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All Journal J-Sil (Jurnal Teknik Sipil dan Lingkungan) TEKNIK MEDIA KOMUNIKASI TEKNIK SIPIL Jurnal Geodesi Undip Jurnal Teknik Sipil dan Perencanaan Journal of Applied Geospatial Information Elipsoida : Jurnal Geodesi dan Geomatika Jurnal Surya Masyarakat Jambura Geoscience Review J-Sil (Jurnal Teknik Sipil dan Lingkungan)
Bashit, Nurhadi
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Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Education Engineering Environmental Science Physics Transportation Other

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COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE ON LANDSAT 8 AND HLS-L30 USING MONO WINDOW AND SPLIT WINDOW ALGORITHMS (CASE STUDY: WKP MOUNT UNGARAN) Nababan, Yolanda Stevany; Putri, Rizki Amara; Bashit, Nurhadi; Hadi, Firman; Ihsanudin, Taufiq
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.28716

Abstract

Advancements in remote sensing technology have enabled the use of satellite imagery, such as Landsat 8 and HLS-L30, for the spatial and temporal estimation of Land Surface Temperature (LST) with improved resolution. In the context of geothermal exploration, the availability of thermal infrared bands in these datasets facilitates more efficient and cost-effective mapping and identification of surface temperature anomalies, particularly across large and inaccessible areas. This study aims to compare LST estimations derived from Landsat 8 and HLS-L30 imagery using the Mono Window Algorithm (MWA) and Split Window Algorithm (SWA) at 18 geothermal manifestation points within the Mount Ungaran Geothermal Working Area (WKP). A Focal Statistic process was applied to 20 LST datasets, resulting in a total of 100 LST layers. From each layer, LST values were extracted at the 18 manifestation points, producing a total of 1,800 data points. A binary logistic regression analysis was conducted using these LST values alongside those from 20 randomly selected comparison points. The results indicate that the median LST derived from HLS-L30 imagery using the Split Window Algorithm with the minimum Focal Statistic yielded the most optimal performance in classifying geothermal manifestation presence. This method achieved statistical significance (p = 0.028), indicating its capability to effectively distinguish between manifestation and non-manifestation points. However, the pseudo-R² value of 0.107 suggests that the model explains approximately 11% of the variance in the data. These findings underscore the potential application of satellite-based LST analysis in the early detection and assessment of geothermal surface anomalies within WKPs.Keywords :  Geothermal, LST, Landsat, HLS-L30, Ungaran
SPATIOTEMPORAL ANALYSIS OF MANGROVE DENSITY DYNAMICS USING SENTINEL-2 IMAGERY AND CLOUD COMPUTING BASED MACHINE LEARNING ALGORITHMS (CASE STUDY: DEMAK REGENCY) Nirwanawati, Raya; Bashit, Nurhadi; Lazuardi, Wahyu
Elipsoida : Jurnal Geodesi dan Geomatika Vol 8, No 2 (2025): Volume 08 Issue 02 Year 2025
Publisher : Department of Geodesy Engineering, Faculty of Engineering, Diponegoro University,Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/elipsoida.2025.28959

Abstract

Mangrove forests are vital coastal ecosystems that protect shorelines and help maintain coastal environmental balance. Mangrove forests undergo changes every year due to degradation or restoration, so monitoring needs to be carried out. This study analyzes the spatiotemporal dynamics of mangrove density in Demak Regency from 2020 to 2025 using Sentinel-2 imagery and the CART (Classification and Regression Tree) algorithm processed through the Google Earth Engine (GEE) platform. Six spectral indices were used as classification inputs, namely NDVI, NDWI, MVI, MI, AMMI, and CMRI. AMMI is the best index in identifying mangroves as a whole, both in narrow and large areas. Based on classification, the mangrove cover area has been restored or significantly increased from 1644.011 ha in 2020 to 2530.522 ha in 2025. The largest increase occurred in the high canopy density class of 960.157 ha. Meanwhile, the medium and low canopy density classes showed a decrease in area. Accuracy assesment showed an overall accuracy value of 99.92% for 2020 and 99.91% for 2025, with kappa accuracy above 97% in both years. These results show that the classification method with the support of cloud computing can be relied on in spatiotemporal monitoring of mangrove changes efficiently and accurately. Keywords:  Mangrove, Sentinel-2, Machine Learning, Classification and Regression Tree, Cloud Computing
Mapping Mangrove Canopy Density Changes in Pekalongan Using Sentinel-2 Red-Edge Damara Santi, Anggit Lejar; Sukmono, Abdi; Bashit, Nurhadi; Sasmito, Bandi
Jambura Geoscience Review Vol 8, No 1 (2026): Jambura Geoscience Review (JGEOSREV)
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jgeosrev.v8i1.27324

Abstract

This study analysed spatiotemporal changes in mangrove canopy density within the coastal areas of Pekalongan Regency and Pekalongan City using multitemporal Sentinel-2 imagery (2019, 2021, and 2024). This study was motivated by the continuous degradation of mangrove ecosystems due to severe tidal flooding, land subsidence, and coastal hydrodynamic disturbances, which necessitate reliable monitoring tools to support mitigation and restoration programs. The objective of this study was to compare the performance of NDVI, NDVI-Red Edge, and mRE-SR vegetation indices in estimating mangrove canopy density and to determine the most accurate index for tidal-affected environments. The methodological framework involved image preprocessing, land cover classification, vegetation index computation, and linear regression modelling validated by in situ canopy measurements obtained through hemispherical photography. The results showed that the mangrove area declined significantly between 2019 and 2021, followed by partial recovery in 2024 in response to rehabilitation efforts. Among the tested indices, NDVI-Red Edge Band 5 yielded the highest accuracy with the lowest RMSE (7.65%), outperforming NDVI and mRE-SR, whereas Bands 6 and 7 showed weak predictive capability. The study concluded that NDVI-RE Band 5 is the most reliable index for mapping mangrove canopy density in dynamic coastal environments affected by tidal inundation. These findings demonstrate the effectiveness of combining Sentinel-2 red-edge information with field-based validation to support mangrove monitoring and coastal management.
Co-Authors Abdi Sukmono, Abdi Adrian, Faizal Ibnu Andri Suprayogi Angga Dwi Prasetyo APRIYANTI, DESSY Arief Laila Nugraha Arwan Putra Wijaya Bambang Sudarsono Bandi Sasmito Damara Santi, Anggit Lejar Desyta Ulfiana Dinoto, Tjiong, Susilo Fadillah, Ma'ruf Arief Fatah, Fakhri Luvian Gusmiarti, Neni Indah Hadi, Firman Hani'ah, Hani'ah Helik Susilo Ihsanudin, Taufiq Kasanah, Nikmatul Kurniawan Teguh Martono Lazuardi, Wahyu Masdian, Azhari Raka Muhammad Adnan Yusuf, Muhammad Adnan Nababan, Yolanda Stevany Nirwanawati, Raya Novia Sari Ristianti Prasetyo, Damar Putri, Erni Dwi Hapsari Putri, Rizki Amara Ramadhani, Sekar Melati Rampu, Jelly Resky Kelana Rassarandi, Farouki Dinda Rosyiidah, Rofi’ Ronaa Sawitri Subiyanto Septiningdiah, Dara Jati Sheehan Maladzi, Havi Shofiyatul Qoyimah, Shofiyatul Sitepu, Selli Angelita Ulfiana, Destya Wijaya, Alfi Dian Ranu Yudi Eko Windarto Yudo Prasetyo