@InProceedings{PereiraAESDJCCO:2018:EfSpRe,
               author = "Pereira, Francisca Rocha de Souza and Assis, Mauro L{\'u}cio 
                         Rodrigues de and Esp{\'{\i}}rito Santo, Ferandno and Sato, 
                         Luciane and Dias, Emily and Jacon, Aline and Carneiro, Heitor 
                         Guerra and Cantinho, Roberta and Ometto, Jean Pierre Henry 
                         Balbaud",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and Minist{\'e}rio da 
                         Ci{\^e}ncia, Tecnologia, Inova{\c{c}}{\~a}o e 
                         Comunica{\c{c}}{\~o}es (MCTIC) and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Minist{\'e}rio da Ci{\^e}ncia, Tecnologia, Inova{\c{c}}{\~a}o 
                         e Comunica{\c{c}}{\~o}es (MCTIC and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "The Effects of the Spatial Resolution of Airborne Lidar Data on 
                         Aboveground Biomass Estimation",
            booktitle = "Proceedings...",
                 year = "2018",
         organization = "IUFRO Conference",
             abstract = "The amount of aboveground biomass (AGB) held in vital components 
                         of vegetation play a significant role in the carbon cycle of 
                         tropical forests. Reducing uncertainty of terrestrial carbon cycle 
                         depend strongly on the accurate estimate of AGB. Lidar remote 
                         sensing provides the most precise methodology to quantify AGB at 
                         large scales, but the effects of the spatial resolution of 
                         airborne lidar data on AGB estimation is unknown. Here we examine 
                         the impact of the minimum spatial resolution threshold of lidar 
                         data to reduce the uncertainty of AGB estimations in tropical 
                         forest. For that we used a sizeable airborne lidar data from 
                         Tapajos National Forest (TNF) and ten permanent field plots. We 
                         compared two approaches: (1) we used general lidar allometric 
                         equation of AGB estimation developed for the Amazon, testing the 
                         best spatial resolution of lidar measurements at 25, 50 and 100 
                         meters and compared with our ground data of forest inventory from 
                         TNF; (2) we developed and tested a new local lidar allometric 
                         equation to quantify AGB in TNF. Although the use of lidar cloud 
                         cover at 50 m provides unbiased estimates of AGB, our results 
                         demonstrated that local forest structure plays a significant role 
                         in this general allometric equations. Our results underscored 
                         three conclusions. First, the effects of the spatial resolution of 
                         airborne lidar data on AGB estimation were significant. We found 
                         that a minimum size-area of 50 meters of lidar is necessary to 
                         produce an unbiased estimate of AGB in a local tropical forest of 
                         Central Amazon. Second, our adjusted allometric equation for TNF, 
                         which was based in mean canopy height model, reduced the 
                         uncertainty of AGB from RMSE%: 36.8% to RMSE%: 26.2% (local 
                         model). Finally, this study highlights the need of lidar 
                         allometric equations based on local forest structure to reduce the 
                         uncertainty of AGB estimations.",
  conference-location = "Posadas, Argentina",
      conference-year = "01-05 oct.",
             language = "en",
           targetfile = "pereira_effects.pdf",
        urlaccessdate = "27 abr. 2024"
}