Estimating Phytoplankton Abundance Using Sentinel 2A Images In Langa-Jampue Water Area, Pinrang Regency

This study aims to estimate the abundance of phytoplankton using Sentinel-2 imagery in the Langa – Jampue water area of Pinrang Regency for Sentinel-2 image recording on February 28, 2022. This study was conducted from January – July 2022 by taking phytoplankton samples, conducting sample analysis in the laboratory, and processing Sentinel-2 image data on February 28, 2022 recording. The results of the study found 5 classes of phytoplankton, namely Bacillariophyceae, Cyanophyceae, Dyanophyceae, Peridinae, and Dinophyceae with a total of 34 phytoplankton genera and 4 dominating genera, namely Astereonolepsis, Rhizosolenia, Chaetoceros, and Ceratium. The highest phytoplankton abundance was obtained in transect 2 on point with an abundance of 977 cells/liter and the lowest abundance in transect 3 on point 24 which was 282 cells/liter. The regression test results between phytoplankton abundance and pixel band values 8, band 3, and band 2 on Sentinel-2 images produced an r-square value of 0.495 and obtained a positive correlation value between the pixel band 8 value and the phytoplankton abundance value with a correlation value of 0.529 which means that band 8 can be used for estimating phytoplankton abundance in marine remote sensing systems. The result of the paired t-test revealed that the abundance of phytoplankton based on the results of image processing and relative laboratory analysis was equal to a significant value of 0.999 Keywords: Phytoplankton, Sentinel-2 Imagery, Band 8 Abstrak Penelitian ini bertujuan untuk mengestimasi kelimpahan fitoplankton menggunakan citra Sentinel-2 di wilayah perairan Langa – Jampue Kabupaten Pinrang untuk perekaman citra Sentinel-2 pada tanggal 28 Februari 2022. Penelitian ini dilakukan pada bulan Januari – Juli 2022 dengan mengambil sampel fitoplankton, melakukan analisis sampel di laboratorium dan mengolah data citra Sentinel-2 pada perekaman 28 Februari 2022. Hasil dari penelitian ditemukan 5 kelas fitoplankton yaitu Bacillariophyceae, Cyanophyceae, Dyanophyceae, Peridinae, Dinophyceae dengan total 34 genus fitoplankton dengan 4 genus yang mendominasi yaitu Astereonolepsis, Rhizosolenia, Chaetoceros dan Ceratium. Kelimpahan fitoplankton tertinggi didapatkan pada transek 2 yaitu pada titik 9 dengan kelimpahan 977 sel/liter dan kelimpahan terendah pada transek 3 yaitu pada titik 24 dengan kelimpahan 282 sel/liter. Hasil uji regresi antara kelimpahan fitoplankton dan nilai pixel band 8, band 3 dan band 2 pada citra Sentinel-2 menghasilkan nilai r-square yaitu 0,495 dan didapatkan nilai korelasi yang positif antara nilai pixel band 8 dan nilai kelimpahan fitoplankton dengan nilai korelasi 0,529 yang berarti band 8 dapat digunakan untuk pendugaan kelimpahan fitoplankton pada sistem penginderaan jauh kelautan. Dari hasil uji-t paired dapat diketahui bahwa kelimpahan fitoplankton dari hasil pengolahan citra dan hasil analisis laboratorium realtif sama dengan nilai signifikan 0,999 Kata kunci: Fitoplankton, Citra Sentinel-2, Band

COMPARISON OF SEAGRASS COVER CLASSIFICATION BASED-ON SVM AND FUZZY ALGORITHMS USING MULTI-SCALE IMAGERY IN KODINGARENG LOMPO ISLAND

