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All Journal Journal of Degraded and Mining Lands Management Pedagogia: Jurnal Pendidikan JPG (Jurnal Pendidikan Geografi) Sumatra Journal of Disaster, Geography and Geography Education Jurnal Pendidikan dan Ilmu Geografi Jurnal Geografi JURNAL BUANA Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat JPIG Journal of Learning Improvement and Lesson Study Al-DYAS : Jurnal Inovasi dan Pengabdian kepada Masyarakat International remote sensing application journal Jurnal Buana JURNAL BUANA

Dian Adhetya Arif

Universitas Negeri Padang

Author-ID : 1176209

Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Computer Science & IT Earth & Planetary Sciences Education Environmental Science Health Professions Medicine & Pharmacology Public Health Social Sciences Other

Published : 26 Documents Claim Missing Document

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Articles

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IDENTIFICATION OF LAND COVER OF SURFACE TEMPERATURE IN KOTA PARIAMAN USING LANDSAT IMAGERY 8-OLI Dedek Putri Bungsu; Dian Adhetya Arif
International Remote Sensing Applied Journal Vol 2 No 1 (2021): international remote sensing application journal (June Edition 2021)
Publisher : Remote Sensing Technology Study Program

Remote sensing is a technology that can overcome data measurement problems for fast and accurate information. This research was carried out in Pariaman City using Landsat 8-OLI imagery with the aim of 1) knowing the identification of land cover based on the classification SNI:7645 (2014) based on Landsat 8-OLI imagery in the Pariaman City area in 2020 2) knowing the identification of surface temperature in the Pariaman City area in 2020. The research was conducted in serval stages, namely pre-processing of image data, selecting band combinations, cutting area studies, image classification, and testing accuracy. At surface temperature using Landsat 8-OLI imagery in 2020, surface temperature values are obtained from the results of thermal band processing. The results of the classification of Landsat 8-OLI images in Pariaman City produce 5 land cover classes, namely water bodies, rice fields, settlements, mixed gardens, and shrubs. This research conducted a classification accuracy test using a confusion matrix accuracy test table. Land cover supervised maximum likelihood method the overall accuracy value is 86.66 %. The results of the surface temperature value in Pariaman City in 2020 obtained the highest temperature value of 30ºC and the lowest surface temperature of 23ºC.

COMPARISON OF SOIL ADJUSTED VEGETATION INDEX (SAVI) AND MODIFIED SOIL ADJUSTED VEGETATION INDEX (MSAVI) METHODS TO VIEW VEGETATION DENSITY IN PADANG CITY USING LANDSAT 8 IMAGE Gilang Novando; Dian Adhetya Arif
International Remote Sensing Applied Journal Vol 2 No 1 (2021): international remote sensing application journal (June Edition 2021)
Publisher : Remote Sensing Technology Study Program

This study aims to see how the shape of the vegetation density map uses the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods in Padang City using remote sensing data in the form of Landsat 8 imagery. This type of research is quantitative using numerical data. and analysis, as well as presenting data in the form of a numerical table to see a comparison of the accuracy of the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods in Padang City. In this study, it was found that the results of the accuracy test of the SAVI (Soil Adjusted Vegetation Index) method were 86.95% while the MSAVI (Modified Soil Adjusted Vegetation Index) method was 91.30%. This research uses the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) vegetation index methods by entering the formula that has been determined for each index to find out how the vegetation density forms in the city of Padang. The results of this study are maps of vegetation density using the SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) methods and tables of SAVI (Soil Adjusted Vegetation Index) and MSAVI (Modified Soil Adjusted Vegetation Index) accuracy test results.

