Satellite Remote Sensing Technology for Forest Type Classification and Inventory in Gunung Stong Forest Reserve, Kelantan, Malaysia
Khuzaimah, Zailani (2000) Satellite Remote Sensing Technology for Forest Type Classification and Inventory in Gunung Stong Forest Reserve, Kelantan, Malaysia. Masters thesis, Universiti Putra Malaysia.
In Malaysia and other developing countries access to the forest area is often difficult and forest information is inadequate. Remote sensing in forestry is very valuable and it has become important due to its capability to collect data from large areas and its capability to generate information. In other words, remote sensing technology offers reliable information essential for forest management and inventory. The objective of this study is to develop a technique for preliminary estimation of timber volume using satellite remote sensing imagery. Based on data analysis of the Landsat TM imagery, six classes of land cover were classified such as Primary Forest, Logged-Over Forest, Degraded Forest/ Plantation, Shrub, Barren Land / Cloud and Water Bodies/ Shadow. Using a forest canopy density technique, three volume intensity categories can be mapped out such as High Density (>35 m3 - 100 m3/ha) , Medium Density (20 m3 - 35 m3/ha) and Low Density (below 20 m3/ha) with an overall accuracy assessment of about 97.30 percent. The results of these studies imply that Malaysian loggers can now utilize these maps to have a preliminary estimate of the timber volume in the concession areas without really "flying over" the inaccessible sites. Consequently from the government's point of view, preliminary estimate of the concession fees can now be imposed on the loggers. It can be concluded that satellite remote sensing (i.e. Landsat TM) can be successfully used in forest type classification and inventory for macro-forest planning in Malaysia. However, high resolution satellite remote sensing data such as IKONOS- 1 or THEMAP airborne data taken from aircraft should be further investigated for micro-planning in forest management due to its high resolution capability for individual tree counting and mapping.
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