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Abstract:
The use of optical remote sensing data for savanna vegetation structure mapping is
hindered by sparse and heterogeneous distribution of vegetation canopy, leading to near-similar
spectral signatures among lifeforms. An additional challenge to optical sensors is the high cloud
cover and unpredictable weather conditions. Longwave microwave data, with its low sensitivity
to clouds addresses some of these problems, but many space borne studies are still limited by low
quality structural reference data. Terrestrial laser scanning (TLS) derived canopy cover and height
metrics can improve aboveground biomass (AGB) prediction at both plot and landscape level. To date,
few studies have explored the strength of TLS for vegetation structural mapping, and particularly
few focusing on savannas. In this study, we evaluate the potential of high resolution TLS-derived
canopy cover and height metrics to estimate plot-level aboveground biomass, and to extrapolate to
a landscape-wide biomass estimation using multi-temporal L-band Synthetic Aperture Radar (SAR)
within a 9 km2 area savanna in Kruger National Park (KNP). We inventoried 42 field plots in the wet
season and computed AGB for each plot using site-specific allometry. Canopy cover, canopy height,
and their product were regressed with plot-level AGB over the TLS-footprint, while SAR backscatter
was used to model dry season biomass for the years 2007, 2008, 2009, and 2010 for the study area.
The results from model validation showed a significant linear relationship between TLS-derived
predictors with field biomass, p < 0.05 and adjusted R2 ranging between 0.56 for SAR to 0.93 for the
TLS-derived canopy cover and height. Log-transformed AGB yielded lower errors with TLS metrics
compared with non-transformed AGB. An assessment of the backscatter based on root mean square
error (RMSE) showed better AGB prediction with cross-polarized (RMSE = 6.6 t/ha) as opposed to
co-polarized data (RMSE = 6.7 t/ha), attributed to volume scattering of woody vegetation along river
valleys and streams. The AGB change analysis showed 32 ha (3.5%) of the 900 ha experienced AGB
loses above an average of 5 t/ha per annum, which can mainly be attributed to the falling of trees by
mega herbivores such as elephants. The study concludes that SAR data, especially L-band SAR, can
be used in the detection of small changes in savanna vegetation over time.