Mapping of Limestone Potential Using Landsat 8 Satellite Imageryin Some Areasof Timpeh Sabrina Roselini; Dian Adhetya Arif; Sri Kandi Putri
International Remote Sensing Applied Journal Vol 3 No 2 (2022): International remote sensing application journal (Dec Edition 2022)
Publisher : Remote Sensing Technology Study Program

Limestone potential is important information that can be obtained from remote sensing data which has advantages and speed in processing results. Remote sensing is a technology that can overcome the problemof measuring data for fast and accurate information. This research was carried out in some areas of the Timpeh sub-district,andDharmasraya districtusing Landsat 8-OLI imagery with the aimof1) identifying the potential of limestone using the Band Ratio method. 2) How to apply remote sensing in mapping the potential of limestoneusing Landsat 8 Oli imagery. This research was carried out in several stages, namely Pre Processing which included radiometric correction and atmospheric correction, image cropping according to the research area, and processing whichincluded making geological maps, making landform maps, making maps of river flow patterns and vegetationindex maps and limestone identification using the RGB band ratio method (5/4;6/3;4/2). The results of field identification in potential limestone areas, where the RGB (Red Green Blue)composite of the band ratio 5/4;6/3;4/2 shows that the presence of limestone is characterized by the appearanceof greenish-brown colored objects. The average pixel value for limestone with a band ratio of 5/4 is 2.475, for a6/3 ratio is 1.275 and for a 4/3 ratio is 0.788. In this study, the potential area of limestone in the research areawasfound,whichwas approximately 2352,14564 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

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.

UTILIZATION OF REMOTE SENSING DATA IN IDENTIFYING COASTLINE CHANGES WITH THE BILKO ALGORITHM METHOD IN 2014, 2018, AND 2022 Basri, Zafini; Arif, Dian Adhetya; Putri, Sri Kandi; Fitriawan, Dedy
International Remote Sensing Applied Journal Vol 4 No 2 (2023): International Remote Sensing Application Journal (December Edition 2023)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v4i2.46

The beach is a form of geology composed of sand located in coastal areas and the position of the coastline is dynamic. Identification of shoreline changes is important information that can be obtained from Remote Sensing Data and Geographic Information Systems (GIS) which has advantages and speed in the results of the process. This research was carried out in the Pasir Baru Beach area, Nagari Pilubang, Sungai Limau District using Landsat 8 OLI Satellite Images in 2014, 2018, and 2022 with the aim of determining changes in coastlines in the 2014-2018 and 2018-2022 ranges and knowing the extent of coastline changes in the 2014-2018 and 2018-2022 ranges. The method used to extract the coastline is obtained from the extraction results from the Landsat 8 OLI Satellite Image using the BILKO algorithm method, for the calculation of distance and rate of change of coastlines using a digital coastline analysis system (DSAS) with two statistical methods, namely Net Shoreline Movement (NSM) and End Point Rate (EPR) and for calculating the area of coastline change using the Calculate Geometry menu using attribute information in the software ArcGIS 10.5 in square meters (m2). Based on the results of the study that the coastal process that occurred in the research area from 2014-2022 was an erosion or abrasion event. The amount of erosion increased from 2018 to 2022 with an average erosion rate of 2.11 m / year, while the average abrasion distance was 7.49 m / year which was characterized by the formation of abrasion gawir and the fall of new trunk trees around the beach due to soil erosion. Meanwhile, the average rate for sedimentation or accretion events in 2018-2022 is 0.04 m/year while the average distance of change due to accretion events is 0.15 m/year. With a total area of erosion or abrasion events in 2018-2022 of 48,220.4 m, with an average annual area change of 12,055 m. Meanwhile, the total area of sedimentation or accretion events in 2018-2022 amounted to 449.3 m with an average annual area change of 112.3 m.

