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…