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Triyatno Triyatno
Lecturer Study Program D3 Remote Sensing Technology
Astronomy Computer Science & IT Earth & Planetary Sciences Education

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DETERMINATION OF COMMUNITY STRUCTURE AND INDEX MANGROVE HEALTH INDEX (MHI) IN DELI SERDANG DISTRICT, PROVINCE NORTH SUMATRA dea lusiyanti; Yudi Antomi; Triyatno Triyatno; Azhari Syarief
International Remote Sensing Applied Journal Vol 4 No 1 (2023): International Remote Sensing Application Journal (June Edition 2023)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v4i1.40

Abstract

This research aims to 1) Know the structure of the mangrove community in Deli Serdang Regency, 2) To find out the differences in the classification of the health level of the mangrove communities in Deli Serdang Regency using Sentinel 2A and Landsat 8OLI imagery in 2022. In determining the structure of the mangrove community carried out by making plot plots to measure trunk circumference and types of mangroves found in Deli Serdang Regency, while to find out differences in the classification of mangrove health levels it was done by comparing the vegetation density values ​​in the field and the canopy density values ​​based on the NDVI vegetation index from Sentinel 2A and Landsat 8OLI imagery. year 2022. The results of this study are, 1) The dominant mangrove species in Deli Serdang Regency are the Avicennia marina, Avicennia alba and Excoecaria agallocha types, with a low level of species diversity. 2) Sentinel 2A imagery is better to use than Landsat 8OLI imagery in determining the Mangrove Health Index (MHI).
UTILIZATION OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS FOR SHRIMP POND IDENTIFICATION USING OBIA METHOD IN BATANG ANAI DISTRICT Diva Valensia; Febriandi Febriandi; Azhari Syarief; Triyatno Triyatno
International Remote Sensing Applied Journal Vol 4 No 1 (2023): International Remote Sensing Application Journal (June Edition 2023)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v4i1.41

Abstract

This study aims to identify shrimp pond objects using Sentinel images in 2017 and 2022 and determine the area of ponds from 2017 to 2022 in Batang Anai District to monitor shrimp pond cultivation, where the amount of production each year always increases. The method used in this study is OBIA (Object Based Image Analysis). Based on the results of image interpretation of the Obia Citra Sentinel-2 method in 2017, it shows that the area of shrimp ponds in Batang Anai District, especially Nagari Katapiang, is only 1.82 ha. Meanwhile, the results of the interpretation of the Obia method image in 2022 show that the area of shrimp ponds in Batang Anai District is 102.75 ha. The Object Base Image Analysis (Obia) method used in Sentinel-2 images in 2017 and 2022 produces segmentation that shapes existing objects into a class that has the same characteristics. Shrimp ponds are segmented with a grayish dark hue, regular shape, boxed pattern, have a smooth texture, water site and associate with rivers. and located on the beach bordering the sea. The identification of obia method ponds in 2017 and 2022 has changed quite drastically in the last 5 years, namely the addition of pond areas of around 100.91 ha. Identification of ponds using the obia method produces segmentation which makes objects look the same into one object.
UTILIZATION OF LANDSAT IMAGERY FOR MAPPING SEAGRASS DISTRIBUTION ON NIRWANA BEACH PADANG CITY Helsa Permata Sari; Dian Adhetya Arif; Febriandi Febriandi; Triyatno Triyatno
International Remote Sensing Applied Journal Vol 4 No 1 (2023): International Remote Sensing Application Journal (June Edition 2023)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v4i1.44

Abstract

Mapping the distribution of seagrass beds at Nirwana Beach in Padang City aims to see changes in seagrass meadow area that occurred within a period of five years, namely from 2017 to 2022.The image used is Landsat 8 Imagery, The method used to detect seagrass beds is the Lyzenga algorithm, this method is used to obtain object information below the surface of the water, Because the information obtained from the initial image is still mixed with other information such as water depth, turbidity, and water table movement. The two channels used in detecting this aquatic bottom information are the blue band and the green band which have wavelengths corresponding to the ratio of attenuation coefficients required by the logarithmic formula of lyzenga. The interpretation results show a decrease in seagrass area within five years, namely from 2017 to 2022 by 6.96 ha. The Lyzenga Algorithm method is the most suitable method for detecting seagrass beds at Nirwana Beach in Padang City.
Co-Authors Azhari Syarief dea lusiyanti Dian Adhetya Arif Diva Valensia Febriandi Febriandi Helsa Permata Sari Yudi Antomi