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@Article{ShinzatoShiCooTomGas:2017:InArIn,
               author = "Shinzato, Emily Tsiemi and Shimabukuro, Yosio Edemir and Coops, 
                         Nicholas C. and Tompalski, Piotr and Gasparoto, Esthevan A. G.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of British 
                         Columbia} and {University of British Columbia} and {Universidade 
                         de S{\~a}o Paulo (USP)}",
                title = "Integrating area-based and individual tree detection approaches 
                         for estimating tree volume in plantation inventory using aerial 
                         image and airborne laser scanning data",
              journal = "Iforest Biogeosciences and Forestry",
                 year = "2017",
               volume = "10",
                pages = "296--302",
                month = "Feb.",
             keywords = "orest Inventory, Airborne Laser Scanning, Treetop Detection, 
                         Eucalyptus Plantation, Area-based Approach, LiDAR.",
             abstract = "Remote sensing has been increasingly used to assist forest 
                         inventory. Airborne Laser Scanning (ALS) systems can accurately 
                         estimate tree height in forests, and are being combined with more 
                         traditional optical images that provide further details about the 
                         horizontal structure of forests. To predict forest attributes two 
                         main techniques are applied to process ALS data: the Area Based 
                         Approach (ABA), and the Individual Tree Detection (ITD). The first 
                         part of this study was focused on the effectiveness of integrating 
                         ALS data and aerial imagery to estimate the wood volume in 
                         Eucalyptus urograndis plantations using the ABA approach. To this 
                         aim, we analyzed three different approaches: (1) using only ALS 
                         points cloud metrics (RMSE = 6.84%); (2) using only the variables 
                         derived from aerial images (RMSE = 8.45%); and (3) the integration 
                         of both 1 and 2 (RMSE = 5.23%), which underestimated the true 
                         volume by 2.98%. To estimate individual tree volumes we first 
                         detected individual trees and corrected the density estimate for 
                         detecting mean difference, with an error of 0.37 trees per hectare 
                         and RMSE of 12.68%. Next, we downscaled the total volume 
                         prediction to single tree level. Our approach showed a better 
                         result of the overall volume in comparison with the traditional 
                         forest inventory. There is a remarkable advantage in using the 
                         Individual Tree Detection approach, as it allows for a spatial 
                         representation of the number of trees sampled, as well as their 
                         volume per unit area - an important metric in the management of 
                         forest resources.",
                  doi = "10.3832/ifor1880-009",
                  url = "http://dx.doi.org/10.3832/ifor1880-009",
                 issn = "1971-7458",
             language = "en",
           targetfile = "shinzato_integrating.pdf",
        urlaccessdate = "26 nov. 2020"
}


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