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.