Forest cover mapping in iskandar Malaysia using satellite data

• Main Authors: Kanniah, K. D., Najib, N. E. M., Vu, T. T.
• Format: Conference or Workshop Item
• Published: International Society for Photogrammetry and Remote Sensing 2016
• Subjects: G70.212-70.215 Geographic information system
• Online Access: http://eprints.utm.my/id/eprint/73060/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-84993997505&doi=10.5194%2fisprs-archives-XLII-4-W1-71-2016&partnerID=40&md5=a0879421d2c7fb18d490967ea87d6b07
• Institution: Universiti Teknologi Malaysia

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.