Abstract
We investigated the use of full-range (400–2,500 nm) hyperspectral data obtained by sampling foliar reflectances to discriminate 46 plant species in a tropical wetland in Jamaica. A total of 47 spectral variables, including derivative spectra, spectral vegetation indices, spectral position variables, normalized spectra and spectral absorption features, were used for classifying the 46 species. The Mann–Whitney U-test, paired one-way ANOVA, principal component analysis (PCA), random forest (RF) and a wrapper approach with a support vector machine were used as feature selection methods. Linear discriminant analysis (LDA), an artificial neural network (ANN) and a generalized linear model fitted with elastic net penalties (GLMnet) were then used for species separation. For comparison, the RF classifier (denoted as RFa) was also used to separate the species by using all reflectance spectra and spectral indices, respectively, without applying any feature selection. The RFa classifier was able to achieve 91.8% and 84.8% accuracy with importance-ranked spectral indices and reflectance spectra, respectively. The GLMnet classifier produced the lowest overall accuracies for feature-selected reflectance spectra data (52–77%) when compared with the LDA and ANN methods. However, when feature-selected spectral indices were used, the GLMnet produced overall accuracies ranging from 79 to 88%, which were the highest among the three classifiers that used feature-selected data. A total of 12 species recorded a 100% producer accuracy, but with spectral indices, and an additional 8 species had perfect producer accuracies, regardless of the input features. The results of this study suggest that the GLMnet classifier can be used, particularly on feature-selected spectral indices, to discern vegetation in wetlands. However, it might be more efficient to use RFa without feature-selected variables, especially for spectral indices.
Highlights
Over the last decade, leaf spectral reflectance has been used successfully to discriminate plant species found in various habitat types/ecosystems [1,2,3,4]
The results from our study suggest that classification performance is improved, at least with the artificial neural network (ANN), when bands from different parts of the spectrum are chosen
We found feature selection to be effective and RFa efficient in distinguishing species based on their foliar reflectance, it should be noted that accuracies might decay markedly at coarser spatial scales [9]
Summary
Leaf spectral reflectance has been used successfully to discriminate plant species found in various habitat types/ecosystems [1,2,3,4]. Species differentiation has been achieved with univariate and multivariate approaches, which include parametric and non-parametric analysis of variance [6,7,8], discriminant analysis [9] and classification and regression tree-based techniques [10]. These methods can be used individually or several methods can be combined to achieve hyperspectral feature reduction. Mather and Koch [11]
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