Optimization of oil palm age recognition using Sentinel -2A and Landsat-8 / Nurul Nadia Zainuleden

Oil palm (Elaeis guineensis) trees have made significant contributions to Southeast Asia recent economic development. Malaysia has made significant strides in the oil palm industry. It began as an ornamental plant in Malaysia and has since grown into a multibillion-dollar industry. It has the highest global demand as an ingredient in food, cosmetics, animal feed, bioenergy, and other products. Malaysia currently ranks second in palm oil production after Indonesia, accounting for 39 percent of global production and 44 percent of palm oil exports. Thus, Malaysia has an important role to play in achieving the growing global demand for oils and fats because it is a major producer and exporter of palm oil and palm oil products. The purpose of this study is to identify the parameter and characteristic of the age of oil palm based on literature research as well to
quantify the oil palm trees based on age distribution derived from satellite and UAV imagery and to compare both Sentinel - 2 and Landsat 8 imagery in their efficiency of age recognition for oil palm trees towards crown diameter extraction from UAV imagery. The satellite images were used are Sentinel-2, Landsat 8, and Unmanned Aerial Vehicle (UAV) dataset imagery were used on the same year in 2018. The process used two methods which are Remote Sensing techniques and Geographic Integration System (GIS) in monitoring and detecting oil palm plantations to maximise productivity. The study outcome is a map distribution of oil palm age and Normalized Differences Index Vegetation (NDVI) in the study
area.