Livelihood, land use and environment interactions in the highlands of East Africa

This study aims at improving understanding of the interactions of livelihoods and the environment focusing on both socio-economic and biodiversity implications of land use change in the context of population pressure, global and local markets, climate change, cultural and regional historical factors in the highlands of East Africa. The study is based on three components (1) two extensive livelihood surveys, one on Mt. Kilimanjaro in Tanzania and the other in the Taita Hills of Kenya, (2) a land use change study of the southern slopes of Mt. Kilimanjaro focusing on land use trends between 1960s and 1980s and 1980s and 2000 and (3) a bird diversity study focusing on the potential impacts of the future land use change on birds in the main land use types on the slopes and the adjacent plains of Mt. Kilimanjaro. In addition, information on the highlands in Embu and the adjacent lowlands in Mbeere of Kenya are added to the discussion. Some general patterns of livelihood, land use and environment interactions can be found in the three sites. However, the linkages are very complex. Various external factors at different times in history have influenced most of the major turning points. Farmers continually make small adaptations to their farming practices, but the locally conceived alternatives are too few. Farmers lack specific information and knowledge on the most suitable crops, market opportunities and the quality requirements for growing the crops for markets. Population growth emerges as the most forceful driver of land use and environmental change. The higher altitudes have become extremely crowded with population densities in some areas higher than typical urban population densities. Natural vegetation has almost totally been replaced by farmland. Decreasing farm size due to population pressure is currently threatening the viability of whole farming systems. In addition, capital-poor intensification has lead to soil fertility depletion. Agricultural expansion to the agriculturally marginal lowlands has created a new and distinct group of farmers struggling constantly with climate variability causing frequent crop failures. Extensification to the fragile drylands is the major cause of fragmentation and loss of wildlife habitat. The linkages between livelihoods, land use and the environment generally point to degradation of the environment leading to reduced environmental services and ecosystem functions. There is no indication that the system is self-regulating in this respect. Positive interventions will be needed to maintain ecosystem integrity.

Geospatial environmental data modelling applications using remote sensing, GIS and spatial statistics

This thesis presents novel modelling applications for environmental geospatial data using remote sensing, GIS and statistical modelling techniques. The studied themes can be classified into four main themes: (i) to develop advanced geospatial databases. Paper (I) demonstrates the creation of a geospatial database for the Glanville fritillary butterfly (Melitaea cinxia) in the Åland Islands, south-western Finland; (ii) to analyse species diversity and distribution using GIS techniques. Paper (II) presents a diversity and geographical distribution analysis for Scopulini moths at a world-wide scale; (iii) to study spatiotemporal forest cover change. Paper (III) presents a study of exotic and indigenous tree cover change detection in Taita Hills Kenya using airborne imagery and GIS analysis techniques; (iv) to explore predictive modelling techniques using geospatial data. In Paper (IV) human population occurrence and abundance in the Taita Hills highlands was predicted using the generalized additive modelling (GAM) technique. Paper (V) presents techniques to enhance fire prediction and burned area estimation at a regional scale in East Caprivi Namibia. Paper (VI) compares eight state-of-the-art predictive modelling methods to improve fire prediction, burned area estimation and fire risk mapping in East Caprivi Namibia. The results in Paper (I) showed that geospatial data can be managed effectively using advanced relational database management systems. Metapopulation data for Melitaea cinxia butterfly was successfully combined with GPS-delimited habitat patch information and climatic data. Using the geospatial database, spatial analyses were successfully conducted at habitat patch level or at more coarse analysis scales. Moreover, this study showed it appears evident that at a large-scale spatially correlated weather conditions are one of the primary causes of spatially correlated changes in Melitaea cinxia population sizes. In Paper (II) spatiotemporal characteristics of Socupulini moths description, diversity and distribution were analysed at a world-wide scale and for the first time GIS techniques were used for Scopulini moth geographical distribution analysis. This study revealed that Scopulini moths have a cosmopolitan distribution. The majority of the species have been described from the low latitudes, sub-Saharan Africa being the hot spot of species diversity. However, the taxonomical effort has been uneven among biogeographical regions. Paper III showed that forest cover change can be analysed in great detail using modern airborne imagery techniques and historical aerial photographs. However, when spatiotemporal forest cover change is studied care has to be taken in co-registration and image interpretation when historical black and white aerial photography is used. In Paper (IV) human population distribution and abundance could be modelled with fairly good results using geospatial predictors and non-Gaussian predictive modelling techniques. Moreover, land cover layer is not necessary needed as a predictor because first and second-order image texture measurements derived from satellite imagery had more power to explain the variation in dwelling unit occurrence and abundance. Paper V showed that generalized linear model (GLM) is a suitable technique for fire occurrence prediction and for burned area estimation. GLM based burned area estimations were found to be more superior than the existing MODIS burned area product (MCD45A1). However, spatial autocorrelation of fires has to be taken into account when using the GLM technique for fire occurrence prediction. Paper VI showed that novel statistical predictive modelling techniques can be used to improve fire prediction, burned area estimation and fire risk mapping at a regional scale. However, some noticeable variation between different predictive modelling techniques for fire occurrence prediction and burned area estimation existed.