WHAT INSIGHTS CAN SENTINEL-2A NDVI AND NUTRIENT VALIDATION PROVIDE FOR OIL PALM HEALTH ASSESSMENT? OBSERVATIONS FROM SOUTHEAST SULAWESI, INDONESIA

Authors

DOI:

https://doi.org/10.2298/IJGI260206011I

Keywords:

NDVI, oil palm health, macronutrients, remote sensing, spatial analysis

Abstract

Monitoring oil palm health using spatial data is essential for optimizing plantation productivity and supporting sustainable management. This study assessed vegetation conditions at Sultra Prima Lestari Ltd. plantation in Southeast Sulawesi, Indonesia, using Sentinel-2A imagery acquired in June 2025 and field data collected from Leaf Sampling Units (LSUs) between April–July 2025. The Normalized Difference Vegetation Index (NDVI) was calculated from the imagery and classified into three health categories: unhealthy (from −1.00 to 0.15), moderately healthy (from 0.15 to 0.35), and very healthy (from 0.35 to 1.00). Field validation across 94 stratified random sampling plots confirmed that NDVI reflects differences in canopy density and leaf greenness. Correlation analyses showed positive associations with plant macronutrients: nitrogen (R² = 0.603), phosphorus (R² = 0.575), potassium (R² = 0.645), and magnesium (R² = 0.520). Spatial analysis revealed heterogeneity in vegetation health across the plantation, with very healthy areas concentrated in central and western divisions, and unhealthy patches primarily in the eastern division. These results indicate that NDVI, when interpreted alongside systematic field measurements, is a cost-effective and spatially explicit indicator of oil palm physiological condition, without implying absolute validation. The findings support targeted, site-specific agronomic interventions and demonstrate the value of integrating moderate-resolution satellite imagery with field-based data for scalable monitoring of large-scale tropical plantations.

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References

Akmal, Z., Wibowo, A., Hernina, R., & Pin, T. G. (2022). Spatial study of oil palm plant health using Sentinel-2 imagery at PTPN VIII Cikasungka Plantation, Bogor, West Java. IOP Conference Series: Earth and Environmental Science, 1111(1), Article 012008. https://doi.org/10.1088/1755-1315/1111/1/012008

Badan Informasi Geospasial. (2023). Geodatabase of Indonesian administrative boundaries (regency/city level), September 2023 edition [Dataset]. Ina-Geoportal. https://tanahair.indonesia.go.id/portal-web

Badan Meteorologi, Klimatologi, dan Geofisika. (2025). Weather forecast for Langgikima District, North Konawe Regency, Southeast Sulawesi. BMKG. https://www.bmkg.go.id/cuaca/prakiraan-cuaca/74.09.03

Banyuriatiga, B., Sari, K., Syaddam, S., Rahman, A., & Adlian, A. (2025). Literature review: IoT-based geographic information system for monitoring soil chemical properties in oil palm plantations. Jurnal Dialektika Informatika, 5(2), 108–115. https://doi.org/10.24176/detika.v5i2.15401

BPS Statistics Indonesia. (2025). Langgikima District in Figures 2025. https://konutkab.bps.go.id/en/publication/2025/09/26/1737d555cb972df1317c4476/langgikima-district-in-figures-2025.html

Charloq, A., Thoha, A. S., Putra, D. Y., & Muammar, A. (2023). Unmanned aerial vehicles (UAV) utilization for mapping the health of oil palm plants (Elaeis guineensis Jacq.). IOP Conference Series: Earth and Environmental Science, 1241(1), Article 012072. https://doi.org/10.1088/1755-1315/1241/1/012072

Congalton, R. G., & Green, K. (2019). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices (3rd ed.). CRC Press. https://doi.org/10.1201/9780429052729

Esri Inc. (2023). ArcGIS Pro (Version 3.1) [Computer software]. https://www.esri.com

European Union, European Space Agency, & Copernicus. (2021). Sentinel-2 MSI Level 2-A (Collection 1) [Data set]. CDSE. https://dataspace.copernicus.eu

