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Fairus Mulia
PT Kandelia Alam

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Growth-site Quality Assessment of Nypa fruticans using Unmanned Aerial Vehicles Images: A case study in Kubu Raya Regency, West Kalimantan Province Adelia Juli Kardika; I Nengah Surati Jaya; Nining Puspaningsih; Fairus Mulia
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 2: February 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i2.pp502-511

Abstract

The growth-site quality is one of the essential information needed to support sustainable forest management particularly in forestry planning. This paper describes the development of a site-quality class of Nypa vegetation by considering the biological and physical factors. The main objective of this study is to develop a discriminant model and to find out major factors that may be used to predict the quality of Nypa growth-sites.The model was developed using variables either measured on UAV images or from field measurement, namely soil texture (X1), water salinity (X2), water pH (X3), crown closure (X4) and stand density (N) measured on the UAV image (X5). The study found that the site quality of Nypa could be indicated by the variation of its biomass content. Then, it was concluded that the major factors that affect the site quality are the soil texture (X1),water salinity (X2),and water pH (X3) with 78.3% of overall accuracy.
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 Adelia Juli Kardika I Nengah Surati Jaya Robert Parulian Silalahi Tatang Tiryana