02815nam a2200517 a 450000100080000000500110000800800410001902000220006010000190008224501330010126001010023430000090033552012980034465000160164265000290165865000220168765000120170965000120172165000100173365000120174365000190175565000150177465000230178965000270181265000260183965000150186565000250188065300090190565300240191465300160193865300210195465300330197565300260200865300390203465300160207365300240208965300210211365300200213465300240215465300150217865300240219365300190221765300190223670000260225570000160228120720212023-11-16       2017    bl uuuu     u00u1 u    #d  a978-85-17-00088-11 aPANTOJA, N. V.  aDetecção da exploração madeireira a partir de imagens Landsat e dados LiDAR no Sudoeste da Amazônia.h[electronic resource]  aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 18., 2017, Santos. Anais... Santos: Inpec2017  a8 p.  aStructural changes on forest canopy produced by selective logging can be identified through satellite images, which shows the location and extent of these areas. The aim of this study was to analyze the detection of logging infrastructure using Landsat images and LiDAR data and verify how long logging scars can be identified through remote sensing. The study was carried out in an annual production unite at the Antimary State Forestry Acre State, western Amazon. We used Non-Photosynthetic Vegetation (NPV) images to identify log landings and compare its location with relative vegetation density models generated from LiDAR data. We also compared the log landing areas identified in the images with the location of 40 log landings obtained in the field through DGPS. The mean area of the detected by landsat images landings was 435 m2 while the undetected landings was 302 m2. The technique tested in this study allowed us to detect 30% of the log landings in NPV images and assisted in the visual interpretation of the canopy opened produced by selective logging. The relative vegetation density model tested in this study successfully identified altered by forest operations area two years after logging, while using Landsat images these areas could be detected only in the logging year.  aCanopy gaps  aEnvironmental monitoring  aForest management  aLandsat  aLásers  aLidar  aLogging  aRemote sensing  aSatellites  aControle ambiental  aDegradação ambiental  aExtração da madeira  aRaio laser  aSensoriamento remoto  aAcre  aAmazônia Ocidental  aBujari (AC)  aDossel florestal  aEspacios vacíos en el dosel  aExplotación forestal  aFloresta Estadual do Antimary (AC)  aGeotécnica  aImagem de satélite  aManejo florestal  aManejo forestal  aMonitoreo ambiental  aSatélites  aSena Madureira (AC)  aTeledetección  aWestern Amazon1 aOLIVEIRA, M. V. N. d'1 aHIGUCHI, N.