Aboveground biomass estimation in Amazonian Tropical Forests: a comparison of aircraft- and GatorEye UAV- borne LiDAR data in the Chico Mendes Extractive Reserve in Acre, Brazil.

Tropical forests are often located in dicult-to-access areas, which make high-quality forest structure information dicult 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-ecient 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 dierences 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/m2). Dierences 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.

Bibliographic Details

Main Authors:	OLIVEIRA, M. V. N. d', BROADBENT, E. N., OLIVEIRA, L. C. de, ALMEIDA, D. R. A., PAPA, D. de A., FERREIRA, M. E., ZAMBRANO, A. M. A., SILVA, C. A., AVINO, F. S., PRATA, G. A., MELLO, R. A., FIGUEIREDO, E. O., JORGE, L. A. de C., JUNIOR, L., ALBUQUERQUE, R. W., BRANCALION, P. H. S., WILKINSON, B., COSTA, M. O. da
Other Authors:	MARCUS VINICIO NEVES D OLIVEIRA, CPAF-AC; Eben N. Broadbent, University of Florida; LUIS CLAUDIO DE OLIVEIRA, CPAF-AC; Danilo R. A. Almeida, University of Florida / USP/ESALQ; DANIEL DE ALMEIDA PAPA, CPAF-AC; Manuel E. Ferreira, Universidade Federal de Goiás; Angelica M. Almeyda Zambrano, University of Florida; Carlos A. Silva, University of Florida / University of Maryland; Felipe S. Avino, WWF-Brasil; Gabriel A. Prata, University of Florida; Ricardo A. Mello, WWF-Brasil; EVANDRO ORFANO FIGUEIREDO, CPAF-AC; LUCIO ANDRE DE CASTRO JORGE, CNPDIA; Leomar Junior, Universidade Federal de Goiás; Rafael W. Albuquerque, Universidade de São Paulo; Pedro H. S. Brancalion, USP/ESALQ; Ben Wilkinson, University of Florida; Marcelo Oliveira-da-Costa, WWF-Brasil.
Format:	Artigo de periódico biblioteca
Language:	Ingles English
Published:	2020-06-01
Subjects:	Bosques tropicales, Inventario forestal, Monitoreo, Biomassa aérea, Teledetección, Vehículos aéreos no tripulados, Drone, GatorEye, Seringal Filipinas (AC), RESEX Chico Mendes, Acre, Amazônia Ocidental, Western Amazon, Amazonia Occidental, Floresta Tropical, Inventário Florestal, Reconhecimento Florestal, Estimativa, Sensoriamento Remoto, Raio Laser, Tropical forests, Forest inventory, Monitoring, Aboveground biomass, Remote sensing, Unmanned aerial vehicles, Lidar,
Online Access:	http://www.alice.cnptia.embrapa.br/alice/handle/doc/1122818
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