About me
I am Yuri Andrei Gelsleichter, Mechanical Technician (IFSC, 2004), Environmental Engineer (UNISUL, 2015), Ph.D. in Science, Technology and Innovation in Agriculture (UFRRJ, 2020), Postdoctoral Researcher (Hungarian University of Agricultural Sciences, 2023).
During this journey, I became a specialist in Digital Soil and Environment Mapping, Precision Agriculture, Analysis and Data Visualization applying the R programming language.
Currently also working with remote sensing, data mining and automation, and research in the area of soil classification and spectroradiometry, and precision agriculture. Co-founder of Data Situ.
Projects
Reproducibility in science needs to be standardized with protocols and checklists
Reproducibility is essential for the quality of Research and Innovation (R&I) but is currently limited. In the project, my role is to test and contribute to computational reproducibility, seeking to develop protocols and checklists for improvement in R&I.
The incredible thing about this project was to apply proximal remote sensing to satellite images to almost double the predictive capacity of the model
Remote Sensing products are applicable for spatial prediction of soil properties, in the process called Digital Soil Mapping (DSM).
The aim of the study was to combine Vis-SWIR soil laboratory spectra with the DSM technique to create new hyperspectral images to use as covariates in a new soil mapping method.
The advantages of the method were demonstrated throughout the spatial prediction of Total Carbon content in soils from the upper part of the Itatiaia National Park. Using information from the Vis-SWIR spectra of the upper soil horizon from 72 points in the upper part of INP, 130 hyperspectral images were generated, that is, subsurface hyperspectral image. The validation methods were 8-fold cross-validation (CV) and external validation executed using the Random Forest algorithm.
The DSM achieves a mean determination coefficient (R2) in the CV of 0.39 and Root Mean Square Error (RMSE) of 4.6, while the creation of a new model with a set of hyperspectral images gave a CV R2 of 0.60 and RMSE of 4.06. The method can be applied in dense or low vegetation areas, for agricultural or conservation purposes, for various soil properties, with all and each wavelength.
International Experiences
In this experience, I am constantly deepening my research strategies and programming in R
Research on classification, mapping, and spectral characterization of soils; Lessons in R programming, soil improvement, and protection
Spatial modeling of land use
Development of land use scenarios for calculating carbon stocks in Argentina
Soil classification provides a foundation for land use planning
International collaboration for the Universal Soil Classification System by including centroids of Brazilian soils
Education
Federal Rural University of Rio de Janeiro
Doctor in Science, Technology and Innovation in Agriculture
2017 - 2020
Founded in 1910, UFRRJ has a tradition in Agronomy research and teaching
At this stage of my professional life, I gained a great deal of knowledge about Soils and Remote Sensing and Geographic Information Systems applied in R programming, where I also learned about data flow and automation
University of Southern Santa Catarina
Bachelor in Environmental and Sanitary Engineering
2009 - 2015
UNISUL is consolidated as an educational hub in Greater Florianopolis
In addition to understanding environmental management, water and waste treatment, I was awakened to Remote Sensing and Geographic Information Systems
Federal Institute of Santa Catarina
Industrial Mechanics Technician
2003 - 2004
Created in 1909 in Florianopolis, Santa Catarina, IFSC has a tradition in teaching
During the course, I gained a lot of knowledge about industrial mechanics including metrology, projects, and manufacturing processes
Recent Publications
Relevant studies:
Enhancing Soil Mapping with Hyperspectral Subsurface Images generated from soil lab Vis-SWIR spectra tested in southern Brazil; 2023; Gelsleichter, Y.A.; Costa, E.M,; Anjos, L.H.C.; Marcondes, R.A.T; doi
Past and Future Responses of Soil Water to Climate Change in Tropical and Subtropical Rainforest Systems in South America; 2023; Arévalo, S.M.M.; Delgado, R.C.; Lindemann, D.S.; Gelsleichter, Y.A.; Pereira, M.G.; Rodrigues, R.A.; Justino, F.B.; Wanderley, H.S.; Zonta, E.; Santana, R.O.; Souza; R.S.; doi
Degradation of South American biomes: What to expect for the future?; Delgado, R.C.; Santana, R.O.; Gelsleichter, Y.A.; Pereira, M.G.; doi
Mapping soil properties in a poorly-accessible area; Costa, E.M.; Pinheiro, H.S.K.; Anjos, L.H.C; Marcondes, R.A.T.; Gelsleichter, Y.A.; doi
Spatial Bayesian belief networks: a participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil; Costa, E.M.; Pinheiro, H.S.K.; Anjos, L.H.C; Gelsleichter, Y.A.; Marcondes, R.A.T.; doi