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All Journal Cyberspace: Jurnal Pendidikan Teknologi Informasi JurTI (JURNAL TEKNOLOGI INFORMASI) Technologia: Jurnal Ilmiah Jurnal Optimalisasi JTIK (Jurnal Teknik Informatika Kaputama) Jurnal Teknologi Informasi Infolitika Journal of Data Science
Abdurrahman Ridho
Universitas Teuku Umar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Other

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THE FEASIBILITY OF FUSING SATELLITE IMAGERIES FOR HIGH-FREQUENCY SEA LEVEL MONITORING Putri, Andriani; Nazhifah, Sri Azizah; Ridho, Abdurrahman; Maghfirah, Hayatun; Mutia, Cut; Niani, Cukri Rahmi; Sanusi, Sanusi
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 8, No 1 (2024)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v8i1.23504

Abstract

Enhancing the capacity to monitor swift environmental shifts at finer scales requires satellite image that offers high spatial and temporal resolution. However, no individual satellite can offer images meeting both criteria simultaneously. To tackle this challenge, spatial temporal fusion algorithms have been developed to derive fine-scale and time-series images. Conversely, effective monitoring of water levels is crucial for preventing natural disaster, such as flood and tsunami mitigation. Yet, monitoring these natural changes regularly poses challenges for remote sensing satellites, given their limitations in either spatial or temporal resolution. For instance, the spatial resolution of 30 meters of Landsat 8 provides imagery with a but lacks the temporal resolution needed to capture dynamic events. On the contrary, the Himawari 8 has the capability to monitor the entire hemisphere every 10 minutes. However, its inadequate resolution affects the precision of sea water change mapping.  This research seeks to utilize Landsat OLI and Himawari-8 images jointly for tracking sea level variation patterns. Our approach involves calculating a water index from both Landsat and Himawari images, then using an image fusion algorithm to merge these indices. Next, we identify water coverage by applying a specific threshold on the water index. The comparison of water percentages with reference water height observations has delivered encouraging outcomes.
FUSING SATELLITE DATA TO MONITOR SEA LEVEL CHANGES: A DEM-BASED NEAREST NEIGHBOR APPROACH Putri, Andriani; Nazhifah, Sri Azizah; Ridho, Abdurrahman; Maghfirah, Hayatun; Mutia, Cut; Niani, Cukri Rahmi
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 8, No 2 (2024)
Publisher : UIN Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v8i2.26369

Abstract

High spatial and temporal resolution satellite imagery is essential for monitoring rapid environmental changes at finer scales. However, no single satellite currently provides images with both high spatial and temporal resolution. To overcome this limitation, spatiotemporal image fusion algorithms have been developed to generate images with improved spatial and temporal detail. Water level monitoring is also crucial for managing natural hazards like floods and tsunamis, but remote sensing satellites face challenges in continuous monitoring due to either low spatial or temporal resolution. For instance, while Landsat 8, with a spatial resolution of 30 meters, has been used for water level detection, it cannot capture fast-changing events because of its low temporal resolution. Conversely, the Advanced Himawari Imager (AHI) 8 offers observations every 10 minutes but has a coarse spatial resolution, limiting its ability to map sea level changes accurately. This study focuses on integrating Landsat and AHI imagery to monitor local and dynamic sea level changes. The process involves calibrating images from the study area to surface reflectance and co-registering them. The Normalized Difference Water Index (NDWI) is calculated from both Landsat and Himawari-8 images, serving as input for image fusion. In the previous study, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used for image fusion. In this study we use the application of Spatial Temporal Adaptive Algorithm for Mapping Reflectance Change (STAARCH) for the image fusion step. Since traditional methods are influenced by land cover changes, this study proposes a method called DEM-based Nearest Neighbor to select appropriate land cover maps for image fusion. Evaluation results demonstrate that this approach can produce accurate water coverage maps with both high spatial and temporal resolution.
Co-Authors Abdiel Khaleil Akmal Amelia Putri Sinaga Andini, Mirna Ria Andriani Putri Cukri Rahmi Niani Cut Mutia Cut Mutia, Cut Hayatun Maghfirah Husdayanti, Noviana Ikhsan Ikhsan Ilham Juliwardi Kasmawati Kasmawati Maghfirah, Hayatun Muhammad Ardiansyah Murhaban Murhaban Nazhifah, Sri Azizah Niani, Cukri Rahmi Putri, Andriani Sanusi Sanusi Shou-Hao Chiang Sofiyanurriyanti Suhendra, Rivansyah Suryadi Suryadi