Abstract:
Structural parameters of the woody component in African savannahs provide estimates of carbon stocks
that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of
households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component
can be characterised by various quantifiable woody structural parameters, such as tree cover, tree
height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast
to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense
the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species
diversity and phenological status – a defining but challenging set of characteristics typical of African
savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic
Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody
component because of their ability to sense within-canopy properties of the vegetation and its insensitivity
to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s
structure can be sensed differently with SAR depending on the frequency or wavelength of the
sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random
Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total
canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band
(RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from
airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency
was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2
of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and
C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths
also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g.
dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration
of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics
(R2 = 0.83 for CC and AGB and R2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison
to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency
datasets for tree structure monitoring in this environment.
