@phdthesis{Frimpong2022, author = {Frimpong, Bernard Fosu}, title = {Analysis of the relationship between spatial urban expansion and temperature utilising remote sensing and GIS techniques in the Accra and Kumasi Metropolises in Ghana}, doi = {10.26127/BTUOpen-6073}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-60730}, school = {BTU Cottbus - Senftenberg}, year = {2022}, abstract = {Accra and Kumasi are the two major cities in Ghana. Spatial urban expansion has been experienced in transforming different non-urban Land Use Land Cover (LULC) types into urban/built-up areas with a potential direct relationship to temperature rise in the cities. Thus, this dissertation aims to establish the relationship between urban spatial expansion and temperature in Accra and Kumasi metropolis. Multi-source datasets such as remote sensing images, different GIS vector layers, reference maps and historical temperature datasets were used for this retrospective study. This research was grouped under three components: environmental science, environmental technology, as well as environmental management and planning. From the environmental science component, LULC maps were produced for different years to assess the trend of temporal change in the various LULC classes in the two metropolises. Remote sensing indices and land surface temperature were retrieved from the remote sensing images to determine their correlations. Temperature time series was analysed by calculating temperature indices and determining temporal trends to reveal changes in air temperature to detect urban warming and its impacts. From the environmental technology, the novel random forest algorithm was utilised to classify the satellite images of both cities since previous works have utilised other traditional classifiers. The satellite imageries were used for point-based estimation of temperature to determine Urban Heat Islands (UHIs) hotspots. For environmental management and planning, spatial urban expansion techniques were utilised to ascertain trends in urban/built-up areas, especially in both cities' sub-metropolitan zones. For prescient purposes, future LULC modelling was implemented to provide insights into the proportions of the various LULC changes in 2025. The analysis identified two salient findings: increased urban/built-up areas at the expense of agricultural and forestlands throughout the study period and the positive correlation between spatial urban expansion and temperature. This indicated warming up of urban temperature in both cities. The major findings in this dissertation provided evidence of how integrated datasets and research techniques can be utilised for LULC changes to determine the relationship between spatial urban expansion and temperature at local scales. Institutions such as metropolitan assemblies and policymakers may adopt the concepts demonstrated in this work to rapidly assess urban environments and investigate the relationship between spatial urban expansion and temperature.}, subject = {Spatial urban expansion; Temperature; Land Use Land Cover; Remote sensing; GIS; Urbane Expansion; Temperatur; Landnutzung; Landbedeckung; Fernerkundung; Ghana; Landnutzung; Großstadt; Temperatur; Fernerkundung; Geoinformationssystem}, language = {en} } @phdthesis{Schiller2025, author = {Schiller, Josepha}, title = {Explainable artificial intelligence for understanding cross-scale diversification in agricultural landscapes}, doi = {10.26127/BTUOpen-7245}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-72457}, school = {BTU Cottbus - Senftenberg}, year = {2025}, abstract = {Agricultural production is expected to cope with numerous challenges in the future. Therefore, the diversification of agricultural systems has gained attention as a promising solution to meet ecological, economic, and social needs in the long term. Despite the overall positive reports of diversification effects, the joint distribution of temporal and spatial diversity and their influencing factors remains largely unexplored. This thesis aims to assess the interrelations of diversity within the agricultural system at the field, farm, and landscape levels and to investigate the links between temporal and spatial crop diversity in Brandenburg, Germany, from 2011 to 2020 using machine learning (ML) combined with explainable artificial intelligence (XAI) tools. This approach involves identifying influencing factors of agricultural diversity such as farm attributes, landscape complexity, and environmental conditions. Data on agricultural land use were derived from the Integrated Administration and Control System (IACS), covering more than 5,642 farms and over 150,000 fields annually. The results revealed (1) a negative relationship between temporal crop diversity at the field level and landscape level complexity associated with soil quality and (2) a positive link between temporal and spatial crop diversity associated with higher crop diversity at the farm level. The potential of using XAI as a trust-enhancing tool for machine learning in such an interdisciplinary research setting was explored by reviewing the recent use of XAI tools in environmental and Earth system sciences in 575 studies. This revealed (3) that despite the increasing use of XAI tools in the environmental and Earth system sciences over time, the question of whether XAI can contribute to increased trust, e.g., in high-stakes decision contexts, has been addressed in only 1.2\% of the studies. These findings contribute to understanding synergies and trade-offs between diversity within agricultural systems across levels and scales and its influencing factors. They also provide insights into the potential of XAI tools toward trustworthy ML applications. These findings can serve to develop targeted diversification strategies that enhance the resilience of agricultural systems and the sustainability of agricultural landscapes and improve the trustworthiness of ML and XAI in interdisciplinary research settings.}, subject = {Explainable artificial intelligence; Machine learning; Agricultural landscapes; Crop diversification; Heterogeneous landscapes; Erkl{\"a}rbare k{\"u}nstliche Intelligenz; Maschinelles Lernen; Kulturpflanzenvielfalt; Landschaftsheterogenit{\"a}t; Agrarlandschaften; Agrarlandschaft; Kulturpflanzen; Diversifikation ; Landschafts{\"o}kologie; Maschinelles Lernen; Erkl{\"a}rbare k{\"u}nstliche Intelligenz}, language = {en} }