Remote Sensing of Woodland Structure and Composition in the Sudano-Sahelian zone : Application of WorldView-2 and Landsat 8

Woodlands constitute the subsistence base of the majority of people in the Sudano-Sahelian zone (SSZ). Trees and grasses provide key ecosystem goods and services, including soil protection, fuelwood, food products and fodder. However, climate change in combination with rapidly increasing populations and altered land use practices put increasing pressure on the vegetation cover in this region. Low availability of in situ data on vegetation structure and composition hampers research and monitoring of this essential resource. Satellite and aerial remote sensing represents important alternative data sources in this context. The main advantages of remote sensing are that information can be collected with high frequency over large geographical areas at relatively low costs. This thesis explores the utility of remote sensing for mapping and analysing vegetation, primarily trees, in the SSZ. A comprehensive literature review was first conducted to describe how the application of remote sensing for analysing vegetation has developed in the SSZ between 1975 and 2014, and to identify important research gaps. Based on the gaps identified in the literature review, the capabilities of two new satellite sensors (WorldView-2 and Landsat 8) for mapping woodland structure and composition were tested in an agroforestry landscape located in central Burkina Faso. The tree attributes in focus included tree crown area (m), tree species, tree canopy cover (%) and aboveground biomass (tons ha). The data processing methods encompassed objectbased image analysis for tree crown delineation, and use of the Random Forest algorithm for tree species classification (WorldView-2) and estimation of tree canopy cover and aboveground biomass (Landsat 8). The literature review revealed that the use of remote sensing has increased extensively in the SSZ, especially since 2010. Remote sensing is increasingly used by diverse scientific disciplines although the contribution from African authors remains relatively low. The main application area has been to analyze changes in vegetation productivity and broad vegetation types, while relatively few studies have used remote sensing to map tree attributes at a higher level of detail, and to analyze interactions between the vegetation cover and environmental factors. This thesis shows that the WorldView-2 satellite represents a useful data source for mapping individual tree attributes, including tree crown area and tree species. The individual tree crown delineation achieved promising results: 85.4% of the reference trees were detected in the WorldView-2 data and tree crown area was estimated with an average error of 45.6%. Both detection and delineation accuracy was influenced by tree size, the degree of crown closure and the composition of the undergrowth vegetation. In addition, WorldView-2 data produced high classification accuracies for five locally important tree species, which are common throughout the SSZ. The highest overall classification accuracy (82.4%) was produced using multi-temporal WorldView-2 data. The dry season is recommended over the wet season for WorldView-2 data acquisition when collection of multi-temporal data is not feasible. Landsat 8 data proved more suitable for mapping tree canopy cover as compared to aboveground biomass in the woodland landscape. Tree canopy cover and aboveground biomass was predicted with 41% and 66% root mean square error, respectively, at pixel level. The most accurate predictions were achieved when spectral, texture and phenology variables derived from Landsat 8 data were combined, which indicates that these three domains contribute complementary information about the tree cover. This thesis demonstrates the potential of easily accessible data from two satellite systems for mapping important tree attributes in woodland areas and discusses how the usefulness of remote sensing for analyzing vegetation can be further enhanced in the SSZ.