Data · dataset · 2018
Using Synergy between Water Limnology and Satellite Imagery to Identify Algal Blooms Extent in a Brazilian Amazonian Reservoir
Listed in DANS Data Station Physical and Technical Sciences
Monitoring algal blooms from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms.
Description
Cyanobacteria are strategic organisms adapted to a wide variety of environmental conditions. In high concentrations, they form scum on the water surface, which is a concern for public health due to the production of toxins, as well as being a nuisance.
Knowledge of the ecological role of these organisms is, therefore, essential when trying to estimate their extent from satellite-based data. We present a multidisciplinary approach, based on both the ecological and the optical perspective. This approach is applied in a Brazilian Amazonian reservoir using spatial and temporal scales.
Links
Where it is published
- Dataverse dataset page phys-techsciences.datastations.nl/dataset.xhtml?persistentId=doi%3A10.17026%2FDANS-ZSM-N3S6 ↗
landing page · from phys techsciences datastations nl
- DOI doi.org/10.17026/dans-zsm-n3s6 ↗
DOI / persistent id · from phys techsciences datastations nl
Catalogue records · 1
- Dataverse API phys-techsciences.datastations.nl/api/datasets/:persistentId/?persistentId=doi%3A10.17026%2FDANS… ↗
metadata API · from phys techsciences datastations nl
Topics
- Stated by source
- Earth and Environmental Sciences · Medicine, Health and Life Sciences
- From keywords
- Earth & Environmental Science · Life Sciences · Medicine & Health
- Inferred from text
- Satellite remote sensing 65%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DANS Data Station Physical and Technical Sciences | doi:10.17026/DANS-ZSM-N3S6 | 10 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].dataverse_subject:medicine-health-and-life-sciences | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:medicine-health | mapping · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[modality].local:modality:remote-sensing | enrichment · phys techsciences datastations nl | keyword-concept-rules@1.0.0 | title+description (65%) |
| created_date | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | |
| description | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /description |
| publication_date | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | |
| title | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /name |
| updated_date | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | |
| version_label | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 |