Classification of sugarcane areas in Landsat images using machine learning algorithms
Conteúdo do artigo principal
Resumo
Monitoring sugarcane areas through remote sensing is essential for planning and management of the national sugarcane industry. The use of machine learning algorithms has provided many benefits to remote sensing. This article aims to compare the prediction quality of three important machine learning methods in identifying sugarcane areas using Landsat images: Logistic Regression (LR), Decision Tree (DT) and RandomForest (RF). LR was applied in three versions: LR without penalization, LR with Ridge penalization (LR-R) and LR with Lasso penalization (LR-L). Data obtained in this study refer to a region of approximately 306,000 ha located in the state of São Paulo, Brazil, which was segmented into approximately 46,000 polygons (observations). Six spectral bands and vegetation indices observed along 17 months resulted in 102 covariates, which were reduced via Principal Component Analysis (PCA). In total, 19 Principal Components were chosen to account for 94.61% of the cumulative explained variance ratio and were used in machine learning methods to classify each polygon as sugarcane or other land covers. The method with the highest accuracy considering testing sample of 20% of data was RF (78.51%), followed by DT (72.30%), LR-L (69.64%), LR-R (69.64%), and LR (69.52%).
Detalhes do artigo
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Referências
Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and Regression Trees. (Wadsworth, 1984).
De Souza Dias, M. O., Maciel Filho, R., Mantelatto, P. E., Cavalett, O., Rossell, C. E. V., Bonomi, A. & Leal, M. R. L. V. Sugarcane processing for ethanol and sugar in Brazil. Environmental Development 15, 35–51. ISSN: 2211-4645. https://www.sciencedirect.com/science/ article/pii/S2211464515000147 (2015).
Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (Springer New York Inc., 2001).
Huete, A., Liu, H. & van Leeuwen, W. The use of vegetation indices in forested regions: issues of linearity and saturation in IGARSS’97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development 4 (1997), 1966–1968 vol.4.
Izbicki, R. & dos Santos, T. M. Aprendizado de máquina: uma abordagem estatística 268 pp. (2020).
James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning: with Applications in R (Springer, 2013).
Johnson, B. A., Scheyvens, H. & Shivakoti, B. R. An ensemble pansharpening approach for finer-scale mapping of sugarcane with Landsat 8 imagery. International Journal of Applied Earth Observation and Geoinformation 33, 218–225. ISSN: 1569-8432. https://www.sciencedirect.com/ science/article/pii/S0303243414001366 (2014).
Johnson, R. & Wichern, D. Applied multivariate statistical analysis (Prentice hall Upper Saddle River, NJ, 2002).
Karp, S. G., Medina, J. D. C., Letti, L. A. J.,Woiciechowski, A. L., de Carvalho, J. C., Schmitt, C. C., de Oliveira Penha, R., Kumlehn, G. S. & Soccol, C. R. Bioeconomy and biofuels: the case of sugarcane ethanol in Brazil. Biofuels, Bioproducts and Biorefining 15, 899–912. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/bbb.2195. https://onlinelibrary.wiley.com/ doi/abs/10.1002/bbb.2195 (2021).
Lantz, B. Machine learning with R: expert techniques for predictive modeling (Packt publishing ltd, 2019).
Lu, M., Bi, Y., Xue, B., Hu, Q., Zhang, M.,Wei, Y., Yang, P. &Wu, W. Genetic Programming for High-Level Feature Learning in Crop Classification. Remote Sensing 14, 16 (2022).
Luciano, A., Picoli, M., Rocha, J., Franco, H., Sanches, G., Leal, M. & leMaire, G. Generalized space-time classifiers for monitoring sugarcane areas in Brazil. Remote Sensing of Environment 215 (July 2018).
Luciano, A. C. d. S., Campagnuci, B. C. G. & le Maire, G. Mapping 33 years of sugarcane evolution in São Paulo state, Brazil, using landsat imagery and generalized space-time classifiers. Remote Sensing Applications: Society and Environment 26, 100749. ISSN: 2352-9385. https://www.sciencedirect.com/science/article/pii/S235293852200057X (2022).
Luciano, A. C. d. S., Picoli, M. C. A., Rocha, J. V., Duft, D. G., Lamparelli, R. A. C., Leal, M. R. L. V. & Le Maire, G. A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm. International Journal of Applied Earth Observation and Geoinformation 80, 127–136. ISSN: 1569-8432. https://www.sciencedirect.com/science/article/pii/S0303243418311917 (2019).
McCullagh, P. & Nelder, J. Generalized Linear Models, Second Edition (Chapman & Hall, 1989).
McFEETERS, S. K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17, 1425–1432. https://doi.org/10.1080/01431169608948714 (1996).
Morettin, P. A. & Singer, J. d. M. Estatística e ciência de dados 454 pp. (2022).
Rouse, J., Hass, R., Schell, J. & Deering, D. Monitoring vegetation systems in the great plains with ERTS. Third Earth Resour. Technol. Satell. Symp. 1, 309–317. https://cir.nii.ac.jp/crid/1573105975083785728 (1973).
Rudorff, B. F. T., Aguiar, D. A., Silva, W. F., Sugawara, L. M., Adami, M. & Moreira, M. A. Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data. Remote Sensing 2, 1057–1076. ISSN: 2072-4292. https://www.mdpi.com/2072-4292/2/4/1057 (2010).
Silva, C. B., de Moraes, M. A. F. D. & Molin, J. P. Adoption and use of precision agriculture technologies in the sugarcane industry of São Paulo state, Brazil. Precision Agriculture 12, 67–81 (2011).
Wang, M., Liu, Z., Ali Baig, M. H., Wang, Y., Li, Y. & Chen, Y. Mapping sugarcane in complex landscapes by integrating multi-temporal Sentinel-2 images and machine learning algorithms. Land Use Policy 88, 104190. ISSN: 0264-8377. https://www.sciencedirect.com/science/article/pii/S0264837719307185 (2019).
Wilson, E. H. & Sader, S. A. Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment 80, 385–396. ISSN: 0034-4257. https://www.sciencedirect.com/science/article/pii/S0034425701003182 (2002).
Zhou, Z., Huang, J.,Wang, J., Zhang, K., Kuang, Z., Zhong, S. & Song, X. Object-oriented classification of sugarcane using time-series middle-resolution remote sensing data based on AdaBoost. PloS one 10, e0142069 (2015).