Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency
Abstract
The advancement of remote sensing technology has led to the development of sophisticated image processing methods that yield highly accurate land cover classification information, minimizing misinterpretations. The Normalized Difference Vegetation Index (NDVI) is a widely utilized method in remote sensing for measuring green vegetation. A significant portion of the Jember Regency area is covered by vegetation. This study aimed to identify various land cover types in the Jember Regency area, quantify the area for each classification, and establish the NDVI value ranges for each type of cover. Sentinel-2 was employed as the primary data source, and the NDVI method was utilized for land cover classification in the Jember Regency. The region exhibited diverse land cover types. Data from Sentinel-2A captured in June and October 2019 were chosen due to their accessibility, open-source nature, and adequate spectral, spatial, and temporal resolution. The classification in this study encompassed five classes: water bodies, settlements, dry fields, irrigated paddy fields, and forests. Error analysis was conducted using a confusion matrix with the Overall and Kappa algorithms. The accuracy results for June indicated a Kappa Accuracy of 37.7% and Overall Accuracy of 54.5%. In October, the Kappa Accuracy increased to 39.9%, and the Overall Accuracy reached 56.5%. In conclusion, the NDVI method did not meet the criteria for accurately interpreting land cover classification.
References
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Arnanto, A. (2013). Pemanfaatan Transformasi Normalized Difference Vegetation Index (NDVI) Citra Landsat TM untuk zonasi vegetasi di lereng merapi bagian selatan. Geomedia: Majalah Ilmiah Dan Informasi Kegeografian, 11(2), 155–170. https://doi.org/10.21831/gm.v11i2.3448
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Clerici, N., Valbuena Calderón, C. A., & Posada, J. M. (2017). Fusion of sentinel-1a and sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia. Journal of Maps, 13(2), 718–726. https://doi.org/10.1080/17445647.2017.1372316
Cui, L., Li, G., Ren, H., He, L., Liao, H., Ouyang, N., & Zhang, Y. (2014). Assessment of atmospheric correction methods for historical Landsat TM images in the coastal zone: A case study in Jiangsu, China. European Journal of Remote Sensing, 47(1), 701–716. https://doi.org/10.5721/EuJRS20144740
da Silva, M. R., de Carvalho, O. A., Guimarães, R. F., Trancoso Gomes, R. A., & Rosa Silva, C. (2020). Wheat planted area detection from the MODIS NDVI time series classification using the nearest neighbour method calculated by the Euclidean distance and cosine similarity measures. Geocarto International, 35(13), 1400–1414. https://doi.org/10.1080/10106049.2019.1581266
Dronova, I. (2015). Object-based image analysis in wetland research: A review. Remote Sensing, 7(5), 6380–6413. https://doi.org/10.3390/rs70506380
Filgueiras, R., Mantovani, E. C., Althoff, D., Fernandes Filho, E. I., & Cunha, F. F. da. (2019). Crop NDVI monitoring based on sentinel 1. Remote Sensing, 11(12), 1441. https://doi.org/10.3390/rs11121441
Firmansyah, S., Gaol, J. L., & Susilo, S. B. (2019). Perbandingan klasifikasi SVM dan Decision Tree untuk pemetaan mangrove berbasis objek menggunakan citra satelit Sentinel-2B di Gili Sulat, Lombok Timur. Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 9(3), 746-757. https://doi.org/10.29244/jpsl.9.3.746-757
Goncalves, R. M., Saleem, A., Queiroz, H. A. A., & Awange, J. L. (2019). A fuzzy model integrating shoreline changes, NDVI and settlement influences for coastal zone human impact classification. Applied Geography, 113, 102093. https://doi.org/10.1016/j.apgeog.2019.102093
Huang, S., Ming, B., Huang, Q., Leng, G., & Hou, B. (2017). A case study on a combination NDVI forecasting model based on the entropy weight method. Water Resources Management, 31(11), 3667–3681. https://doi.org/10.1007/s11269-017-1692-8
Indarto. (2014). Teori dan Praktik Pengindraan jauh. Andi Offset.
