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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用于马来西亚半岛森林覆盖测绘的光学和雷达遥感数据

这项研究的目的是利用卫星图像绘制马来西亚半岛的森林覆盖图，因为近几十年来森林砍伐一直备受关注，这也是未来的重要环境问题。本研究使用卡内基Landsat分析系统-Lite（CLASlite）程序，使用Landsat卫星数据检测马来西亚半岛的森林覆盖率。研究结果表明，CLASlite算法将某些油棕，橡胶和城市地区误分类为森林植被。首先将Landsat和ALOS PALSAR图像进行组合，以识别油棕，橡胶和市区，然后将其移除，从而生成了可靠的森林覆盖图。ALOS PALSAR（阈值法）的HH和HV极化数据可以检测出油棕种植园，占总精度的85.26％。对于市区检测，使用Landsat的光谱带增强构建指数（EBBI），可提供94％的更高整体精度。这些方法得出的森林覆盖率读数为5 914 421公顷，总分类准确率为94.5％。在这项研究中检测到的森林覆盖率（包括橡胶区）比马来西亚半岛林业部检测到的2010年森林覆盖率高0.38％。本文中描述的技术为更新马来西亚的森林覆盖图提供了另一种可行的方法。比马来西亚半岛林业局2010年的森林覆盖率高出38％。本文中描述的技术为更新马来西亚的森林覆盖图提供了另一种可行的方法。比马来西亚半岛林业局2010年的森林覆盖率高出38％。本文中描述的技术为更新马来西亚的森林覆盖图提供了另一种可行的方法。