Data, research code, and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Pedology, Hyperspectral Soil Mapping, and Spectral studies
Authors/Creators
- 1. Federal Rural University of Rio de Janeiro (UFRRJ)
Contributors
Supervisor:
- 1. Federal Rural University of Rio de Janeiro (UFRRJ)
Description
Data, research code (in R language), and metadata related to the Soils of the Upper Part of Itatiaia National Park (INP) for Hyperspectral Soil Mapping
Up to this point, this data, and code were used in the Doctoral thesis of Elias Mendes Costa and Yuri Andrei Gelsleichter, and the following publications.
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Dados, código de pesquisa (em linguagem R) e metadados relacionados aos Solos e ao Mapeamento da Parte Superior do Parque Nacional de Itatiaia (PNI) para o Mapeamento Hiperespectral de Solos
Até este momento, esses dados, e códigos foram utilizados nas teses de doutorado de Elias Mendes Costa e de Yuri Andrei Gelsleichter e nas seguintes publicações.
Gelsleichter, Y. A., Costa, E. M., dos Anjos, L. H. C., & Marcondes, R. A. T. (2023). Enhancing soil mapping with hyperspectral subsurface images generated from soil lab vis-SWIR spectra tested in southern Brazil. Geoderma Regional, e00641.
doi: https://doi.org/10.1016/j.geodrs.2023.e00641
link: https://www.sciencedirect.com/science/article/pii/S2352009423000378
Costa, E. M., Pinheiro, H. S. K., Anjos, L. H. C. dos, Marcondes, R. A. T., & Gelsleichter, Y. A. (2020). Mapping soil properties in a poorly-accessible area. Revista Brasileira de Ciência Do Solo, 44.
doi: https://doi.org/10.36783/18069657rbcs20190107
link: https://www.rbcsjournal.org/article/mapping-soil-properties-in-a-poorly-accessible-area/
Costa, E. M., dos Anjos, L. H. C., Pinheiro, H. S. K., Gelsleichter, Y. A., & Marcondes, R. A. T. (2020). Spatial Bayesian belief networks: A participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil. Environmental Earth Sciences, 79, 1–13.
doi: https://doi.org/10.1007/s12665-020-09099-9
link: https://link.springer.com/article/10.1007/s12665-020-09099-9#citeas
The code is arranged to deliver the outputs in their respective folders.
Notes
Files
Hyperspectral_Soil_Mapping__Gelsleichter_et_al_2023.zip
Additional details
References
- Gelsleichter, Y. A., Costa, E. M., dos Anjos, L. H. C., & Marcondes, R. A. T. (2023). Enhancing soil mapping with hyperspectral subsurface images generated from soil lab vis-SWIR spectra tested in southern Brazil. Geoderma Regional, e00641. doi: https://doi.org/10.1016/j.geodrs.2023.e00641
- Costa, E. M., Pinheiro, H. S. K., Anjos, L. H. C. dos, Marcondes, R. A. T., & Gelsleichter, Y. A. (2020). Mapping soil properties in a poorly-accessible area. Revista Brasileira de Ciência Do Solo, 44. doi: https://doi.org/10.36783/18069657rbcs20190107
- Costa, E. M., dos Anjos, L. H. C., Pinheiro, H. S. K., Gelsleichter, Y. A., & Marcondes, R. A. T. (2020). Spatial Bayesian belief networks: A participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil. Environmental Earth Sciences, 79, 1–13. doi: https://doi.org/10.1007/s12665-020-09099-9