Assessment sg detection by remote sensing
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This document discusses using remote sensing to monitor seagrass ecosystems. Seagrass provides important coastal ecosystem functions but faces threats from natural disasters and human activities. Remote sensing allows monitoring of large areas in a cost-effective manner, though resolution and water conditions can pose difficulties. The document analyzes Landsat satellite imagery from 1988-2003 to detect changes in seagrass cover in Wallis Lake and Chwaka Bay in Australia, finding both losses and gains. While remote sensing shows potential for seagrass monitoring, challenges remain in species discrimination, sensor resolution, and accounting for water quality.
Assessment sg detection by remote sensing
- 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