Applied k-Means Algorithm to the Visualization of Digital Maps of Soil Elevation in the Recôncavo Baiano Region
Abstract
The economic impacts due to soil degradation are increasingly evident. Data from the United Nations (UN) show that due to soil compaction, floods and droughts are increasingly common in the world. Therefore, the analysis of soil data is crucial for environmental planning, agricultural production, land use, and population well-being. Digital soil mapping consists of techniques that facilitate such analyses, aggregating information from laboratory experiments with information from satellite images and drones, with mathematical techniques to generate more accurate descriptive soil models. In this context, this work demonstrates an application of digital soil mapping, specifically the k-Means algorithm used to construct a digital elevation model of the Recôncavo Baiano region.
References
Borra, S., Thanki, R., Dey, N. (2019). Satellite image analysis: clustering and classification. Springer.
Burrough, P. A. (1986). Principles of geographical information systems for land resources assessment. clarendon.
Burrough, P.A., McDonnell, R.A., Lloyd,C.D.(2015).Principles of geographical information systems. Oxford university press.
Collins, H. S. (1981). Algorithms for dense digital terrain models. PHOTOCRAMMETERNIGCI NEERINANGD REMOTES ENSING, 47:71–76.
Davies, J. (2017). The business case for soil. Nature, 543:309–311.
Farr, T.G. et al. (2007). The shuttle radar topography mission. Reviews of Geophysics, 45.
Fortes, L., Costa, S., Lima, M., Fazan, J., Santos, M. (2007). Accessing the new sirgas2000 reference frame through a modernized brazilian active control network. Dynamic Planet. International Association of Geodesy Symposia, 130.
Han, J., Pei, J., Kamber, M. (2011). Data mining: concepts and techniques. Elsevier.
Klingebiel, A., Horvath, E., Moore, D., Reybold, W. (1987). Use of slope, aspect, and elevation maps derived from digital elevation model data in making soil surveys. Soil survey techniques, 20:77–90.
Lagacherie, P., Legros, J.P., Burrough, P. (1995). Soil survey procedure using the knowledge of soil pattern established on a previously mapped reference area. Geoderma, pages 283–301.
Lagacherie, P., Mcbratney, A.B. (2007). Spatial soil information systems and spatial soil inference systems: perspectives for digital soil mapping. Digital Soil Mapping: an introductory perspective. Amsterdam:. Elsevier, pages 3–22.
Likas, A., Vlassis, N., Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2):451–461. Biometrics.
McBratney, A., Santos, M.M., Minasny, B. (2003). On digital soil mapping. Geoderma, 117(1):3–52.
McBratney, A.B., Odeh, I.O., Bishop, T.F., Dunbar, M.S., Shatar, T.M. (2000). An overview of pedometric techniques for use in soil survey. Geoderma, 97(3):293–327.
Piloyan, A. (2017). Semi-automated classification of landform elements in armenia based on srtm dem using k-means unsupervised classification. Quaestiones Geographicae, 36:93–103.
TOPODATA (2021). Banco de dados geomorfométricos do brasil | topodata. Disponível em: http://www.dsr.inpe.br/topodata/dados.php. Acesso em: 04 set 2021.
