The dynamic nature and inaccessibility of wetland ecosystems restricts in situ data collection and promote the use of various remote sensing platforms. This is because of their ability to record large areas in comparatively short time periods and map physically unreachable areas. Sensors in the optical and microwave range of the electromagnetic spectrum play a critical role in wetlands detection and delineation, as they complement each other in data collection. This study examined the potential of optical and microwave remote sensing in detecting the diversity of small wetlands (<500 ha) in the semi-arid and sub humid parts of Laikipia and Pangani plains and the humid parts of Mt. Kenya and Usambara highlands in Kenya and Tanzania, respectively. An intensive field survey was conducted to supplement the remotely sensed data. Decision tree, supervised and unsupervised classification techniques, facilitated the detection of floodplains and inland valley wetlands within the study sites. The results reveal that although optical and microwave data work effectively in the detection of wetlands the latter would be more effective in larger wetlands than those in the scope of this study.
ii 1 Introduction: Knowledge Clusters for Development 1
Monitoring landscape changes, especially conservation sites, is essential to sustain the environment and landscape diversity (towNseNd et al. 2009). By now, it is evident that the thematic resolution of classified data sets affects the results of land use/land cover and landscapestructure analysis. However, uncertainty regarding the ambiguity of classification schemes and the impact of generalizations have not been sufficiently addressed until now (lechNer et al. 2012). This study applies digital vector data of biotope types and land use mapping to gain further systematic insights into these questions. The data sets are available area-wide for 2 years (1993 and 2006), using the example of the Rhoen biosphere reserve situated in central Germany. The objectives of the study are 1) to consider the effect of thematic resolution on the magnitude of land use and land cover changes, 2) to assess the impact of thematic resolution on the analysis of landscape patterns, and 3) to investigate which thematic resolution is most suitable to detect differences between the biosphere reserve zones regarding the temporal development of the landscape structure. To achieve the objectives, the initial data are reclassified into data sets, encompassing 9, 27, 59, and 204 classes. Results indicate a considerable effect on the magnitude of detectable landscape changes at low or very high thematic resolutions and a high sensitivity of landscape metrics. However, landscape metric values show not only quantitative (discrepancies of values) but also qualitative (divergences of direction of change) impacts. Zusammenfassung: Das Monitoring von Landschaften und insbesondere von Schutzgebieten ist zur Erhaltung der Umwelt und Landschaftsvielfalt unerlässlich (towNseNd et al. 2009). Es ist offensichtlich, dass dabei die thematische Auflösung der verwendeten klassifizierten Datensätze Einfluss auf Ergebnisse von Landnutzungs-/-bedeckungs-und Landschaftsstrukturanalysen hat. Dennoch werden Unsicherheiten hinsichtlich der mangelnden Eindeutigkeit von Klassifikationsschemata und der Einfluss von Generalisierungen nicht ausreichend behandelt (lechNer et al. 2012). Um diese Zusammenhänge systematisch zu untersuchen, werden in der vorgestellten Studie Biotoptypen-und Nutzungskartierungen verwendet, die als Vektordatensätze vorliegen. Die Datensätze stehen flächendeckend für zwei Zeitschritte (1993 und 2006) für das Biosphärenreservat Rhön zur Verfügung. Mit der Studie soll der Einfluss der thematischen Auflösung von klassifizierten Datensätzen 1) auf die Veränderung von Landnutzung und-bedeckung sowie 2) auf die Analyse der Landschaftsstruktur ermittelt werden. Darüber hinaus wird untersucht, 3) welche thematische Auflösung am besten zur Analyse von unterschiedlich verlaufenden Landschaftsstrukturveränderungen in den Zonen des Biosphärenreservats geeignet ist. Um die Forschungsfragen zu beantworten, werden die Ausgangsdaten in 9, 27, 59 und 204 Klassen differenziert. Die Ergebnisse zeigen, dass bei geringen und sehr hohen thematischen Auflösungen der E...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.