Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1085664
Research center of Embrapa/Collection: Embrapa Amazônia Oriental - Artigo em anais de congresso (ALICE)
Date Issued: 2017
Type of Material: Artigo em anais de congresso (ALICE)
Authors: ADAMI, M.
GOMES, A. R.
BELUZZO, A.
COELHO, A. dos S.
VALERIANO, D. de M.
RAMOS, F. de S.
NARVAES, I. da S.
BROWN, I. F.
OLIVEIRA, I. D. de
SANTOS, L. B.
MAURANO, L. E. P.
WATRIN, O. dos S.
GRAÇA, P. M. L. de A.
Additional Information: MARCOS ADAMI, INPE; ALESSANDRA RODRIGUES GOMES, INPE; AMANDA BELUZZO, COLABORADORA CPATU; ANDREA DOS SANTOS COELHO, INPE; DALTON DE MORISSON VALERIANO, INPE; FELIPE DE SOUZA RAMOS, INPA; Igor da Silva Narvaes, INPE; Irvin Foster Brown, UFAC; Ivanilson Dias de Oliveira, UFAC; Lucyana Barros Santos, INPE; Luis Eduardo P. Maurano, INPE; ORLANDO DOS SANTOS WATRIN, CPATU; Paulo Maurício Lima de Alencastro Graça, INPA.
Title: A confiabilidade do PRODES: estimativa da acurácia do mapeamento do desmatamento no estado Mato Grosso.
Publisher: In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... São José dos Campos: INPE, 2017.
Pages: p. 4189-4196.
Language: pt_BR
Keywords: Acurácia.
Description: PRODES is completing almost thirty years of uninterrupted monitoring of clear-cut deforestation over the Brazilian Amazon. Until now, no estimate of its mapping accuracy has been made. In this sense, this article brings a first approximation of mapping accuracy estimation of PRODES deforested areas, taking as example the state of Mato Grosso for the year 2014. For this, a random sampling panel was constructed, stratified with two strata, the deforestation of 2014 and the remaining forest. The sample size was calculated using the binomial function. In addition, a web platform was built to evaluate the points drawn by three independent evaluators. The global accuracy of the mapping of deforestation for the state of Mato Grosso, for the year 2014 was 94.5%, and may vary between 92.4% and 96.5%, in the evaluated scenario there was no class discordance to be found. Regarding the Forest class, the user accuracy was 90.5% and the producer's accuracy was 88.4%, this imbalance between user accuracy and producer accuracy indicates that there is a tendency for the forest class area to be underestimated for this mapping, in this year.
Thesagro: Desmatamento.
Data Created: 2018-01-17
Appears in Collections:Artigo em anais de congresso (CPATU)

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