@Article{LechlerPiSoSaChVe:2020:ExNaHa,
author = "Lechler, S. and Picoli, Michelle Cristina Ara{\'u}jo and Soares,
Anderson Reis and Sanchez Ipia, Alber Hamersson and Chaves, Michel
Eust{\'a}quio Dantas and Vertegen, J.",
affiliation = "{University of Munster} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University
of Munster}",
title = "Exploring nasa's harmonized landsat and sentinel-2 (HLS) dataset
to monitor deforestation in the amazon rainforest",
journal = "International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences",
year = "2020",
volume = "43",
number = "B3",
pages = "705--711",
month = "Aug.",
note = "2020 24th ISPRS Congress - Technical Commission III; Nice,
Virtual; France; 31 August 2020 through 2 September 2020",
keywords = "HLS dataset, BFAST monitor, Random Forest, Brazilian Legal Amazon,
Deforestation.",
abstract = "Deforestation is a threat to biodiversity and the worlds climate.
As agriculture and mining areas grow, forest loss becomes
unbearable for the environment. Consequently, monitoring
deforestation is crucial for decision makers to create polices.
The most reliable deforestation data about the Amazon forest is
generated by the Brazils National Institute for Space Research
(INPE) through its PRODES project. This effort is labor and time
intensive because it depends on visual interpretation from
experts. Additionally, frequent Amazons atmospheric phenomena,
such as clouds, difficult image analysis which induces alternative
approaches such as time series analysis. One way to increase the
number of images of an area consists of using images from
different satellites. NASA provides the Harmonized Landsat and
Sentinel-2 (HLS) dataset solving spectral dissimilarities of
satellite sensors. In this paper, the possibilities of HLS for
forest monitoring are explored by applying two deforestation
detection methods, Break Detection for Additive Season and Trend
(BFAST) monitor and Random Forest, over four different vegetation
indices, NDVI, EVI, GEMI and SAVI. The SAVI index used as input
for BFAST monitor performed the best in this data setup with
95.23% for deforested pixel, 53.69% for non-deforested pixels.
Although the HLS data is described as analysis ready, further
pre-processing can enhance the outcome of the analysis.
Especially, since the cloud and cirrus cover in the Amazon causes
gaps in the dataset, a best pixel method is recommended to create
patched images and thus a continuous time series as input for any
land cover and land use classification.",
doi = "10.5194/isprs-archives-XLIII-B3-2020-705-2020",
url = "http://dx.doi.org/10.5194/isprs-archives-XLIII-B3-2020-705-2020",
issn = "0256-1840",
language = "en",
targetfile = "lechler_exploring.pdf",
urlaccessdate = "21 maio 2024"
}