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

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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.
COMPARISON OF RANDOM FOREST AND MAXIMUM LIKELIHOOD CLASSIFICATION METHODS FOR LAND COVER IN LANDSAT 9 IMAGES IN LUBUK KILANGAN DISTRICT Fitri Hayati; Febriandi Febriandi; Ernawati Ernawati; Sri Kandi Putri
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.v4i1.42

Abstract

Information on land cover is needed in various sectors including management and resources which can be obtained through data processing using remote sensing satellite imagery. This research was conducted in Lubuk Kilangan District using Landsat 9 imagery, with the aim of (1) knowing the land cover classification using the random forest method, (2) knowing the land cover classification using the maximum likelihood classification method, and (3) knowing the best method for obtaining land cover information based on the accuracy value between the random forest method and the maximum likelihood classification. The method used is a comparative quantitative method by comparing the random forest method and the maximum likelihood classification of land cover in Lubuk Kilangan District. This study performs classification accuracy test calculations using Kappa with the help of a confusion matrix. The results of the study obtained 13 land cover classes from were found from taking training samples showing (1) the random forest land cover classification method was able to classify images properly. This is proven by findings in the field where 86% of pixels are classified correctly. Meanwhile, (2) the maximum likelihood classification method of land cover classification is not able to classify images properly. This is proven by findings in the field where 55% of pixels are classified correctly. (3) the Kappa accuracy value found for the random forest method is 0.81, while the maximum likelihood classification method is 0.51. This shows that the random forest method is better at obtaining information on the land cover than the maximum likelihood classification method.
ESTIMATION OF MANGROVE FOREST CARBON STOCK USING THE VEGETATION INDEX METHOD IN PADANG PARIAMAN DISTRICT Insanul Putri; Yudi Antomi; Febriandi Febriandi; 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.v4i1.43

Abstract

Padang Pariaman Regency is categorized as a coastal district because it has a coastline of 42.11 km. Padang Pariaman Regency has resources, one of which is mangrove forests. Mangrove forests are scattered in several sub-districts in Padang Pariaman Regency. This study aims to determine the estimated carbon stock value of mangrove forests in Padang Pariaman District using the Geographic Information System and Landsat 8 imagery, and to determine the accuracy of the carbon stock estimation results from the Landsat 8 imagery vegetation index. The method used in this study isNormalized Difference Vegetation Index (NDVI). Based on the estimation results of the above surface biomass values ​​obtained from the calculation of the correlation and regression equations in band 6 Landsat 8 imagery shows that the estimation results of the above surface biomass of mangrove forests in Padang Pariaman District obtain a maximum value of 644.85 tons/ha and a minimum value of 487, 92 tons/ha to obtain an estimated carbon stock value of 46% of the biomass value and an estimated maximum carbon stock value of 296.63 tons/ha and a minimum of 224.44 tons/ha.
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 Dian Adhetya Arif Diva Valensia Ernawati Ernawati Fitri Hayati Helsa Permata Sari Insanul Putri Sri Kandi Putri Triyatno Triyatno Yudi Antomi