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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
- Source :
- Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), instacron:EMBRAPA, Remote Sensing; Volume 12; Issue 11; Pages: 1754, Remote Sensing, Vol 12, Iss 1754, p 1754 (2020), Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP
- Publication Year :
- 2020
-
Abstract
- 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. Made available in DSpace on 2020-06-02T04:38:55Z (GMT). No. of bitstreams: 1 27002.pdf: 8268657 bytes, checksum: 92fd75c7b1786acaf00f080246b7eef4 (MD5) Previous issue date: 2020
- Subjects :
- 010504 meteorology & atmospheric sciences
Tropical forests
Amazonian
0211 other engineering and technologies
Point cloud
Estimativa
02 engineering and technology
Vehículos aéreos no tripulados
01 natural sciences
remote sensing
Floresta Tropical
Teledetección
forest inventory
Digital elevation model
Inventario forestal
Seringal Filipinas (AC)
Raio Laser
Lidar
Biomassa aérea
Amazon rainforest
Inventário Florestal
Crown (botany)
Monitoreo
Aboveground biomass
Remote sensing
RESEX Chico Mendes
forest structure
Sensoriamento Remoto
Monitoring
Science
forest monitoring
Reconhecimento Florestal
Unmanned aerial vehicles
Acre
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Estimation
Forest inventory
TECNOLOGIA LIDAR
Drone
General Earth and Planetary Sciences
Environmental science
Amazonia Occidental
GatorEye
Amazônia Ocidental
Western Amazon
Bosques tropicales
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA-Alice), Empresa Brasileira de Pesquisa Agropecuária (Embrapa), instacron:EMBRAPA, Remote Sensing; Volume 12; Issue 11; Pages: 1754, Remote Sensing, Vol 12, Iss 1754, p 1754 (2020), Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual), Universidade de São Paulo (USP), instacron:USP
- Accession number :
- edsair.doi.dedup.....d12975113e1a8db3df51044bdcad9180