基于光谱特征变量的湿地典型植物生态类型识别方法

Abstract:

Certain spectral characteristic variables have a direct impact to the accuracy and efficiency of identifying the wetland vegetation. The Wild Duck Lake, a typical freshwater wetland of North China, was selected as the study area of this research. The canopy hyperspectral reflectances of typical wetland vegetation were measured by Field-Spec 3 high-spectrum radiometer. In this research, a total of seven vegetation covers were measured. The reflectance spectrum of submerged vegetation species was special because of the influence of water body, suspended solid in water, etc. The reflectance spectra of the other six vegetation covers were similar. But different vegetation cover had different feature, such as plant morphology, water content and chlorophyll content so that they had their own spectral reflectance characteristics. Thus, based on the canopy spectral reflectance, first-derivative and continuum removal were applied to analyze and contrast spectral features of different vegetation types. The spectral characteristic variables were selected for identifying plant ecological. The research established eight distinct spectral characteristic variables (red edge position WP_r, red edge amplitude Dr, green peak position WP_g, green peak amplitude Rg, absorption depth around 510 nm and 675 nm, absorption area around 510 nm and 675 nm) that have importance for mapping vegetation cover types in the Wild Duck Lake wetland. In addition, the absorption features of seven vegetation types were larger differences than first-derivative spectral features. Excepting WP_r and WP_g, the average of Rg and Dr of submerged plant were minimum, the average of Rg of hygrophilous plant was maximum (0.164) and the average of Dr of cultivated plant was maximum (0.012). The DEP-675 and AREA-675 around 675 nm of seven vegetation types were higher than the DEP-510 and AREA-510 around 510 nm. Absorption depth and absorption area of other six vegetation types had shown fall after rise along with the water environment gradient change except cultivated plant. Then we made use of single factor analysis of variance (One-way ANOVA) to verify the discrimination of the spectral characteristic variables. When the confidence level was less than or equal to 0.01, the selected spectral characteristic variables could distinguish seven plant ecological types better with a minimum discrimination of 13 and a maximum of 18. Moreover, the discrimination of absorption characteristic parameters was better than first-derivative parameters. Finally the nonlinear back propagation artificial neural network (BP-ANN) and fisher linear discriminant analysis (FLDA) were applied to identify the wetland vegetation types and the selected spectral characteristic variables were also included in. According to accuracy test, the results of overall accuracy of two methods were 85.5% and 87.98%, respectively. The single factor analysis of variance (One-way ANOVA) and the classification accuracy of different classifiers indicated that the selected eight spectral characteristic variables had great applicability and reliability. The results of this paper would not only provide a scientific base for hyperspectral remote sensing image processing and wetland vegetation mapping in the Wild Duck Lake, but also supply reference for identifying and classification of freshwater wetland vegetation applying remote sensing technology. In future research, we should increase sample numbers and refine the research objectives. Applying the results of this research to hyperspectral image interpretations, we can fully explore the potential and advantage of hyperspectral remote sensing technology.