@Article{BispoRZMCFBLRGERPGRBFSABWSOTB:2020:WoAbBi,
author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and
Rodr{\'{\i}}guez-Veiga, Pedro and Zimbres, Barbara and Miranda,
Sabrina do Couto de and Cezare, Cassio Henrique Giusti and
Fleming, Sam and Baldacchino, Francesca and Louis, Valentin and
Rains, Dominik and Garcia, Mariano and Esp{\'{\i}}rito Santo,
Fernando Del Bon and Roitman, Iris and Pacheco Pascagaza, Ana
Mar{\'{\i}}a and Gou, Yaqing and Roberts, John and Barrett,
Kirsten and Ferreira, Laerte Guimaraes and Shimbo, Julia Zanin and
Alencar, Ane and Bustamante, Mercedes and Woodhouse, Iain Hector
and Sano, Edson Eyji and Ometto, Jean Pierre Henry Balbaud and
Tansey, Kevin and Balzter, Heiko",
affiliation = "{University of Manchester} and {University of Leicester} and
{Amazon Environmental Research Institute (IPAM)} and {Universidade
do Estado de Go{\'{\i}}as (UEG)} and {Universidade Federal de
Goi{\'a}s (UFG)} and {Carbomap Ltd.} and {Carbomap Ltd.} and
{University of Leicester} and {Ghent University} and {University
of Alcal{\'a} de Henares} and {University of Leicester} and
{Universidade de Bras{\'{\i}}lia (UnB)} and {University of
Leicester} and {University of Leicester} and {University of
Leiceste} and {University of Leicester} and {Universidade Federal
de Goi{\'a}s (UFG)} and {Amazon Environmental Research Institute
(IPAM)} and {Amazon Environmental Research Institute (IPAM)} and
{Universidade de Bras{\'{\i}}lia (UnB)} and {Carbomap Ltd.} and
{Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University
of Leicester} and {University of Leicester}",
title = "Woody aboveground biomass mapping of the brazilian savanna with a
multi-sensor and machine learning approach",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "17",
pages = "e3262",
month = "Sept.",
keywords = "aboveground biomass, Cerrado ecosystem, random forest, SAR.",
abstract = "The tropical savanna in Brazil known as the Cerrado covers circa
23% of the Brazilian territory, but only 3% of this area is
protected. High rates of deforestation and degradation in the
woodland and forest areas have made the Cerrado the second-largest
source of carbon emissions in Brazil. However, data on these
emissions are highly uncertain because of the spatial and temporal
variability of the aboveground biomass (AGB) in this biome.
Remote-sensing data combined with local vegetation inventories
provide the means to quantify the AGB at large scales. Here, we
quantify the spatial distribution of woody AGB in the Rio Vermelho
watershed, located in the centre of the Cerrado, at a high spatial
resolution of 30 metres, with a random forest (RF)
machine-learning approach. We produced the first high-resolution
map of the AGB for a region in the Brazilian Cerrado using a
combination of vegetation inventory plots, airborne light
detection and ranging (LiDAR) data, and multispectral and radar
satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of
random forest (RF) models and jackknife analyses enabled us to
select the best remote-sensing variables to quantify the AGB on a
large scale. Overall, the relationship between the ground data
from vegetation inventories and remote-sensing variables was
strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58
Mg ha-1 and a bias of 0.43 Mg ha-1.",
doi = "10.3390/RS12172685",
url = "http://dx.doi.org/10.3390/RS12172685",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-12-02685-v3.pdf",
urlaccessdate = "27 abr. 2024"
}