| Texto completo | |
| Autor(es): Mostrar menos - |
d'Oliveira, Marcus V. N.
[1]
;
Broadbent, Eben N.
[2]
;
Oliveira, Luis C.
[1]
;
Almeida, Danilo R. A.
[2, 3]
;
Papa, Daniel A.
[1]
;
Ferreira, Manuel E.
[4]
;
Zambrano, Angelica M. Almeyda
[2]
;
Silva, Carlos A.
[5, 6]
;
Avino, Felipe S.
[7]
;
Prata, Gabriel A.
[2]
;
Mello, Ricardo A.
[7]
;
Figueiredo, Evandro O.
[1]
;
de Castro Jorge, Lucio A.
[8]
;
Junior, Leomar
;
Albuquerque, Rafael W.
[9]
;
Brancalion, Pedro H. S.
[3]
;
Wilkinson, Ben
[6]
;
Oliveira-da-Costa, Marcelo
[7]
Número total de Autores: 18
|
| Afiliação do(s) autor(es): | [1] Embrapa Acre, Rodovia BR 364, Km 14, BR-69900056 Rio Branco, Acre - Brazil
[2] Univ Florida, Sch Forest Resources & Conservat, Spatial Ecol & Conservat SPEC Lab, Gainesville, FL 32611 - USA
[3] Univ Sao Paulo USP ESALQ, Luiz de Queiroz Coll Agr, Dept Forest Sci, BR-1289 Piracicaba, SP - Brazil
[4] Image Proc & GIS Lab LAPIG, BR-74001970 Goiania, Go - Brazil
[5] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 - USA
[6] Univ Florida, Sch Forest Resources & Conservat, Dept Geog Sci, Gainesville, FL 32611 - USA
[7] WWF Brazil, CLS 114, Bloco D-35, BR-70377540 Brasilia, DF - Brazil
[8] Embrapa Instrumentacao, Rua XV Novembro 1452, BR-13564030 Sao Carlos, SP - Brazil
[9] Univ Sao Paulo, Inst Energy & Environm, Prof Luciano Gualberto Ave 1289, Sao Paulo, SP - Brazil
Número total de Afiliações: 9
|
| Tipo de documento: | Artigo Científico |
| Fonte: | REMOTE SENSING; v. 12, n. 11 JUN 2020. |
| Citações Web of Science: | 0 |
| Resumo | |
Tropical forests are often located in difficult-to-access areas, which make high-quality forest structure information difficult and expensive to obtain by traditional field-based approaches. LiDAR (acronym for Light Detection And Ranging) data have been used throughout the world to produce time-efficient and wall-to-wall structural parameter estimates for monitoring in native and commercial forests. In this study, we compare products and aboveground biomass (AGB) estimations from LiDAR data acquired using an aircraft-borne system in 2015 and data collected by the unmanned aerial vehicle (UAV)-based GatorEye Unmanned Flying Laboratory in 2017 for ten forest inventory plots located in the Chico Mendes Extractive Reserve in Acre state, southwestern Brazilian Amazon. The LiDAR products were similar and comparable among the two platforms and sensors. Principal differences between derived products resulted from the GatorEye system flying lower and slower and having increased returns per second than the aircraft, resulting in a much higher point density overall (11.3 +/- 1.8 vs. 381.2 +/- 58 pts/m(2)). Differences in ground point density, however, were much smaller among the systems, due to the larger pulse area and increased number of returns per pulse of the aircraft system, with the GatorEye showing an approximately 50% higher ground point density (0.27 +/- 0.09 vs. 0.42 +/- 0.09). The LiDAR models produced by both sensors presented similar results for digital elevation models and estimated AGB. Our results validate the ability for UAV-borne LiDAR sensors to accurately quantify AGB in dense high-leaf-area tropical forests in the Amazon. We also highlight new possibilities using the dense point clouds of UAV-borne systems for analyses of detailed crown structure and leaf area density distribution of the forest interior. (AU) | |
| Processo FAPESP: | 19/14697-0 - Monitoramento da demografia e diversidade de florestas em processo de restauração usando um sistema drone-lidar-hiperespectral |
| Beneficiário: | Danilo Roberti Alves de Almeida |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado |
| Processo FAPESP: | 18/21338-3 - Monitoramento da restauração de paisagens florestais usando veículo aéreo não tripulado com sensoriamento remoto Lidar e hiperespectral |
| Beneficiário: | Danilo Roberti Alves de Almeida |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |