Mapping cryptic invaders and invasibility of tropical forest ecosystems: Chromolaena odorata in Nepal

This thesis explores the different methods used to map the distribution of Chromolaena odorata in the tropical forests of Nepal. Chapter 1 provides a general introduction on invasion and invasive species, and the application of GIS and remote sensing techniques in mapping and modelling the distribution of invasive species. It also outlines the shortcomings of current attempts in mapping invasive species. Chapter 2 reviews the application of remote sensing and GIS in mapping the actual and predicting the potential distribution of invasive species. In Chapter 3, the performance of an artificial neural network, a multiple linear regression analysis, the forest canopy density mapper, and a maximum likelihood classification method was compared for the prediction of forest canopy density using a Landsat ETM+ image. In Chapter 4, the possibilities of not only mapping the presence and absence but also seed productivity of C. odorata are revealed by applying a canopy density mapping classifier in combination with GIS expert knowledge, biophysical factors and field data. Chapter 5 investigates how the vegetative spread of C. odorata is determined by light intensity. Chapter 6 discusses the factors that made C. odorata a successful invader in the Nepalese Terai. Chapter 7 presents the expert, empirical expert and principal component analysis based model to predict the presence and absence of C. odorata in Nepal. Chapter 8 assesses the spatial extent and severity of deforestation and forest degradation in the Terai district of Nepal. Finally, Chapter 9 synthesizes the findings and conclusion of the previous chapters and discusses the research approach applied in mapping, modelling and managing cryptic invasive species.