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@Article{LeitoldKelMorCooShi:2015:OpTrRE,
               author = "Leitold, Veronika and Keller, Michael and Morton, Douglas C. and 
                         Cook, Bruce D. and Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and NASA 
                         and NASA and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Airborne lidar-based estimates of tropical forest structure in 
                         complex terrain: opportunities and trade-offs for REDD+",
              journal = "Carbon Balance and Management",
                 year = "2015",
               volume = "10",
               number = "3",
             keywords = "Tropical montane forest, Airborne lidar, Digital Terrain Model, 
                         Elevation accuracy, Data thinning, Canopy height, Biomass 
                         estimation, REDD+.",
             abstract = "Background: Carbon stocks and fluxes in tropical forests remain 
                         large sources of uncertainty in the global carbon budget. Airborne 
                         lidar remote sensing is a powerful tool for estimating aboveground 
                         biomass, provided that lidar measurements penetrate dense forest 
                         vegetation to generate accurate estimates of surface topography 
                         and canopy heights. Tropical forest areas with complex topography 
                         present a challenge for lidar remote sensing. Results: We compared 
                         digital terrain models (DTM) derived from airborne lidar data from 
                         a mountainous region of the Atlantic Forest in Brazil to 35 ground 
                         control points measured with survey grade GNSS receivers. The 
                         terrain model generated from full-density (~20 returns 
                         m\−2) data was highly accurate (mean signed error of 0.19  
                         0.97 m), while those derived from reduced-density datasets (8 
                         m\−2,4m\−2,2m\−2 and 1 m\−2) were 
                         increasingly less accurate. Canopy heights calculated from 
                         reduced-density lidar data declined as data density decreased due 
                         to the inability to accurately model the terrain surface. For 
                         lidar return densities below 4 m\−2, the bias in height 
                         estimates translated into errors of 80125 Mg ha\−1 in 
                         predicted aboveground biomass. Conclusions: Given the growing 
                         emphasis on the use of airborne lidar for forest management, 
                         carbon monitoring, and conservation efforts, the results of this 
                         study highlight the importance of careful survey planning and 
                         consistent sampling for accurate quantification of aboveground 
                         biomass stocks and dynamics. Approaches that rely primarily on 
                         canopy height to estimate aboveground biomass are sensitive to DTM 
                         errors from variability in lidar sampling density.",
                  doi = "10.1186/s13021-015-0013-x",
                  url = "http://dx.doi.org/10.1186/s13021-015-0013-x",
                 issn = "1750-0680",
             language = "en",
           targetfile = "leitold_airborne.pdf",
        urlaccessdate = "02 dez. 2020"
}


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