%0 Conference Proceedings
%4 sid.inpe.br/marte2/2019/09.17.13.25
%2 sid.inpe.br/marte2/2019/09.17.13.25.15
%@isbn 978-85-17-00097-3
%T Estimating forest attributes in industrial Pinus taeda L. forest plantations in Brazil using simulated NASA's GEDI spaceborne LiDAR data
%D 2019
%A Silva, Carlos Alberto,
%A Duncanson, Laura,
%A Hancock, Steven,
%A Klauberg, Carine,
%A Hudak, Andrew T.,
%A Dubayah, Ralph,
%@affiliation NASA Goddard Space Flight Center
%@affiliation NASA Goddard Space Flight Center
%@affiliation University of Maryland
%@affiliation Universidade Federal de São João Del-Rei (UFSJ)
%@affiliation US Forest Service (USDA)
%@affiliation University of Maryland
%@electronicmailaddress carlos_engflorestal@outlook.com
%@electronicmailaddress lauraiduncanson@gmail.com
%@electronicmailaddress hancock@umd.edu
%@electronicmailaddress carine_klauberg@hotmail.com
%@electronicmailaddress ahudak@fs.fed.us
%@electronicmailaddress dubayah@umd.edu
%E Gherardi, Douglas Francisco Marcolino,
%E Sanches, Ieda DelArco,
%E Aragão, Luiz Eduardo Oliveira e Cruz de,
%B Simpósio Brasileiro de Sensoriamento Remoto, 19 (SBSR)
%C Santos
%8 14-17 abril 2019
%I Instituto Nacional de Pesquisas Espaciais (INPE)
%J São José dos Campos
%P 1047-1050
%S Anais
%1 Instituto Nacional de Pesquisas Espaciais (INPE)
%K spaceborne lidar, forest attributes, stand modeling, pine plantations.
%X 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.
%9 LIDAR: sensores e aplicações
%@language pt
%3 97584.pdf