The Use of Remote Sensing and Geographic Information System to Determine the Spatial Distribution of Melaleuca cajuputi as a Major Bee Plant in Marang, Terengganu
Saberioon, Mohammadmehdi (2009) The Use of Remote Sensing and Geographic Information System to Determine the Spatial Distribution of Melaleuca cajuputi as a Major Bee Plant in Marang, Terengganu. Masters thesis, Universiti Putra Malaysia.
In Malaysia, honey is chiefly obtained from species of honeybees known as Apis dorsata and to a lesser extent Apis cerana. Honey from Apis dorsata is a supplementary source of income to many rural poor in the district of Marang, Terengganu. The colonies of A. dorsata are found to nest in aggregates on tall bee trees (tree emergent) in the open, as well as, nesting singly in concealed locations when nesting low, especially in the submerged forest of Melaleuca cajuputi as in the vast hectare (> 200,000 hectares) of Melalueca forest along the coastal areas of Terengganu. So, Melaleuca forest mapping and flower mapping can be reliable methods for determining this species distribution as the main source of nectar and pollen for these aforementioned honey bees. In ecology, biomass can be defined as accumulation of living matter which is useful as a biophysical index for mapping of flower in forest. In this study, we used SPOT-5 and RADARSAT-1 for inventory of Melaleuca forest in Marang and developed Above Ground Biomass (AGB) estimation model as indirect index for obtaining and producing distribution of Melaleuca cajuputi flowers. Also, Apis dorsata colonies distribution and motorbike parking points of honey hunters were collected using GPS in field survey to determine distribution of colonies and improve searching ability in Apis dorsata colonies harvesting by honey hunters in the study area. The Melaleuca forest, located in Marang, Terengganu, Malaysia which is lying in upper left latitude 5°17'15.473"N, and longitude 103°05'25.021"E and lower right latitude 4°37'55.236" N, longitude 103°45'47.568"E was chosen for this research. SPOT-5 was enhanced, classified and vectorized using image processing software for the purpose of Melaleuca forest mapping. Based on the image analysis of the SPOT-5 image the Melaleuca forest were classified as five classes Melaleuca Cajuputi, Acacia auriculiformis, non-vegetation, water bodies and Cloud/haze/Shadow. The analysis showed that Melaleuca cajuputi covered 76,061.73ha (61.72%), Acacia auriculiformis 24,484.32ha (19.88%), non-vegetation 9,991.76ha (8.11%), water bodies 2,203.47ha (1.79%) and Cloud/Haze/Shadow 10,491.86ha (8.51%) with an overall classification accuracy of 91.79% while the statistics value obtained from kappa coefficient was more than 0.86 which is relatively quite good results for image processing. Based on Melaleuca forest inventory, 10 plots of 10m
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