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All Journal Asian Journal of Aquatic Sciences Jurnal Laot Ilmu Kelautan PELAGICUS: Jurnal IPTEK Terapan Perikanan dan Kelautan Nekton Jurnal Ilmiah Pengabdian dan Inovasi Journal of Fisheries & Marine
Herlina Adelina Meria Uli Sagala
Politeknik Kelautan Dan Perikanan Karawang
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Environmental Science Social Sciences

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Journal : Asian Journal of Aquatic Sciences

 1 
GROWTH RATE OF Nannochloropsis sp ACCORDING TO ADDITING CONCENTRATION OF ZINC (ZN) AND COOPER (CU) Herlina Adelina Meria Uli Sagala; Chrisoetanto P Pattirane; Roni Sewiko; Ully Wulandari; Beta Indi Sulistyowati
Asian Journal of Aquatic Sciences Vol 5 No 3 (2022): December
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ajoas.5.3.315-321

Abstract

Zinc and copper are micronutrients that are the factors that support microalgae growth rate besides macronutrients, CO2, temperature, salinity, and pH. Growth of Nannochlopsis sp was observed to determine the influence of the addition of Zn and Cu concentration. Microalgae cultivation was located outdoors on the semi-mass scale using 800 L medium on mesophilic condition in 3 treatments with the addition of 1 ppm, 3 ppm, and 5 ppm concentrations of Zn and Cu metals. Cell density was calculated using a hemocytometer, assays the absorbed metal content was measured by AAS. The study aimed to test the growth response of Nannochloropsis sp cells to Zn and Cu metals. The growth rate of microalgae was observed for the addition of Zn and Cu concentration in the cultivation medium. The result showed the highest Nannochloropsis sp growth rate for Zn addition was 0.053/day and for Cu addition was 0.279/day. Between Zn and Cu addition, the highest growth rate was observed in the addition of 1 ppm Cu metal (Cu1) which was equal to 0.279/day. This shows that adding micronutrients are not automatically will increase the growth rate of microalgae as well.
THE USE OF DRONE AND VISIBLE ATMOSPHERICALLY RESISTANT INDEX (VARI) ALGORITHM IMPLEMENTATION IN MANGROVE ECOSYSTEM HEALTH’S MONITORING Roni Sewiko; Herlina Adelina Meria Uli Sagala
Asian Journal of Aquatic Sciences Vol 5 No 3 (2022): December
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/ajoas.5.3.322-329

Abstract

Operational limitations are the main problem in monitoring 3.31 million hectares of mangrove forest areas throughout Indonesia. However, with the disruption of technology, there are currently many approaches and methods that can be adapted to answer these problems. One of them is drone technology. This technology can be utilized in high-resolution rapid mapping for limited areas. The output from the data acquired by the drone can be analyzed for various purposes, including assessing the health condition of the vegetation. In this study, the results of the acquisition of unmanned aircraft on mangrove vegetation are used to determine the health level of vegetation in mangrove conservation areas. The research was conducted on 46 hectares of the mangrove conservation area. The acquisition process was divided into four flying missions with a flight height of 150 m, 80% patching, and using the Hasselblad L1D-20c camera with a 1-inch sensor. The acquisition results are processed using the online photogrammetry method through the cloud-based photogrammetry service from DroneDeploy. Processing uses standard mode, where this mode is designed to produce good image quality with a relatively fast processing time. The acquisition results of 1614 photos were 100% successfully aligned, with 3.50 cm/px GSD resolution. Based on the application of the VARI algorithm to the resulting orthophoto, it is known that 30.2692% of the AOI is an area and/or dead or non-vegetated vegetation. Then 59.3887% is vegetation in an unhealthy condition, 10.3405% is considered as vegetation in a healthy condition, and 0.0015% is vegetation in a very healthy condition
Co-Authors Aris Kabul Bayu Oktavian Chrisoetanto P. Pattirane Fafthia Karimatul Ulya Neneng Purwanti Novhitasari Simbolon Nur Atikah Pasaribu, Roberto Patar Setya Indra Padma Putri Sewiko, Roni Sulistyowati, Beta Indi Syahrial Syahrial Ully Wulandari Welmar Olfan Basten Barat Yulandhita Yulandhita Yulina Sari