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Dissolved Oxygen in the East Bolaang Mongondow Waters, North Sulawesi Patty, Simon I.; Rizqi, Marenda Pandu; Huwae, Rikardo
Jurnal Ilmiah Platax Vol. 10 No. 1 (2022): ISSUE JANUARY-JUNE 2022
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35800/jip.v10i1.40434

Oxygen in seawater comes from the air through diffusion and photosynthesis. This study aims to determine the dissolved oxygen content, NAEC (Normal Atmospheric Equilibrium Concentration), and AOU (Apparent Oxygen Utilization) in East Bolaang Mongondow waters. The analysis showed that the dissolved oxygen content in the surface layer ranged from 6.01-7.05 mg/l; 5.91-6.98 mg/l (5 m depth); and 5.75-6.90 mg/l (near bottom depth). NAEC is closely related to water temperature and implicitly increases with depth. The highest NAEC value of 5.90 mg/l (t=28.0ÂºC) was found at the bottom depth, and the lowest NAEC of 5.63 mg/l (t=30.0ÂºC) was found at the surface layer. The dissolved oxygen saturation level of more than 100% and the average positive AOU value at 0.59-0.81 mg/l describe the amount of oxygen available in the surface layer.Keywords: Dissolved Oxygen; Normal Atmospheric Equilibrium Concentration (NAEC); Apparent Oxygen Utilization (AOU); East Bolaang Mongondow.AbstrakOksigen dalam air laut bersumber dari udara melalui proses difusi dan hasil fotosintesis. Penelitian ini bertujuan untuk mengetahui kandungan oksigen terlarut, NAEC (normal atmospheric equilibrium concentration) dan AOU (apparent oxygen utilization) di perairan Bolaang Mongondow Timur. Hasil analisis menunjukkan bahwa kandungan oksigen terlarut di lapisan permukaan berkisar antara 6,01-7,05 mg/l; kedalaman 5 meter 5,91-6,98 mg/l dan dekat dasar 5,75-6,90 mg/l. NAEC sangat berhubungan dengan suhu air dan secara implisit konsentrasinya akan semakin tinggi dengan bertambahnya kedalaman. Nilai NAEC tertinggi 5,90 mg/l (t=28,0ÂºC) ditemukan pada kedalaman dekat dasar danÂ NAEC terendah 5,63 mg/l (t=30,0ÂºC) ditemukan pada lapisan permukaan. Tingkat kejenuhan oksigen terlarut >100 % dan rata-rata nilai AOU positif (0.59-0.81 mg/l) menggambarkan banyaknya kandungan oksigen yang tersedia pada lapisan permukaan.Kata kunci: oksigen terlarut; normal atmospheric equilibrium concentration (NAEC); apparent oxygen utilization (AOU); Bolaang Mongondow Timur.

Analysis of Mangrove Vegetation and Distribution Using Landsat 8 Images In Bolaang Mongondow East, North Sulawesi Patty, Simon I.; Nurdiansah, Doni; Rizqi, Marenda Pandu; Huwae, Rikardo
Jurnal Ilmiah Platax Vol. 10 No. 2 (2022): ISSUE JULY-DECEMBER 2022
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35800/jip.v10i2.41069