Kawamuna, A., Suprayogi, A., & Wijaya, A. P. (2017). Analisis Kesehatan Hutan Mangrove Berdasarkan Metode Klasifikasi NDVI pada Citra Sentinel-2(Studi Kasus : Teluk Pangpang Kabupaten Banyuwangi). Geodesi Undip, 6, 277–284. doi.org/10.14710/jgundip.2017.15439
Kristianingsih, L., Wijaya, A. P., & Sukmono, A. (2016). Analisis pengaruh koreksi atmosfer terhadap estimasi kandungan klorofil-a menggunakan citra landsat 8. Jurnal Geodesi Undip, 5(4), 56–64. https://doi.org/10.14710/jgundip.2016.13876
LAPAN. (2014). Penyusunan pedoman pengolahan digital klasikasi penutup lahan menggunakan penginderaan jauh. Pusat Pemanfaatan Penginderaan Jauh Lembaga Penerbangan dan Antariksa Nasional.
Menteri Kehutanan Republik Indonesia. (2012). Peraturan Menteri Kehutanan Republik Indonesia Nomor 32 Tahun 2012 Tentang Tata Cara Penyusunan Rencana Teknik Rehabilitasi Hutan Dan Lahan Daerah Aliran Sungai. Menteri Kehutanan Republik Indonesia.
Osgouei, P. E., Kaya, S., Sertel, E., & Alganci, U. (2019). Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery. Remote Sensing, 11(3). https://doi.org/10.3390/rs11030345
Pandey, P., Dewangan, K. K., & Dewangan, D. K. (2017). Enhancing the quality of satellite images by preprocessing and contrast enhancement. 2017 International Conference on Communication and Signal Processing (ICCSP), 56–60.
Patel, S. K., Verma, P., & Singh, G. S. (2019). Agricultural growth and land use land cover change in peri-urban India. Environmental Monitoring and Assessment, 191(9), 1–17. https://doi.org/10.1007/s10661-019-7736-1
Preidl, S., Lange, M., & Doktor, D. (2020). Introducing APiC for regionalised land cover mapping on the national scale using Sentinel-2A imagery. Remote Sensing of Environment, 240, 111673. https://doi.org/10.1016/j.rse.2020.111673
Putra, A., Tanto, T. A., Farhan, A. R., Husrin, S., & Pranowo, W. S. (2017). Pendekatan metode Normalized Difference Vegetation Index (NDVI) dan Lyzenga untuk pemetaan sebaran ekosistem perairan di kawasan pesisir teluk Benoa-Bali. J. Ilmiah Geomatika, 23(2), 87–94.
Putra, B. T. W., Soni, P., Marhaenanto, B., Harsono, S. S., & Fountas, S. (2019). Using information from images for plantation monitoring: A review of solutions for smallholders. Information processing in agriculture, 7(1), 109-119.. https://doi.org/10.1016/j.inpa.2019.04.005
Saini, R., & Ghosh, S. K. (2021). Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery. Geocarto international, 36(19), 2141-2159. https://doi.org/10.1080/10106049.2019.1700556
Simamora, F. B., Sasmito, B., & Hani’ah. (2015). Kajian metode segmentasi untuk identifikasi tutupan lahan dan luas bidang tanah menggunakan citra pada google earth (Studi Kasus : Kecamatan Tembalang, Semarang). Jurnal Geodesi Undip, 4(4), 43–51. https://doi.org/10.14710/jgundip.2015.9909
Sotille, M. E., Bremer, U. F., Vieira, G., Velho, L. F., Petsch, C., & Simões, J. C. (2020). Evaluation of UAV and satellite-derived NDVI to map maritime Antarctic vegetation. Applied Geography, 125, 102322. https://doi.org/10.1016/j.apgeog.2020.102322
Sun, R., Chen, S., Su, H., Mi, C., & Jin, N. (2019). The Effect of NDVI time series density derived from spatiotemporal fusion of multisource remote sensing data on crop classification accuracy. ISPRS International Journal of Geo-Information, 8(11), 502. https://doi.org/10.3390/ijgi8110502
Tao, H., Li, M., Wang, M., & Lü, G. (2019). Genetic algorithm-based method for forest type classification using multi-temporal NDVI from Landsat TM imagery. Annals of GIS, 25(1), 33–43. https://doi.org/10.1080/19475683.2018.1552621
Tsakmakis, I. D., Gikas, G. D., & Sylaios, G. K. (2021). Integration of Sentinel-derived NDVI to reduce uncertainties in the operational field monitoring of maize. Agricultural Water Management, 255, 106998. https://doi.org/10.1016/j.agwat.2021.106998
USGS. (2019). EarthExplorer - Home. In U.S. Geological Survey.
