Abstract:
Rangelands play a vital role in developing
countries’ biodiversity conservation and economic
growth, since most people depend on rangelands for
their livelihood. Aboveground-biomass (AGB) is an
ecological indicator of the health and productivity of
rangeland and provides an estimate of the amount of
carbon stored in the vegetation. Thus, monitoring seasonal
AGB is important for understanding and managing
rangelands’ status and resilience. This study
assesses the impact of seasonal dynamics and fire
on biophysical parameters using Sentinel-1 (S1) and
Sentinel-2 (S2) image data in the mesic rangeland of
Limpopo, South Africa. Six sites were selected (3/
area), with homogenous vegetation (10 plots/site of
30m2).
The seasonal measurements of LAI and biomass
were undertaken in the early summer (December
2020), winter (July–August 2021), and late
summer (March 2022). Two regression approaches,
random forest (RF) and stepwise multiple linear
regression (SMLR), were used to estimate seasonal
AGB. The results show a significant difference (p <
0.05) in AGB seasonal distribution and occurrence
between the fire (ranging from 0.26 to 0.39 kg/m2)
and non-fire areas (0.24–0.35 kg/m2). In addition,
the seasonal predictive models derived from random
forest regression (RF) are fit to predict disturbance
and seasonal variations in mesic tropical rangelands.
The S1 variables were excluded from all models due
to high moisture content. Hence, this study analyzed
the time series to evaluate the correlation between
seasonal estimated and field AGB in mesic tropical
rangelands. A significant correlation between
backscattering, AGB and ecological parameters was
observed. Therefore, using S1 and S2 data provides
sufficient data to obtain the seasonal changes of biophysical
parameters in mesic tropical rangelands after
disturbance (fire) and enhanced assessments of critical
phenology stages.
