@Article{AdornoKörtAmar:2023:CoTiDa,
author = "Adorno, Bruno Vargas and K{\"o}rting, Thales Sehn and Amaral,
Silvana",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Contribution of time-series data cubes to classify urban
vegetation types by remote sensing",
journal = "Urban Forestry and Urban Greening",
year = "2023",
volume = "79",
pages = "e127817",
month = "Jan.",
keywords = "CBERS-4A WPM, Multisource image analysis, Object-based
classification, Per-pixel classification, Sentinel-2 MSI.",
abstract = "Mapping urban vegetation types is important for urban planning and
assessing environmental justice. Nowadays, despite data cubes
projects are providing Analysis Ready Data to facilitate
time-series analysis, we did not found studies employing these
data for improving urban vegetation mapping. By relying solely on
open data and software, this work proposes and evaluates the
integration of time-series data cubes in a hybrid image
classification method to map the intra-urban space,
differentiating Tree cover and Herb-shrub. The urban area of
Goi{\^a}nia, Goi{\'a}s, Brazil, is the study area. The hybrid
method combined object-based classification of a pan-sharpened
CBERS-4A WPM image (spatial resolution of 2 m) with the
pixel-based classification of Sentinel-2 MSI time-series data
cubes (10 m). Both approaches used the Random Forest algorithm.
Objects from the CBERS-4A segmentation composed the spatial unit
of analysis and the class assignment depended on the Sentinel-2
time-series urban land cover probabilities. Based on both Maps
probabilities, Shannon entropy was calculated to attribute the
final urban land cover to the objects. Urban land cover
probabilities presented similar spatial distribution patterns for
both classification approaches. Regarding the thematic maps, the
Herb-shrub cover area was 35% higher in Sentinel-2 time-series
classification than in GEOBIA classification, but Tree cover was
21% lower. In general, 75% of the study area was equally
classified by the initial approaches. However, for 9% of the
remaining area, the hybrid classification improved vegetation
classes accuracies by 35%, contributing to the vegetation covers
identification. Thus, this study contributes to methodological
procedures for urban land cover study and demonstrates that hybrid
maps based on open data are effective to reduce classification
mistakes, allowing more accurate monitoring, planning, and
designing of different urban vegetation types. Future research
efforts should focus on scale compatibility between data of
different spatial resolutions and expand the use of data cubes to
integrate time-series information into the GEOBIA
classification.",
doi = "10.1016/j.ufug.2022.127817",
url = "http://dx.doi.org/10.1016/j.ufug.2022.127817",
issn = "1618-8667",
language = "en",
targetfile = "1-s2.0-S1618866722003600-main.pdf",
urlaccessdate = "15 jun. 2024"
}