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Tatang Tiryana
Bogor Agricultural University

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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
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 3: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i3.pp722-730

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%.
Co-Authors Fairus Mulia I Nengah Surati Jaya Robert Parulian Silalahi