Padang lamun mempunyai peranan ekologi bagi lingkungan laut dangkal yaitu sebagai habitat biota, produsen primer, penangkap sedimen serta berperan sebagai pendaur zat-zat hara. Mengingat pentingnya peranan ekosistem padang lamun maka kelestarian sumber daya alam ini perlu dijaga, oleh karena itu pemetaan dan pemantauan yang terus-menerus terhadap keberadaan padang lamun sangat penting dilakukan. Metode penginderaan jauh merupakan metode yang dapat digunakan untuk memetakan dan memantau kondisi padang lamun. Perkembangan teknologi sensor satelit yang pesat saat ini, khususnya resolusi spasial dan spektral sensor meningkatkan kualitas peta sebaran lamun. Penggunaan metode dan skema klasifikasi yang kurang tepat dalam klasifikasi kondisi lamun dari citra satelit juga termasuk hal yang dapat memengaruhi akurasi peta, sehingga dibutuhkan berbagai alternatif kajian algoritma yang digunakan. Pada penelitian ini digunakan algoritma Support Vector Machine dan Logika Fuzzy menggunakan citra satelit WorldView-2 dan Sentinel-2 di Pulau Kodingareng Lompo dengan empat kelas tutupan lamun yaitu jarang (0-25%), sedang (26-50%), padat (51-75%), dan sangat padat (76-100%). Hasil yang diperoleh adalah algoritma Logika Fuzzy menggunakan citra WorldView-2 memiliki akurasi keseluruhan klasifikasi tutupan lamun yang paling baik sebesar 78,60%.Seagrass beds play an ecological role in the shallow marine environment, such as a habitat for biota, primary producers, and sediment traps. They also act as nutrient recyclers. Since they have such an important role, this natural resource needs to be preserved. Therefore, continuous monitoring and mapping of seagrass beds, especially by remote sensing methods, is paramount. The current rapid development of satellite sensor technology, especially its spatial and spectral resolutions, has improved the quality of the seagrass distribution map. The use of proper classification methods and schemes in the classification of seagrass distribution based on satellite imagery can affect the accuracy of the map, which is why various alternative algorithm studies are required. In this study, the Support Vector Machine and Fuzzy Logic algorithms were used to classify the WorldView-2 and Sentinel-2 satellite imageries on Kodingareng Lompo Island with four classes of seagrass cover, sparse (0–25%), moderate (26–50%), dense (51–75%), and very dense (76–100%). The result showed that the Fuzzy Logic algorithm applied to WorldView-2 imagery has the best overall accuracy of 78.60% seagrass cover classification

Estimasi Daya Dukung Padang Lamun Di Paparan Terumbu Karang Kepulauan Derawan Sebagai Feeding Habitat Bagi Penyu Hijau

Padang lamun pada paparan terumbu karang adalah habitat potensial bagi hewanhewan\ud langka\ud dilindungi\ud seperti\ud penyu\ud hijau\ud (Chelonia\ud mydas).\ud Hewan\ud ini\ud \ud memakan\ud daun-daun\ud lamun\ud secara\ud massive\ud sehingga\ud sangat\ud penting\ud dalam\ud daur\ud \ud nutrien\ud pada\ud daerah\ud padang lamun\ud dan\ud berperan\ud penting\ud secara\ud ekologi\ud dalam\ud \ud mengendalikan\ud aliran\ud materi\ud dan\ud energi\ud antara\ud ekosistem\ud padang\ud lamun\ud dan\ud \ud terumbu\ud karang.\ud Selain\ud itu,\ud hewan\ud kharismatik\ud ini\ud juga\ud penting\ud sebagai\ud ikon\ud \ud ekowisata\ud laut.\ud Di kawasan\ud Indo-Pasifik,\ud salah\ud satu\ud habitat\ud utama\ud penyu\ud hijau\ud \ud untuk\ud mencari\ud makan\ud (feeding\ud ground)\ud dan peneluran\ud adalah\ud pada\ud pulau-pulau\ud \ud karang\ud Kepulauan\ud Derawan,\ud Kalimantan\ud Timur\ud yang\ud terletak\ud di kawasan\ud laut\ud \ud semi\ud tertutup\ud Sulu\ud Sulawesi.\ud Luasan,\ud komposisi\ud jenis,\ud persen\ud penutupan,\ud dan\ud \ud produksi\ud biomassa\ud padang\ud lamun\ud secara\ud simultan\ud memberi\ud pengaruh\ud terhadap\ud \ud daya\ud dukung padang\ud lamun\ud dalam\ud menyediakan\ud makanan\ud bagi\ud penyu\ud hijau\ud di\ud \ud daerah\ud ini.\ud Penelitian\ud ini\ud dilakukan untuk mengetahui\ud kondisi\ud padang\ud lamun\ud di\ud \ud Kepulauan\ud Derawan\ud dan memetakan\ud luasan\ud dan sebaran\ud padang\ud lamun\ud sebagai\ud \ud feeding\ud habitat\ud penyu\ud hijau\ud di\ud daerah\ud tersebut.\ud Hasil penelitian\ud menunjukkan\ud \ud bahwa\ud padang\ud \ud lamun di Kepulauan Derawan (Pulau Derawan, Gusung\ud Masimbung, Pulau Maratua) didominasi oleh jenis lamun Halodule uninervis,\ud sedangkan padang lamun di Pulau Panjang didominasi oleh Cymodocea\ud rotundata. Selanjutnya hasil pengukuran morfologi lamun penyusun yang\ud dominan meliputi panjang daun, lebar daun, dan jarak internode rhizoma,\ud mengindikasikan adanya tingkat pemangsaan yang intensif oleh megaherbivora \ud penyu hijau terhadap lamun di daerah tersebut. Luas padang lamun di Kepulauan\ud Derawan yaitu 2507.54 Ha dengan tingkat penutupan berkisar 20.4% ??? 39.5%

