Soil pedogeochemical attributes prediction by interpolators in ice-free areas of Antarctica

Authors

DOI:

https://doi.org/10.33448/rsd-v11i4.27542

Keywords:

Predictive covariates; Interpolation; Digital mapping.

Abstract

The main objective of this paper is to predict soil attributes in unsampled areas using geostatistical models, By improving the prediction parameters of selected data, using environmental covariates characteristic of Antarctic ice free areas. In this study, 58 soil samples from a grid were collected at 0-10 cm depth in Keller Peninsula, King George Island, Antarctica. The soil chemical analysis was performed, and the values of potassium, calcium and magnesium were determined for each soil sampled. Simple kriging (SK) and Random Forest interpolator were used in this work to predict the values of the studied soil attributes in non-sampled areas. We used a Terrestrial Laser Scanner (TLS) to generate a cloud of points, to obtain digital elevation models (DEMs) of 1, 5, 10, 20 and 30 meters cell size. The use of covariates did not improve the parameters of soil bases prediction in the studied area. The final maps did not show great differences based on RMSEs, mainly related to the great spatial variability of soil attributes in this region, despite soil thematic maps evidencing visual difference.

References

Breiman, L., Breiman, L. (2001). Statistical Modeling: The Two Cultures. Statistical Science, 16(3): 199–215.

Brenning, A. (2008). Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models. In: SAGA--Seconds Out (Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie, 19: 23–32.

Carneiro, A. P. B., Polito, M. J., Sander, M. W Z. (2010). Trivelpiece. Abundance and spatial distribution of sympatrically breeding Catharacta spp. (skuas) in Admiralty Bay, King George Island, Antarctica. Polar Biology, 33(5): 673–682.

Embrapa. (2011). Manual de métodos de análise de solo, 3rd ed. EMBRAPA/CNPS, Rio de Janeiro (212 pp).

Ferreira, Í. O., Santos, G. R. DOS, Rodrigues, D. D. (2013). Estudo sobre a utilização adequada da krigagem na representação computacional de superfícies batimétricas. Revista Brasileira de Cartografia, 65(5): 831–842.

Francelino, M. R., Schaefer, C. E. G. R., Simas, F. N. B., Filho, E. I. F., Souza, J. J. L. L., Costa, L. M., (2011). Geomorphology and soils distribution under paraglacial conditions in an ice-free area ofAdmiralty Bay, King George Island, Antarctica. Catena 85: 194–204.

Hijmans, R. J. (2015). Geographic Data Analysis and Modeling. R package version 2.4-20. http://CRAN.R-project.org/package=raster.

INPE, 2015. Instituto Nacional de Pesquisas Espaciais-CPTEC. http://antartica.cptec.inpe. br/~antar/weatherdata.shtml.

Kirkwood, C., Cave, M., Beamish, D., Grebby, S., & Ferreira, A. (2016). A machine learning approach to geochemical mapping. Journal of Geochemical Exploration, 167.

Thomazini, A., Teixeira, D. B., Turbay, C. V. G., La Scala Jr, N., Schaefer, C. E. G. R., Mendonça, E. S., (2014). Spatial variability of CO2 emissions from newly exposed paraglacial soils at a glacier retreat zone on King George Island, Maritime Antarctica. Permafr. Periglac. Process. 25: 233–242.

R Core Team. R. (2015). A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL https://www.R-project.org/, consulted novenber 2015.

Mendonça, E. S., La Scala, N., Panosso, A. R., Simas, F. N. B., Schaefer, C. E. G. R., (2010). Spatial variability models of CO2 emissions from soils colonized by grass (Deschampsia antarctica) and moss (Sanionia uncinata) in Admiralty Bay, King George Island. Antarctic Science. 23: 27–33.

Simas, F. N. B., Schaefer, C. E. G. R., Albuquerque Filho, M. R., Francelino, M. R., Fernandes Filho, E. I., & Costa, L. M. (2006). Genesis, properties and classification of Cryosols from Admiralty Bay, Maritime Antarctica. Geoderma 144: 116–122.

Schünemann, A. L., Almeida, P. H. A., Thomazini, A, Fernandes Filho, E I., Francelino, M, R., Schaefer, C. E. G. R., & Pereira, A B. (2018). High-resolution topography for Digital Terrain Model (DTM) in Keller Peninsula, Maritime Antarctica. Anais da Academia Brasileira de Ciências, 90(2, Suppl. 1), 2001-2010.

Vaysse, K., Lagacherie, P., (2015). Evaluating digital soil mapping approaches for mapping GlobalSoilMap soil properties from legacy data in Languedoc-Roussillon (France). Geoderma Reg. 4: 20–30.

Vieira, S. R. (2000) Geoestatística em estudos de variabilidade espacial do solo. In: Novais, R.F., Alvarez V., V.H., Schaefer, G.R., eds. Tópicos em ciência do solo. Viçosa, Sociedade Brasileira de Ciência do Solo, 1, 1-54.

Wasklewicz,. T., STaley D M., Reavis K., & Oguchi T. (2013). 3.6 Digital Terrain Modeling. In: Treatise on Geomorphology, p. 130-216.Webster, R., Oliver, M.A., (1990). Statistical Methods in Soil and Land Resource Survey. Oxford University Press, Oxford, p. 316.

Willmott, C. J, & Matsuura, K. (2005). Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30:79–82.

Downloads

Published

25/03/2022

How to Cite

SCHÜNEMANN, A. L. .; THOMAZINI, A. .; ALMEIDA, P. H. A. .; FRANCELINO, M. R. .; FERNANDES FILHO, E. I. .; SANTOS, G. R. dos .; PAULA, M. D. de .; SCHAEFER, C. E. G. R. .; PEREIRA, A. B. Soil pedogeochemical attributes prediction by interpolators in ice-free areas of Antarctica. Research, Society and Development, [S. l.], v. 11, n. 4, p. e51411427542, 2022. DOI: 10.33448/rsd-v11i4.27542. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/27542. Acesso em: 28 dec. 2024.

Issue

Vol. 11 No. 4

Section

Agrarian and Biological Sciences

License

Copyright (c) 2022 Adriano Luis Schünemann; André Thomazini; Pedro Henrique Araújo Almeida; Márcio Rocha Francelino; Elpídio Inácio Fernandes Filho; Gérson Rodrigues dos Santos; Mayara Daher de Paula; Carlos Ernesto Gonçalves Reynaud Schaefer; Antonio Batista Pereira

Creative Commons License

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

Authors who publish with this journal agree to the following terms:

1) 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.

2) 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.

3) 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.