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Data · collection · 2024

LCC - Bahia - S2 10m 1M STK

Listed in NASA Earthdata CMR

This land cover classification refers to a study area in Bahia state, in the Cerrado biome.

Description

For this map, the Sentinel-2 monthly data cube was used, with a spatial resolution of 10 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. This experiment uses the time series of an agricultural calendar year, from September 2018 to August 2019, extracted from the Sentinel-2 data cube.

The classification was made using 922 samples (Pasture: 258; Agriculture: 242; Natural Vegetation: 422). The spectral band used were B01, B02, B03, B04, B05, B06, B07, B08, B8A, B11, and B12 along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask4 algorithm and estimated using linear interpolation. We trained a multi-layer perceptron for a deep learning classification network to classify the data cube using sits R package.

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Validation was done using good practice guidelines by Olofsson. The validation was done independently for each map using the PRODES Cerrado data of 2019. This data obtained overall accuracy (OA) 0.87.

For more information see the paper <a href="mdpi.com/2072-4292/12/24/4033" target="_blank">Earth Observation Data Cubes for Brazil: Requirements, Methodology and Products.</a> This product was funded by the Brazilian Development Bank (BNDES).

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Inferred from text
Satellite remote sensing 65%
Provenance · 1 source records, 16 field assertions
SourceKeyLast seenRaw
NASA Earthdata CMRC3560374496-INPE7 d agoJSON v1
FieldAssertionExtractorEvidence
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concepts[measured_variable].gcmd:earth-science/human-dimensions/environmental-governance-management/land-management/land-use-land-cover-classificationsource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[measured_variable].gcmd:earth-science/land-surface/land-use-land-cover/land-use-classessource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[measured_variable].gcmd:earth-science-services/models/machine-learning-models/classificationsource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[measured_variable].gcmd:earth-science/spectral-engineering/visible-wavelengthssource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[modality].local:modality:remote-sensingenrichment · NASA CMRkeyword-concept-rules@1.0.0title+description (65%)
concepts[platform].gcmd_platform:sentinel-2asource · NASA CMRconnector:nasa_cmr@1.0.0
concepts[platform].gcmd_platform:sentinel-2bsource · NASA CMRconnector:nasa_cmr@1.0.0
created_datesource · NASA CMRconnector:nasa_cmr@1.0.0
descriptionsource · NASA CMRconnector:nasa_cmr@1.0.0/umm/Abstract
publication_datesource · NASA CMRconnector:nasa_cmr@1.0.0
spatialsource · NASA CMRconnector:nasa_cmr@1.0.0/umm/SpatialExtent
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version_labelsource · NASA CMRconnector:nasa_cmr@1.0.0