03791naa a2200673 a 450000100080000000500110000800800410001902200140006002400280007410000260010224502060012826000090033452018240034365000240216765000210219165000100221265000150222265000190223765000210225665000290227765000150230665000220232165000260234365000150236965000290238465000250241365300090243865300240244765300240247165300200249565300230251565300100253865300130254865300240256165300140258565300230259965300280262265300190265065300370266965300190270670000210272570000230274670000220276970000190279170000200281070000230283070000170285370000170287070000170288770000170290470000220292170000230294370000150296670000230298170000250300470000180302970000200304777300500306721228182021-06-28       2020    bl uuuu     u00u1 u    #d  a2072-42927 a10.3390/rs121117542DOI1 aOLIVEIRA, M. V. N. d'  aAboveground biomass estimation in Amazonian Tropical Forestsba comparison of aircraft- and GatorEye UAV- borne LiDAR data in the Chico Mendes Extractive Reserve in Acre, Brazil.h[electronic resource]  c2020  aTropical 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.  aAboveground biomass  aForest inventory  aLidar  aMonitoring  aRemote sensing  aTropical forests  aUnmanned aerial vehicles  aEstimativa  aFloresta Tropical  aInventário Florestal  aRaio Laser  aReconhecimento Florestal  aSensoriamento Remoto  aAcre  aAmazonia Occidental  aAmazônia Ocidental  aBiomassa aérea  aBosques tropicales  aDrone  aGatorEye  aInventario forestal  aMonitoreo  aRESEX Chico Mendes  aSeringal Filipinas (AC)  aTeledetección  aVehículos aéreos no tripulados  aWestern Amazon1 aBROADBENT, E. N.1 aOLIVEIRA, L. C. de1 aALMEIDA, D. R. A.1 aPAPA, D. de A.1 aFERREIRA, M. E.1 aZAMBRANO, A. M. A.1 aSILVA, C. A.1 aAVINO, F. S.1 aPRATA, G. A.1 aMELLO, R. A.1 aFIGUEIREDO, E. O.1 aJORGE, L. A. de C.1 aJUNIOR, L.1 aALBUQUERQUE, R. W.1 aBRANCALION, P. H. S.1 aWILKINSON, B.1 aCOSTA, M. O. da  tRemote Sensinggv. 12, n. 11, 1754, May 2020.