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  • Marcus Vinicio Neves d'Oliveira
    Embrapa Acre
  • Evandro Orfanó Figueiredo
    Embrapa Acre
  • Danilo Roberti Alves de Almeida
    University of Sao Paulo
  • Luis Claudio Oliveira
    Embrapa Acre
  • Carlos Alberto Silva
    University of Florida
  • Bruce Walker Nelson
    National Institute for Amazon Research (INPA)
  • Renato Mesquita da Cunha
    Instituto de Meio ambiente do Acre
  • Daniel de Almeida Papa
    Embrapa Acre
  • Scott C. Stark
    Michigan State University
  • Ruben Valbuena
Sustainable 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.

Keywords

  • Unmanned aerial vehicle, Forest monitoring, Remote sensing, Digital terrain model, LiDAR, Amazon forest
Original languageEnglish
Article number119648
JournalForest Ecology and Management
Volume500
Early online date28 Aug 2021
DOIs
Publication statusPublished - 15 Nov 2021

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