{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:49:57Z","timestamp":1784764197558,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2020,5,29]],"date-time":"2020-05-29T00:00:00Z","timestamp":1590710400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 \u00b1 1.8 vs. 381.2 \u00b1 58 pts\/m2). 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 \u00b1 0.09 vs. 0.42 \u00b1 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.<\/jats:p>","DOI":"10.3390\/rs12111754","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T09:19:27Z","timestamp":1591089567000},"page":"1754","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["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"],"prefix":"10.3390","volume":"12","author":[{"given":"Marcus","family":"d\u2019Oliveira","sequence":"first","affiliation":[{"name":"Embrapa Acre, Rodovia BR-364, km 14, CEP 69900-056 Rio Branco, Acre, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eben","family":"Broadbent","sequence":"additional","affiliation":[{"name":"Spatial Ecology and Conservation (SPEC) Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis","family":"Oliveira","sequence":"additional","affiliation":[{"name":"Embrapa Acre, Rodovia BR-364, km 14, CEP 69900-056 Rio Branco, Acre, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danilo","family":"Almeida","sequence":"additional","affiliation":[{"name":"Spatial Ecology and Conservation (SPEC) Lab, School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"},{"name":"Department of Forest Sciences, \u201cLuiz de Queiroz\u201d College of Agriculture, University of S\u00e3o Paulo (USP\/ESALQ), 1289 Piracicaba-SP, 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School of Forest Resources and Conservation, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Mello","sequence":"additional","affiliation":[{"name":"WWF-Brazil, CLS 114, Bloco D-35, 70377-540 Bras\u00edlia-DF, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evandro","family":"Figueiredo","sequence":"additional","affiliation":[{"name":"Embrapa Acre, Rodovia BR-364, km 14, CEP 69900-056 Rio Branco, Acre, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L\u00facio","family":"Jorge","sequence":"additional","affiliation":[{"name":"Embrapa Instrumenta\u00e7\u00e3o, Rua XV de Novembro, 1452, CEP 13564-030 S\u00e3o Carlos-SP, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leomar","family":"Junior","sequence":"additional","affiliation":[{"name":"Image Processing and GIS Lab (LAPIG), Universidade Federal de Goi\u00e1s, 74001-970 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