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.

The purpose of this study was to determine the variability of Sea Surface Temperature (SST) and chlorophyll-a concentration (Chl-a) in the waters of Palabuhanratu and its surroundings. This study used a combination of NOAA-AVHRR and MODIS images data for monthly SST, SeaWiFS and MODIS images for monthly Chl-a, and monthly surface wind data from the European Center for Medium Range Weather Forecasts (ECMWF). Monthly data of El Nino Southern Oscillation (ENSO) index and the Dipole Mode Index (DMI) were also used. The results showed that the range of SST for 21 years (1997-2018) was 23.14-33.45°C where the maximum SST value occurred at the end of Transition Season 1 (May), while the minimum low value was found at the beginning of Transition Season 2 (September). Chl-a for 21 years ranged from 0.063-3.363 mg/m³ where the maximum Chl-a occurred during the beginning of Transition Season 2 (September), while the minimum value was found at the end of Transition Season 1 (May). During ENSO and positive IOD occurred together, it triggered stronger Southeast wind speed and intensified upwelling event along the Palabuhanratu and its surrounding waters and caused lower SST and higher Chl-a than normal. Variabilities of the SST and Chl-a values were related to the seasonal winds, the intensity of solar radiation, the phenomenon of climate anomalies of ENSO, and Indian Ocean Dipole Mode (IODM). The evidence of higher concentration of chlorophyll-a around August-October coincided with the evidence of lower SST in the same seasons was due to the upwelling phenomenon.