Penelitian pemetaan habitat bentik di Pulau Wangi-wangi masih sangat sedikit dilakukan, sehingga ketersediaan data spasial habitat bentik di daerah ini sangat terbatas. Penelitian ini bertujuan untuk memetakan habitat bentik perairan dangkal menggunakan citra Sentinel-2 dengan metode klasifikasi berbasis objek/OBIA dan menghitung tingkat akurasi hasil klasifikasi habitat bentik di perairan Pulau Wangi-wangi Kabupaten Wakatobi. Penelitian ini dilaksanakan di perairan Pulau Wangi-wangi, khususnya perairan Sombu Dive dan sekitarnya. Penelitian ini menggunakan data satelit Sentinel-2 dengan resolusi spasial 10x10 m2 yang diakuisisi pada tanggal 4 April 2017 dan pengambilan data lapangan dilakukan pada bulan Maret - April 2017. Klasifikasi citra dengan metode OBIA menggunakan metode contextual editing pada level 1. Level 2 menggunakan klasifikasi terbimbing dengan beberapa algoritma klasifikasi yaitu support vector machine (SVM), decision tree (DT), Bayesian, dan k-nearest neighbour (KNN) dengan input themathic layer dari data lapangan. Klasifikasi habitat bentik dilakukan pada 12 dan 9 kelas dengan penerapan optimasi skala segmentasi yaitu 1, 1,5, 2, dan 2,5. Berdasarkan metode OBIA, habitat bentik dapat dipetakan dengan tingkat akurasi sebesar 60,4% dan 64,1% pada citra klasifikasi 12 dan 9 kelas secara berturut-turut pada nilai optimum skala segmentasi 2 dengan algoritma SVM.

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.

Chlorophyll-a is a phytoplankton pigment involved in photosynthesis. Chlorophyll-a concentration detection through satellite orbiting can only be infered the concentration of chlorophyll-a at sea surface and could not estimate the sea primary productivity. Sea Primary productivity may last up to a depth of compensation or the depth at which the intensity of light stayed at least 1% of sea surface light intensity. However, the aim of this study is to find out the relationships between the concentration of chlorophyll-a and primary productivity so that the concentration of chlorophyll-a could be used to predict primary productivity. The linear regression equation have been applied to construct model explaing relationship between the chlorophyll-a concentration and sea primary productivity. The equation explaing on chlorophyll-a concentrations with primary productivity is PP = 22.746 + 95.536Keu (R²) = 0.66 where PP is the sea primary productivity, Keu is the average of chlorophyll-a concentration throughout the water column. The results of these equations can be applied to satellite imagery so that it can assist in monitoring water quality conditions. Keyword: Chlorophyll-a concentration, light intensity, primary productivity, satellite imagery ABSTRAK Klorofil-a merupakan pigmen fitoplankton yang berperan dalam proses fotosintesis. Deteksi konsentrasi klorofil-a melalui satelit hanya dapat menduga konsentrasi klorofil-a permukaan dan bukan produktivitas primer. Produktivitas primer dapat berlangsung sampai kedalaman kompensasi atau kedalaman dimana intensitas cahaya tinggal 1% dari intensitas cahaya permukaan. Penelitian ini bertujuan untuk mencari hubungan antara konsentrasi klorofil-a dengan produktivitas primer sehingga konsentrasi klorofil-a dapat digunakan untuk menduga produktivitas primer. Analisis regresi linier dilakukan terhadap model hubungan antara konsentrasi klorofil-a dengan produktivitas primer. Persamaan hubugan antara konsentrasi klorofil dengan produktivitas primer adalah PP = 22.746 + 95.536Keu dengan (R²) = 0.66 dimana PP adalah produktivitas primer dan Keu adalah konsentrasi klorofil-a rata-rata di seluruh kolom perairan. Hasil persamaan tersebut dapat diaplikasikan untuk citra satelit sehingga dapat membantu dalam memonitoring kondisi kualitas perairan.Kata kunci: Konsentrasi klorofil-a, intensitas cahaya, produktivitas primer, citra satelit

Abstract. Indonesian waters containing many small islands and shallow waters leads to a less accurate of sea surface height (SSH) estimation from satellite altimetry. Little efforts are also given for the validation of SSH estimation from the satellite in Indonesian waters. The purpose of this research was to identify and retrack waveforms of Jason-2 altimeter satellite data in southern Java island waters and Java Sea using several retrackers and performed improvement percentage analyses for new SSH estimation. The study used data of the Sensor Geophysical Data Record type D (SGDR-D) of Jason-2 satellite altimeter of the year 2010 in the southern Java island waters and 2012-2014 in Java Sea. Waveform retracking analyses were conducted using several retrackers (Offset Center of Gravity, Ice, Threshold, and Improved Threshold) and examined using a world reference undulation geoid of EGM08 and Oceanic retracker. Result showed that shape and pattern of waveforms were varied in all passes, seasons, and locations specifically along the coastal regions. In general, non-Brownish and complex waveforms were identified along coastal region specifically within the distance of 0-10 km from the shoreline. In contrary, generally Brownish waveforms were found in offshore. However, Brownish waveform can also be found within coastal region and non-Brownish waveforms within offshore region. The results were also showed that the four retrackers produced a better SSH estimation in coastal region. However, there was no dominant retracker to improve the accuracy of the SSH estimate.

The ocean color satellite can only sense a water column up to one optical depth. However, literatur regarding the depth of one optical depth is very limited to none. This study aimed to determine light propagation, attenuation coefficient (Kd), and the depth of one optical depth in different water types. We used in situ data of downwelling irradiance (Ed) with depths taken using the instrument of submersible marine environmental radiometer (MER) in the northeastern gulf of mexico (NEGOM) in April 2000. We also used SeaWiFS data such as water leaving radience (Lw ), remote sensing refectance (Rrs), and chlorophyll-a concentration (Chla). The results showed that the light propagation pattern generally decreased with increasing depth. The reduction in light intensity with depth was very strong in the red wavelengths, lower in the green wavelengths, and the lowest in the blue wavelengths. In contrast, Kd values were generally found the lowest at the blue wavelengths, slightly increase at the purple and green wavelengths, and the highest at the red wavelengths. The depth of one optical depth in the case-1 waters was found as deep as 39.79 m (λ=475 nm), followed by intermediate water of 31.79 m (λ=475 nm), and in the case-2 waters of 16.08 m (λ=490 nm). Both Kd (490) in situ and modelled results showed a good correlation (r=0.83-0.84) and R2 values of 0.68-0.71.