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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Majalah Geografi Indonesia Jurnal Kelautan : Indonesian Journal of Marine Science and Technology JGE (Jurnal Geofisika Eksplorasi) Jurnal Geosaintek Jurnal Kelautan Tropis Journal of Science and Applicative Technology JURNAL ILMIAH GEOMATIKA Jurnal Geografi Jurnal Geografi, Edukasi dan Lingkungan J SIG (Jurnal Sains Informasi Geografi) Jurnal Geosains dan Remote Sensing (JGRS) Jurnal Riset Kelautan Tropis (Journal Of Tropical Marine Research) (J-Tropimar) Jurnal Riset Kelautan Tropis (J-Tropimar) Jurnal Ilmiah Geomatika Journal of Infrastructure Policy and Management (JIPM) TeknoKreatif: Jurnal Pengabdian kepada Masyarakat Journal of Sciencce Technology and Visual Culture Jurnal Geosaintek Geoid - Journal of Geodesy and Geomatics
Nadzir, Zulfikar Adlan
Program Studi Teknik Geomatika, Institut Teknologi Sumatera, Lampung Selatan, 35365, Indonesia
Agriculture, Biological Sciences & Forestry Arts Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Journal : jurnal geografi

 1 
APPLICATION OF SPOT6/7 SATELLITE IMAGERY FOR RICE FIELD MAPPING BASED ON TRANSFORMATIVE VEGETATION INDICES Nirmawana Simarmata; Zulfikar Adlan Nadzir; Lea Kristi Agustina
JURNAL GEOGRAFI Vol 14, No 1 (2022): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v14i1.29036

Abstract

Agriculture plays an essential role in national economic development. This fact made agricultural land one of the main unique production factors irreplaceable due to its importance in the agricultural business processes. However, a persistent problem of arable land conversion and land degradation have become more massive throughout the years. Meanwhile, the continuation of existing agricultural land and transformation into new agricultural land is inherently small. This research aimed to map agricultural land in sustainable agricultural development. Several transformative vegetation indices: NDVI, SAVI, and TSAVI, applied SPOT 6/7 satellite imagery in Lampung Province. Results show that the TSAVI value is the highest, with a 1.80 value, which indicates that this index value is very dense vegetation. Meanwhile, the NDVI index, which has a minimum value of -1.02, suggests that this index value is a non-vegetation object. However, high or low value does not indicate the rigorousness and Accuracy of an index. All three indices’ results are then overlaid with the satellite imagery classification process result. The accuracy result shows that the agricultural land has a maximum of 100% producer accuracy while the user accuracy value is 87.87%. Overall, for NDVI, the Accuracy was valued at 90.25%, which could be classified as a reasonable classification result. SAVI has a PA value of 97.85%, UA 85.20% and OA 86.63%, while the TSAVI Index has a PA value of 98.23%, UA 86.16% and OA 87.63%. This accuracy value indicates that the map has good results but judging from the magnitude of the highest accuracy value obtained from NDVI, it can be concluded that NDVI is the best index to determine paddy fieldsKeywords: Agricultural Land, SPOT 6/7, NDVI, SAVI, TSAVI.
Monitoring Sugarcane Phenology Using Sentinel-1 SAR and Machine Learning in Central Lampung Simarmata, Nirmawana; Agustina, Lea Kristi; Rahadianto, Muhammad Ario Eko; Sari, Ratna Mustika; Nadzir, Zulfikar Adlan; Nabila, Choirun Nisa
JURNAL GEOGRAFI Vol. 18 No. 1 (2026): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/46hfx180

Abstract

Sugarcane is a vital commodity in the national sugar industry, requiring accurate growth monitoring to support precision agriculture. In Indonesia, conventional monitoring methods remain limited in spatial and temporal coverage. This study aims to monitor sugarcane phenology using multitemporal Sentinel-1 Synthetic Aperture Radar and to develop a machine learning–based growth phase classification model. The study was conducted in Central Lampung during one complete growing season from November 2023 to December 2024. Time-series analysis of VV and VH backscatter coefficients and the Normalised Polarisation Ratio was applied to capture sugarcane growth dynamics. Growth phase classification models were developed using Random Forest (RF) and Support Vector Machine (SVM) and evaluated using the confusion matrix, overall accuracy, Kappa coefficient, coefficient of determination, and root mean square error. The results indicate that RF consistently outperformed SVM across all model configurations. The best performance was achieved using combined VV–VH polarization, yielding an R² of 0.929, RMSE of 0.295, and classification accuracy of 93%. In contrast, SVM models showed weak predictive performance with negative R² values and classification accuracy below 32%. These findings demonstrate that multitemporal Sentinel-1 SAR data combined with RF provide an effective approach for spatial and temporal monitoring of sugarcane phenology
Co-Authors Agung, Haikhal Nuri Al Farishi, Bilal Albertus Sulaiman, Albertus Aliffia Aliffia Alvito, Fahri Dean Anita Eka Jayanti Annisa Permata Sari, Annisa Permata Apriani, Mutia Rahma Atmojo, Aulia Try Devianti Natalia Lase Fahrurozi, Fikri Giovani, Muhammad Hasan, Muhammad Akmal Hendrawan, Rezki Naufan Jaelani, Lalu Muhamad Jayanti, Anita Eka Julianto, Romi Lea Kristi Agustina Lea Kristi Agustina Lea Kristi Agustina Mhd Irfansyah Munthe, Manson Brian Utama Nabila, Choirun Nisa Nirmawana Simarmata Nurhayati, Misfallah Ongky Anggara Rahadianto, Muhammad Ario Eko Rahmadhani, Nina Ratna Mustika Sari, Ratna Mustika Satrio Muhammad Alif Satrio Muhammad Alif Simarmata, nirmawana Simbolon, Karti Sulaiman, Albertus Tri Kies Welly Welly, Tri Kies Wijayanti, Goldie Melinda