Recovery of forest structure dynamics following selective logging in lowland dipterocarp Peninsular Malaysia

Abstract

Abstract The selective management system (SMS) practised in Malaysia has emerged to optimise the sustainability of permanently reserved forest management. SMS requires a management regime (felling) to ensure economical harvesting and appropriate residual stands for the logging cycle, including ecological balance and environmental quality. SMS includes a Post-Felling Forest Inventory (Post-F) sequence. Post-F is used to obtain information on the remaining stands and other plants to determine the silvicultural treatment of a logged area. However, data gathered from Post-F are insufficient to track and collect information on forest structure dynamics recovery after logging. Thus, this study is to further understand the applicability of remote sensing technologies for forest recovery structure assessment after selective logging in the lowland dipterocarp forest of Peninsular Malaysia. Understanding the structural and composition changes occurring in lowland dipterocarp forests is vital for forecasting these ecosystems in the future. This study uses temporal dynamics (5, 9, 16, 26 and 32 months) of canopy cover images obtained from Landsat 8 after selective logging. Using the Carnegie Landsat Analysis System (CLASlite), this study applies Automated Monte Carlo Unmixing Analysis (AutoMCU) algorithm to derive per-pixel fractional cover estimates of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil. The relationship between these three indicators has shown the dynamic growth pattern of the forest area after logging. Differences can be seen via changes in the PV, NPV and bare soil image over various time periods. The information derived from this study is vital for forest conservation strategies after logging, thus enhancing Sustainable Forest Management in Peninsular Malaysia.