Fragastia, V. A., Batubara, R. O., Putra, A., & Afilla, M. R. (2024). Development of a geographic information system–based palm tree census tool for oil palm plantations (case study: Langkat Regency). CSRID Journal, 16(4), 389–403. https://doi.org/10.22303/csrid.1.1.2022.01-10

Hardanto, A., Röll, A., Niu, F., Meijide, A., Hendrayanto, & Hölscher, D. (2017). Oil palm and rubber tree water use patterns: Effects of topography and flooding. Frontiers in Plant Science, 8, Article 452. https://doi.org/10.3389/fpls.2017.00452

Hariadi, B., Sastrohartono, H., Krisdiarto, A. W., Sukarman, S., Sagoro, T. H., Primananda, S., Akbar, A. R. M., & Uktoro, A. I. (2024). The Use of the Normalized Difference Red Edge (NDRE) Vegetation Index from Multispectral Cameras Mounted on Unmanned Aerial Vehicle to Estimate the Nutrient Content in Oil Palm Leaves. Jurnal Teknik Pertanian Lampung, 13(4), 1051–1063. https://doi.org/10.23960/jtep-l.v13i4.1051-1063

Judith, J., Tamilselvi, R., Beham, M. P., Lakshmi, S. S. P., Panthakkan, A., Al Mansoori, S., & Al Ahmad, H. (2025). Remote sensing based crop health classification using NDVI and fully connected neural networks. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-G-2025 (pp. 739–747). Copernicus Publications. https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-739-2025

Kumar, S., Ghosh, S. S., Mandal, D., Bhattacharya, A., Porwal, A., & Karthikeyan, L. (2025). Enhancing vegetation monitoring: A proposal for a Sentinel-2 based vegetation health index. Frontiers in Remote Sensing, 6, Article 1581355. https://doi.org/10.3389/frsen.2025.1581355

Lee, J., Lim, J., Lee, J., Park, J., & Won, M. (2024). Ground-based NDVI network: Early validation practice with Sentinel-2 in South Korea. Sensors, 24(6), Article 1892. https://doi.org/10.3390/s24061892

Lv, T., Zhou, X., Tao, Z., Sun, X., Wang, J., Li, R., & Xie, F. (2021). Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution. Remote Sensing, 13(14), Article 2674. https://doi.org/10.3390/rs13142674

Masrol, N. N., Salim, M. Z., Nor, N. F., Rosnan, N. N., Ikhsan, R., & Cheak, S. C. (2023). The reliability of normalised difference vegetation index (NDVI) to indicate health condition from vegetative measurement and nutrient content of mature oil palm trees for precision agriculture. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 3562–3565). IEEE. https://doi.org/10.1109/IGARSS52108.2023.10283421

McCoy, R. M. (2005). Field Methods in Remote Sensing (1st ed.). Guilford Press.

Minarto, A., Ramadhan, M. H., Lie, A., & Irwansyah, E. (2024). Oil palm tree counting and abnormality assessment using deep learning and vegetation index. Procedia Computer Science, 245, 768–777. https://doi.org/10.1016/j.procs.2024.10.303

Nadzirah, R., Rizqon, M. K., & Indarto, I. (2024). Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency. Geosfera Indonesia, 9(1), 41–51. https://doi.org/10.19184/geosi.v9i1.28846

Nikolić, N. (2025). Assessing wildfire impact on vegetation in protected areas using the DNBR index: Insights from the designated location in Serbia. Journal of the Geographical Institute “Jovan Cvijić” SASA, 75(3), 453–460. https://doi.org/10.2298/IJGI250225010N

Ningrum Amrul, H. M. Z., Marisa, J., Syahputra, M. W., Rusnanto, Wahyudi, H., & Harahap, A. Z. (2025). Effectiveness of drone utilization and spatial verification of palm oil key points. In Proceedings of the 2nd International Conference on Islamic Community Studies (ICICS): History of Malay civilisation and Islamic human capacity and halal hub in the globalization era (pp. 2590–2594). Universitas Pembangunan Panca Budi. https://proceeding.pancabudi.ac.id/index.php/ICIE/index

