Map The Gap – Using Repeat Airborne LiDAR to Map the Growth of Oil Palm Across a Tropical Landscape.

Research output: Other contribution

Abstract

The ever-increasing demand for palm oil has led to a rapid rise in the clearing of tropical forests, particularly in areas of South East Asia. Oil palm is central to the livelihoods of many people, and it must be ensured that the production of palm oil can occur as sustainably as possible. Thus, it is essential to understand how oil palm growth varies across tropical landscapes in order to optimise yields. In this project, repeat airborne LiDAR data was used to map the height growth of over half a million individual oil palms in Malaysian Borneo over a two-year period which coincided with the 2015-16 global El Niño event. The ability of oil palms to continue growing during this period of uncharacteristically dry and hot weather was investigated, and the ecological and landscape features that contributed most to differences in growth rates across landscapes were explored. Despite the drought conditions, oil palms grew 1.61 m yr-1 in height on average, but growth varied substantially among individuals, with smaller oil palms exhibiting the fastest rates of height growth. Landscape features such as the distance of palms from forest edges, elevation, and terrain ruggedness all had significant effects on height growth, as did relative competition with neighbours. However, effect sizes were weak and collectively these predictors only explained a small portion of the variation in growth among individual oil palms (5%). The project also revealed opportunities for improving the efficiency and yields of oil palm agriculture, but doing so requires further work to pinpoint the factors that contribute most to driving variation in oil palm growth rates across tropical landscapes.
Original languageEnglish
TypeMSc by Research thesis
Media of outputPDF
Number of pages109
Publication statusUnpublished - 2021

Keywords

  • LiDAR; Oil Palm; El Niño; Remote Sensing; Drought; Climate Change; Precision Agriculture

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