03372naa a2200589 a 450000100080000000500110000800800410001902200140006002400540007410000260012824501520015426000090030652016130031565000240192865000250195265000180197765000100199565000120200565000190201765000250203665000290206165000220209065000130211265000270212565000260215265000220217865300090220065300340220965300240224365300240226765300190229165300220231065300110233265300100234365300260235365300390237965300150241865300240243365300290245765300370248665300190252370000220254270000250256470000230258970000170261270000180262970000200264770000190266770000170268670000170270377300620272021339272021-11-19       2021    bl uuuu     u00u1 u    #d  a0378-11277 ahttps://doi.org/10.1016/j.foreco.2021.1196482DOI1 aOLIVEIRA, M. V. N. d'  aImpacts of selective logging on Amazon forest canopy structure and biomass with a LiDAR and photogrammetric survey sequence.h[electronic resource]  c2021  aSustainable forest management relies on good knowledge of forest structure obtained from ground surveys combined with remote sensing. Capable of detecting both the forest floor and canopy elements, airborne LiDAR can estimate forest structure parameters with accuracy and precision, but is still difficult to acquire due to the lake of service provider in remote regions of developing countries. Alternatively if ground surface elevations are known (e.g., from LiDAR), they can be tied to a canopy surface model derived from stereo photogrammetry using RGB images from unmanned aerial vehicles (UAV). Here we assessed whether such photogrammetric canopy measurements offer aboveground biomass (AGB) and disturbance impact estimates from logging that are comparable to LiDAR, and whether the use of both in sequence can provide an efficient post-harvest monitoring system. Specifically, through a combination of forest inventory ground plots, airborne LiDAR data, and a UAV-RGB camera system we (i) automatically located and measured canopy disturbance caused by logging, (ii) compared AGB models produced by LiDAR alone and the combination of LiDAR (for terrain elevation model) and RGB-photogrammetry (for forest surface model), and (iii) estimated the AGB stock loss from logging. The study was carried out in the Antimary State forest located in the southwestern Brazilian Amazon. Our results demonstrate that the use of RGB-photogrammetry in regions where the terrain elevation has already been estimated can be an effective way to rapidly identify selective logging and to accurately monitor its impact.  aAboveground biomass  aEnvironmental impact  aForest canopy  aLidar  aLogging  aPhotogrammetry  aSustainable forestry  aUnmanned aerial vehicles  aAerofotogrametria  aBiomassa  aExploração Florestal  aExtração da Madeira  aImpacto Ambiental  aAcre  aAeronave remotamente pilotada  aAmazonia Occidental  aAmazônia Ocidental  aBiomasa aérea  aCubierta forestal  aDossel  aDrone  aExplotación forestal  aFLoresta Estadual do Antimary (AC)  aRGB images  aSena Madureira (AC)  aSilvicultura sustentable  aVehículos aéreos no tripulados  aWestern Amazon1 aFIGUEIREDO, E. O.1 aALMEIDA, D. R. A. de1 aOLIVEIRA, L. C. de1 aSILVA, C. A.1 aNELSON, B. W.1 aCUNHA, R. M. da1 aPAPA, D. de A.1 aSTARK, S. C.1 aVALBUENA, R.  tForest Ecology and Managementgv. 500, 119648, Nov. 2021.