Pan-Sharpening Analysis for Improved Detection Accuracy and Estimation of Coffee Plantation Land Area (Case Study: South OKU Regency, South Sumatra Province)
DOI:
https://doi.org/10.23960/jtep-l.v14i2.424-436
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Abstract
The use of remote sensing technology in monitoring coffee plantations is becoming increasingly important considering the vital role of coffee in the economy as an export product that increases state revenue. However, challenges remain, especially regarding the low resolution of satellite imagery which hinders accurate and efficient monitoring of coffee fields. This study aims to improve the accuracy of coffee plantation land analysis in South OKU Regency, South Sumatra Province, by using a pan-sharpening method consisting of IHS, Brovey, and Gram-Schmidt and assisted by a composite index. Satellite image sampling data from Landsat-8 was carried out at 1800 points divided into six classes. The results of the study show that the characteristics of coffee plantation land have NDVI, EVI, and ARVI values that tend to be lower, but the NDBI and NDWI values tend to be higher than the non-coffee plantation and forest classes. This study also compares the data from the pan-sharpening method using machine learning and deep learning methods to get the best classification model. The results showed that the SVM model machine learning method on the pan-sharpening brovey data gave the best results with an ACCURACY value of 83.49 and an F1-score of 83.59 percent.
Keywords: Coffee Plantations, Deep Learning, Machine Learning, Pan-sharpening, Remote Sensing.
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Aziz, M.H., & Murti, S.H. (2019) Pemanfaatan citra sentinel-2A untuk estimasi produksi tanaman kopi di sebagian wilayah Kabupaten Temanggung. [Undergraduated Thesis], Gadjah Mada University.
Bezerra, F.G.S., Aguiar, A.P.D., Alvalá, R.C.S., Giarolla, A., Bezerra, K.R.A., Lima, P.V.P.S., do Nascimento, F.R., & Arai, E. (2020). Analysis of areas undergoing desertification, using EVI2 multi-temporal data based on MODIS imagery as indicator. Ecological Indicators, 117, 106579. https://doi.org/10.1016/j.ecolind.2020.106579
BPS (Badan Pusat Statistik). (2023). Luas Tanaman Perkebunan (Hektar), 2021-2022. Accessed on 09 November 2023 from: https://sumsel.bps.go.id/indicator/54/414/1/luas-tanaman-perkebunan.html.
BPS (Badan Pusat Statistik). (2022). Statistik Kopi Indonesia 2021. Badan Pusat Statistik, Jakarta.
Breiman, L. (2001). Random forests. Machine learning, 45, 5-32. https://doi.org/10.1023/A:1010933404324
Budholiya, S., Shrivastava, S.K., & V. SharmaV. (2022). An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University-Computer and Information Sciences, 34(7), 4514-4523. https://doi.org/10.1016/j.jksuci.2020.10.013
Carper, W.J., Lillesand, T.M., & Kiefer, R.W. (1990). The use of intensity-hue-saturation transformations for merging SPOT panchromatic and multispectral image data. Photogrammetric Engineering and Remote Sensing, 56(4), 459-467.
Chavez, P.S., Sides, S.C., & Anderson, J.A. (1991). Comparison of three different methods to merge multiresolution and multispectral data: Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(3), 295-303.
Chen, Y., Dou, P., & Yang, X. (2017). Improving land use/cover classification with a multiple classifier system using AdaBoost integration technique. Remote Sensing, 9(10), 1055. https://doi.org/10.3390/rs9101055
Daenodoro, P. (2012). Pengantar Penginderaan Jauh Digital. Andi.
Damayanti, A.M. & Liyantono. (2022). Identifikasi tanaman kopi (coffea) di Provinsi Jawa Barat berbasis machine learning SVM
dan random forest citra satelit landsat-8. [Undergraduated Thesis], IPB University.
Dibs, H., Hasab, H.A., Mahmoud, A.S., & Al-Ansari, N. (2021). Fusion Methods and multi-classifiers to improve land cover estimation using remote sensing analysis. Geotechnical and Geological Engineering, 39, 5825-5842. https://doi.org/10.1007/s10706-021-01869-x
Finaka, A.W., Nurhanisah, Y., & Devina, C. (2023). Provinsi Penghasil Kopi Terbesar. https://indonesiabaik.id/infografis/provinsipenghasil-kopi-terbesar. Acessed 09 November 2023
Gautam, V.K., Gaurav, P.K., Murugan, P., & Annadurai, M. (2015). Assessment of surface water dynamicsin bangalore using WRI, NDWI, MNDWI, supervised classification and KT transformation. Aquatic Procedia, 4, 739-746. https://doi.org/10.1016/j.aqpro.2015.02.095
Haub, C., Kleinewillinghöfer, L., Millan, V.G., & Gregorio, A.D. (2015). Protocol for land cover validation. SIGMA – Stimulating Innovation for Global Monitoring of Agriculture.
