Validação do mapeamento de uma área de floresta tropical com o uso imagens de videografia aérea e dados de levantamento de campo Validation of tropical forest area mapping using aerial videography images and data from field work survey

Este trabalho apresenta o mapeamento da cobertura vegetal da região da Floresta Nacional do Tapajós (FNT) no Pará, realizado por imagens multitemporais do satélite Landsat. Para a validação do mapeamento, foram utilizadas imagens de videografia aérea e dados de levantamento de campo. Através da análise da matriz de confusão, foram observados uma exatidão global de classificação de 84,5% e um índice kappa de 80,9%. O uso dos mosaicos de videografia aérea e dos pontos de levantamento de campo, dentro de um sistema de informação geográfica, permitiu caracterizar e avaliar a qualidade do mapeamento da região da FNT.<br>A vegetation cover mapping of Tapajós National Forest (FNT) in the State of Pará, by Landsat multitemporal images is presented. For mapping validation purposes, aerial videography images and field work survey were used. The confusion matrix analysis gave a 84.5% global classification accuracy and a kappa coefficient of 80.9%. The use of aerial videography mosaics and plots of field work survey allowed to characterize and to evaluate the quality of FNT region mapping

Digital soil mapping: strategy for data pre-processing

The region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance