1. Adapted from: Dekker et al. (2005) & Gullstrom et al. (2006)
Presented by:
Fiddy Semba Prasetiya
2. Introduction
Seagrass as one of the important
coastal resources:
- Highly productive ecosystem
- Important physical and
ecological function
Threats on seagrass ecosystem:
- Natural disaster
- Anthrophogenic pressure
Monitoring is needed..
Remote sensing as an option
3. 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
4. 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 1972
4
68 m x 80 m
185 km
16-18 days
917 km
99.2°
1984
7
30 m x 30 m
185 km
16 days
705 km
98.2°
1999
7
30 m x 30 m
185 km
16 days (233orbit)
705 km
98.2°
Objective:
To investigate the possibility of using satellite remote
sensing technique for assessment spatial and temporal
dynamics of Submerged Aquatic Vegetation (SAV)
5. 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
6. Methodology
Measuring 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
7. Changes in seagrass cover in Wallis lake
Changes in substrate cover from
1988-2002 for Zostera, Posidonia
and Ruppia/Halopila
= loss = gain = no change
8. 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
9. Conclussions
Remote sensing can be used as an effective and
cost efficient monitoring tools:
Future trends
Good resolution and accuracy (up to 70%)
More objective and repeatable
10. 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