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Registro Completo
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Biblioteca(s): |
Embrapa Acre. |
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Data corrente: |
28/07/2023 |
|
Data da última atualização: |
28/07/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
|
Circulação/Nível: |
A - 3 |
|
Autoria: |
QUEIROZ, R. F. P.; OLIVEIRA, M. V. N. d'; REZENDE, A. V.; ALENCAR, P. A. L. de. |
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Afiliação: |
ROBERTA FRANCO PEREIRA DE QUEIROZ, UNIVERSIDADE DE BRASILIA; MARCUS VINICIO NEVES D OLIVEIRA, CPAF-AC; ALBA VALÉRIA REZENDE, UNIVERSIDADE DE BRASILIA; PAOLA AIRES LÓCIO DE ALENCAR, UNIVERSIDADE DE BRASILIA. |
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Título: |
Estimation of aboveground biomass stock in tropical savannas using photogrammetric imaging. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Drones, v. 7, n. 8, 493, July 2023. |
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ISSN: |
2504-446X |
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DOI: |
https://doi.org/10.3390/drones7080493 |
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Idioma: |
Inglês |
|
Conteúdo: |
The use of photogrammetry technology for aboveground biomass (AGB) stock estimation in tropical savannas is a challenging task and is still at a preliminary stage. This work aimed to use metrics derived from point clouds, constructed using photogrammetric imaging obtained by an RGB camera on board a remotely piloted aircraft (RPA), to generate a model for estimating AGB stock for the shrubby-woody stratum in savanna areas of Central Brazil (Cerrado). AGB stock was estimated using forest inventory data and an allometric equation. The photogrammetric digital terrain model (DTM) was validated with altimetric field data, demonstrating that the passive sensor can identify topographic variations in sites with discontinuous canopies. The inventory estimated an average AGB of 18.3 (±13.3) Mg ha-1 at the three sampled sites. The AGB model selected was composed of metrics used for height at the 10th and 95th percentile, with an adjusted R2 of 93% and a relative root mean squared error (RMSE) of 16%. AGB distribution maps were generated from the spatialization of the metrics selected for the model, optimizing the visualization and our understanding of the spatial distribution of forest AGB. The study represents a step forward in mapping biomass and carbon stocks in tropical savannas using low-cost remote sensing platforms. |
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Palavras-Chave: |
Aerial photogrammetry; Árboles forestales; Biomasa aérea; Distrito Federal (DF); Drone; Estimación; Sabanas; Teledetección; Vehículos aéreos no tripulados. |
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Thesagro: |
Aerofotogrametria; Árvore Florestal; Biomassa; Cerrado; Estimativa; Parte Aérea; Sensoriamento Remoto. |
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Thesaurus NAL: |
Aboveground biomass; Estimation; Forest trees; Remote sensing; Savannas; Unmanned aerial vehicles. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1155389/1/27471.pdf
|
|
Marc: |
LEADER 02598naa a2200445 a 4500
001 2155389
005 2023-07-28
008 2023 bl uuuu u00u1 u #d
022 $a2504-446X
024 7 $ahttps://doi.org/10.3390/drones7080493$2DOI
100 1 $aQUEIROZ, R. F. P.
245 $aEstimation of aboveground biomass stock in tropical savannas using photogrammetric imaging.$h[electronic resource]
260 $c2023
520 $aThe use of photogrammetry technology for aboveground biomass (AGB) stock estimation in tropical savannas is a challenging task and is still at a preliminary stage. This work aimed to use metrics derived from point clouds, constructed using photogrammetric imaging obtained by an RGB camera on board a remotely piloted aircraft (RPA), to generate a model for estimating AGB stock for the shrubby-woody stratum in savanna areas of Central Brazil (Cerrado). AGB stock was estimated using forest inventory data and an allometric equation. The photogrammetric digital terrain model (DTM) was validated with altimetric field data, demonstrating that the passive sensor can identify topographic variations in sites with discontinuous canopies. The inventory estimated an average AGB of 18.3 (±13.3) Mg ha-1 at the three sampled sites. The AGB model selected was composed of metrics used for height at the 10th and 95th percentile, with an adjusted R2 of 93% and a relative root mean squared error (RMSE) of 16%. AGB distribution maps were generated from the spatialization of the metrics selected for the model, optimizing the visualization and our understanding of the spatial distribution of forest AGB. The study represents a step forward in mapping biomass and carbon stocks in tropical savannas using low-cost remote sensing platforms.
650 $aAboveground biomass
650 $aEstimation
650 $aForest trees
650 $aRemote sensing
650 $aSavannas
650 $aUnmanned aerial vehicles
650 $aAerofotogrametria
650 $aÁrvore Florestal
650 $aBiomassa
650 $aCerrado
650 $aEstimativa
650 $aParte Aérea
650 $aSensoriamento Remoto
653 $aAerial photogrammetry
653 $aÁrboles forestales
653 $aBiomasa aérea
653 $aDistrito Federal (DF)
653 $aDrone
653 $aEstimación
653 $aSabanas
653 $aTeledetección
653 $aVehículos aéreos no tripulados
700 1 $aOLIVEIRA, M. V. N. d'
700 1 $aREZENDE, A. V.
700 1 $aALENCAR, P. A. L. de
773 $tDrones$gv. 7, n. 8, 493, July 2023.
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