@Article{BendiniFoScRuKöKoHo:2020:CaStBr,
author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and
Schwieder, M. and Rufin, P. and K{\"o}rting, Thales Sehn and
Koumrouyan, Adriana and Hostert, P.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Humboldt-Universitat
zu Berlin} and {Humboldt-Universitat zu Berlin} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Humboldt-Universitat zu Berlin}",
title = "Combining environmental and landsat analysis ready data for
vegetation mapping: a case study in the Brazilian Savanna Biome",
journal = "International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences",
year = "2020",
volume = "43",
number = "B3",
pages = "953--960",
month = "Aug.",
note = "2020 24th ISPRS Congress - Technical Commission III; Nice,
Virtual; France; 31 August 2020 through 2 September 2020",
keywords = "Vegetation mapping, Cerrado, Phenology, Data mining, Random
Forest.",
abstract = "The Cerrado biome in Brazil covers approximately 24% of the
country. It is one of the richest and most diverse savannas in the
world, with 23 vegetation types (physiognomies) consisting mostly
of tropical savannas, grasslands, forests and dry forests. It is
considered as one of the global hotspots of biodiversity because
of the high level of endemism and rapid loss of its original
habitat. This work aims to analyze the potential of Landsat
Analysis Ready Data (ARD) in combination with different
environmental data to classify the vegetation in the Cerrado in
two different hierarchical levels. Here we present results of a
pixel-based modelling exercise, in which field data were combined
with a set of input variables using a Random Forest classification
approach. On the first hierarchical level, with the three classes
savanna, grasslands and forest, our model results reached
f1-scores of 0.86, 0.87 and 0.85 leading to an overall accuracy of
0.86. In the second hierarchical level we differentiated a total
of 12 vegetation physiognomies with an overall accuracy of 0.77.",
doi = "10.5194/isprs-archives-XLIII-B3-2020-953-2020",
url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-953-2020",
issn = "0256-1840",
language = "en",
targetfile = "bendini_combining.pdf",
urlaccessdate = "25 abr. 2024"
}