Vali, A., Comai, S., & Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sensing, 12(15), 2495. https://doi.org/10.3390/rs12152495
Vani, V., & Mandla, V. R. (2017). Comparative Study of NDVI and SAVI vegetation Indices in Anantapur district semi-arid areas. Int. J. Civ. Eng. Technol, 8(4).
Veloso, A., Mermoz, S., Bouvet, A., Le Toan, T., Planells, M., Dejoux, J.-F., & Ceschia, E. (2017). Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199, 415–426. https://doi.org/10.1016/j.rse.2017.07.015
Vijith, H., & Dodge-Wan, D. (2020). Applicability of MODIS land cover and Enhanced Vegetation Index (EVI) for the assessment of spatial and temporal changes in strength of vegetation in tropical rainforest region of Borneo. Remote Sensing Applications: Society and Environment, 18, 100311. https://doi.org/10.1016/j.rsase.2020.100311
Wong, M. M. F., Fung, J. C. H., & Yeung, P. P. S. (2019). High-resolution calculation of the urban vegetation fraction in the Pearl River Delta from the Sentinel-2 NDVI for urban climate model parameterization. Geoscience Letters, 6(1), 1–10. https://doi.org/10.1186/s40562-019-0132-4
Yang, Y., Luo, J., Huang, Q., Wu, W., & Sun, Y. (2019). Weighted double-logistic function fitting method for reconstructing the high-quality sentinel-2 NDVI time series data set. Remote Sensing, 11(20), 2342. https://doi.org/10.3390/rs11202342
Zhang, H. K., Roy, D. P., Yan, L., Li, Z., Huang, H., Vermote, E., Skakun, S., & Roger, J.-C. (2018). Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences. Remote Sensing of Environment, 215, 482–494. https://doi.org/10.1016/j.rse.2018.04.031
Zhang, T.-X., Su, J.-Y., Liu, C.-J., & Chen, W.-H. (2019). Potential bands of sentinel-2A satellite for classification problems in precision agriculture. International Journal of Automation and Computing, 16(1), 16–26. https://doi.org/10.1007/s11633-018-1143-x
Zhao, L., Shi, Y., Liu, B., Hovis, C., Duan, Y., & Shi, Z. (2019). Finer classification of crops by fusing UAV images and Sentinel-2A data. Remote Sensing, 11(24), 3012. https://doi.org/10.3390/rs11243012
Published
2024-04-07
How to Cite
NADZIRAH, Rufiani; RIZQON, Mochammad Kevin; INDARTO, Indarto.
Application of Sentinel-2A Images for Land Cover Classification Using NDVI in Jember Regency.
Geosfera Indonesia, [S.l.], v. 9, n. 1, p. 41-55, apr. 2024.
ISSN 2614-8528.
Available at: <https://jurnal.unej.ac.id/index.php/GEOSI/article/view/28846>. Date accessed: 20 nov. 2024.
doi: https://doi.org/10.19184/geosi.v9i1.28846.
Section
Original Research Articles
