Although much emphasis has been given to the analysis of continuous forest
conversion in tropical regions, our understanding in detecting, mapping and
interpreting the spatial pattern of woodland deforestation and degradation is still
limited. This thesis focuses on two factors contributing to this limitation: uncertainties
in retrieving woodland change from remote sensing imagery, and the complex
processes that may cause woodland deforestation and degradation. Firstly, I investigate
approaches to minimising uncertainty in ALOS PALSAR-derived biomass maps by
modifying a widely used processing chain, with the aim of provide recommendations
for producing radar-based biomass maps with reduced uncertainty. Secondly, to further
improve the retrieval of woody biomass from ALOS PALSAR imagery, the semi-empirical
Water Cloud Model (WCM) is introduced to account for backscattering from
soil. In wooded areas with low canopy (such as the miombo woodland which
dominates the study area) the effect from soil moisture on the received backscattered
signal is critical. Thirdly, based on the biomass maps retrieved from the refined radar-remote-sensing-based methodology discussed above, the influence of driving variables
of the woodland deforestation and degradation, and how they alter the spatial patterns
of these two processes, are analysed. The threshold for defining woodland
deforestation and degradation in terms of biomass loss intensity is generated through
integration of radar-based biomass loss maps, an optical forest cover change map and
fieldwork investigation. Multi-linear model simulations of the spatial variation of
deforestation and degradation events were constructed at a district and 1 km resolution
respectively to rank the relative importance of driving variables.
Results suggest that biomass-backscatter relationships based on plots of approximately
1 ha, and processed with high resolution DEMs, are needed for low uncertainty
biomass maps using ALOS PALSAR data. Although plots sizes of 0.1 - 0.5 ha lead to
large uncertainties, aggregating 0.1 ha plots into larger calibration sites shows some
promise even in hilly terrain, potentially opening up the use of common forest
inventory data to calibrate remote-sensing-based biomass retrieval models. Such
relationships appear to hold across the miombo woodland ecoregion, which implies
that there is a consistent relationship at least in the miombo woodland. From this I infer
that random error, different processing methods and fitting techniques, and data from
small plots are the source of the differences in the savanna biomass-backscatter
relationships seen in the literature.
The interpreted WCM presented in this study for L-band backscatter at HV
polarisation improves biomass retrieval for areas with a biomass value less than 15
tC/ha (or 0.025 m2/m2 in backscatter). Use of the WCM also results in better quality
regional biomass mosaics. This is because the WCM helped to improve the correlation
of biomass estimation for overlay areas by reducing bias between adjacent paths,
especially the bias introduced by changes in soil moisture conditions between different
acquisition dates for different paths. Result shows that active and combined soil
moisture datasets (from the Climate Change Initiative Soil Moisture Dataset) can be
used as effective soil moisture proxies in the WCM for biomass retrieval.
This quantitative analysis on the driving variables of woodland deforestation and
degradation suggests that large uncertainty exists in modelling the occurrence of
deforestation and degradation, especially at a 1 km scale. The spatial patterns of
woodland deforestation and degradation differ in terms of shape, size, intensity, and
location. Agriculture-related driving variables account for most of the explained
variance in deforestation, whereas for degradation, distance to settlements also plays
an important role. Deforestation happens regardless of the original biomass levels,
while degradation is likely to happen at high biomass areas. The sizes of degradation
events are significantly smaller than those of deforestation events, with 90% of
deforestation events sharing boundaries with degradation events.
This thesis concludes by outlining the importance and difficulties in integrating ‘distal’
(underlying) drivers in modelling the spatial dynamics of deforestation and
degradation. Further work on the causal connection between deforestation and
degradation is also needed. The processing chain and biomass retrieval models
presented in this study could be used to support monitoring and analysis of biomass
change elsewhere in the tropics, and should be compatible with data derived from
ALOS-2 and the future SAOCOM and BIOMASS satellite missions