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Vegetation abundance is a critical indicator for measuring the status of vegetation.It is also important for revealing the rules of spatial change and evaluating the eco-environment of wetland.In this paper,vegetation abundance is estimated through a Linear Spectral Mixture Model(LSMM) using Landsat Thematic Mapper(TM) data within the Wild Duck Lake Wetland,a typical freshwater wetland in North China.Considering the biodiversity of wetland,it is necessary to select enough endmembers to characterize the spectral variability of features.However,a maximum of four endmembers is usually used in spectral mixture analysis when using Landsat TM data due to the high correlation coefficients among the three visible bands.Thus,the Normalized Difference Vegetation Index(NDVI),a widely used vegetation index,is included along with the six reflective bands of Landsat TM image,which extended the dimensionality of the TM data and allowed the use of five endmembers.After performed the Minimum Noise Fraction(MNF) transformation and Pixel Purity Index(PPI) algorithm,five endmembers are selected including Terrestrial plants,Aquatic plants,High albedo,Low albedo and Bare soil using n-Dimensional Visualizer Tool of ENVI software.At the same time,a four-endmember model which consists Plants,High albedo,Low albedo and Bare soil was established using the six reflective bands of Landsat TM data.For both four-and five-endmember model,we used a fully constrained LSMM algorithm to obtain the vegetation abundance of the study area.We modified our result using the pure water information of Wild Duck Lake Wetland,which was obtained by using NDVI and Normalized Difference Water Index(NDWI).The accuracy of vegetation abundance is validated by WorldView-2 multispectral image with a 1.8 meter spatial resolution through randomly selected 60 samples.The result showed that although the vegetation abundance extracted by four-and five-endmember model share almost the same spatial distribution,their values vary much from each other.Generally,the five-endmember model results a much higher value than the four-endmember model and it can reflect the vegetation abundance of aquatic plants better than the other.After the analysis of relationship between predicted and inspection values of both four-and five-endmember models,we found that the correlation coefficients are 0.8671 and 0.9023,respectively.Additionally,the Root Mean Square Error(RMSE) clearly showed that the five-endmember model can achieve a much better result than the other model: the former is 0.0939 and the latter is 0.1771.Our results suggest that it is feasible to improve the number of endmembers by extending the dimension of remote sensing image.By using NDVI as an additional data dimension along with the reflective bands of Landsat TM data,a five-endmember model can estimate vegetation abundance of Wild Duck Lake wetland better due to the inclusion of the fifth endmember.Moreover,four endmembers may be inadequate to spectrally characterize the heterogeneous landscapes of wetland.The use of five endmembers may further improve the representation of the spectral variability of land-cover types within the study area and thus potentially improve the accuracy for estimating vegetation abundance of wetlands. |
