Tree species diversity estimation using airborne imaging spectroscopy

With the ongoing global biodiversity loss, approaches to measuring and monitoring biodiversity are necessary for effective conservation planning, especially in tropical forests. Remote sensing is a very potential tool for biodiversity mapping, and high spatial resolution imaging spectroscopy allows for direct estimation of tree species diversity based on spectral reflectance. The objective of this study is to test an approach for estimating tree species alpha diversity in a tropical montane forest in the Taita Hills, Kenya. Tree species diversity is estimated based on spectral variation of high spatial resolution imaging spectroscopy data. The approach is an unsupervised classification, or clustering, applied to objects that represent tree crowns. Airborne imaging spectroscopy data and species data from 31 field plots were collected from the study area. After preprocessing of the spectroscopic imagery, a minimum noise fraction (MNF) transformation with a subsequent selection of 13 bands was applied to the data to reduce its noise and dimensionality. The imagery was then segmented to obtain objects that represent tree crowns. A clustering algorithm was applied to the segments, with the aim of grouping spectrally similar tree crowns. Experiments were made to find the optimal range for the number of clusters. Tree species richness and two diversity indices were calculated from the field data and from the clustering results. The clusters were assumed to represent species in the calculations. Correlation analysis and linear regression analysis were used to study the relationship between diversity measures from the field data and from the clustering results. It was found that the approach succeeded well in revealing tree species diversity patterns with all three diversity measures. Despite some factors that added error to the relationship between field-derived and clustering-derived diversity measures, high correlations were observed. Especially tree species richness could be modelled well using the approach (standard error: 3 species). The size of the considered trees was found to be an important determinant of the relationships. Finally, a tree species richness map was created for the study area. With further development, the presented approach has potential for other interesting applications, such as estimation of beta diversity, and tree species identification by linking the reflectance properties of individual crowns to their corresponding species

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