Integrating RADAR and optical imagery improve the modelling of carbon stocks in a mopane-dominated African savannah dry forest
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Date
2022-12-25Author
Mpakairi, Kudzai S.
Gara, Tawanda W.
Nampira, Tinotenda C.
Appiah, Joseph Oduro
Muumbe, Tasiyiwa P.
Dube, Timothy
Metadata
Show full item recordAbstract
This study examined the integration of two satellite data sets, that is Landsat 7 ETM+
and ALOS PALSAR (Advanced Land Observing Satellite Phased Array type L-band
Synthetic Aperture RADAR) in estimating carbon stocks in mopane woodlands of
north-western
Zimbabwe. Mopane woodlands cover large spatial extents and provide
ecosystem benefits to the rural economies and grazing resources for both livestock
and wildlife. In this study, artificial neural networks (ANN) were used to estimate
carbon stocks based on spectral metrics derived from Landsat 7 ETM+ and ALOS
PALSAR. To determine the utility of the two satellite-derived
metrics, a two-pronged
modelling framework was adopted. Firstly, we used spectral bands and vegetation indices
from the two satellite data sets independently, and subsequently, we integrated
the metrics from the two satellite data sets into the final model. Results showed that
the ALOS PALSAR (R2 = 0.75 and nRMSE = 0.16) and Landsat ETM+ (R2 = 0.78 and
nRMSE = 0.14) derived spectral bands and vegetation indices comparatively yielded
accurate estimations of carbon stocks. Integrating spectral bands and vegetation
indices from both sensors significantly improved the estimation of carbon stocks
(R2 = 0.84 and nRMSE = 0.12). These findings underscore the importance of integrating
satellite data in vegetation biophysical assessment and monitoring in poorly
documented ecosystems such as the mopane woodlands.