Mangrove is one of the objects that can be identified by remote sensing technology using satellite imagery. Analysis of the distribution and density of mangrove vegetation using Landsat 8 imagery was carried out in Bolaang Mongondow Timur, North Sulawesi in September 2020. This study aims to map the distribution of mangroves and determine the correlation between NDVI values, canopy cover, and mangrove density. The data analysis used Landsat 8 images with ENVI 5.3 and ArcGIS 10.1 software. Maximum likelihood classification is used to separate mangrove and non-mangrove features. The calculation of mangrove vegetation density using the NDVI algorithm and single-channel classification using the density slice method to divide mangrove density based on the range of pixel values of the NDVI image. Next, to test the accuracy of the classification results using an error matrix (confusion matrix) and the NDVI vegetation index correlation test compared with canopy cover and density data. The classification resulted in four different land cover classes with an overall accuracy of 97.70% and a kappa coefficient of 0.9688. The mangrove vegetation distribution from the classification results is 524.75 ha. The NDVI correlation with the percentage of canopy cover is very significant with a correlation coefficient (r) = 0.9516, while the NDVI correlation with density resulted in moderate correlation (r = 0.5315).Keywords: density; mangrove; Landsat 8; NDVIÂ Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â AbstrakMangrove merupakan salah satu objek yang dapat diidentifikasi menggunakan teknologi penginderaan jauh yakni memanfaatkan citra satelit. Analisis sebaran dan kerapatan vegetasi mangrove menggunakan citra Landsat 8 telah dilakukan di Bolaang Mongondow Timur, Sulawesi Utara pada bulan September 2020. Penelitian ini bertujuan untuk memetakan sebaran mangrove dan mengetahui hubungan korelasi antara nilai NDVI dengan tutupan kanopi dan kerapatan mangrove. Pengolahan data citra Landsat 8 dengan perangkat lunak ENVI 5.3 dan ArcGIS 10.1. Klasifikasi maximum likelihood digunakan untuk memisahkan fitur mangrove dan non mangrove. Perhitungan kerapatan vegetasi mangrove dengan algoritma NDVI dan klasifikasi saluran tunggal menggunakan metode density slice untuk membagi kerapatan mangrove berdasarkan rentang nilai piksel citra NDVI. Uji akurasi hasil klasifikasi menggunakan matriks kesalahan (confussion matriks) dan uji korelasi indeks vegetasi NDVI dengan data tutupan kanopi dan kerapatan. Hasil klasifikasi mendapatkan empat kelas tutupan lahan yang berbeda dengan overall akurasi sebesar 97,70 % dengan kappa coefisien sebesar 0,9688. Luas sebaran vegetasi mangrove dari hasil klasifikasi adalah 524,75 ha. Korelasi NDVIÂ dengan persentase tutupan kanopi termasuk korelasi sangat kuat dengan koefisien korelasi r = 0,9516 sedangkan korelasi NDVIÂ dengan kerapatan termasuk korelasi sedang (r = 0,5315).Kata kunci: kerapatan; mangrove; Landsat 8; NDVIÂ

Mapping Benthic Habitat Distribution and Coral Reef Health Index in Ternate Island and Tidore Island, North Maluku Patty, Simon I.; Souhoka, Jemmy; Makatipu, Petrus C.; Rizqi, Marenda Pandu
Jurnal Ilmiah Platax Vol. 12 No. 1 (2024): ISSUE JANUARY-JUNE 2024
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35800/jip.v12i1.54054

Coral reefs are underwater marine organisms whose coverage can be identified using remote sensing technology utilizing satellite imagery. The benthic habitats classification and coral reef health index analysis were carried out on the Ternate and Tidore islands in June 2021. This study aims to map the distribution of benthic habitat as well as calculate the value of the coral reef health index. The processing of satellite imagery data from Landsat 8, using ENVI 5.3 and ArcGIS 10.1 software. The "depth invariant index" algorithm is used in the image sharpening process (Lyzenga, 1981). The maximum likelihood classification technique is used to identify substrate type, with a confusion matrix used to test its accuracy. The coral reef health index assessment is based on the condition of live coral cover, resilience level, and reef fish biomass. The results from water column image classification showed that there were four classes of habitat, i.e. coral, seagrass, sand, and rubble with an overall accuracy value of 85.86% and kappa coefficient of 0.80605. The total area of classified coral reefs is 1152.85 hectares. The coral reef health index values ranged from 2 to 8 with an average of 5, which translates to a moderate percentage of live coral cover, high resilience, and low fish biomass. Keywords: coral reefs, benthic habitat, health index, Landsat 8 images. Abstrak Terumbu karang merupakan objek tutupan bawah air laut yang dapat diidentifikasi menggunakan teknologi penginderaan jauh yakni memanfaatkan citra satelit. Klasifikasi habitat bentik dan analisis indeks kesehatan terumbu karang telah dilakukan di pulau Ternate dan pulau Tidore pada bulan Juni 2021. Penelitian ini bertujuan untuk memetakan sebaran habitat bentik serta mengetahui nilai indeks kesehatan terumbu karang. Pengolahan data citra Landsat 8 dengan perangkat lunak ENVI 5.3 dan ArcGIS 10.1. Proses penajaman citra menggunakan algoritma “depth invariant index” (Lyzenga, 1981). Teknik klasifikasi maximum likelihood digunakan untuk identifikasi objek dasar perairan dan uji akurasi menggunakan confusion matrix. Penilaian indeks kesehatan terumbu karang berdasarkan kondisi tutupan karang hidup, tingkat resiliensi dan biomassa ikan karang. Hasil klasifikasi citra kolom air mendapatkan empat kelas habitat yaitu karang, lamun, pasir dan pecahan karang mati (rubble) dengan nilai akurasi keseluruhan sebesar 85,86 % dan koefisien kappa sebesar 0,80605. Total luasan area terumbu karang hasil klasifikasi adalah 1152,85 ha. Nilai indeks kesehatan terumbu karang berkisar antara 2 sampai 8, dengan rata-rata 5 yang menggambarkan persentase tutupan karang hidup sedang, tingkat resiliensi tinggi dan biomassa ikan rendah. Kata kunci: terumbu karang, habitat bentik, indeks kesehatan, citra Landsat 8.