Nițu, A., Florea, C., Ivanovici, M., & Racoviteanu, A. (2025). NDVI and beyond: Vegetation indices as features for crop recognition and segmentation in hyperspectral data. Sensors, 25(12), Article 3817. https://doi.org/10.3390/s25123817

Nuthammachot, N., & Stratoulias, D. (2023). Exploring Sentinel-2 satellite imagery-based vegetation indices for classifying healthy and diseased oil palm trees. Journal of Oil Palm Research, 35(3), 517–527. https://doi.org/10.21894/jopr.2022.0068

Potić, I., Stojanović, M., Ćurčić, N., Đorđević, D., & Banković, R. (2024). Development of geospatial passability maps: A multi-criteria analysis approach. Journal of the Geographical Institute “Jovan Cvijić” SASA, 74(1), 29–45. https://doi.org/10.2298/IJGI230822002P

Prasvita, D. S., Arymurthy, A. M., & Chahyati, D. (2023). Deep learning model for automatic detection of oil palm trees in Indonesia with YOLO-V5. In Proceedings of the 8th International Conference on Sustainable Information Engineering and Technology (SIET ’23) (pp. 39–44). Association for Computing Machinery. https://doi.org/10.1145/3626641.3626924

Putra, F. A., Hermawan, E., & Hudjimartsu, S. A. (2025). Analysis of oil palm plant nutrient prediction using the random forest regression method. E-Jurnal Penyelidikan dan Inovasi, 12(5), 257–271. https://doi.org/10.53840/ejpi.v12i5.322

Puttinaovarat, S., Chai-Arayalert, S., Saetang, W., Khaimook, K., Plaiklang, S., & Horkaew, P. (2025). Innovative Flood Impact Monitoring and Harvest Analysis in Oil Palm Plantations Utilizing Geographic Information Systems and Deep Learning. AgriEngineering, 7(2), Article 44. https://doi.org/10.3390/agriengineering7020044

Qin, Q., Xu, D., Hou, L., Shen, B., & Xin, X. (2021). Comparing vegetation indices from Sentinel-2 and Landsat 8 under different vegetation gradients based on a controlled grazing experiment. Ecological Indicators, 133, Article 108363. https://doi.org/10.1016/j.ecolind.2021.108363

Rahmasari, A. N., Supriatna, & Rustanto, A. (2022). Estimation of oil palm plant productivity using Sentinel-2A imagery at Cikasungka Plantation PTPN VIII, Bogor, West Java. International Journal of Remote Sensing and Earth Sciences, 19(1), 31–38. https://doi.org/10.30536/ijreses.v19i1.13782

Rudorff, N., Rudorff, C. M., Kampel, M., & Ortiz, G. (2018). Remote sensing monitoring of the impact of a major mining wastewater disaster on the turbidity of the Doce River plume off the eastern Brazilian coast. ISPRS Journal of Photogrammetry and Remote Sensing, 145(Part B), 349–361. https://doi.org/10.1016/j.isprsjprs.2018.02.013

Saputro, W. J., Luthfi, M. A. K., & Machfud, S. (2024). Mapping of Darunnajah 6 Muko-Muko Bengkulu oil palm plantation using GIS. Journal of Information System and Technology, 1(2), 59–66. https://ejournal.darunnajah.ac.id/index.php/joistech/article/view/322

Sultra Prima Lestari Ltd. (2025). Plantation boundary [Data set]. Sultra Prima Lestari Ltd.

