Identification of Rice Fields Using the Random Forest Method Based on Spectral and Physical Parameters (Case Study: Bandung Regency)

Jania Sutia Rahayu, Albertus Deliar, Riantini Virtriana

Abstract


This research aims to identify paddy fields in Bandung Regency using the Random Forest method by incorporating spectral and physical parameters. Typically, mapping paddy fields requires several satellite images over different periods due to rice's planting cycle. To reduce time and cost, this study optimizes Random Forest classification with single-time satellite images, adding physical characteristics of paddy fields. Data includes NDVI, NDWI, LST, SMI, BSI, SAVI, slope, elevation, soil pH, total nitrogen, and clay content. Results show that combining these parameters enhances classification accuracy, achieving 91% overall accuracy and a Kappa of 0.89. This approach is both effective and efficient for mapping paddy fields, crucial for agricultural management and food security, and supports sustainable monitoring of paddy field distribution in Bandung Regency. By integrating spectral parameters (NDVI, NDWI, LST, SMI, BSI, SAVI) and physical characteristics (slope, elevation, soil pH, total nitrogen, and clay content), the Random Forest method significantly improves the classification accuracy of paddy fields compared to using only spectral data. The classification results indicated a substantial improvement, with the overall accuracy reaching 91% and a Kappa coefficient of 0.89. This methodological approach not only demonstrates its effectiveness and efficiency but also plays a vital role in agricultural management and food security. It provides a sustainable solution for monitoring paddy field distribution in Bandung Regency.


Keywords


Paddy fields; Bandung Regency; Random Forest method; Spectral parameters; Physical parameters

References


Aldiansyah, S., Mandini Mannesa, M. D., and Supriatna, S. (2021). Monitoring of Vegetation Cover Changes With Geomorphological Forms using Google Earth Engine in Kendari City. Jurnal Geografi Gea, 21(2), 159–170. https://doi.org/10.17509/gea.v21i2.37070

Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.

Cleophas, F., Isidore, F., Musta, B., Mohd Ali, B. N., Mahali, M., Zahari, N. Z., and Bidin, K. (2022). Effect of soil physical properties on soil infiltration rates. Journal of Physics: Conference Series, 2314(1). https://doi.org/10.1088/1742-6596/2314/1/012020

Dzikrillah, G. F., Anwar, S., and Sutjahjo, S. H. (2017). Analisis Keberlanjutan Usahatani Padi Sawah Di Kecamatan Soreang Kabupaten Bandung. Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan (Journal of Natural Resources and Environmental Management), 7(2), 107–113. https://doi.org/10.29244/jpsl.7.2.107-113

Fauzi, A., Supriyadi, R., & Maulidah, N. (2020). Detection of breast cancer with feature selection based on Principal Component Analysis and Random Forest. Jurnal Infortech, 2(1), 96-101. https://doi.org/10.31294/infortech.v2i1.8079

Feizizadeh, B., and Blaschke, T. (2013). Land suitability analysis for Tabriz County, Iran: A multi-criteria evaluation approach using GIS. Journal of Environmental Planning and Management, 56(1), 1–23. https://doi.org/10.1080/09640568.2011.646964

Gregorutti, B., Michel, B., and Saint-Pierre, P. (2017). Correlation and variable importance in random forests. Statistics and Computing, 27, 659–678.

Kohavi, R., and Edu, S. (1993). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence, 2, 1137–1143.

Li, S., and Chen, X. (2014). A new bare-soil index for rapid mapping developing areas using landsat 8 data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 40(4), 139–144. https://doi.org/10.5194/isprsarchives-XL-4-139-2014

Medhina, F. M., Harjanti, D. T., and Buwono, S. (2023). Jurnal Geografi Gea Biomass Percentage of Organic Soil Layers in The Rasau Jaya Tiga Village. Jurnal Geografi GEA, 23(2), 144–158.

Mulyani, A., and Agus, F. (2018). The Need and Availability of Reserved Land to Realize Indonesia’s Aspiration as the World’s Food Barn in 2045. Analisis Kebijakan Pertanian, 15(1), 1.

