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In systems with strong seasonal difference in vegetation structure and appearance, multi-temporal imagery can be particularly useful for community- and species-level discrimination. And, since the availability of past data for one source of time series images may be limited, so we need to develop multi-temporal and multi-source method for wetland ecosystem monitoring. To perform this type of analysis, the image spectral characteristics comparison between different aquatic macrophytes and different sensors should be studied firstly. We used TM images, Beijing-1 images and HJ-1 images for this analysis and based on the determination of aquatic plant functional types (PFTs). The objectives of this study were: (1) single-sensor single-date aquatic PFT analysis; (2) multi-source single-date diagnostic spectral characteristics analysis and comparison for different aquatic PFTs; (3) multi-source multitemporal diagnostic spectral characteristics analysis for different aquatic PFTs. From this analysis we found that: (1) For the single-date TM data, the diagnostic spectral band and indexes are Band 2, 4, 5, NDVI, and MNDWI; the best temporal for discriminating different Nonpersistent Emergent Wetland PFTs are in low water level periods, and water infilling and subsiding periods for seasonal submerged and floating aquatic macrophyte. Multi-spectral Decision Tree classification method lead the more good results for most of PFTs; (2) the same type of aquatic PFTs have similar and comparable reflectance characteristics between multi-sensor optical data which could satisfy the time series analysis by compensating more available past images; (3) phenological curves and relative canopy moisture curves extracted from time series remote sensing images provide important information for distinguish different PFTs.
Geoinformatics, 2010 18th International Conference on
Date of Conference: 18-20 June 2010