Understanding the usage of land use and land cover classifiers in scientific research

Authors

DOI:

https://doi.org/10.32358/rpd.2023.v9.660

Keywords:

land use and land cover, classification, bibliometrics

Abstract

Purpose: assess the primary methods utilized in land use and land cover classification research to determine the most frequently applied techniques and potential trends Methodology/Approach: a bibliometric study was carried out in the Scopus scientific article databases, counting the use of classification methods between 2012 and 2022. The obtained data was converted into SQL database tables and processed using queries, looking for articles whose abstracts contains keywords related to land cover and land use methods. Findings: a general growth trend in the studies of this area was discovered, with an increase in the use of machine learning-based methodologies and stabilization in the use of other methods. Statistical methods were also heavily used, while others were used less frequently. Research Limitation/implication: it’s important to note that keywords present in abstract sections does not necessarily correspond to the mentioned methods being applied in the studies. This leads to some expected degree of imprecision, but the data remains a reasonable representation of the popularity of each method. Originality/Value of paper: considering that the scientific literature lacks a quantitative understanding of the usage of land use and land cover classifiers, this work aims to fill that gap by providing usable data.

Downloads

Download data is not yet available.

Author Biographies

Vitor da Silva Gonçalves, Universidade Candido Mendes

Master in Operational Research and Computational Intelligence.

Italo de Oliveira Matias, Universidade Candido Mendes University

Graduated in Computer Science from the Federal University of Paraíba (1998). Master's degree from COPPE/UFRJ (2001) in Systems and Computer Engineering in the area of Computer Graphics. PhD from COPPE/UFRJ (2007) in Civil Engineering in the area of Computer Systems/Computational Intelligence. Post-Doctorate in Engineering and Materials Sciences from Universidade Norte-Fluminense (2011). He is a professor at UCAM (Campos dos Goytacazes) where he teaches and supervises the POIC master's degree and the doctorate in Regional Planning and City Management. He works in the areas: Artificial Intelligence, Data Science, Big Data, Digital Image Processing, Geoprocessing, Computer Vision and Emerging Digital Technologies.

Aldo Shimoya, Candido Mendes University

Aldo Shimoya has a degree in Agronomy from the Federal University of Mato Grosso (1982), a master's degree (1987) and a doctorate (2000) in Genetics and Breeding from the Federal University of Viçosa. Professor at Universidade Candido Mendes, in undergraduate courses in Production, Civil and Mechanical Engineering; in the master's degree in Operational Research and Computational Intelligence; and master's and doctorate degrees in Regional Planning and City Management.

References

Alzubi, J., Nayyar, A., & Kumar, A. (2018). Machine Learning from Theory to Algorithms: An Overview. Journal of Physics: Conference Series, 1142(1), 012012. https://doi.org/10.1088/1742-6596/1142/1/012012

Belgiu, M., & Csillik, O. (2018). Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote Sensing of Environment, 204, 509–523. https://doi.org/10.1016/j.rse.2017.10.005

Borra, S., Thanki, R., & Dey, N. (2019). Satellite Image Analysis: Clustering and Classification. Springer.

Chaves, M. E. D., Picoli, M. C. A., & Sanches, I. D. (2020). Recent Applications of Landsat 8/OLI and Sentinel-2/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sensing, 12(18), Article 18. https://doi.org/10.3390/rs12183062

Frazier, B. E., & Shovic, H. (1980). Statistical methods for determining land-use change with aerial photographs. Photogrammetric Engineering and Remote Sensing, 46(8), 1067–1077.

Horning, N. (2004). Land cover classification methods, Version 1.0. American Museum of Natural History, Center for Biodiversity and Conservation. https://www.amnh.org/content/download/74344/1391366/file/LandCoverClassification_Final.pdf

Jozdani, S. E., Johnson, B. A., & Chen, D. (2019). Comparing Deep Neural Networks, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification. Remote Sensing, 11(14), Article 14. https://doi.org/10.3390/rs11141713

Lei, T., & Nandi, A. K. (2022). Image Segmentation: Principles, Techniques, and Applications. Wiley.

Mai, S. D., & Ngo, L. T. (2018). Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification. Engineering Applications of Artificial Intelligence, 68, 205–213. https://doi.org/10.1016/j.engappai.2017.11.007

Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817. https://doi.org/10.1080/01431161.2018.1433343

Mora, B., Tsendbazar, N.-E., Herold, M., & Arino, O. (2014). Global Land Cover Mapping: Current Status and Future Trends. In I. Manakos & M. Braun (Eds.), Land Use and Land Cover Mapping in Europe: Practices & Trends (pp. 11–30). Springer Netherlands. https://doi.org/10.1007/978-94-007-7969-3_2

Phiri, D., & Morgenroth, J. (2017). Developments in Landsat Land Cover Classification Methods: A Review. Remote Sensing, 9(9), Article 9. https://doi.org/10.3390/rs9090967

Rebala, G., Ravi, A., & Churiwala, S. (2019). Machine Learning Definition and Basics. In G. Rebala, A. Ravi, & S. Churiwala (Eds.), An Introduction to Machine Learning (pp. 1–17). Springer International Publishing. https://doi.org/10.1007/978-3-030-15729-6_1

Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A., & Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review. Remote Sensing, 12. https://doi.org/10.3390/rs12071135

Wang, J., Bretz, M., Dewan, M. A. A., & Delavar, M. A. (2022). Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects. Science of The Total Environment, 822, 153559. https://doi.org/10.1016/j.scitotenv.2022.153559

Wulder, M. A., Coops, N. C., Roy, D. P., White, J. C., & Hermosilla, T. (2018). Land cover 2.0. International Journal of Remote Sensing, 39(12), 4254–4284. https://doi.org/10.1080/01431161.2018.1452075

Downloads

Published

2023-12-29

How to Cite

Gonçalves, V. da S., Matias, I. de O., & Shimoya, A. (2023). Understanding the usage of land use and land cover classifiers in scientific research. Revista Produção E Desenvolvimento, 9(1), e660. https://doi.org/10.32358/rpd.2023.v9.660

Issue

Section

Information Studies