Analysing the spatial pattern of deforestation and degradation in miombo woodland: methodological issues and practical solutions
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.