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UNIVERSITAS NEGERI PADANG (UNP) Address: Prof. Dr. Hamka Street, Air Tawar, Padang - West Sumatra -Indonesia
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INDONESIA
International remote sensing application journal
Published by Universitas Negeri Padang
ISSN : -     EISSN : 27753409     DOI : https://doi.org/10.24036/irsaj.v3i2.34
Core Subject : Science, Education,
Astronomy Computer Science & IT Earth & Planetary Sciences Education
This journal covers the scope of remote sensing which includes: (1) data acquisition; (2) processing data; (3) data storage and distribution; (4) application and utilization of information from remote sensing data. The focus of this journal includes: 1. Remote sensing applications 2. Multi-spectral and hyperspectral remote sensing 3. Active and passive microwave remote sensing 4. Lidar and laser scanning 5. Geometric reconstruction 6. Physical modeling and signatures 7. Change detection 8. Image processing and pattern recognition 9. Data fusion and data assimilation 10. Dedicated satellite missions 11. Operational processing facilities 12. Spaceborne, airborne and terrestrial platforms
Arjuna Subject : Ilmu Bumi dan Planet (semua kategori) - Ilmu Bumi dan Planet (Lain-Lain)
Articles 52 Documents
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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.
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.
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

Abstract

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

Abstract

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

Abstract

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.
THE ROLE OF REMOTE SENSING AND SIG DATA FOR MAPPING LAND PRICE ESTIMATION IN PAYAKUMBUH CITY Ramanda, Risa; Syahar, Fitriana; Antomi, Yudi; Ramadhan, Risky
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.v4i2.49

Abstract

Land is a field that has a strategic role in the development of an urban area, land located in a strategic location of economic activity, easy accessibility, and complete infrastructure can be said that the land has a high land price. Factors that influence land prices in urban areas are increasing population, land change, and regional development. Payakumbuh City is a city that is strategically located and close to the city center, has complete public facilities, adequate road access, proximity of land to economic areas, and urban development and buildings affect land prices in Payakumbuh City. The research objectives (1) identify factors that affect land prices in Payakumbuh City (2) analyze the spatial distribution of land price estimates in Payakumbuh City. The research method to determine land price estimation is the overlay and weighting method. The parameters used in this study are land use using Pleiades imagery, land accessibility, and completeness of public facilities. Based on the results of the research, the factors that affect land prices in Payakumbuh City are land use that is closer to the city center will be more expensive, land accessibility that facilitates road access, and completeness of public facilities. Based on the results of data processing, it is found that there are 4 classes of land price estimation in Payakumbuh City, namely the very high class has a price of Rp. 5,000,000.00 - Rp. 10,000,000.00, the high class has a land price of Rp. 2,000,000.00 - Rp. 5,000,000.00, the medium class has a land price of Rp. 1,000,000.00 - Rp. 2,000,000.00, and the low class has a land price of Rp. 200,000.00 - Rp. 500,000.00.
LAND COVER CLASSIFICATION WITH OBIA METHOD (OBJECT BASED IMAGE ANALYSIS) IN PADANGWEST DISTRICT, PADANG CITY Salsabila, Rania; Putri, Sri Kandi; Syahar, Fitriana; 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.v4i2.50

Abstract

High population growth has an impact on the development of a region. Therefore, the need for the latest information regarding land cover obtained through data processing using remote sensing techniques. This land cover monitoring utilizes object-based SPOT 7 satellite imagery data (OBIA) in West Padang District, Padang City. This research was conducted with the aim of knowing the level of accuracy of the OBIA method in land cover classification on SPOT 7 Imagery. The OBIA method consists of two stages, namely segmentation and classification with the Train Maximum Likelihood Classifier algorithm. In this study, there were 10 land cover classifications and resulted in an overall accuracy of 95% and a kappa accuracy of 94%.

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