@Article{GhizoniSMGJGADO:2019:MuApEs,
               author = "Ghizoni, Dos Santos Erone and Shimabukuro, Yosio Edemir and Moura, 
                         Yhasmin Mendes de and Gon{\c{c}}alves, F{\'a}bio Guimar{\~a}es 
                         and Jorge, Anderson and Gasparini, Kaio Alan and Arai, Egidio and 
                         Duarte, Valdete and Ometto, Jean Pierre Henry Balbaud",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of 
                         Leicester} and {Canopy Remote Sensing Solutions} 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 {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Multi-scale approach to estimating aboveground biomass in the 
                         Brazilian Amazon using Landsat and LiDAR data",
              journal = "International Journal of Remote Sensing",
                 year = "2019",
               volume = "40",
               number = "22",
                pages = "8635--8645",
                month = "Nov.",
             abstract = "Forest degradation from either natural or anthropogenic drivers 
                         involves processes that change the capacity of the ecosystem to 
                         provide services. In Brazil, estimates of carbon emissions do not 
                         currently take into account emissions from forest degradation 
                         caused by fire or by selective logging. Here, we present a 
                         methodology to estimate aboveground biomass in forest 
                         degradedareas, that can be accounted to estimate carbon emissions. 
                         We explored a multi-scale and temporal approach involving Airborne 
                         Laser Scanning (ALS) and orbital images from Landsat 8 Operational 
                         Land Imager (OLI) sensor to estimate the aboveground biomass. 
                         Cross-validation results showed that 49% of the variation in 
                         biomass could be explained using this approach, with an estimation 
                         error 58 Mg ha(-1) (49.08%). Due to the difficulty in measuring 
                         biomass in tropical forests, the proposed methodology can be an 
                         alternative in future works to estimate aboveground biomass in 
                         order to improve the estimates of carbon emissions by the 
                         governmental organizations.",
                  doi = "10.1080/2150704X.2019.1619955",
                  url = "http://dx.doi.org/10.1080/2150704X.2019.1619955",
                 issn = "0143-1161",
             language = "en",
           targetfile = "Multi scale approach to estimating aboveground biomass in the 
                         Brazilian Amazon using Landsat and LiDAR data.pdf",
        urlaccessdate = "27 abr. 2024"
}