Oil Palm Plantation

Abstract

Oil palm is recognized as the golden crop. It produces the highest oil yield among oil seed crops. Malaysia,
as the world's second largest producer of palm oil, has 16 % of its lands planted with oil palms. Multisensor remote
sensing plays an important role by providing relevant, timely and accurate information that can be developed into a
plantation monitoring system to optimize production and sustainability. Synthetic aperture radar sensors deliver `cloudfree' images. Optical remote sensing provides important physical parameters of the plantation using multispectral data
acquisition. Both types of data are complementary and need to be exploited simultaneously to obtain a holistic view on
the plantation. The research described in this paper anticipates the development of a multisensor image and data fusion
system for oil palm plantation management.
Oil palm is cultivated extensively in the humid tropical land. It the most productive oil seed in the world
because the economic importance of oil palm is in two distinct products; the palm oil and kernel oil. Historically, oil
palm is native to West African coast and the palm oil is mainly used for cooking. Oil palm expansion and production in
Ghana within the last 2 decades were due to factors such as commodity price, market availability and government
intervention. Juaben oil mills located in Ejisu-Juaben district is one of the oldest mills in the country established during
the post-independence era. Since its privatisation in 1992, the supply of adequate fresh fruits bunches has been a
challenge due to demand. So the Ghanaian government with assistance separately from World Bank and Africa
Development fund in 1997 and 2004 respectively launched oil palm plantation initiatives to boost palm oil production,
improve employment opportunities while at the same time control rural-urban migration. However, the cultivation of oil
palm has raised issues of environmental sustainability. To assess sustainability of palm oil production and oil palm
expansion, the roundtable for sustainable palm oil has defined principles and criteria. Several of these criteria link to
land use and land cover. Yet, there is insufficient guidance from roundtable for sustainable palm oilon how to map and
quantify oil palm related land cover changes. So there is a need to develop a methodology to map oil palm related land
cover changes at the local level. The study objective seeks to map oil palm related land cover of a section from northern
portion of Ejisu-Juaben district in the Ashanti Region of Ghana using support vector machine (SVM) with Landsat
ETM+. The districtlies within Longitude 6° 15‟ N and 7° 00‟ N and Latitude 1° 15‟ W and 1 ° 45‟ W and is
characterised by both agricultural and socio-economic activities. The Landsat ETM+ data acquired in 2010 was used
for processing and image classification. Field data were acquired in October 2011 through stratified random sampling.
A total of 343 samples were collected for classification and accuracy assessment. The classification was carried out
using MLC and SVM based on best three band combination from the image. The SVM and MLC performance evaluation
was done using overall accuracy assessment and kappa statistics procedure. The results of separability analysis showed
that ETM+ data provides spectral discrimination of land cover types found in the study area. The best three bands that
provided the optimum spectral separability based on Bhattacharyya distance are 4, 5, and 3.