Data · collection · 2024
LCC - Mata Atlantica - LC8 30m 16D STK
Listed in NASA Earthdata CMR
This is a land cover classification map of Brazilian Mata Atlantica, from January to December of 2017.
Description
This classification was made on top of Landsat-8 days data cubes with spatial resolution of 30 meters, using the best pixel composition function named as Least Cloud Cover First (LCF), which was previously named Stack in BDC older versions. The input datacube was Landsat-8 - OLI - Cube Stack 16 days - v001, which was deprecated.
The classification model was trained using 13442 sample points of the classes Agriculture 2668, Planted forest 823, Forest (Formação florestal) 3754, Pasture 6197. The spectral bands used were B1, B2, B3, B4, B5, B6, B7, along with the vegetation indices EVI and NDVI; the clouded observation were identified using the Fmask algorithm and estimated using linear interpolation. The classification algorithm was Random Forest.
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The post-processing consisted on cropping the images to the biome's boundary. This product was funded by the Brazilian Development Bank (BNDES).
Links
Get the data
- DescribedBy fedeo-client.ceos.org/?uid=LCC_L8_30_16D_STK_MataAtlantica-1 ↗
download · from NASA CMR
- DescribedBy fedeo.ceos.org/collections/series/items/LCC_L8_30_16D_STK_MataAtlantica-1?htt… ↗
download · from NASA CMR
Catalogue records · 2
- CMR record cmr.earthdata.nasa.gov/search/concepts/C3560374933-INPE.html ↗
catalogue entry · from NASA CMR
- CMR UMM-JSON cmr.earthdata.nasa.gov/search/concepts/C3560374933-INPE.umm_json ↗
metadata API · from NASA CMR
Topics
- Stated by source
- Classification · LANDSAT-8 · Land Use Classes · Land Use/Land Cover Classification · Operational Land Imager · Random Forest
- Inferred from text
- Image 65%
Provenance · 1 source records, 15 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| NASA Earthdata CMR | C3560374933-INPE | 7 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[instrument].gcmd_instrument:oli | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[measured_variable].gcmd:earth-science/human-dimensions/environmental-governance-management/land-management/land-use-land-cover-classification | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[measured_variable].gcmd:earth-science/land-surface/land-use-land-cover/land-use-classes | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[measured_variable].gcmd:earth-science-services/models/machine-learning-models/classification | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[measured_variable].gcmd:earth-science-services/models/machine-learning-models/ensemble-models/random-forest | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · NASA CMR | keyword-concept-rules@1.0.0 | title+description (65%) |
| concepts[platform].gcmd_platform:landsat-8 | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| created_date | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| description | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/Abstract |
| publication_date | source · NASA CMR | connector:nasa_cmr@1.0.0 | |
| spatial | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/SpatialExtent |
| temporal | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/TemporalExtents |
| title | source · NASA CMR | connector:nasa_cmr@1.0.0 | /umm/EntryTitle |
| version_label | source · NASA CMR | connector:nasa_cmr@1.0.0 |