Informing reforestation practices : quantifying live forest above ground biomass of a randomly mixed natural forest plantation using GIS and remote sensing models.
Hlatshwayo, Sizwe Thamsanqa.
MetadataShow full item record
Restoration of natural forests is viewed as one of the effective and viable approaches for mitigating and adapting to climate change. However, maximising the carbon capture and storage of naturally mixed forest plantations is currently a challenge for forest managers, due to the complex nature of species interaction and environmental controls that inhibit the distribution and growth rates of certain species. Monitoring the amount of carbon captured and stored in natural forest ecosystem is vital in verifying their productivity and detecting areas of concern that could be unproductive. In this study the productivity of the Buffelsdraai reforestation site was monitored using above ground biomass (AGB) of planted trees. While there are traditional approaches for monitoring forest AGB with high accuracy, these approaches are unfavourable because they are timeous and spatially restricted. Fortunately, the inception of remote sensing has provided viable approaches for estimating forest AGB at a synoptic scale and with low cost. The purpose of this study was to apply remote sensing and GIS models to quantify the ecological benefits of the Buffelsdraai reforestation project on AGB productivity. The study investigated the potential of the spatially optimised three band texture combinations in predicting and mapping forest AGB and structural diversity. This research study has potential to contribute to the importance of spatial planning and design of naturally mixed forest plantations to improve their diversity and AGB productivity. The first part of the study focused on mapping the temporal and spatial distribution of forest AGB using spatially optimised three band texture combinations computed from SPOT-6 imagery and random forest regression algorithm. The results indicated that the three band texture combinations were superior in predicting forest AGB compared to raw texture bands and two band texture combinations. The second part of the thesis focussed on assessing the effects of forest structural diversity and topographic variables on forest AGB productivity using GIS and remotely sensed data. The forest structural diversity measures were predicted using three band texture combinations modelled using random forest and stochastic gradient boosting algorithms. The topographic variables were derived using the digital elevation model in ArcMap 10.3. Results indicated that random forest yielded overall higher accuracies in predicting the forest structural diversity measures compared to stochastic gradient boosting. More importantly, the study showed that forest diversity and topographic variables have significant influences on forest AGB variability. Overall the study provided insight into the management of natural forests and to the importance of spatial planning and design of these mixed forests.