Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Biodiversitas Journal of Biological Diversity
Vincentius P. Siregar
Department of Marine Science and Technology, Faculty of Fisheries and Marine Science, Institut Pertanian Bogor. Jl. Agatis, Kampus IPB Dramaga, Bogor 16680, West Java, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu, Indonesia Hartoni Hartoni; Vincentius P. Siregar; Sam Wouthuyzen; Syamsul Bahri Agus
Biodiversitas Journal of Biological Diversity Vol. 23 No. 1 (2022)
Publisher : Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/biodiv/d230155

Abstract

Abstract. Hartoni, Siregar VP, Wouthuyzen S, Agus SB. 2021. Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu. Biodiversitas 23: 514-520. Benthic habitats have very high complexity and are home to many types of aquatic organisms. Benthic habitats have various functions, including habitat for flora and fauna, sediment traps, nursery areas, and foraging areas for aquatic fauna that are susceptible to damage due to human activities or natural factors. Therefore, more accurate spatial information is needed. The purpose of this study was to examine the ability of object-based classification techniques for mapping shallow waters benthic habitats using Sentinel 2A imagery. The two classification algorithms used are support vector machine (SVM) and random forest (RF). The input image layer (IIL) used for classification is the natural color band (Band 432). The results showed that the SVM and RF classification algorithms could classify eight classes of benthic habitats. The overall accuracy (OA) of the SVM algorithm is 65%, while the RF accuracy is 67%, with kappa values of 0.59 and 0.60, respectively. The significant test applied to Sentinel 2 images with SVM and RF algorithms for benthic habitats has a Z test value of-0.41. These results indicate that the classification results between the SVM and RF algorithms are not significantly different.
Geomorphological classification of benthic structures of Kaledupa Atoll Wakatobi National Park, Indonesia Alim Setiawan; Vincentius P. Siregar; Setyo Budi Susilo; Ani Mardiastuti; Syamsul Bahri Agus
Biodiversitas Journal of Biological Diversity Vol. 23 No. 7 (2022)
Publisher : Society for Indonesian Biodiversity

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/biodiv/d230755

Abstract

Abstract. Setiawan A, Siregar VP, Susilo SB, Mardiastuti A, Agus SB. 2022. Geomorphological classification of benthic structures of Kaledupa Atoll Wakatobi National Park, Indonesia. Biodiversitas 23: 3784-3792. Geomorphological mapping is one solution to understanding the complexity of the seabed pattern. This research aims to map the benthic structure of Kaledupa Atoll using the Remote Sensing and Geographic Information System with the Benthic Terrain Modeler (BTM) extension. The data used is in situ bathymetry data using Mapsounder 585 and Sentinel 2A satellite image data downloaded for free. BTM is a technique for examining benthic habitat and shallow water geomorphology. BTM was used to analyze integrated depth data to determine the bathymetric position index (BPI), slope, and classification of reef geomorphological structures. Thirteen structural classes of habitat geomorphology resulting from this research are Flat plain with the largest area of ??17142.35 ha, and the class with the smallest area is Crevices class, with narrow gullies rock outcrops 1.64 ha. While the other classes in a row are Broad slope 9104.53 ha; Flat ridge tops, upper slopes 4262.67 ha; Current scoured depressions on a slope 3420.98 ha; Narrow depressions at the base of rock outcrops 2348.20 ha; Rock outcrop highs, narrow ridges 1792.45 ha; Local ridges, boulders, pinnacles on slopes 879.29 ha; Scarp, cliff or small local depressions on a slope 703.02 ha; Local ridges, boulders, pinnacles on broad flat 490.17 ha; Local depressions, current scoured on flat 416.39 ha; Local ridges, boulders, pinnacles on broad depressions 16.63 ha; and Steep slopes 2.88 ha.
Co-Authors Alim Setiawan Ani Mardiastuti Hartoni Hartoni Sam Wouthuyzen Setyo Budi Susilo Syamsul Bahri Agus