Shallow-water benthic habitat mapping using drone with object based image analyses

• Main Authors: Nababan, Bisman, Mastu, La Ode Khairum, Idris, Nurul Hazrina, Panjaitan, James P.
• Format: Article
• Language: English
• Published: MDPI 2021
• Subjects: G Geography (General); VM Naval architecture. Shipbuilding. Marine engineering
• Online Access: http://eprints.utm.my/id/eprint/94811/1/NurulHazrina2021_ShallowWaterBenthicHabitat.pdf; http://eprints.utm.my/id/eprint/94811/; http://dx.doi.org/10.3390/rs13214452

Spatial information on benthic habitats in Wangiwangi island waters, Wakatobi District, Indonesia was very limited in recent years. However, this area is one of the marine tourism destinations and one of the Indonesia’s triangle coral reef regions with a very complex coral reef ecosystem. The drone technology that has rapidly developed in this decade, can be used to map benthic habitats in this area. This study aimed to map shallow-water benthic habitats using drone technology in the region of Wangiwangi island waters, Wakatobi District, Indonesia. The field data were collected using a 50 × 50 cm squared transect of 434 observation points in March–April 2017. The DJI Phantom 3 Pro drone with a spatial resolution of 5.2 × 5.2 cm was used to acquire aerial photographs. Image classifications were processed using object-based image analysis (OBIA) method with contextual editing classification at level 1 (reef level) with 200 segmentation scale and several segmentation scales at level 2 (benthic habitat). For level 2 classification, we found that the best algorithm to map benthic habitat was the support vector machine (SVM) algorithm with a segmentation scale of 50. Based on field observations, we produced 12 and 9 benthic habitat classes. Using the OBIA method with a segmentation value of 50 and the SVM algorithm, we obtained the overall accuracy of 77.4% and 81.1% for 12 and 9 object classes, respectively. This result improved overall accuracy up to 17% in mapping benthic habitats using Sentinel-2 satellite data within the similar region, similar classes, and similar method of classification analyses.