Coffee Plantation Area Classification in Highlands Using Pixel Based Approaches from Sentinel-2 Multi-Spectral Data and Auxiliary Geographical Features
Article Sidebar
Main Article Content
Abstract
This research aimed to identify the key factors and conditions influencing the classification of coffee-growing areas and to develop a highland coffee plantation classification technique using point-based image analysis from multispectral Sentinel-2 satellite data. The methodology utilizes spectral reflectance values that influence the digital signature of coffee plantation areas, in combination with spectral indices derived from various Sentinel-2 bands. Field surveys were conducted in highland coffee-growing regions, and custom scripts were developed to process multispectral satellite data. Purposive sampling was used to select suitable study sites in three provinces of northern Thailand: Chiang Mai, Chiang Rai, and Nan. The research results showed that 1) the cultivation areas, consisting of montane forests and deciduous forests, affect yields, and that an altitude of 850 meters above sea level is suitable for Arabica coffee cultivation. 2) The results of using satellite data and scripting to process the cultivation area classification using the Random Forest model showed that the coffee growing area classification technique using Sentinel-2 satellite data can produce highly accurate classification results, with an overall classification accuracy of 89.70 percent. This can be used for strategic planning to effectively manage coffee growing in highlands.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Adugna, D. & Struik, P. C. (2011). Effects of Shade on Growth, Production and Quality of Coffee (Coffea Arabica) in Ethiopia. Journal of Horticulture and Forestry, 3(11), 336-341. https://www.researchgate.net/publication/ 215979044_Effects_of_shade_on_growth_production_and_quality_of_coffee_Coffea_arabica_in_Ethiopia
Anhar, A., Rasyid, U. H. Ar., Muslih, A. M., Baihaqi, A., Romano & Abubakar Y. (2021). Sustainable Arabica Coffee Development Strategies in Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 667. https://doi.org/10.1088/1755-1315/667/1/012106
Bourgoin, C., Oszwald, J., Bourgoin, J., Gond, V., Blanc, L., Dessard, H., Phan, T. V., Sist, P., Läderach, P. & Reymondin, L. (2020). Assessing the Ecological Vulnerability of Forest Landscape to Agricultural Frontier Expansion in the Central Highlands of Vietnam. International Journal of Applied Earth Observation and Geoinformation, 84, 101958.https:// doi.org/10.1016/j.jag.2019.101958
Digital Economy Promotion Agency. (2025). Remote Sensing for Precision Agriculture with Remote Sensing Technology. Retrieved August 2025, from https://www.depa.or.th/th/article-view/remote-sensing-20210903
Dai, J., Ding, L., Zhang, T., Qin, Z. & Wang, J. (2025). RCFC: Radiometric Resolution Compression of Remote Sensing Images Considering Feature Reconstruction and Color Consistency. Digital Signal Processing, 164(2025), 105255. https://doi.org/10.1016/j.dsp.2025.105255
European Space Agency. (2024). Sentinel-2. Retrieved December 2024, from https://sentiwiki.copernicus.eu/ web/ sentinel-2
European Space Agency. (2025) Applications Sentinel-2. Retrieved Mar 2025, from https://www. esa.int/Applications/ Observing_the_Earth/Copernicus/Sentinel-2
European Space Operations Centre. (2024). Mission Sentinel-2B. Retrieved April 2025, from https://esoc.esa.int/ content/sentinel-2b
Fassnacht, F., White, J., Wulder, M. & Næsset, E. (2024). Remote Sensing in Forestry: Current Challenges, Considerations and Directions. Forestry: An International Journal of Forest Research, 97(1), 11-37.https://doi.org/ 10.1093/forestry/cpad024
Highland Research and Development Institute (Public Organization). (2024). History of Arabica Coffee in Thailand. Retrieved December 2024, from https://research.hrdi.or.th/public/upload/fj11zku0i6.pdf
Huang, Z., Zhong, L., Zhao, F., Wu. J., Tang, H., LV, Z., Xu, B., Zhou, L., Sun, R & Meng, R. (2023), A Spectral-Temporal Constrained Deep Learning Method for Tree Species Mapping of Plantation Forests using Time Series Sentinel-2 Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 204, 397-420. https://doi.org/10.1016/j.isprsjprs. 2023.09.009
Hunt, D. A., Tabor, K., Hewson, J. H., Wood, M. A., Reymondin, L., Koenig, K., Schmitt-Harsh, M. & Follett, F. (2020). Review of Remote Sensing Methods to Map Coffee Production Systems. Remote Sensing, 12(12), 2041.https:// doi.org/10.3390/rs12122041
Le, T., Dang, K. B., Giang, T. L., Tong, T. H. A., Nguyen, V. G., Nguyen, T. D. L. N. & Yasir, M. (2022). Deep Learning Model Development for Detecting Coffee Tree Changes Based on Sentinel-2 Imagery in Vietnam. IEEE Access, 10, 109097-109107. https://doi.org/10.1109/access.2022.3203405
Maxar. (2025). Extract more data in greater detail. Retrieved January 2025, from https://www. maxar.com/maxar-intelligence/imagery-leadership#high-resolution
Muangpha, S. & Mensin, S. (2021). Royal Project Research Project on Breeding and Value-Added Arabica Coffee Products. Subproject 1: Selection of Quality Coffee Varieties and Study of Environmentally Friendly High-Quality Coffee Cultivation Systems. Retrieved January 2025, from https:// research.hrdi.or.th/public/upload/2yl594gciy.pdf
Office of the Permanent Secretary for Ministry of Agriculture and Cooperatives. (2025). Where are the important Arabica growing areas in Thailand?. Retrieved August 2025, from https://www.opsmoac. go.th/news-preview-471991792943
Oliveira, M., Santos, A., Kazama, E. & Rolim, G. & Silva, R. (2021). Determination of Application Volume for Coffee Plantations using Artificial Neural Networks and Remote Sensing. Computers and Electronics in Agriculture,184, 106096. https://doi.org/10.1016/j.compag.2021.106096
Qin, H., Zhou, W., Yao, Y. & Wang, W. (2022). Individual Tree Segmentation and Tree Species Classification in Subtropical Broadleaf Forests using UAV-based LiDAR, hyperspectral, and Ultrahigh-resolution RGB data. Remote Sensing of Environment, 280, 113143. https://doi.org/10.1016/j.rse.2022.113143
Schneider, B., Alves, M. & Ferreira, V. (2025). Coffee Crop Detection and Mapping using Sentinel-2 Data and Spatial Coherence. Theoretical and Applied Engineering, 9(3), 1-10. https://doi.org/10.31422/ taae.v9i3.64
Tu, Y., Johansen, K., Aragon, B., Hajj, M. & McCabe, M. (2022). The Radiometric Accuracy of the 8-band Multi-spectral Surface Reflectance from the Planet SuperDove Constellation, International Journal of Applied Earth Observation and Geoinformation, 114,103035. https://doi.org/10.1016/j.jag. 2022.103035
