Oil palm is recognized as a golden crop, as it produces the highest oil yield among oil seed crops. Malaysia is the world’s second largest producer of palm oil; 16% of its land is planted with oil palm. To cope with the ever-increasing global demand on edible oil, additional areas of oil palm are forecast to increase globally by 12 to 19 Mha by 2050. Multisensor remote sensing plays an important role in providing relevant, timely, and accurate information that can be developed into a plantation monitoring system to optimize production and sustainability. The aim of this study was to simultaneously exploit the synthetic aperture radar ALOS PALSAR 2, a form of microwave remote sensing, in combination with visible (red) data from Landsat Thematic Mapper to obtain a holistic view of a plantation. A manipulation of the horizontal–horizontal (HH) and horizontal–vertical (HV) polarizations of ALOS PALSAR data detected oil palm trees and water bodies, while the red spectra L-band from Landsat data (optical) could effectively identify built up areas and vertical–horizontal (VH) polarization from Sentinel C-band data detected bare land. These techniques produced an oil palm area classification with overall accuracies of 98.36% and 0.78 kappa coefficient for Peninsular Malaysia. The total oil palm area in Peninsular Malaysia was estimated to be about 3.48% higher than the value reported by the Malaysian Palm Oil Board. The over estimation may be due the MPOB’s statistics that do not include unregistered small holder oil palm plantations. In this study, we were able to discriminate most of the rubber areas.

ABSTRACT: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.

This study aims to map forest cover in Peninsular Malaysia using satellite images as deforestation is of concern in the recent decades, and is an important environmental issue for the future too. The Carnegie Landsat Analysis System‐Lite (CLASlite) program was used in this study to detect forest cover in Peninsular Malaysia using Landsat satellite data. The results of the study show that CLASlite algorithm misclassified some oil palm, rubber and urban areas as forest vegetation. A reliable forest cover map was produced by first combining Landsat and ALOS PALSAR images to identify oil palm, rubber and urban areas, and then subsequently removing them. The HH and HV polarization data of ALOS PALSAR (threshold method) could detect oil palm plantations with 85.26 per cent of overall accuracy. For urban area detection, Enhance Build up Index (EBBI) using spectral bands from Landsat provided higher overall accuracy of 94 per cent. These methods produced a forest cover reading of 5 914 421 ha with an overall classification accuracy of 94.5 per cent. The forest cover (including rubber areas) detected in this study is 0.38 per cent higher than the percentage of 2010 forest cover detected by the Forestry Department of Peninsular Malaysia. The technique described in this paper presents an alternative and viable approach for updating forest cover maps in Malaysia.

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