Vorliegende Arbeit zeigt die jüngsten Möglichkeiten hochauflösender Fernerkundung am Beispiel von Stadtbäumen in Berlin, Deutschland. Es wurden neuste methodische Ansätze eingesetzt, wie beispielsweise maschinelles Lernens und individuelle Baumdetektion. Sie erwiesen sich von großem Vorteil für die detaillierte Analyse urbaner Ökosystemdienstleistungen in einer heterogenen Umwelt. Neueste Fernerkundung von hoher zeitlicher Auflösung hat Möglichkeiten gezeigt, Veränderungen des Stadtwaldes präziser zu untersuchen. Diesbezüglich konnten Baumspezies klassifiziert werden auf Grundlage saisonaler Veränderungen, die mittels Fernerkundungsdaten aufgenommen wurden. Dies ist für den urbanen Bereich einmalig und über große Flächen noch nicht durchgeführt worden. Darüber hinaus haben diese Baumarten einzelnen Bäumen zugeordnet werden können, deren Abmessung fernerkundlich erfasst worden ist. Diese neu erzeugten Umweltinformationen einzelner Bäume können damit verbundene urbane Ökosystemdienstleistungen präzise aktualisieren. Zum Beispiel haben so Unsicherheiten in der Schätzung zur Kohlenstoffspeicherung städtischer Wälder reduziert werden können. Es ist zudem von Vorteil gewesen, den gegenwärtigen Mangel an räumlich expliziten dreidimensionalen Informationen über Stadtwälder anzusprechen. Allerdings ist die Rolle städtischen Wälder, das Treibhausgas CO2 langfristig auszugleichen, immer noch wenig untersucht. Gerade der Mangel an präzisen, konsistenten und aktuellen Details führt zu großen Unsicherheiten im Rahmen von Lebenszyklus-Analysen. Auf Grund des aktuellen Fortschritts in hochauflösender Fernerkundung könnten diese Unsicherheiten reduziert werden. Dazu werden Möglichkeiten ausgiebig kritisch bewertet und anhand einer Lebenszyklus-Analyse am Beispiel Berlin andiskutiert, inwieweit sie präzisere langfristige Prognosen zum Stadtwald als Kohlenstoffspeicher liefern. / This work shows recent options for implementing high resolution remote sensing in assessing urban trees in Berlin, Germany. State-of-the-art methodological approaches like machine learning and individual tree detection proved to be highly advantageous for analyzing details of urban ecosystem services within a heterogeneous urban environment. Recent remote sensing of high temporal resolution offers new options for more precisely addressing urban forest dynamics. This successfully shows that tree species could be identified from seasonal changes of remotely sensed imagery, though this has not yet been applied across cities. Furthermore, these tree species results could be combined with remotely sensed individual tree dimensions. This newly generated data can be suggested to update spatially explicit information on related urban ecosystem services. For example, this could reduce the uncertainties of such estimates as urban forest carbon storage, and also address the present lack of spatially explicit three-dimensional information on urban forests. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging within the scope of life cycle assessments. This can cause high uncertainties in urban forest carbon offset. Although, recent progress in high resolution remote sensing is promising to reduce these uncertainties. For this purpose, remote sensing options are extensively reviewed and briefly discussed using an example of life cycle assessment for Berlin, which allow more precise long-term prognoses of urban forest carbon offset.

This dissertation deals with the long-term dynamics and fluctuations of settlements on Gotland for the period from AD 200 up until early modern times. The settlement structure on Gotland is most often described as very stable and consisting of solitary farms, established in the Iron Age. A contrasting view is presented by analyses of a vast source material from different periods. The source material consists of both physical remains, noted in the Swedish national Archaeological Sites Information System, FMIS and large scale historical maps, as well as other written sources. For the first studied period, the locations of some 2 000 houses are known, since they were constructed with sturdy stone walls and are thus preserved. The source material for the following periods is scarcer, but some hundred Viking Age sites are identified, mainly by the find places of silver hoards. By retrogressive analyses of historical maps, from the decades around the year 1700, and other written sources, later periods are analysed. All available data are gathered in geodatabases, which enables both generalised and detailed spatial and statistical analyses. The results of the analyses show a more varied picture, with great fluctuations in the number of farms; the existence of villages is also clearly indicated in a large part of the settlements. The villages are centred on kinship and the lack of strong royal power or landed gentry meant they were not fixed in cadastres, as fiscal units, as villages were on the Swedish mainland. Two peaks, followed by major dips, were identified in the number of settlements and thus in the population. The first peak occurred during the late Roman Iron Age/Migration period, which was followed by a reduction in the Vendel period of possibly up to 30-50%. After this, a recovery started in the Viking Age, which culminated during the heydays of Gotland in the High Middle Ages, with population numbers most probably not surpassed until late in history. This upward trend was broken by the diminishing trade of Gotland, the Medieval agrarian crisis, The Danish invasion and later events. All this resulted in a decline, probably as great as after the Migration period.