Nguyen, C. T., Chidthaisong, A., Diem, P. K., and Huo, L. Z. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in southeast asia using landsat 8. Land, 10(3), 1–18. https://doi.org/10.3390/land10030231

Noi Phan, T., Kuch, V., and Lehnert, L. W. (2020). Land cover classification using google earth engine and random forest classifier-the role of image composition. Remote Sensing, 12(15), 1–22. https://doi.org/10.3390/RS12152411

Probst, P., Wright, M. N., and Boulesteix, A. L. (2019). Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), 1–19. https://doi.org/10.1002/widm.1301

Ridwana, R., Al Kautsar, A., Saleh, F., Himayah, S., Arrasyid, R., & Pamungkas, T. D. (2022, November). Spatiotemporal monitoring of rice crops in the covid-19 pandemic period for local food security using sentinel 2b imagery case ctudy: tasikmalaya city. In IOP Conference Series: Earth and Environmental Science (Vol. 1089, No. 1, p. 012039). IOP Publishing.

Ridwana, R., Kamal, M., Arjasakusuma, S., & Rabbi, M. F. A. (2024). Evaluation of Machine Learning Models for Mapping Food Crops using Sentinel-2A Imagery in West Java, Indonesia. In E3S Web of Conferences (Vol. 600, p. 03007). EDP Sciences.

Ridwana, R., Kamal, M., Arjasakusuma, S., Sugandi, D., & Sakti, A. D. (2024). Bibliometric computation mapping analysis of publication machine and deep learning for food crops mapping using VOSviewer. Journal of Advanced Research in Applied Sciences and Engineering Technology, 50(2), 42-59.

Rimal, Y., Sharma, N., and Alsadoon, A. (2024). The accuracy of machine learning models relies on hyperparameter tuning: student result classification using random forest, randomized search, grid search, bayesian, genetic, and optuna algorithms. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-024-18426-2

Sari, I. L., Weston, C. J., Newnham, G. J., and Volkova, L. (2022). Developing multi-source indices to discriminate between native tropical forests, oil palm and rubber plantations in indonesia. Remote Sensing, 14(1). https://doi.org/10.3390/rs14010003

Schulthess, U., Rodrigues, F., Taymans, M., Bellemans, N., Bontemps, S., Ortiz-Monasterio, I., Gérard, B., and Defourny, P. (2023). Optimal Sample Size and Composition for Crop Classification with Sen2-Agri’s Random Forest Classifier. Remote Sensing, 15(3). https://doi.org/10.3390/rs15030608

Shaharum, N. S. N., Shafri, H. Z. M., Ghani, W. A. W. A. K., Samsatli, S., Al-Habshi, M. M. A., and

Yusuf, B. (2020). Oil palm mapping over Peninsular Malaysia using Google Earth Engine and machine learning algorithms. Remote Sensing Applications: Society and Environment, 17. https://doi.org/10.1016/j.rsase.2020.100287

Wang, Y., Liu, X., Wang, H., Li, Y., Li, Y., Liu, F., Xiao, R., Shen, J., and Wu, J. (2017). Rice agriculture impacts catchment hydrographic patterns and nitrogen export characteristics in subtropical central China: a paired-catchment study. Environmental Science and Pollution Research, 24(18), 15700–15711. https://doi.org/10.1007/s11356-017-9044-z

Wei, G., Cao, J., Xie, H., Xie, H., Yang, Y., Wu, C., Cui, Y., and Luo, Y. (2022). Spatial-Temporal Variation in Paddy Evapotranspiration in Subtropical Climate Regions Based on the SEBAL Model: A Case Study of the Ganfu Plain Irrigation System, Southern China. Remote Sensing, 14(5). https://doi.org/10.3390/rs14051201

Wei, P., Ye, H., Qiao, S., Liu, R., Nie, C., Zhang, B., Song, L., and Huang, S. (2023). Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm. Remote Sensing, 15(13), 1–18. https://doi.org/10.3390/rs15133212




DOI: https://doi.org/10.17509/gea.v25i2.72852

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.