@InProceedings{SilvaDuHaKlHuDu:2019:FoPlBr,
               author = "Silva, Carlos Alberto and Duncanson, Laura and Hancock, Steven and 
                         Klauberg, Carine and Hudak, Andrew T. and Dubayah, Ralph",
          affiliation = "{NASA Goddard Space Flight Center} and {NASA Goddard Space Flight 
                         Center} and {University of Maryland} and {Universidade Federal de 
                         S{\~a}o Jo{\~a}o Del-Rei (UFSJ)} and {US Forest Service (USDA)} 
                         and {University of Maryland}",
                title = "Estimating forest attributes in industrial Pinus taeda L. forest 
                         plantations in Brazil using simulated NASA's GEDI spaceborne LiDAR 
                         data",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "1047--1050",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "spaceborne lidar, forest attributes, stand modeling, pine 
                         plantations.",
             abstract = "Remote sensing technologies can dramatically increase the 
                         efficiency of plantation management by reducing or replacing 
                         time-consuming field sampling. In this study, we evaluated the 
                         capability of the NASAs Global Ecosystem Dynamic Investigation 
                         (GEDI) spaceborne lidar system for estimating forest attributes at 
                         footprint level in industrial Pinus teada L. forest plantations in 
                         Southern Brazil. In the field, 100 field plots were measured and 
                         top canopy height (HMAX; m) and timber volume (V; m3/ha) were 
                         computed. GEDI-derived metrics were simulated using airborne lidar 
                         (ALS) data. We used multiple linear regression for modeling HMAX 
                         and V from GEDI-like metrics, and we found that models defined as 
                         a function of only three GEDI-like metrics (RH98: canopy height at 
                         98 percentiles of energy, COV: canopy cover; FHD: foliage height 
                         diversity) had a very strong and unbiased predictive power. The 
                         promising results presented herein show that GEDI, during its 
                         lifetime time of two years, may provide an appropriate technology 
                         to assist forest managers towards more cost effective and 
                         efficient forest inventory in industrial pine forest 
                         plantations.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3U3RNTS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U3RNTS",
           targetfile = "97584.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "2024, May 02"
}