Woody biomass dynamics are an expression of ecosystem function, yet biomass estimates
do not provide information on the spatial distribution of woody vegetation within the vertical
vegetation subcanopy. We demonstrate the ability of airborne light detection and ranging
(LiDAR) to measure aboveground biomass and subcanopy structure, as an explanatory
tool to unravel vegetation dynamics in structurally heterogeneous landscapes. We sampled
three communal rangelands in Bushbuckridge, South Africa, utilised by rural communities
for fuelwood harvesting. Woody biomass estimates ranged between 9 Mg ha-1 on gabbro
geology sites to 27 Mg ha-1 on granitic geology sites. Despite predictions of woodland depletion
due to unsustainable fuelwood extraction in previous studies, biomass in all the communal
rangelands increased between 2008 and 2012. Annual biomass productivity
estimates (10–14% p.a.) were higher than previous estimates of 4% and likely a significant
contributor to the previous underestimations of modelled biomass supply. We show that biomass
increases are attributable to growth of vegetation <5 m in height, and that, in the high
wood extraction rangeland, 79% of the changes in the vertical vegetation subcanopy are
gains in the 1-3m height class. The higher the wood extraction pressure on the rangelands,
the greater the biomass increases in the low height classes within the subcanopy, likely a
strong resprouting response to intensive harvesting. Yet, fuelwood shortages are still occurring,
as evidenced by the losses in the tall tree height class in the high extraction rangeland. Loss of large trees and gain in subcanopy shrubs could result in a structurally simple landscape
with reduced functional capacity. This research demonstrates that intensive harvesting
can, paradoxically, increase biomass and this has implications for the sustainability of ecosystem service provision. The structural implications of biomass increases in communal
rangelands could be misinterpreted as woodland recovery in the absence of three-dimensional,
S1 Dataset. Biomass model data. Data include 2012 LiDAR-derived average height and canopy
cover extraction metrics, as well as field-work based allometry. Each line item is per 25 m x
25 m grid cell. Metadata are included in the dataset.
S2 Dataset. Biomass and subcanopy data. Data include 2008 and 2012 biomass estimates derived
from biomass models as well as % subcanopy returns for voxel data for the height class
categories: 1-3m, 3-5m, 5-10m and >10m. Each line item is per 25 m x 25 m grid cell. Data are
organized per land extraction category into separate worksheets. Metadata are included in
S3 Dataset. Biomass changes (Mg ha-1) in relation to relative height and canopy cover
change. Data include biomass change estimates (2008–2012), percentage height and canopy
cover changes for each 25 m x 25 m grid cell. Each height class (relative to height in 2008) are
shown on separate worksheets. Metadata are included in the dataset.
S1 Fig. Site-specific biomass model residuals. The residual spread demonstrates heteroskedasticity
with increasing biomass fitted values for rangelands with a) high, b) intermediate and
c) low extraction pressure.
S2 Fig. Biomass changes (%) relative to height-specific change in subcanopy returns (%).
Height categories are: 1–3 m, 3–5 m, 5–10 m and >10 m.