HORIZONTAL COORDINATE ACCURACY OF GOOGLE EARTH ON THE COVERAGE OF SMALL ISLANDS OF MAKASSAR CITY, INDONESIA

The presence of high-resolution satellite imagery on Google Earth provides an opportunity for the availability of maps that can be used as a reference for accurate coordinates. Google Earth has been developed to contain high-resolution images, but it warns users about the accuracy of the data regarding the coordinates of the objects covered. Coordinate inaccuracies have the potential to cause problems when used for navigational purposes, or in technical tasks requiring high accuracy such as surveying and mapping applications. Despite these warnings, users are often forced to refer to Google Earth as a reliable data source due to the absence of other data sources. The purpose of this study is to evaluate the accuracy of Google Earth's horizontal coordinates and determine the maximum map scale that can be made based on coordinate data from Google Earth on the coverage of small islands in the Makassar City area. The method used is to compare the object coordinate obtained from Google Earth and the coordinate measured in the field at the same object point. The calculation results show the RMSEH is 2.49 meters and the horizontal accuracy is 4.28 meters. These results indicate that the horizontal coordinates on Google Earth can be referenced to produce a map with a maximum scale of 1: 10,000

PROFIL DISTRIBUSI DAN KONDISI MANGROVE BERDASARKAN PASANG SURUT AIR LAUT DI PULAU BANGKOBANGKOANG KECAMATAN LIUKANG TUPABBIRING KABUPATEN PANGKEP

Mangrove forests are a community of tropical and subtropical beach vegetation, capable of growing and developing in tidalareas. This study aims to obtain information on the type and density of mangrove species by using remote sensing applicationsand to obtain mangrove distribution profile based on tidal. This research has been conducted in Bangkobangkoang IslandTupabbiring Sub-district of Pangkep Regency in September-October 2016. This research covers species inventory, mangrovedensity level using Landsat 8 image with Acquisition 6 June 2016 and mangrove distribution based on sea tides. The resultsshowed that mangrove vegetation density conditions in Bangkobangkoang island were generally in good condition. The typesof mangroves on the island of Bangkobangkoang are Rhizophora stylosa, Rhizophora apiculata, Rhizophora mucronata,Sonneratia alba, and Avicennia marina. The dominant mangrove species are Rhysophora stylosa and Rhizophora apiculata.Mangrove distributed at the highest tide with Rhyzophora stylosa type will be submerged while at lowest tide generally nomangrove is submerged except on the western island with the same type of Rhyzophora stylosaKey words: Mangrove, Landsat-8, Density, Ddistributions profil

