@Article{BispoPPKBRSRTSA:2019:MaFoSu,
author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and Pardini, Matteo and
Papathanassiou, Konstantinos P. and Kluger, Florian and Balzter,
Heiko and Rains, Dominik and Santos, Jo{\~a}o Roberto dos and
Rizaev, Igor G. and Tansey, Kevin and Santos, Maiza Nara dos and
Araujo, Luciana Spinelli",
affiliation = "{University of Leicester} and {German Aerospace Center (DLR)} and
{German Aerospace Center (DLR)} and {German Aerospace Center
(DLR)} and {University of Leicester} and {Ghent University} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University
of Bristol} and {University of Leicester} and {Empresa Brasileira
de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and {Empresa Brasileira
de Pesquisa Agropecu{\'a}ria (EMBRAPA)}",
title = "Mapping forest successional stages in the Brazilian Amazon using
forest heights derived from TanDEM-X SAR interferometry",
journal = "Remote Sensing of Environment",
year = "2019",
volume = "232",
pages = "111194",
month = "Oct.",
keywords = "Tropical forests, Successional stages, Forest height, Synthetic
Aperture Radar, Interferometry, TanDEM-X.",
abstract = "Knowledge of the spatial patterns of successional stages (i.e.,
primary and secondary forest) in tropical forests allows to
monitor forest preservation, mortality and regeneration in
relation to natural and anthropogenic disturbances. Different
successional stages have also different capabilities of
re-establishing carbon stocks. Therefore, a successful
discrimination of successional stages over wide areas can lead to
an improved quantification of above ground biomass and carbon
stocks. The reduction of the mapping uncertainties is especially a
challenge due to high heterogeneity of the tropical vegetation. In
this framework, the development of innovative remote sensing
approaches is required. Forests (top) height (and its spatial
distribution) are an important structural parameter that can be
used to differentiate between different successional stages, and
can be provided by Interferometric Synthetic Aperture Radar
(InSAR) acquisitions. In this context, this paper investigates the
potential of forest heights estimated from TanDEM-X InSAR data and
a LiDAR digital terrain model (DTM) for separating successional
stages (primary or old growth and secondary forest at different
stages of succession) by means of a maximum likelihood
classification. The study was carried out in the region of the
Tapaj{\'o}s National Forest (Par{\'a}, Brazil) in the Amazon
biome. The forest heights for three years (2012, 2013 and 2016)
were estimated from a single-polarization in bistatic mode using
InSAR model-based inversion techniques aided by the LiDAR digital
terrain model. The validation of the TanDEM-X forest heights with
independent LiDAR H100 datasets was carried out in the location of
seven field inventory plots (measuring 50 × 50 m, equivalent to
0.25 ha), also allowing for the validation of the LiDAR datasets
against the field data. The validation of the estimated heights
showed a high correlation (r = 0.93) and a low uncertainty (RMSE =
3 m). The information about the successional stages and forest
heights from field datasets was used to select training samples in
the LiDAR and TanDEM-X forest heights to classify successional
stages with a maximum likelihood classifier. The identification of
different stages of forest succession based on TanDEM-X forest
heights was possible with an overall accuracy of about 80%.",
doi = "10.1016/j.rse.2019.05.013",
url = "http://dx.doi.org/10.1016/j.rse.2019.05.013",
issn = "0034-4257",
language = "en",
targetfile = "bispo_mapping.pdf",
urlaccessdate = "21 maio 2024"
}