Ooi Lean Sim, Rosmadi Fauzi, Phang Siew Moi
Author Affiliations +
Proceedings Volume 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments; 71452H (2008) https://doi.org/10.1117/12.813073
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
The objective of this article is to represent spatial patterns of seaweed distribution in Malaysia. Seaweeds have been collected since 1984 along coastlines of 4675 km of peninsular Malaysia, Sabah, and Sarawak. However, there is no seaweed database and they cannot be displayed in a geographic view. Therefore, a database with 805 georeferenced observations was setup and GIS is used to analyze seaweed diversity based on this database. The highest number of observations is 94 which occur along east coastline of peninsular Malaysia. The highest number of species richness is 82 which are also along east coastline of peninsular Malaysia. Rhodophyta has the highest species richness while Chlorophyta has the least species richness.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Du Hai Lian, Jillian Ooi Lean Sim, Rosmadi Fauzi, and Phang Siew Moi "Spatial patterns of seaweed distribution in Malaysia using GIS", Proc. SPIE 7145, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Monitoring and Assessment of Natural Resources and Environments, 71452H (3 November 2008); https://doi.org/10.1117/12.813073
Lens.org Logo
CITATIONS
Cited by 4 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Databases

Geographic information systems

Oceanography

Analytical research

Statistical analysis

Data modeling

Environmental monitoring

Back to Top