Hidayati, I.N., Suharyadi, R., & Danoedoro, P. (2018) Developing an extraction method of urban built- up area based on remote sensing imagery transformation index. Forum Geografi, 96-108. https://doi.org/10.23917/forgeo.v32i1.5907
Hoeser, T., Bachofer, F., & Kuenzer, C. (2020). Object detection and image segmentation with deep learning on earth observation data: A review—Part II: Applications. Remote Sensing, 12(18), 3053. https://doi.org/10.3390/rs12183053
Hu, F., Xia, G-S., Hu, J., & Zhang, L. (2015). Transferring deep convolutional neural networks for the scene classification of highresolution remote sensing imagery. Remote Sensing, 7(11), 14680-14707. https://doi.org/10.3390/rs71114680
Jiang, W., He, G., Long, T., Ni, Y., Liu, H., Peng, Y., Lv, K., & Wang, G. (2018). Multilayer perceptron neural network for surface water extraction in landsat 8 OLI satellite images. Remote Sensing, 10(5), 755. https://doi.org/10.3390/rs10050755
Jiang, Z., Huete, A.R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote sensing of Environment, 112(10), 3833-3845. https://doi.org/10.1016/j.rse.2008.06.006
Lillesand, T.M., & Kiefer, R.W. (2015). Remote Sensing and Image Interpretation 7th Edition. Wiley, New York.
Peryanto, A., Yudhana, A., & Umar, R. (2020). Klasifikasi citra menggunakan convolutional neural network dan k fold cross validation. Journal of Applied Informatics and computing, 4(1), 45-51. https://doi.org/10.30871/jaic.v4i1.2017
Putra, M.S.T., & Azhar, Y. (2021). Perbandingan model logistic regression dan artificial neural network pada prediksi pembatalan hotel. JISKA (Jurnal Informatika Sunan Kalijaga), 6(1), 29-37. https://doi.org/10.14421/jiska.2021.61-04
Research and Markets. (2023). Global Coffee Shops Strategic Business Report 2023: Contact-less Machines and Robots Address the Shifting Consumer Mindset. Accessed 09 November 2023 from: https://finance.yahoo.com/news/global-coffee-shops-strategicbusiness-085800157.html.
Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote Sensing of Environment, 55(2), 95-107. https://doi.org/10.1016/0034-4257(95)00186-7
Sitanggang, G., Carolita I., & Trisasongko, B.H. (2004). Aplikasi teknik dan metode fusi data optik ETM-Plus landsat dan sar radarsat untuk ekstraksi informasi geologi pertambangan batu bara. Jurnal Lapan, 6(1), 11-30.
Triscowati, D.W., Sartono, B., Kurnia, A., Domiri, D.D., & Wijayanto, A.W. (2019). Multitemporal remote sensing data for classification of food crops plant phase using supervised random forest. Sixth Geoinformation Science Symposium, 1131102. https://doi.org/10.1117/12.2547216
USDA. (2024). Coffee: World Markets and Trade. United States Department of Agriculture Foreign Agricultural Service, https://apps.fas.usda.gov/psdonline/circulars/coffee.pdf
USGS. Landsat 8 Band Designations. https://www.usgs.gov/media/images/landsat-8-band-designations. Accessed 8 March 2022.
Verde, N., Kokkoris, I.P., Georgiadis, C., Kaimaris, D., Dimopoulos, P., Mitsopoulos, I., & Mallinis, G. (2020). National scale land cover classification for ecosystem services mapping and assessment, using multitemporal copernicus EO data and google earth engine. Remote Sensing, 12(20), 3303. https://doi.org/10.3390/rs12203303
Wandayani, A. (2007). Perbandingan metode brovey dan PCA dalam fusi citra panchromatic dan multispectral. [Final Assignment Report], Department of Computer Science, Faculty of Math and Natural Sciences, Institut Pertanian Bogor.
Yusof, N., Shafri, H.Z.M., & Shaharum, N.S.N. (2021). The use of Landsat-8 and Sentinel-2 imageries in detecting and mapping rubber trees. Journal of Rubber Research, 24, 121-135. https://doi.org/10.1007/s42464-020-00078-0
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