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Erscheinungsjahr
- 2020 (2) (entfernen)
Dokumenttyp
- Dissertation (2) (entfernen)
Institut
- Institut für Ökologie (IE) (2) (entfernen)
Tropical forests worldwide support high biodiversity and contribute to the sustenance of local people’s livelihoods. However, the conservation and sustainability of these forests are threatened by land-use changes and a rapidly increasing human population. This dissertation, therefore, aimed to characterize biodiversity patterns in the moist Afromontane forests of southwestern Ethiopia and to examine how biodiversity patterns are affected by land-use and land-use changes (mediated by coffee management intensity, landscape attributes and housing development) in a context of a rapidly growing rural population. To achieve this goal, the author takes an interdisciplinary approach where, first, she examined the effects of coffee management intensity on diversity patterns of woody plants and birds, spanning a gradient of site-level disturbance from nearly undisturbed forest interior to highly managed shade coffee forests. Results showed that specialized species of woody plants (forest specialists) and birds (forest specialists, insectivores and frugivores) were affected by coffee management intensity. The richness of forest specialist trees and the richness and/or abundance of insectivores, frugivores and forest specialist birds decrease with increasing levels of disturbance. Second, the author investigated the effects of landscape context on woody plants, birds and mammals. Community composition and specialist species of woody plants and birds were sensitive to landscape context, where woody plants responded positively to gradients of edge-interior and birds to gradients of edge-interior and forest cover. Further results showed that a diverse mammal community, with 26 species, occurs at the forest edge of shade coffee forests and that the leopard, an apex predator in the region depended on large areas of natural forest. A closer examination of leopard activity patterns revealed a shift in the diel activity as a response to human disturbance inside the forest, further highlighting the importance of natural undisturbed forests for leopards in the region. Together, these findings demonstrate the value of low managed shade coffee forests for biodiversity, and importantly, emphasize the irreplaceable value of undisturbed natural forests for biodiversity. Third, the researcher investigated the effects of prospective rural population growth (mediated by housing development) on the forest mammal community. Here, population growth was projected to negatively influence several mammal species, including the leopard. Housing development that encroached the forest entailed worse outcomes for biodiversity than a combination of prioritized development in already developed areas and coffee forest protection. Fourth, to understand the motivations behind high human fertility rates in the region, she examined the determinants of women fertility preferences, including their perceptions on social and biophysical stressors affecting local livelihoods such as food insecurity and environmental degradation. Fertility preferences were influenced by underlying social norms and mindsets, a perceived utilitarian value of children and male dominance within the household, and were only marginally affected by perceptions of social and biophysical stressors. The findings suggest the need for new deliberative and culturally sensitive approaches that engage with pervasive social norms to slow down population growth. Overall, this dissertation demonstrates the key value of moist Afromontane forests in southwestern Ethiopia for biodiversity conservation. It indicates the need to promote coffee management practices that reduce forest degradation and highlights that high priority should be given to the conservation of undisturbed natural forests. It also suggests the need to integrate conservation goals with housing development in landscape planning. A promising approach to achieve the above conservation priorities would be the creation of a Biosphere Reserve and to promote the ecological connectivity between the larger forest remnants in the region. Finally, this dissertation demonstrates the importance of placed-based holistic approaches in conservation that consider both proximate and distal drivers of forest biodiversity decline.
Sustainable landscape development is the main goal of decision makers worldwide. Achieving this goal in the long term leads to achieving social, economic and environmental sustainability. Remote sensing has been playing an essential role in monitoring remote areas. This study has employed part of the role of remote sensing in supporting the direction of decision makers towards sustainable landscape development. The study has focused on some of the main elements affecting sustainable environment: land uses, specifically agricultural land uses, water quality, forests, and water hazards such as floods. Three research programs were undertaken to investigate the role of Terrasar-x imagery, as a source of remote sensing data, in monitoring the environment and achieving the previous stated elements. The investigation was intended to investigate the effectiveness of TSX imagery in identifying the cropping pattern of selected study areas by employing a pixel-based supervised maximum likelihood classifier, as published in Paper I, assessment of the efficiency of using TSX imagery in determining land use and the flood risk maps by applying an object-based decision tree classifier as published in Paper II, and determination of the potential of inferential statistics tests such as the two samples Z-test and multivariate analysis, for example Factor Analysis, for identifying the kind of forest canopy, based on the backscattering coefficient of TSX imagery of forest plots, as presented in Paper III. Papers I and II covered two pilot areas in the Lower Saxonian Elbe Valley Biosphere Reserve “das Biosphärenreservat "Niedersächsische Elbtalaue" around Walmsburger Werder and Wehninger Werder. Paper III focused on the Fuhrberger Feld water protection area near Hanover in Germany. The inputs for this research were mainly SAR Imagery and the ground truth data collected from field surveys, in addition to databases, geo-databases and maps. The study presented in Paper I used two filters to decrease speckle noise namely De-Grandi as multi-temporal speckle filter, and Lee as an adaptive filter. A multi-temporal classification method was used to identify the different crops using a pixel-based maximum likelihood classifier. The classification accuracy was assessed based on the external user accuracy for each crop, the external producer accuracy for each crop, the Kappa index and the external total accuracy for the entire classification. Three cropping pattern maps were produced namely the cropping pattern map of Wehninger Werder in 2011 and the cropping pattern maps of Walmsburger Werder in 2010 and in 2011. The study showed that image filtering was essential for enhancing the accuracy of crop classification. The multi-temporal filter De-Grandi enhanced the producer accuracy by about 10% compared to the Lee filter. Furthermore, gathering and utilizing large ground truth data greatly enhanced the accuracy of the classification. The research verified that using sequence images covering the growing season usually improved the classification results. The results exposed the effect of the polarization and demonstrate that the majority of the classifications produced according to the crop calendar had higher total producer accuracy than using all acquisitions. The study demonstrated undertaken in Paper II applied the decision tree object-based classifier in determining the major land uses and the inundation extent areas in 2011 and 2013 using the Lee-filtered imagery. Based on the maps produced for the land uses and inundation areas, the hazard areas due to the floods in 2011 and 2013 were identified. The study illustrated that 95% of the inundated area was classified correctly, that 90% of vegetated lands were accurately determined, and around 80% of the forest and the residential areas were correctly recognized. The research undertaken in Paper III statistically analyzed the backscattering coefficient of the Lee-filtered TSX in some forest plots by the Factor Analysis and two sample Z-test. The study showed that Factor analysis tools succeeded in differentiating between the coniferous forest and the deciduous forest and mixed forest, but failed to discriminate between the deciduous and the mixed forest. On one hand, only one factor was extracted for each sample plot of the coniferous forest with approximately equal loadings during the whole acquisition period from March 2008 to January 2009. On the other hand, two factors were extracted for each deciduous or mixed forest sample plot, where one factor had high loadings during the leaf-on period from May to October, and the other one had high loadings during the leaf-off period from November to April. Furthermore, the research revealed that the two sample Z-test enabled not only differentiation between the deciduous and the mixed forest against the coniferous forest, but also discrimination between deciduous forest and the mixed forest. Statistically significant differences were observed between the mean backscatter values of the HH-polarized acquisitions for the deciduous forest and the mixed forest during the leaf-off period, but no statistically significant difference was found during the leaf-on period. Moreover, plot samples for the deciduous forest had slightly higher mean backscattering coefficients than those for the mixed forest during the leaf-off period.