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