MAPPING THE DISTRIBUTION OF SEAGRASS IN NIRWANA BEACH, PADANG CITY USING SENTINEL-2 IMAGERY Sepriani, Nur Astri; Arif, Dian Adhetya; Iswandi, Iswandi; Triyatno, Triyatno
International Remote Sensing Applied Journal Vol 4 No 2 (2023): International Remote Sensing Application Journal (December Edition 2023)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v4i2.47

Seagrass (Lamun) is a flowering plant (Angiospermae) that thrives in shallow marine environments. Seagrass meadows play a crucial role in aquatic ecosystems, and the degradation or loss of seagrass can impact the balance of these ecosystems. The use of remote sensing technology in mapping the distribution of seagrass beds can support monitoring efforts and contribute to the conservation and protection of marine ecosystems. This research aims to map and measure the extent of seagrass beds in Nirwana Beach, Padang City, in the year 2022. The method employed involves using Sentinel-2A imagery from 2022 and the Object-Based Image Analysis (OBIA) approach for seagrass detection. The Sentinel-2A imagery is processed using ArcGIS and eCognition software, including atmospheric correction, data clipping, composite image creation, segmentation, image classification, and accuracy assessment. The results of processing the Sentinel-2A data in 2022 for Nirwana Beach, Padang City, indicate that seagrass beds are distributed along the Nirwana Beach area, particularly in the eastern and southern regions. The detected seagrass bed covers an approximate area of 25.06 hectares. The use of Sentinel-2A imagery with the OBIA method has proven to be effective in detecting the distribution of seagrass beds in Nirwana Beach, Padang City.

IDENTIFICATION OF LAND USE CHANGES USING THE OBJECT BASED IMAGE ANALYSIS (OBIA) METHOD IN BUNGUS TELUK KABUNG DISTRICT Wahyuni, Sri Agustia; Fitriawan, Dedy; Triyatno, Triyatno; Arif, Dian Adhetya
International Remote Sensing Applied Journal Vol 4 No 2 (2023): International Remote Sensing Application Journal (December Edition 2023)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v4i2.48

Object-based image analysis (OBIA) is an image classification that considers not only the spectral aspects of objects, but also their spatial aspects. This classification is guided by objects that have distribution patterns from object samples which are used as references for their accuracy. However, this object-based classification process must be taken into account when looking at color and calculating it so that there is no error in classification. In this research, the OBIA method was used to identify changes in land use in the Bungus Teluk Kabung District in 2012, 2017 and 2022. By using the OBIA method, identification results were obtained in areas where land use changes occurred between 2012 and 2017, which were identified as having changed from open land to built-up land. with an area of 355.84ha, plantations 22.62ha and rice fields 20.97ha. From 2017 to 2022, it was identified that there was a change in land use from dry land forests to 6.30ha of built-up land. The change in open land to built-up land was 7.47ha. Plantations experienced changes to 6.21ha of built-up land and 9.27ha of rice fields. Meanwhile, bushes/shrubs experienced changes in plantations of 2.47ha.

UTILIZATION OF SENTINEL-2 IMAGES FOR MAPPING THE CORAL REEF AREA IN THE CONSERVATION AREA OF PIEH ISLAND WATERS 2022 Amatullah, Ulfi Rahmi; Febriandi, Febriandi; Ernawati, Ernawati; Arif, Dian Adhetya
International Remote Sensing Applied Journal Vol. 5 No. 2 (2024): International Remote Sensing Application Journal (December Edition 2024)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v5i2.63

Coral reefs are known as complex and productive shallow tropical marine ecosystems. They serve as a habitat for various species of marine plants, marine animals, and marine microorganisms. The deterioration of coral reefs threatens the survival of this shallow marine ecosystem. Mapping the distribution of coral reefs using remote sensing technology is a crucial instrument in the effort to monitor and protect coral reefs while preserving the marine environment. This research aims to map and measure the extent of coral reefs in the Pieh Islands Marine Conservation Area in 2022. In this study, we used Sentinel-2 imagery from 2022 and applied the Object-Based Image Analysis (OBIA) method to detect the extent of coral reefs. Sentinel-2 imagery was processed using ArcGIS and eCognition software, involving atmospheric correction, image clipping, image compositing, segmentation, image classification, and accuracy testing. The data processing results indicate that coral reefs are distributed around the waters of Pieh Islands, with the highest density located to the south of Pieh Islands's waters. The total extent of detected coral reefs in this study is 15.76 hectares. The use of Sentinel-2 imagery with the OBIA method has proven to be effective in detecting the extent of coral reefs in the Pieh Islands Marine Conservation Area.

