Assessment sg detection by remote sensing


Published on

Published in: Education, Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Assessment sg detection by remote sensing

  1. 1. Adapted from: Dekker et al. (2005) & Gullstrom et al. (2006) Presented by: Fiddy Semba Prasetiya
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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