Assessing the Crown Closure of Nypa on UAV Images using Mean-Shift Segmentation Algorithm

Robert Parulian Silalahi, I Nengah Surati Jaya, Tatang Tiryana, Fairus Mulia

Abstract


Utilization of very high-resolution images becomes a new trend in forest management, particularly in the detection and identification of forest stand variables. This paper describes the use of mean-shift segmentation algorithm on unmanned aerial vehicles (UAV) images to measure crown closure of nypa (Nypa fructicans) and gap. The 27 combinations of the parameter values such as spatial radius (hs), range radius (hr), and minimum region size (M). Gap detection and nypa crown closure measurements were performed using a hybrid between pixel-based (maximum likelihood classifier) and object-based approaches (segmentation).  For evaluation of the approach performance, the accuracy assessment was done by comparing object-based classification results (segmentation) and visual interpretation (ground check). The study found that the best combination of segmentation parameter was the combination of hs 10, hr 10 and M 50, with the overall accuracy of 76,6% and kappa accuracy of 55.7%.


Keywords


nypa, unmanned aerial vehicles (UAV), mean-shift, segmentation

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DOI: http://doi.org/10.11591/ijeecs.v9.i3.pp722-730

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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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