As we move towards a digital based society, technology continues to improve. It is important to take advantage of this to inform and facilitate our sustainable development goals in the most cost-effective and time efficient manner. By utilising the best available technologies, not only can time savings be achieved, but scope of works can be dramatically increased, particularly with ecological data collection. This study will focus on collecting ecological data (tree species) using developing modern technologies (satellites) with the aim of reaching classification accuracies comparable with ground truthed (real life) records. The study area is in central Burkina Faso approximately 30km south of the capital and is generally described as an agroforestry parklands area. The region suffers greatly from poverty and many people are heavily dependent on the agricultural sector and subsistence farming. As these agroforestry parklands are so critical to many people’s livelihoods, it is important to assess the natural resources available within them to provide the best food security management for the people. Tree species locations were overlayed on two satellite images acquired during different stages of the annual growing periods in the agroforestry parklands of the study area. From these images, segmentation of individual tree crowns was done manually and used as the reference data for an object-based classification model, which were assessed for the classification accuracies that can be achieved. Three satellite image scenarios were assessed for classification accuracy, including two single image scenarios and a multi-imagery dataset combining both images. Results indicate that combined images perform the best in terms of overall classification accuracies, closely followed by the end of the wet season growing period. The image acquisition from the end of the dry season was quite poor in comparison, having an overall classification accuracy more than 10% lower than the other scenarios. Of the focus species assessed in this study, Azadirachta Indica was the clear loser in terms of the number of correctly classified individuals from each model scenario. All other focus species were relatively well classified achieving close to or above 60% accuracies in the multi-imagery classification scenario.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-166250 |
| Date | January 2020 |
| Creators | Lunn, Simon |
| Publisher | Linköpings universitet, Institutionen för tema |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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