Forests cover a significant portion of the earth’s surface and provide a range of services. The services and benefits of forests vary depending on the level of human influence. For example, the services of natural forests and plantation forests are very different for biodiversity as natural forests have high species diversity, while plantations commonly have mono-specific alien tree plantings. In addition, natural forests are very beneficial in balancing the Earth’s carbon cycle through its CO2 absorption, while plantations do not serve as carbon sinks – only absorbing carbon temporarily. Therefore, monitoring and mapping natural vs plantation forests is important for understanding their services for different systems such as biodiversity, ecosystem, terrestrial carbon cycle and livelihood.
Due to their similarity in spectral characteristics between natural forests and forest plantations, identifying forest plantations from natural forest has been a challenging task. In addition, small scale plantations that are for example common in tropical and sub-tropical regions in Africa, makes it further challenging to characterize as compared with large-scale industrial plantations. Different remote sensing data and algorithms have been tested for to characterize forest plantations in the literature, however which sensor(s) and algorithms are best suited for automated and large scale monitoring of forest plantations in tropical and sub-tropical regions? Should we look into temporal characteristics of the forest plantations in addition to the spectral characteristics? Or should we look into spatial detail?
The aim of this MSc thesis research is to address these questions and map forest plantations in a sub-tropical region of Africa. The findings from this work can be useful to other researchers to create global or continental scale forest plantation maps towards better understanding of influence of forest plantations in different systems of the Earth. If you are interested in forestry, land-use, forest managements, automatization, machine learning and cloud computing, as well as have some experience in some of these, you can contact us for further details.
Theme(s): Sensing & measuring, Integrated Land Monitoring