@InProceedings{SantosJorgShimGonį:2017:EsBiAc,
author = "Santos, Erone Ghizoni dos and Jorge, Anderson and Shimabukuro,
Yosio Edemir and Gon{\c{c}}alves, Fabio Guimar{\~a}es",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Estimativa de biomassa acima do solo para uma {\'a}rea queimada e
uma {\'a}rea de corte seletivo no munic{\'{\i}}pio de Feliz
Natal MT por meio de dados LiDAR",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "4321--4328",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Remote sensing techniques have aided measurement and estimation of
forest area and the identification of deforestation and forest
degradation. Light Detection and Ranging (LiDAR) allows mapping
the vertical structure of forests and helps obtaining information
in areas of difficult access. This study was conducted in two
areas in the municipality of Feliz Natal, Mato Grosso, Brazil. The
first area (area 1) was burned in 2006, 2008 and 2011, while the
second area (area 2) was subjected to selective logging in 2006
and 2007. Both areas were inventoried in the field: area 1 in 2013
and area 2 in 2015, totalizing 27 samples. In addition to the
field data, airborne LiDAR data were acquired for the two areas in
August 2013. The objective of this study was to use LiDAR data to
estimate aboveground biomass (AGB) in these areas and understand
the differences in their carbon stocks as a result of fire and
selective logging. Structure metrics extracted from the point
cloud data were linearly and highly correlated with AGB. The
multiple regression model created with the stepwise procedure
presented an R2 of 0.96 and a root mean square error of 8.7 Mg/ha
(25.3%). Using LiDAR data, it was possible to model the
relationship between AGB and LiDAR metrics for areas that have
been degraded by fire and selective logging. The results showed a
difference in carbon stocks of 15.8% for these areas, indicating
that the degradation by fire was considerably more intense in this
site.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59392",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSM2SA",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM2SA",
targetfile = "59392.pdf",
type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
urlaccessdate = "15 jun. 2024"
}