Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648249
Title: Remote sensing methods for environmental monitoring of human impact on sub-Arctic ecosystems in Europe
Author: Shipigina, Ekaterina
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2013
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Abstract:
The role and scale of human impact on the global environment is a question of special importance to the scientific community and the world as a whole. This impact has dramatically increased since the beginning of industrialisation, yet its understanding remains patchy. The sub-Arctic plays a central role in forming the global environment due to the vast territory of boreal forest and tundra. Severe climatic conditions make its ecosystems highly sensitive to any natural and human disturbances. In this context, the dynamics of boreal vegetation, and of the forest/tundra interface (the treeline), is the most representative indicator of environmental changes in the sub-Arctic. For some time now, monitoring land cover and vegetation changes using remote sensing techniques have been a powerful method for studying human impact on environment from landscape to global scales. It is particularly efficient when applied to the sub-Arctic ecosystems. Remote sensing gives access to accurate and specific information about distant and hard-to-reach areas across forest and tundra. Despite all the e orts, there is a lack of uniformity in studying human impact, a shortage of mapping of impact over large territories and a lack of understanding of the relation between human activity and environmental response. This dissertation develops a systematic approach to monitoring land cover and vegetation changes under human impact over northern Fennoscandia. The study area extends north and south of the treeline and covers around 400,000km2 reaching from Finnmark in Norway, through Norrbotten in Sweden, Lapland in Finland up to the Murmansk region in Russia. This is the most populated and industrially developed region of the whole sub-Arctic and, therefore, suffering most from human impact. This dissertation identifies industrial atmospheric pollution, reindeer herding, forest logging, forest fires and infrastructure development as the primary types of human impact close to the treeline. For each type characteristic hotspots are identified and human impact is analysed in the context of physical environment as well as cultural, economical and political development of the area. This dissertation presents an automated workflow enabling large-scale land cover mapping in northern Fennoscandia with high throughput. It starts with automated image pre-processing using image metadata and ends with automated mapping of classification results. A single classifier for multispectral Landsat data is trained on extensive field data collected across the whole region. Open source tools are used extensively to set up the processing scripts enabling rapid and reproducible analysis. Using the developed advanced remote sensing methodology land cover maps are constructed for all identified hotspots and types of human impact. Changes in vegetation are analysed using three or four historical land cover maps for each hotspot. More than 35 Landsat TM and ETM+ images covering the period from the 1980s until 2011 are processed in an automated manner. A strong correlation between the level of impact and the scale of vegetation change is confirmed and analysed. The structure and dynamics of the local treeline and the quality of environment are analysed and assessed in the context of changing levels of impact at each hotspot and regionally.
Supervisor: Rees, Gareth Sponsor: Trinity Eastern European Research Studentship, Cambridge Overseas Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.648249  DOI:
Keywords: remote sensing ; monitoring ; ecosystems ; human impact ; climate change ; Fennoscandia ; vegetation change detection ; land cover ; Landsat ; R ; image processing ; environmental assessment
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