Analysis of Mangrove Vegetation Index Using Landsat 8 Images in Dodinga Bay, West Halmahera Patty, Simon I.; Nurdiansah, Doni; Rizqi, Marenda Pandu; Akbar, Nebuchadnezzar; Huwae, Rikardo
Jurnal Ilmiah Platax Vol. 13 No. 1 (2025): ISSUE JANUARY-JUNE 2025
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35800/jip.v13i1.60830

Mangrove vegetation can be easily recognized from remote-sensing satellite images compared to other terrestrial vegetation. The vegetation index is applied to the satellite images to highlight the aspect of vegetation density. This study aims to determine the correlation between the value of the vegetation index and mangrove canopy cover data to achieve a proper vegetation index to estimate the density of the mangrove canopy. The data needed are satellite imagery from Landsat 8 and mangrove canopy cover in sampling locations along the coast of Dodinga Bay, West Halmahera. Image data analysis includes radiometric correction, image sharpening, masking, classification, and accuracy tests. The vegetation index algorithms used were NDVI, GNDVI, and IM, and regression analysis was carried out for correlation tests. The analysis results obtained four different land cover classes with an overall accuracy of 97.70% and a kappa coefficient of 0.9688. The IM vegetation index showed an excellent correlation with mangrove canopy cover compared to GNDVI and NDVI. The determination coefficient (R²) of the IM is 0.6765; GNDVI (0.4897) and NDVI (0.4825). The IM classification produces four levels of mangrove canopy density, i.e., sparse (7.40 ha), moderate (628.33 ha), dense (921.22 ha), and very dense (16.45 ha). Keywords: mangrove, Landsat 8 images, vegetation index, Dodinga Bay Abstrak Objek vegetasi mangrove paling mudah diidentifikasi dengan menggunakan citra satelit penginderaan jauh dibandingkan objek vegetasi darat lainnya. Indeks vegetasi diterapkan terhadap citra untuk menonjolkan aspek kerapatan vegetasi. Penelitian ini bertujuan mengetahui korelasi antara nilai indeks vegetasi dengan data tutupan kanopi mangrove, sehingga didapatkan indeks vegetasi yang sesuai untuk menduga kerapatan kanopi mangrove. Data yang diperlukan yaitu citra Landsat 8 dan tutupan kanopi mangrove di lapangan. Analisis data citra terdiri dari koreksi radiometrik, penajaman citra, masking, klasifikasi dan uji akurasi. Algoritma indeks vegetasi yang digunakan yaitu NDVI, GNDVI dan IM, serta dilakukan analisis regresi untuk uji korelasi. Hasil analisis mendapatkan empat kelas tutupan lahan yang berbeda dengan overall akurasi sebesar 97,70 % dan kappa coefisien sebesar 0,9688. Indeks vegetasi IM menunjukkan korelasi sangat baik dengan tutupan kanopi mangrove dibandingkan GNDVI dan NDVI. Koefisien determinasi (R²) IM adalah 0,6765; GNDVI (0,4897) dan NDVI (0,4825). Klasifikasi IM menghasilkan empat tingkat kerapatan kanopi mangrove yaitu mangrove jarang (7,40 ha), mangrove sedang (628,33 ha), mangrove lebat (921,22 ha), dan mangrove sangat lebat (16,45 ha). Kata kunci: mangrove, citra Landsat 8, indeks vegetasi, Teluk Dodinga

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