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Adapted from: Dekker et al. (2005) & Gullstrom et al. (2006)
Presented by: Fiddy Semba Prasetiya
Introduction
Seagrass as one of the important coastal resources: - Highly productive ecosystem- Important physical and
ecological functionThreats on seagrass ecosystem:
- Natural disaster- Anthrophogenic pressure
Monitoring is needed..Remote sensing as an option
Remote sensing on seagrass
Why remote sensing: Cover large area Better spectral resolution Cost effective
Basic principle in remote sensing on seagrass habitat
Potential difficulties: Resolution and patchiness Attenuation by pure water Spectral scattering and
absorption by phytoplankton, SOM/SiOM, DOM
Satellite used in Seagrass mapping
Characteristic/Satellite Landsat MSS (1-3) Landsat TM (5) Landsat ETM (7)
• Operational date• Band• Spatial resolution• Swath width• Repeat coverage interval• Altitude• Inclination
Since 1972468 m x 80 m185 km16-18 days917 km99.2°
1984730 m x 30 m185 km16 days705 km98.2°
1999730 m x 30 m185 km16 days (233orbit)705 km98.2°
Objective:To investigate the possibility of using satellite remote sensing technique for assessment spatial and temporal dynamics of Submerged Aquatic Vegetation (SAV)
Case study: Wallis lake & Chwaka bay
Benthic substrate classification/Submerged Aquatic Vegetation (SAV) using Landsat 5&7: Change detection analysis done
(1988-2003) using archived Landsat data
Chwaka bay Wallis lake
MethodologyMeasuring the spectral
characterization of seagrass and macroalgae species, focusing on: Estimating the optical properties of
water column by profiling downwelling&upwelling irradiance by RAMSES spectroradiometer
Estimating the optical properties of substrate vegetation (also by RAMSES spectroradiometer )
Measuring the spectral characterization of waters: In situ samples for
spectrophotometric measurement of the phytoplankton and CDOM absorption
Changes in seagrass cover in Wallis lakeChanges in substrate cover from
1988-2002 for Zostera, Posidonia and Ruppia/Halopila
= loss = gain = no change
Changes SAV in Chwaka bay
Changes in SAV distribution between 1987-2003
Colours represent change and unchanged areas:Bare sediment to SAV (yellow)SAV to bare sediment (orange)Unchanged SAV (green)Unchanged bare sediment (brown)
Positive correlation between pairs of images in different years
Conclussions
Remote sensing can be used as an effective and cost efficient monitoring tools: Future trendsGood resolution and accuracy (up to 70%)More objective and repeatable
Challenges
Advance techniques in discriminating seagrass species and macroalgae
Satellite sensor data with higher spatial resolution, better signal to noise ratio
Enhancement on multispectral and hyperspectral data
Higher radiometric sensitivity of Landsat sensor for better accuracy (at ´pixel to pixel´ instead of at group pixel scale)
Monitoring on water quality recomended
Thank youThank you for your attention, questions are always welcome… for your attention, questions are always welcome…