@Article{CostaSBLMLSAASGCFHMCMHDMHZVFSAFLCK:2021:MaToAb,
author = "Costa, M{\'a}ira Beatriz Teixeira da and Silva, Carlos Alberto
and Broadbent, Eben North and Leite, Rodrigo Vieira and Mohan,
Midhun and Liesenberg, Veraldo and Stoddart, Jaz and Amaral,
Cibele Hummel do and Almeida, Danilo Roberti Alves de and Silva,
Anne Laura da and Goya, Lucas Ruggeri R{\'e} Y. and Cordeiro,
Victor Almeida and Franciel, Rex and Hirsch, Andre and Marcatti,
Gustavo Eduardo and Cardil, Adrian and Mendon{\c{c}}a, Bruno
Ara{\'u}jo Furtado de and Hamamura, Caio and Dalla Corte, Ana
Paula and Matricardi, Eraldo Aparecido Trondoli and Hudak, Andrew
T. and Zambrano, Ang{\'e}lica Maria Almeyda and Valbuena, Ruben
and Faria, Bruno Lopes de and Silva J{\'u}nior, Celso Henrique
Leite and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and
Ferreira, Manuel Eduardo and Liang, Jingjing and Carvalho, Samuel
de P{\'a}dua Chaves e and Klauberg, Carine",
affiliation = "{Universidade de Bras{\'{\i}}lia (UnB)} and {University of
Florida} and {University of Florida} and {Universidade Federal de
Vi{\c{c}}osa (UFV)} and {University of California Berkeley} and
{Universidade do Estado de Santa Catarina (UDESC)} and {Bangor
University} and {Universidade Federal de Vi{\c{c}}osa (UFV)} and
{Universidade de S{\~a}o Paulo (USP)} and {Universidade Federal
de S{\~a}o Jo{\~a}o Del-Rei (UFSJ)} and {Universidade Federal de
S{\~a}o Jo{\~a}o Del-Rei (UFSJ)} and {Universidade Federal de
S{\~a}o Jo{\~a}o Del-Rei (UFSJ)} and {Universidade Federal do
Parn{\'a} (UFPR)} and {Universidade Federal de S{\~a}o Jo{\~a}o
Del-Rei (UFSJ)} and {Universidade Federal de S{\~a}o Jo{\~a}o
Del-Rei (UFSJ)} and {Technosylva Inc} and {Universidade Federal
Rural do Rio de Janeiro (UFRRJ)} and {Instituto Federal de
S{\~a}o Paulo (IFSP)} and {Universidade Federal do Paran{\'a}
(UFPR)} and {Universidade de Bras{\'{\i}}lia (UnB)} and US
Department of Agriculture, Forest Service and {University of
Florida} and {Bangor University} and {University of Florida} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade Estadual
do Maranh{\~a}o (UEMA)} and {Purdue University} and {Universidade
Federal de Mato Grosso (UFMT)} and {Universidade Federal de
S{\~a}o Jo{\~a}o Del-Rei (UFSJ)}",
title = "Beyond trees: Mapping total aboveground biomass density in the
Brazilian savanna using high-density UAV-lidar data",
journal = "Forest Ecology and Management",
year = "2021",
volume = "491",
pages = "e119155",
month = "July",
keywords = "Biomass, Vegetation, Tropical savanna, Remote sensing, Cerrado,
Mapping, GatorEye.",
abstract = "Tropical savanna ecosystems play a major role in the seasonality
of the global carbon cycle. However, their ability to store and
sequester carbon is uncertain due to combined and intermingling
effects of anthropogenic activities and climate change, which
impact wildfire regimes and vegetation dynamics. Accurate
measurements of tropical savanna vegetation aboveground biomass
(AGB) over broad spatial scales are crucial to achieve effective
carbon emission mitigation strategies. UAV-lidar is a new remote
sensing technology that can enable rapid 3-D mapping of structure
and related AGB in tropical savanna ecosystems. This study aimed
to assess the capability of high-density UAV-lidar to estimate and
map total (tree, shrubs, and surface layers) aboveground biomass
density (AGBt) in the Brazilian Savanna (Cerrado). Five ordinary
least square regression models estimating AGBt were adjusted using
50 field sample plots (30 m × 30 m). The best model was selected
under Akaike Information Criterion, adjusted coefficient of
determination (adj.R2), absolute and relative root mean square
error (RMSE), and used to map AGBt from UAV-lidar data collected
over 1,854 ha spanning the three major vegetation formations
(forest, savanna, and grassland) in Cerrado. The model using
vegetation height and cover was the most effective, with an
overall model adj-R2 of 0.79 and a leave-one-out cross-validated
RMSE of 19.11 Mg/ha (33.40%). The uncertainty and errors of our
estimations were assessed for each vegetation formation
separately, resulting in RMSEs of 27.08 Mg/ha (25.99%) for
forests, 17.76 Mg/ha (43.96%) for savannas, and 7.72 Mg/ha
(44.92%) for grasslands. These results prove the feasibility and
potential of the UAV-lidar technology in Cerrado but also
emphasize the need for further developing the estimation of
biomass in grasslands, of high importance in the characterization
of the global carbon balance and for supporting integrated fire
management activities in tropical savanna ecosystems. Our results
serve as a benchmark for future studies aiming to generate
accurate biomass maps and provide baseline data for efficient
management of fire and predicted climate change impacts on
tropical savanna ecosystems.",
doi = "10.1016/j.foreco.2021.119155",
url = "http://dx.doi.org/10.1016/j.foreco.2021.119155",
issn = "0378-1127",
language = "en",
targetfile = "costa_beyonds.pdf",
urlaccessdate = "21 maio 2024"
}