Investigating the relationship between tree species diversity and Landsat-8 spectral heterogeneity across multiple phenological stages

Abstract

The emergence of the spectral variation hypothesis (SVH) has gained widespread attention in the remote sensing community as a method for deriving biodiversity information from remotely sensed data. SVH states that spectral heterogeneity on remotely sensed imagery reflects environmental heterogeneity, which in turn is associated with high species diversity and, therefore, could be useful for characterizing landscape biodiversity. However, the effect of phenology has received relatively less attention despite being an important variable influencing plant species spectral responses. The study investigated (i) the effect of phenology on the relationship between spectral heterogeneity and plant species diversity and (ii) explored spectral angle mapper (SAM), the coefficient of variation (CV) and their interaction effect in estimating species diversity. Stratified random sampling was adopted to survey all tree species with a diameter at breast height of >10 cm in 90 × 90 m plots distributed throughout the study site. Tree species diversity was quantified by the Shannon diversity index (H0 ), Simpson index of diversity (D2 ) and species richness (S). SAM and CV were employed on Landsat-8 data to compute spectral heterogeneity. The study applied linear regression models to investigate the relationship between spectral heterogeneity metrics and species diversity indices across four phenological stages. The results showed that the end of the growing season was the most ideal phenological stage for estimating species diversity, following the SVH concept. During this period, SAM and species diversity indices (S, H0 , D2 ) had an r 2 of 0.14, 0.24, and 0.20, respectively, while CV had an r 2 of 0.22, 0.22, and 0.25, respectively. The interaction of SAM and CV improved the relationship between the spectral data and H0 and D2 (from r 2 of 0.24 and 0.25 to r 2 of 0.32 and 0.28, respectively) at the end of the growing season. The two spectral heterogeneity metrics showed differential sensitivity to components of plant diversity. SAM had a high relationship with H0 followed by D2 and then a lower relationship with S throughout the different phenological stages. Meanwhile, CV had a higher relationship with D2 than other plant diversity indices and its relationship with S and H0 remained similar. Although the coefficient of determination was comparatively low, the relationship between spectral heterogeneity metrics and species diversity indices was statistically significant (p < 0.05) and this supports the assertion that SVH could be implemented to characterize plant species diversity. Importantly, the application of SVH should consider (i) the choice of spectral heterogeneity metric in line with the purpose of the SVH application since these metrics relate to components of species diversity differently and (ii) vegetation phenology, which affects the relationship that spectral heterogeneity has with plant species diversity.