Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data
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Date
2024
Authors
Journal Title
Journal ISSN
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Publisher
Remote Sensing
Abstract
This study utilized a remotely sensed dataset with a high spatial resolution of 3 m to
predict species diversity in the Bobiri Forest Reserve (BFR), a moist semi-deciduous tropical forest
in Ghana. We conducted a field campaign of tree species measurements to achieve this objective
for species diversity estimation. Thirty-five field plots of 50 m × 20 m were established, and the
most dominant tree species within the forest were identified. Other measurements, such as diameter
at breast height (DBH ≥ 5 cm), tree height, and each plot’s GPS coordinates, were recorded. The
following species diversity indices were estimated from the field measurements: Shannon–Wiener
(H′
), Simpson diversity index (D2
), species richness (S), and species evenness (J
′
). The PlanetScope
surface reflectance data at 3 m spatial resolution was acquired and preprocessed for species diversity
prediction. The spectral/pixel information of all bands, except the coastal band, was extracted
for further processing. Vegetation indices (VIs) (NDVI—normalized difference vegetation index,
EVI—enhanced vegetation index, SRI—simple ratio index, SAVI—soil adjusted vegetation index,
and NDRE—normalized difference red edge index) were also calculated from the spectral bands
and their pixel value extracted. A correlation analysis was then performed between the spectral
bands and VIs with the species diversity index. The results showed that spectral bands 6 (red) and
2 (blue) significantly correlated with the two main species diversity indices (S and H′
) due to their
influence on vegetation properties, such as canopy biomass and leaf chlorophyll content. Furthermore,
we conducted a stepwise regression analysis to investigate the most important spectral bands to
consider when estimating species diversity from the PlanetScope satellite data. Like the correlation
results, bands 6 (red) and 2 (blue) were the most important bands to be considered for predicting
species diversity. The model equations from the stepwise regression were used to predict tree species
diversity. Overall, the study’s findings emphasize the relevance of remotely sensed data in assessing
the ecological condition of protected areas, a tool for decision-making in biodiversity conservation.
Description
Research Article
Keywords
Planet Scope data, Shannon diversity, species richness, tropical forest
Citation
Citation: Njomaba, E.; Ofori, J.N.; Guuroh, R.T.; Aikins, B.E.; Nagbija, R.K.; Surový, P. Assessing Forest Species Diversity in Ghana’s Tropical Forest Using PlanetScope Data. Remote Sens. 2024, 16, 463. https://doi.org/10.3390/ rs16030463