This study assessed the feasibility of spectral mixture analysis (SMA) of Landsat thematic mapper (TM) data for monitoring estuarine vegetation at species level. SMA modelling was evaluated, using 2 test, by comparing SMA fraction images with a precisely classified QuickBird image that has a higher spatial resolution. To clearly understand the strengths and weaknesses of SMA, eight SMA models with different endmember combinations were assessed. When the TM data dimension for SMA and the endmember number required were balanced, a model with three endmembers representing water and two vegetation types was most accurate, whereas a model with five endmembers approximated the actual surface situation and generated a relatively accurate result. Our results indicate that an SMA model with appropriate endmembers had relatively satisfactory accuracy in monitoring vegetation. However, errors might occur in SMA fraction images, especially in models with an inappropriate endmember combination, and the errors were mainly distributed in areas filled with water or near water. Therefore, short vegetation usually submerged during high tide tended to be poorly predicted by SMA models. These results strongly suggest that tide water has a great influence on SMA modelling, especially for short vegetation.
[1]Fudan Univ, Coastal Ecosyst Res Stn Yangtze River Estuary, Key Lab Biodivers Sci & Ecol Engn, Inst Biodivers Sci,Minist Educ, Shanghai 200433, Peoples R China
(8.0+极速模式)