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Remotely sensed monitoring of land surface albedo and ecosystem dynamics  

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Alibakhshi, Sara
dc.date.accessioned 2020-11-13T10:01:29Z
dc.date.available 2020-11-13T10:01:29Z
dc.date.issued 2020
dc.identifier.isbn 978-952-64-0130-0 (electronic)
dc.identifier.isbn 978-952-64-0129-4 (printed)
dc.identifier.issn 1799-4942 (electronic)
dc.identifier.issn 1799-4934 (printed)
dc.identifier.issn 1799-4934 (ISSN-L)
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/47625
dc.description Follow dissertation remotely from here on 4.12.2020 12:00 – 16:00: https://aalto.zoom.us/j/61998840053
dc.description.abstract The earth is under unprecedented pressure, which is reflected in rapid ecosystem changes around the globe. Over just the past three decades, the earth has lost over 178 million hectares of its forests. The rapidly growing evidence of the loss of resilience in ecosystems (i.e., recovery from disturbances slows down) due to climate change has become a global concern. Our limited knowledge of ecosystem dynamics and their key parameters, such as albedo, has also hindered our ability to manage ecosystems appropriately. The main aim of this dissertation is to contribute to elucidating ecosystem dynamics by exploiting remotely sensed satellite data. Additionally, this dissertation aims to explore the dynamics of albedo (reflectivity) in response to forest structure (forest density, tree cover, and leaf area index) variations and forest disturbances (fire and drought). To address specific research questions discussed throughout this dissertation, various study sites extending from local to global scales are considered. The results showed that using an appropriate ecosystem state variable that can represent the state of an ecosystem makes it is possible to achieve a reliable and timely evaluation of wetlands or forests state change. To that end, a new remotely sensed index called the modified vegetation water ratio (MVWR) was developed which improved the ability to understand the dynamics of wetlands. A new approach was also developed based on incorporating local spatial autocorrelation. The efficiency of this approach in measuring the state of drought-affected forests was demonstrated. To provide a convenient implementation of the presented approaches, a new R package, "stew", was developed. Investigating the dynamics of forest albedo on disturbances revealed that precipitation and burn severity only weakly explained the temporal dynamics of albedo. In contrast, it was shown that the number of fire events and the leaf area index strongly explained temporal albedo dynamics. According to the results, although fires could lead to abrupt decreases in the temporal dynamics of albedo, droughts caused abrupt increases in the temporal dynamics of albedo. Finally, a global-scale study was conducted to explore the links between forest structure and albedo during the peak growing season. The results demonstrated that forest structure might significantly explain albedo in most of the forests around the world. It was also found that the response of the shortwave albedo (300–5000 nm) to the variations of the leaf area index was always positive. The first map representing the links between forest structure and albedo was provided which highlights the importance of the role of forests in modulating albedo on a global scale. It is expected that the implications of the results of this dissertation contribute to future climate mitigation plans, the monitoring of ecosystem dynamics, and sustainable development in general. en
dc.format.extent 77 + app. 141
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Aalto University en
dc.publisher Aalto-yliopisto fi
dc.relation.ispartofseries Aalto University publication series DOCTORAL DISSERTATIONS en
dc.relation.ispartofseries 183/2020
dc.relation.haspart [Publication 1]: Alibakhshi, S., Groen, T.A., Rautiainen, M., and Naimi, B., 2017. Remotely-sensed early warning signals of a critical transition in a wetland ecosystem. Remote sensing, 9(4), p.352. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201705114258. DOI: 10.3390/rs9040352'
dc.relation.haspart [Publication 2]: Alibakhshi, S. Spatio-temporal analysis of remote sensing images provides early warning signals of forest mortality. Submitted to Science of the Total Environment on 22nd of May 2020 and revised according to pre-examination comments
dc.relation.haspart [Publication 3]: Alibakhshi, S., Hovi, A., and Rautiainen, M., 2019. Temporal dynamics of albedo and climate in the sparse forests of Zagros. Science of the Total Environment, 663, pp.596-609. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-201902251852. DOI: 10.1016/j.sci- totenv.2019.01.253
dc.relation.haspart [Publication 4]: Alibakhshi, S., Naimi, B., Hovi, A., Crowther, T.W., and Rautiainen, M., 2020. Quantitative analysis of the links between forest structure and land surface albedo on a global scale. Remote Sensing of Environment, 246, p.111854. Full text in Acris/Aaltodoc: http://urn.fi/URN:NBN:fi:aalto-202006013426. DOI: 10.1016/j.rse.2020.111854
dc.subject.other Environmental science en
dc.title Remotely sensed monitoring of land surface albedo and ecosystem dynamics   en
dc.type G5 Artikkeliväitöskirja fi
dc.contributor.school Insinööritieteiden korkeakoulu fi
dc.contributor.school School of Engineering en
dc.contributor.department Rakennetun ympäristön laitos fi
dc.contributor.department Department of Built Environment en
dc.subject.keyword ecosystem dynamics en
dc.subject.keyword land surface albedo en
dc.subject.keyword disturbances en
dc.subject.keyword forest mortality en
dc.subject.keyword critical transition en
dc.subject.keyword satellite images en
dc.identifier.urn URN:ISBN:978-952-64-0130-0
dc.type.dcmitype text en
dc.type.ontasot Doctoral dissertation (article-based) en
dc.type.ontasot Väitöskirja (artikkeli) fi
dc.contributor.supervisor Rautiainen, Miina, Prof., Aalto University, Department of Built Environment, Finland
dc.opn Vastaranta, Mikko, Prof., University of Eastern Finland, Finland
dc.contributor.lab Geoinformatics en
dc.rev Verbesselt, Jan, Prof., Wageningen University, Netherlands
dc.rev Riihelä, Aku, Prof., Finnish Meteorological Institute, Finland
dc.date.defence 2020-12-04
local.aalto.acrisexportstatus checked 2020-12-28_1732
local.aalto.formfolder 2020_11_13_klo_09_47
local.aalto.archive yes

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