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Unter dem Titel «Object-based remote sensing for modelling scenarios of rural livelihoods in the highly structured farmland surrounding Kakamega Forest, western Kenya» habe ich an der Fakultät Umweltwissenschaften der TU Dresden zum Dr.-Ing. promoviert.

Betreut wurde ich durch Prof. Dr. phil. habil. M. F. Buchroithner (TU Dresden) sowie Prof. Dr.-Ing. G. Schaab (HS Karlsruhe – Technik und Wirtschaft). Begutachtet wurde die Arbeit durch Prof. Dr. phil. habil. M. F. Buchroithner (TU Dresden), Prof. Dr.-Ing. G. Schaab (HS Karlsruhe – Technik und Wirtschaft) und Prof. Dr. habil. E. Csaplovics (TU Dresden).


Weitere Informationen zur Arbeit können auf Qucosa abgerufen werden:

Über den folgenden Link kann die Arbeit auch direkt heruntergeladen werden:
urn:nbn:de:bsz:14-qucosa-150628 (PDF, 95 MB)

Kurzfassung (Englisch)

This thesis analyses the highly structured and densely populated farmland surrounding Kakamega Forest (western Kenya) in a spatially-explicit manner. The interdisciplinary approach combines methodologies and technologies from different scientific disciplines: remote sensing with OBIA, GIS and spatially explicit modelling (geomatics and geographic science) with socio-economic as well as agro-economic considerations (human and social sciences) as well as cartographic science. Furthermore, the research is related to conservation biology (biological sciences).

Based on an in-situ ground truthing and visual image interpretation, very high spatial resolution QuickBird satellite imagery covering 466 km² of farmland was analysed using the concept of object-based image analysis (OBIA) (cf. Figure). In an integrative workflow, statistical analysis and expert knowledge were combined to develop a sophisticated rule set. The classification result distinguishing 15 LULC classes was used alongside with temporally extrapolated and spatially re-distributed population data as well as socio-/agro-economic factors in order to create a spatially-explicit typology of the farmland and to model scenarios of rural livelihoods.

The farmland typology distinguishes ten types of farmland: 3 sugarcane types (covering 48% of the area), 3 tea types (30%), 2 transitional types (15%), 1 steep terrain type (2%), and 1 central type (5%). The scenarios consider different developments of possible future yields and prices for the main agricultural products sugarcane, tea, and maize. Out of all farmland types, the ‘marginal sugarcane type’ is best prepared to cope with future problems. Besides a comparably low population density, a high share of land under cultivation of food crops coupled with a moderate cultivation of cash crops is characteristic for this type.

As part of the research conducted, several novel methodologies were introduced. These include a new conceptual framework for categorizing parameter optimization studies, the area fitness rate (AFR) as a novel discrepancy measure, the technique of ‘classification-based nearest neighbour classification’ for classes which are difficult to separate from others, and a novel approach for accessing the accuracy of OBIA classifications. Finally, this thesis makes a number of recommendations and elaborates promising starting points for further scientific research.