Application of optical remote sensing technology for oil palm management
Ibrahim, Zainal Abidin Hasan & Mariamni Khalid
Abstract
Recent advances in remote sensing technology and applications, with concurrent advances in global positioning systems (GPS) and the ubiquitous use of geographic information systems (GIS), have provided a powerful analysis tool for precision agriculture. These advances have also led to intense informational requirements. Image-based remote sensing may provide the timely, spatially distributed information on crop and soil conditions that is needed to implement precision agriculture. The aim of this paper is to investigate the relationship between Landsat Thematic Mapper (TM) relative radiance and phenology of oil palm for commercial estates in Selangor, Malaysia. This study focused on estimating oil palm phenology with special emphasis to planting age (1- >20 years) and Leaf Area Index (LAI) to spectral radiance via multiple linear regression models. The carrier (predictor) variables that was considered for multiple regression are the six Landsat TM bands, Shadow Canopy Index (SI), Bare Soil Index (BIO), Normalised Vegetation index (NDVI), Advanced Vegetation Index (AVI), planting densities, soil units and relief. The relative radiance recorded by Landsat TM for oil palm was negatively correlated with stand age. Middle Infrared wavebands (MIR) band 7 and 5 showed high correlation (> 0.74) and more discriminating power (Lambda Wilk's coefficient) to discriminate the oil palm age classes. However, the visible and Near Infrared bands displayed insignificant correlation (< -0.05). Nevertheless, it is concluded that the developed regression model was able to produce reasonably accurate oil palm age classes at estate level from satellite image.
Introduction
Oil palm has become an important crop in Malaysia. The country is the world's largest exporter of palm oil. Within a relatively short period the planted areas under oil palm have increased tremendously from 54,656 hectares in 1960 to 300,800 hectares in 1970 and 1.98 million hectares in 1990. In 1993, the total area under oil palm was estimated to be 2.2 million hectares (Primary Industries Department of Malaysia, 1998). Oil palm cultivation in Malaysia is largely based on the estate management system and organised smallholders under The Federal Land Development Authority (FELDA), Federal Land Consolidation and Rehabilitation Authority (FELCRA), The Rubber Industry Smallholders Development Authority (RISDA) and the State Economic Development Corporation (SEDCs). This has enabled a better utilisation of resources as well as the application of advanced management and planning techniques throughout most of the industry.
Because of the importance of oil palm to the country, accurate and reliable information is needed for oil palm management, especially on plant quality, phenology, health and yield prediction. However, in the context of Malaysia, lack of data, cost effective and timely processing of information for oil palm management planning are major constraints which hold up decision making. Currently data collection for oil palm planning and management depend mainly on traditional methods of sample surveys in the field. The main task of Malaysian agriculture agencies is to improve the reliability, timeliness and cost effectiveness of data collection techniques. Remote sensing was identified as an effective recent agricultural crop information for the global oil palm plantation industry planning (MACRES, 1998).
In Malaysia, the usefulness of remote sensing in crop management has not been intensively explored. Remote sensing in conjunction with a land information system is believed to be a good technique to assist the Malaysian Agriculture Planning Unit and estate managers in making fast decisions. Therefore, based on the importance of oil palm to the country and the lack of reliable, timely and cost effective data, a study of application of satellite images to plantation information management and planning is justified. The aim of this paper is to elaborate the relationship between Landsat TM radiance and the phenology of oil palm on commercial estates in Selangor, Malaysia (Tuan Mee and Balau Estate), especially the ability to characterise age since field planting and to acquire knowledge on producing oil palm vigour map from remote sensing.
Methodology
Study area
Balau and Tuan Mee estates are situated at Semenyih and Kuala Selangor, respectively, in the state of Selangor, Malaysia They were selected as study areas because of the availability of the ground-surveyed data supplied by Applied Agriculture Research Sendirian Berhad (AARSB), the agriculture adviser to both estates. Balau Estate is relatively flat with less than 17 degree slope whereas Tuan Mee Estate is undulating with 10 to 20 degree slopes.
Landsat imagery
The Landsat TM scene used was path 127 row 58 for 6 March 1996, bands 1,2,3,4,5 and 7, pre-processed by the ground station to level 4 (radiometrically corrected and across track geometric correction applied) (Figure 3.2 and Figure 3.3). The imagery was supplied by the Malaysian Centre for Remote Sensing (MACRES) who acquired it from Thailand's ground receiving station. The Landsat TM imagery was atmospherically adjusted using the dark-pixel subtraction method (Foody, 1997). Dark object subtraction methods are based on the assumption that within an image some pixels will lie in areas of complete shadow and so any radiance measured from them by the sensor must be attributable to the atmosphere requisite that image has been radiometrically calibrated.
Pixel sampling
A simple stratified sampling scheme (Gallego, 1995) with strata based on oil palm age was used to extract the Landsat TM spectral radiance values. A sample of 373 and 757 pixels, each 30 X 30 m were distributed randomly were selected from Balau and Tuan Mee Estate, respectively. Samples were selected based on visual interpretation of the Landsat-5 image, the topographic map, and oil palm years planting map. A number of considerations were taken into account during the sampling procedure: 1: The location of training areas near the boundary of different planting stages was avoided. 2: Extreme care was taken to prevent the selection of training areas within or near to shadow areas.
