@Article{BispoSaVaGrBaFrBi:2016:PrMoPr,
author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and Santos, Jo{\~a}o
Roberto dos and Valeriano, M{\'a}rcio de Morisson and
Gra{\c{c}}a, Paulo Maur{\'{\i}}cio Lima de Alencastro and
Baltzer, Heiko and Fran{\c{c}}a, Helena and Bispo, Pit{\'a}goras
da Concei{\c{c}}{\~a}o",
affiliation = "{Universidade Federal do ABC (UFABC)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas da
Amaz{\^o}nia (INPA)} and {University of Leicester} and
{Universidade Federal do ABC (UFABC)} and {Universidade Estadual
Paulista (UNESP)}",
title = "Predictive models of primary tropical forest structure from
geomorphometric variables based on SRTM in the Tapajo's region,
Brazilian Amazon",
journal = "PLoS One",
year = "2016",
volume = "11",
number = "4",
pages = "e0152009",
month = "Apr.",
keywords = "altitude, Brazil, canopy, forest structure, genetic polymorphism,
height, model, multiple linear regression analysis, tropical rain
forest, uncertainty, validation process, vegetation.",
abstract = "Surveying primary tropical forest over large regions is
challenging. Indirect methods of relating terrain information or
other external spatial datasets to forest biophysical parameters
can provide forest structural maps at large scales but the
inherent uncertainties need to be evaluated fully. The goal of the
present study was to evaluate relief characteristics, measured
through geomorphometric variables, as predictors of forest
structural characteristics such as average tree basal area (BA)
and height (H) and average percentage canopy openness (CO). Our
hypothesis is that geomorphometric variables are good predictors
of the structure of primary tropical forest, even in areas, with
low altitude variation. The study was performed at the Tapajo's
National Forest, located in the Western State of Par{\'a},
Brazil. Forty-three plots were sampled. Predictive models for BA,
H and CO were parameterized based on geomorphometric variables
using multiple linear regression. Validation of the models with
nine independent sample plots revealed a Root Mean Square Error
(RMSE) of 3.73 m2/ha (20%) for BA, 1.70 m (12%) for H, and 1.78%
(21%) for CO. The coefficient of determination between observed
and predicted values were r2 = 0.32 for CO, r2 = 0.26 for H and r2
= 0.52 for BA. The models obtained were able to adequately
estimate BA and CO. In summary, it can be concluded that relief
variables are good predictors of vegetation structure and enable
the creation of forest structure maps in primary tropical
rainforest with an acceptable uncertainty. © 2016 Bispo et al.
This is an open access article distributed under the terms of the
Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the
original author and source are credited.",
doi = "10.1371/journal.pone.0152009",
url = "http://dx.doi.org/10.1371/journal.pone.0152009",
issn = "1932-6203",
language = "en",
targetfile = "bispo_predictive.PDF",
urlaccessdate = "27 abr. 2024"
}