• International Journal of Technology (IJTech)
  • Vol 10, No 7 (2019)

The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

Title: The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images
Lim Soon Eng, Rozita Ismail, Wahidah Hashim, Aslina Baharum

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Eng, L.S., Ismail, R., Hashim, W., Baharum, A., 2019. The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images. International Journal of Technology. Volume 10(7), pp. 1385-1394

Lim Soon Eng College Of Computer Science and Information Technology, University Tenaga Nasional (UNITEN)
Rozita Ismail College Of Computer Science and Information Technology, University Tenaga Nasional (UNITEN)
Wahidah Hashim Institute of Informatics and Computing in Energy, University Tenaga Nasional (UNITEN)
Aslina Baharum Faculty of Computing and Informatics (FKI), University Malaysia Sabah (UMS), Jalan UMS 88400 Kota Kinabalu, Sabah, Malaysia
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The Use of VARI, GLI, and VIgreen Formulas in Detecting Vegetation In aerial Images

Vegetation monitoring is a task that requires much time and human effort, but by using an unmanned aerial vehicle with a system that can store captured data digitally, the task can be more manageable and efficient. Past research has shown many formulas were developed by researchers to capture vegetation data in varying conditions and equipment. This paper discusses an experiment conducted to test three of those formulas using visible band data images. The formulas are the visible atmospherically resistant index, the green leaf index, and the visible atmospherically resistant indices green. The objective of this paper is to report and discuss our findings from experiments conducted using each formula as well as to compare the accuracy of these formulas.

Green leaf index; Vegetation indices; Vegetation monitoring; Visible atmospherically resistant index


Planting vegetation benefits every living being on earth (William et al., 2012; Pauline et al.,  2013). According to the book The Benefits of Urban Vegetation, a survey found that green vegetation can directly affect human health by reducing stress, encouraging physical activity and improving the living environment (Pauline et al., 2013).  Most importantly, it also helps reduce the environment temperatures of urban areas (William et al., 2012). However, vegetation can be a problem, too. For example, in Bakersfield, CA, a developer had planted 300 redwoods directly under power line cables. As a result, tree trimming caused challenges that may have reduced the benefits of having vegetation (William et al., 2012). Monitoring vegetation is another issue for some agencies. Research found that a power energy provider had spent and allocated around USD2 billion to USD10 billion per year on vegetation management in order to have proper maintenance at their facilities and provide reliable electricity delivery to consumers (Rancea, 2014). Meanwhile, according to a power provider in South Australia known as SA Power Network, they completed vegetation inspections using a four-wheel-drive vehicle to inspect every potential vegetation encroachment (Lewis, 2018). According to research conducted by de Ronde et al. (2007), the main requirement of vegetation monitoring is to do comparability studies  on data  between different years.  Visible atmospherically resistant index (VARI),  green leaf index (GLI) and visible atmospherically resistant indices green (VIgreen) have been used to perform vegetation monitoring by just using visible band (RGB) data. We have conducted an experiment to study and understand the behavior of vegetation formulas on the aerial images captured by unmanned aerial vehicles of different environments. The motivation is to familiarize and determine the differences in the results between each formula, which can be used by utilities industries when verifying vegetation encroachment in power transmission corridors. The purpose of conducting this research is to find out the most suitable formula to perform post-processing of the images captured using consumer drones to highlight vegetation objects from other objects. It can reduce the time taken to perform monitoring for encroachment and trimming of large areas of vegetation.


Throughout this experiment, it was found that the GLI formula proved the most suitable technique for highlighting vegetation in both urban areas and forest areas based on its sensitivity in detecting the differences between vegetation and non-vegetation. Overall, the three formulas can show green objects with minimal false detection in single-band pseudocolor. As for future work, we will include machine-learning techniques and deep-learning analysis in the data processing in order to enhance the vegetation detection to a minimal percentage of misinterpretation.


This research was supported by UNITEN internal grant, project code J510050744. We acknowledge the use of facilities and equipment provided by Micro Multi Copter Aero Science & Technology and Kembara Impian Technologies Sdn. Bhd. We would like to thank Tuan Hussin Daud. and Mr. Alan from Kembara Impian as well as Mr. Edison and Mr. Ken from Micro Multi Copter Aero Science & Technology for providing us suggestions based on their expertise and experience throughout the research.


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