Tamburadzhiev, I. (2024). Analysis of remote sensing data pertaining to debris flows: Insights from selected drainage basins in Bulgaria. Journal of the Geographical Institute “Jovan Cvijić” SASA, 74(3), 311–327. https://doi.org/10.2298/IJGI2403311T

Taufik, V. V., Sukmono, A., & Firdaus, H. S. (2020). Estimasi produktivitas kelapa sawit menggunakan metode NDVI (Normalized Difference Vegetation Index) dan ARVI (Atmospherically Resistant Vegetation Index) dengan citra Sentinel-2A (studi kasus: beberapa wilayah di Provinsi Riau) [Estimation of oil palm productivity using NDVI (Normalized Difference Vegetation Index) and ARVI (Atmospherically Resistant Vegetation Index) methods with Sentinel-2A imagery (case study: several regions in Riau Province)]. Jurnal Geodesi Undip, 10(1), 153–162. https://doi.org/10.14710/jgundip.2021.29636

Wibowo, H., Sitanggang, I. S., Mushthofa, M., & Adrianto, H. A. (2022). Large-scale oil palm trees detection from high-resolution remote sensing images using deep learning. Big Data and Cognitive Computing, 6(3), Article 89. https://doi.org/10.3390/bdcc6030089

Wijaya, D., & Irwansyah, E. (2023). Tree counting with deep learning algorithm and carbon evaluation using Pleiades satellite imagery data in Kulon Progo, Yogyakarta, Indonesia. In Proceedings of the 2023 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) (pp. 22–26). IEEE. https://doi.org/10.1109/ICIMCIS60089.2023.10348982

Yarak, K., Witayangkurn, A., Kritiyutanont, K., Arunplod, C., & Shibasaki, R. (2021). Oil palm tree detection and health classification on high-resolution imagery using deep learning. Agriculture, 11(2), Article 183. https://doi.org/10.3390/agriculture11020183

Yuniasih, B., & Adjie, A. R. P. (2022). Evaluasi Kondisi Kebun Kelapa Sawit Menggunakan Indeks NDVI dari Citra Satelit Sentinel 2. Teknotan: Jurnal Industri Teknologi Pertanian, 16(2), 127–132. https://doi.org/10.24198/jt.vol16n2.10

Yuniasih, B., Adji, A. R. P., & Budi, B. (2022). Evaluation of Pre-Replanting Oil Palm Plant Health using the NDVI Index from Landsat 8 Satellite Imagery. Jurnal Teknik Pertanian Lampung, 11(2), 304–313. https://doi.org/10.23960/jtep-l.v11i2.304-313

Yurianda, R. B., Setyawan, D., & Warsito. (2023). Metode klasifikasi normalized difference vegetation index berbasis citra Landsat 8I untuk identifikasi sebaran kondisi kesehatan tanaman kelapa sawit di PT. Andira Agro, Sumatera Selatan [Normalized Difference Vegetation Index classification methods based on Landsat 8 imagery for identifying the distribution of oil palm health conditions at PT. Andira Agro, South Sumatra]. Pedontropika: Jurnal Ilmu Tanah dan Sumber Daya Lahan, 8(2), 15–24. https://doi.org/10.26418/pedontropika.v8i2.56431

Zhao, Q., & Qu, Y. (2024). The retrieval of ground NDVI (normalized difference vegetation index) data consistent with remote-sensing observations. Remote Sensing, 16(7), Article 1212. https://doi.org/10.3390/rs16071212

Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152–171. https://doi.org/10.1016/j.rse.2014.01.011

Zhu, Z., Zhang, J., Yang, Z., Aljaddani, A. H., Cohen, W. B., Qiu, S., & Zhou, C. (2020). Continuous monitoring of land disturbance based on Landsat time series. Remote Sensing of Environment, 238, Article 111116. https://doi.org/10.1016/j.rse.2019.03.009

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Published

20-10-2026

How to Cite

Salihin, L. M. I., Safuan, L. O., Usman, I., Jaya, L. M. G., Muhidin, Sutariati, G. A. K., … Hasddin. (2026). WHAT INSIGHTS CAN SENTINEL-2A NDVI AND NUTRIENT VALIDATION PROVIDE FOR OIL PALM HEALTH ASSESSMENT? OBSERVATIONS FROM SOUTHEAST SULAWESI, INDONESIA. Journal of the Geographical Institute “Jovan Cvijić” SASA, 76(3), 349–366. https://doi.org/10.2298/IJGI260206011I

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