USE OF MEDIUM RESOLUTION IMAGERY FOR PREDICTION MAPPING OF LAND COVER CHANGES IN SOLOK DISTRICT Sari, Yolanda Indah Permata; Fitriawan, Dedy; Antomi, Yudi; Arif, Dian Adhetya
International Remote Sensing Applied Journal Vol. 5 No. 2 (2024): International Remote Sensing Application Journal (December Edition 2024)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v5i2.65

This research aims to determine changes in land cover from 2017-2022 in Solok Regency, to find out predictions of changes in land cover until 2032 in Solok Regency, to find out the results of land cover accuracy tests in Solok Regency. This research uses the Supervised (Maximum Likelihood) method to identify changes in land cover in Solok Regency in 2017 and 2022. This research was carried out in several stages, namely the preprocessing stage including radiometric and atmospheric correction, image cropping according to the research area. The processing stage uses the Supervised (Maximum Likelihood) method to determine the classification, then creating a land cover change identification matrix, creating sample points in the field, accuracy testing, and finally making predictions using the Cellular Automata model to predict land cover in 2032. Identification results in areas there was a change in land cover from 2012 to 2017 to 2022, land cover that changed, namely primary forest in 2012 to 2017 experienced a change in 2022 to 206,362.04ha, built-up land also experienced an area change of 3,162.37ha, followed by open land experiencing changes 283.98ha, mixed plantation land experienced a change of 78,176.71ha, wetland farming experienced a change of 12,751.07ha and dry land farming experienced a change of 20,707.08ha in 2022. Then the results of land cover predictions in 2032 are forest land area primary area in Solok Regency changed to 207,382.99ha, while the area of water bodies changed to 6,889.05ha, then built-up land experienced a change of 3,288.13ha, then open land cover changed to 77,912.95ha, then mixed plantation cover changed to 13,248.51 , in wetland agriculture it changed to 13,248.51ha and dryland agriculture to 19,164.11ha.

Co-Authors Adek Andreas Afifu Rahman Akmelliona, Akmelliona Amatullah, Ulfi Rahmi Anwar, Yaskinul Aprizon Putra Ayu Fathonah Azani, Fadli Haikhal Basri, Zafini Bayu Wijayanto Bayu Wijayanto Bigharta Bekti Susetyo Deded Chandra Dedek Putri Bungsu Dedy Fitriawan Devi L Maria Eri Barlian Ernawati Ernawati Ernawati Ernawati Febriandi Febriandi Febriandi Febriandi, Febriandi Firma Maulidna Fitri Aji Gilang Novando Gina Rahayu Helsa Permata Sari Hendry Frananda, Hendry Indah Fultriasantri Indang Dewata Iswandi Iswandi Iswandi U Lailatur Rahmi Lintang Ronggowulan M. Mursyid Alfahri Marya, Sri Mufti Khairatunnisa Muhammad Noval Nailul Insani Nova, Sari NUR EFENDI Nurfajri Indra Putri, Sri Kandi Rafiqah Indah Sari Ramadhan, Risky Ratna Wahyu Kusuma Arum Ratna Wilis rexy elnando Reza Nofri Andika Rikki Afrizal Ronal Wilnika Sabilau, Oldheva Genisa Sabrina Roselini Sari, Yolanda Indah Permata Sepriani, Nur Astri Sildila Sari Sri Kandi Putri Sri Kandi Putri Syah, Nur Hasan Thuba Imam Fauzi Triyatno Triyatno Triyatno Triyatno Umar, Iswandi Wahyuni, Sri Agustia Widya Prarikeslan Yudi Antomi Yulfa, Arie Yunus Aris Wibowo Yuswanti Ariani Wirahayu

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