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All Journal Indonesian Journal of Geography Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Sumatra Journal of Disaster, Geography and Geography Education Pendas : Jurnah Ilmiah Pendidikan Dasar JURNAL PENDIDIKAN TAMBUSAI International Journal of Educational Dynamics (IJEDS) JRTI (Jurnal Riset Tindakan Indonesia) geoedusains: Jurnal Pendidikan Geografi Tsaqofah: Jurnal Penelitian Guru Indonesia ALSYS : Jurnal Keislaman dan Ilmu Pendidikan International remote sensing application journal
erna, ernawati
Jurusan Geografi Fakultas Ilmu Sosial Universitas Negeri Padang
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Journal : International remote sensing application journal

 1 
UTILIZATION OF SATELLITE IMEGERY FOR MAPPING SETTLEMENT DEVELOPMENT TRENDS IN THE CITY BUKITTINGGI Sherena Aurelia Anwar; Ernawati Ernawati
International Remote Sensing Applied Journal Vol 3 No 1 (2022): international remote sensing application journal (June Edition 2022)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (485.885 KB) | DOI: 10.24036/irsaj.v3i1.32

Abstract

This research was conducted to see the trend of the development of residential areas in the City of Bukittinggi using remote sensing methods. This technique is considered important and effective in providing spatial information on the earth's surface quickly, precisely and easily. This study aims to classify land use for residential areas using Landsat 8 OLI (Operational Land Imaginer) imagery. In this study, the maximum likelihood classification (MLC) method was used. The research used is descriptive with a quantitative approach, namely using numerical data, analysis, interpretation and presenting data in numerical form for sampling in identifying the results of land use for settlements in the City of Bukittinggi. The research results have changed in the last 5 years, it was found that there was an increase in residential areas of 7.92 ha in 2016 and 2021 using Landsat imagery. The results of the research in the form of a map are land use maps in the City of Bukittinggi to see the distribution of residential areas.
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.
FLOOD IDENTIFICATION BY UTILIZING REMOTE SENSING AND SPATIAL ANALYSIS TECHNIQUES IN PADANG CITY Islami, Refki Addea; Ernawati, Ernawati; Edial, Helfia; Syarief, Azhari
International Remote Sensing Applied Journal Vol 5 No 1 (2024): International Remote Sensing Application Journal (June Edition 2024)
Publisher : Remote Sensing Technology Study Program

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/irsaj.v5i1.55

Abstract

Natural disasters floods are the most common disasters found almost everywhere, floods can cause damage and can even take lives. The city of Padang is often hit by flood disasters which result in damage and loss for those affected. This research aims to 1) To find out the results of identifying flood-prone areas by utilizing remote sensing and spatial analysis techniques in Padang City; 2) To determine the level of flood vulnerability by utilizing remote sensing and spatial analysis techniques in Padang City. This research uses an overlay method which combines several parameters. Parameters for identifying areas that have the potential for flooding in this research include slope, rainfall, land use, elevation, soil type, and river buffer. Each of these parameters is given a different scoring value and weight, then an overlay analysis is carried out and a flood hazard map will be produced as a result of the combination of parameters used. After obtaining the results from the overlay analysis of all parameters, the map of potential flood areas will be divided into 3 vulnerability classes, namely low, medium and high vulnerability classes. The results of the research are that the low vulnerability class has an area of ​​33854.4 ha with a percentage of 49% of the total area of ​​Padang City, the medium vulnerability class has an area of ​​26337.6 with a percentage of 38.2% of the total area, and the high vulnerability class has an area of ​​8823.3 ha with a percentage of 12.8% of the total area.
Co-Authors Adri, Fitrah Maulana Andika Rahman Aulia, A'isyah Azhari Syarief Destri Purwati Detti Wijayanti dion, nofrion Emillia Shandia Dewi Febriandi Febriandi Fitri Hayati Genta Ilahi Pratama Harby Prasetya Helfia Edial Heru Pratama Rosyidin Heru Pratama Rosyidin, Heru Pratama Islami, Refki Addea Mutia Ayulanda Nada Nilam Suri Osmi Zein R Rahmayoni Rahmayoni Resti Yolanda Putri Rizki Naldi Santoso, Titis Sherena Aurelia Anwar Sri Kandi Putri Suhadi Rahman Titi Syafura Yudi Antomi Yurni Suasanti Yurni Suasti Yurni Suasti Zulva, MartaPoli