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Evaluating spatial coverage of data on the aboveground biomass in undisturbed forests in the Brazilian Amazon.
Tejada, Graciela; Görgens, Eric Bastos; Espírito-Santo, Fernando Del Bon; Cantinho, Roberta Zecchini; Ometto, Jean Pierre.
Afiliação
  • Tejada G; Earth System Science Center (CCST), National Institute for Space Research (INPE), Av dos Astronautas 1758, São José dos Campos, SP, 12227-010, Brazil. gracielatejadap@gmail.com.
  • Görgens EB; Department of Forestry Engineering, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Campus JK, Rod. MGT 367, km 583 5000, Alto do Jacuba, Diamantina, MG, 39100-000, Brazil.
  • Espírito-Santo FDB; Centre for Landscape and Climate Research (CLCR) and Leicester Institute for Space and Earth Observation (LISEO), School of Geography, Geology and Environment, University of Leicester, University Road, Leicester, LE1 7RH, UK.
  • Cantinho RZ; United Nations Development Programme (UNDP), SEN 802, 17, Conj. C-St. Mans̃oes DB, Brasília, DF, 70800-400, Brazil.
  • Ometto JP; Earth System Science Center (CCST), National Institute for Space Research (INPE), Av dos Astronautas 1758, São José dos Campos, SP, 12227-010, Brazil.
Carbon Balance Manag ; 14(1): 11, 2019 Sep 03.
Article em En | MEDLINE | ID: mdl-31482475
ABSTRACT

BACKGROUND:

Brazilian Amazon forests contain a large stock of carbon that could be released into the atmosphere as a result of land use and cover change. To quantify the carbon stocks, Brazil has forest inventory plots from different sources, but they are unstandardized and not always available to the scientific community. Considering the Brazilian Amazon extension, the use of remote sensing, combined with forest inventory plots, is one of the best options to estimate forest aboveground biomass (AGB). Nevertheless, the combination of limited forest inventory data and different remote sensing products has resulted in significant differences in the spatial distribution of AGB estimates. This study evaluates the spatial coverage of AGB data (forest inventory plots, AGB maps and remote sensing products) in undisturbed forests in the Brazilian Amazon. Additionally, we analyze the interconnection between these data and AGB stakeholders producing the information. Specifically, we provide the first benchmark of the existing field plots in terms of their size, frequency, and spatial distribution.

RESULTS:

We synthesized the coverage of forest inventory plots, AGB maps and airborne light detection and ranging (LiDAR) transects of the Brazilian Amazon. Although several extensive forest inventories have been implemented, these AGB data cover a small fraction of this region (e.g., central Amazon remains largely uncovered). Although the use of new technology such as airborne LiDAR cover a significant extension of AGB surveys, these data and forest plots represent only 1% of the entire forest area of the Brazilian Amazon.

CONCLUSIONS:

Considering that several institutions involved in forest inventories of the Brazilian Amazon have different goals, protocols, and time frames for forest surveys, forest inventory data of the Brazilian Amazon remain unstandardized. Research funding agencies have a very important role in establishing a clear sharing policy to make data free and open as well as in harmonizing the collection procedure. Nevertheless, the use of old and new forest inventory plots combined with airborne LiDAR data and satellite images will likely reduce the uncertainty of the AGB distribution of the Brazilian Amazon.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article