2 | Elias M. Costa

Use of Airborne Radar Images and Machine Learning Algorithms to Map Soil Clay, Silt, and Sand Contents in Remote Areas under the Amazon Rainforest

Digital soil mapping; soil texture; radar P-band; reference area; soil survey

Ana Carolina de S. Ferreira, Marcos Bacis Ceddia, Elias Mendes Costa, Érika F. M. Pinheiro, Mariana Melo do Nascimento, Gustavo M. Vasques

Use of Airborne Radar Images and Machine Learning Algorithms to Map Soil Clay, Silt, and Sand Contents in Remote Areas under the Amazon Rainforest

Training pedologist for soil mapping-Contextualizing methods and its accuracy using the project pedagogy approach

The PPA demonstrates relevance in the teaching-learning process of soil mapping. Combination of data-driven and expert knowledge methods are advised for future courses. Soil mapping using new tools involves multidisciplinary knowledge. This course can serve as a guide to meet the needs of PronaSolos

Elias Mendes Costa, Marcos Bacis Ceddia, Felipe Nascimento dos Santos, Laiz de Oliveira Silva, Igor Prata Terra de Rezende, Douglath Alves Correa Fernandes

Training pedologist for soil mapping-Contextualizing methods and its accuracy using the project pedagogy approach

Mapping soil properties in a poorly-accessible area

DSM tools are useful for soil sampling and mapping in areas with limited access. Non-linear models are more effective to map soil properties than linear models. GAM_scorpan model can improve soil properties predictions. GAM smooth functions can successfully map soil properties in 3-D. The high functionality of 2-D and 3-D maps benefits decision-makers.

Elias Mendes Costa, Helena Saraiva Koenow Pinheiro, Lúcia Helena Cunha dos Anjos, Robson Altiellys Tosta Marcondes, Yuri Andrei Gelsleichter

Spatial Bayesian belief networks a a participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil

The objective of this work was to use information on soils, land use/cover, climate, relief and parent material to create a BBN for analysing environmental vulnerability.

Elias Mendes Costa, Lúcia Helena Cunha dos Anjos, Helena Saraiva Koenow Pinheiro, Yuri Andrei Gelsleichter, Robson Altiellys Tosta Marcondes

Spatial Bayesian belief networks a a participatory approach for mapping environmental vulnerability at the Itatiaia National Park, Brazil

Digital elevation model quality on digital soil mapping prediction accuracy

The study was developed by Elias Mendes Costa, Alessandro Samuel-Rosa, and Lúcia Helena Cunha dos Anjos as part of the Master Thesis of Elias Mendes Costa presented before the Post-Graduate Course in Agronomy-Soil Science of the Federal Rural University of Rio de Janeiro on 26 February 2015.

Elias Mendes Costa, Alessandro Samuel Rosa, Lúcia Helena Cunha dos Anjos

Digital elevation model quality on digital soil mapping prediction accuracy

Mapping soil organic carbon and organic matter fractions by Geographically Weighted Regression

The study was developed by Elias Mendes Costa, Wagner de Souza Tassinari, Helena Saraiva Koenow Pinheiro, Sidinei Julio Beutler and Lúcia Helena Cunha dos Anjos as part of the Master Thesis of Elias Mendes Costa presented before the technical course in statistics at Federal Rural University of Rio de Janeiro on July 2017.

Elias Mendes Costa, Wagner de Souza Tassinari, Helena Saraiva Koenow Pinheiro, Sidinei Julio Beutler, Lúcia Helena Cunha dos Anjos

$Mapping soil organic carbon and organic matter fractions by Geographically Weighted Regression$

RapidEye image processing for soil use mapping in rugged landscape accuracy

The objective of this work was to analyse RapidEye satellite image characteristics, as well as to assess its orthorectification geometric accuracy and its application for land use mapping, in a rugged landscape.

Elias Mendes Costa, Mauro Antonio Homem Antunes, Paula Debiasi, Lúcia Helena Cunha dos Anjos