Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images
We integrated hyperspectral and field-measured chlorophyll-a (Chl-a) data from the Kristalbad constructed wetland in the Netherlands. We developed a best-fit band ratio empirical algorithm to generate a distribution map of Chl-a concentration (C chla) from SPOT 6 imagery. The C...
Guardado en:
| Autores principales: | , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
De Gruyter
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/84019df85bb24f4eb9734a0e27e7c230 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | We integrated hyperspectral and field-measured chlorophyll-a (Chl-a) data from the Kristalbad constructed wetland in the Netherlands. We developed a best-fit band ratio empirical algorithm to generate a distribution map of Chl-a concentration (C
chla) from SPOT 6 imagery. The C
chla retrieved from remote sensing was compared with a water quality model established for a wetland pond system. The retrieved satellite results were combined with a water quality model to simulate and predict the changes in phytoplankton levels. The regression model provides good retrievals for Chl-a. The imagery-derived C
chla performed well in calibrating the simulation results. For each pond, the modeled C
chla showed a range of values similar to the Chl-a data derived from SPOT 6 imagery (10–25 mg m−3). The imagery-derived and prediction model results could be used as the guiding analytical tools to provide information covering an entire study area and to inform policies. |
|---|