Your browser doesn't support javascript.
loading
Artificial Neural Network and Remote Sensing combined to predict the Aboveground Biomass in the Cerrado biome.
Oliveira, Paula L G; Matricardi, Eraldo A T; Miguel, Eder P; Marimon Júnior, Ben Hur; Rezende, Alba Valéria.
Afiliación
  • Oliveira PLG; University of Brasilia - UnB, College of Technology, Forestry Department, Campus Darcy Ribeiro, 70910-000 Brasília, DF, Brazil.
  • Matricardi EAT; University of Brasilia - UnB, College of Technology, Forestry Department, Campus Darcy Ribeiro, 70910-000 Brasília, DF, Brazil.
  • Miguel EP; University of Brasilia - UnB, College of Technology, Forestry Department, Campus Darcy Ribeiro, 70910-000 Brasília, DF, Brazil.
  • Marimon Júnior BH; Mato Grosso State University - UNEMAT, College of Agrarian, Biological and Applied Social Sciences, Campus Nova Xavantina, Parque Municipal Mário Viana, 78690-000 Nova Xavantina, MT, Brazil.
  • Rezende AV; University of Brasilia - UnB, College of Technology, Forestry Department, Campus Darcy Ribeiro, 70910-000 Brasília, DF, Brazil.
An Acad Bras Cienc ; 96(3): e20221041, 2024.
Article en En | MEDLINE | ID: mdl-39194050
ABSTRACT
Cerrado is the second largest biome in Brazil, and it is responsible for providing us several ecosystem services, including the functions of storing Carbon and biodiversity conservation. In this study, we developed a modeling approach to predict the Aboveground biomass (AGB) in Cerrado vegetation using Artificial Neural Networks (ANNs), vegetation indices retrieved from RapidEye satellite imagery, and field data acquired within the Federal District territory, Brazil. Correlation testing was performed to identify potential vegetation index candidates to be used as input in the AGB modeling. Several ANNs were trained to predict the AGB in the study area using vegetation indices and field data. The optimum ANN was selected according to criteria of mean error of the estimate, correlation coefficient, and graphical analysis. The best performing ANN showed a predictive power of 90% and RMSE less than 17%. The validation tests showed no significant difference between the observed and ANN-predicted values. We estimated an average AGB of 16.55± 8.6 Mg.ha-1 in shrublands in the study area. Our study results indicate that vegetation indices and ANNs combined could accurately estimate the AGB in the Cerrado vegetation in the study area, showing to be a promising methodological approach to be broadly applied throughout the Cerrado biome.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Biomasa / Tecnología de Sensores Remotos País/Región como asunto: America do sul / Brasil Idioma: En Revista: An Acad Bras Cienc Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Brasil
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Biomasa / Tecnología de Sensores Remotos País/Región como asunto: America do sul / Brasil Idioma: En Revista: An Acad Bras Cienc Año: 2024 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Brasil