Detection of rubber tree using image classification and vegetation indices / Nurhidayah Abidin

Abidin, Nurhidayah (2022) Detection of rubber tree using image classification and vegetation indices / Nurhidayah Abidin. Degree thesis, Universiti Teknologi MARA, Perlis.


The rubber tree (Hevea brasiliensis is a tree of the Euphorbiaceous family and the most important member of the genus Hevea. The growth of rubber trees should be considered when making land-use decisions. The conventional way of obtaining this information is time-consuming, expensive, and imposes restrictions on access to certain locations. The information on existing rubber cultivation acreages and tree growth conditions is critical for plantation management functions such as field management planning and decision-making. Therefore, the aim of this study is to detect the rubber trees using image classification and vegetation indices. The objectives of this study were i)to determine land use the accuracy of using supervised classification and ii)to identify the range of vegetation index for rubber trees using NDVI. The method used in this study is by using supervised classification and the use of the method is the maximum possibility. The study identified vegetation indices using NDVI. This study is very important to farmers because it can show the area of rubber trees using remote sensing method as well as to know the concentration of rubber trees area. The result will be demonstrated through an assessment of the accuracy of image classification and production of an NDVI map.


Item Type: Thesis (Degree)
Email / ID Num.
Abidin, Nurhidayah
Email / ID Num.
Nasron, Nursyahani
Subjects: G Geography. Anthropology. Recreation > G Geography (General) > Remote Sensing
G Geography. Anthropology. Recreation > G Geography (General) > Geomatics
Divisions: Universiti Teknologi MARA, Perlis > Arau Campus > Faculty of Architecture, Planning and Surveying
Programme: Bachelor of Surveying Science and Geomatics (Honours)
Keywords: Rubber tree, supervised classification, vegetation index, remote sensing
Date: 2022
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