SPATIAL-TEMPORAL DISTRIBUTION OF CHLOROPHYLL-A IN SOUTHERN PART OF THE MAKASSAR STRAIT

Surface chlorophyll-a (Chl-a) distribution have been analyzed with seasonal variation during southeast monsoon in southern part of Makassar Strait and Flores Sea. Satellite data of Landsat-8 is applied to this study to formulate the distribution of chlorophyll concentration during monsoonal wind period. The distribution of chlorophyll concentration was normally peaked condition in August during southeast monsoon. Satellite data showed that a slowdown in the rise of the distribution of chlorophyll in September with a lower concentration than normal is likely due to a weakening the strength of southeast trade winds during June – July – August 2016. Further analysis shows that the southern part of the Makassar strait is likely occurrence of upwelling characterized by increase in surface chlorophyll concentrations were identified as the potential area of fishing ground

CALENDAR FOR PLANTING SEAWEED EUCHEUMA SP. IN MALLASORO BAY, JENEPONTO DISTRICT, BASED ON LANDSAT-8 IMAGES

Seaweed cultivation activities in Jeneponto Regency have been practised for a long time and have become the main livelihood for most of the Mallasoro Bay community. In cultivating seaweed, obstacles often arise in the form of failures experienced by seaweed farmers or poor-quality yields. This study was aimed to develop alternative planting calendars for Eucheuma sp. in Mallasoro Bay, Jeneponto Regency based on sea surface temperature and distribution of chlorophyll-a obtained from Landsat-8 imagery. Image Processing Sea Surface Temperature and Chlorophyll-a were processed using ENVI 4.8 AND 5.3 software, the satellite images used were clean and without cloud disturbance. In this study, data analysis was carried out descriptively. The water temperature that is good for seaweed growth is 27-30˚C, for Mallasoro Bay Sea Surface Temperature which is suitable for seaweed cultivation, namely April, May, June, July, August, September, October and November. While the classification is based on the criteria for chlorophyll-a trophic status in marine waters, namely the range 5 mg/L is classified as Hypertrophic. , from the results of image analysis for the distribution of chlorophyll-a in Mallasoro Bay, it shows that Mallasoro Bay is at the Mesotrophic level throughout the year or the fertility level of the waters is quite fertile because it is in the range of ≥ 1–3 mg/L. so that a seaweed planting calendar can be obtained in Mallasoro Bay, namely in January, February and December, preparation of tools such as cleaning and repair of seaweed planting tools can be carried out, then at the end of March, the end of May, the end of July and the end of September, the procurement of seaweed seeds is carried out. , in early April, early June, early August, and early October, seaweed seeds can be spread, then in mid-May, mid-July, mid-September, and mid-November, harvesting can be carried out, so that seaweed cultivation can be carried out 4 times in one year. cycle

ESTIMATION OF SEAGRASS COVERAGE BY DEPTH INVARIANT INDICES ON QUICKBIRD IMAGERY

Management of seagrass ecosystem requires availability of information on the actual condition of seagrass coverage. Remote sensing technology for seagrass mapping has been used to detect the presence of seagrass coverage, but so far no information on the condition of seagrass could be obtained. Therefore, a research is required using remote sensing imagery to obtain information on the condition of seagrass coverage.The aim of this research is to formulate mathematical relationship between seagrass coverage and depth invariant indices on Quickbird imagery. Transformation was done on multispectral bands which could detect sea floor objects that are in the region of blue, green and red bands.The study areas covered are the seas around Barranglompo Island and Barrangcaddi Island, westward of Makassar city, Indonesia. Various seagrass coverages were detected within the region under study.Mathematical relationship between seagrass coverage and depth invariant indices was obtained by multiple linear regression method. Percentage of seagrass coverage (C) was obtained by transformation of depth invariant indices (Xij) on Quickbird imagery, with transformation equation as follows:C = 19.934 – 63.347 X12 + 23.239 X23.A good accuracy of 75% for the seagrass coverage was obtained by transformation of depth invariant indices (Xij) on Quickbird imagery