GI_Forum 2020, Volume 8, Issue 1 Journal for Geographic Information Science
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2020, Volume 8, Issue 1 Journal for Geographic Information Science
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8740-0 Online Edition
Irada Ismayilova,
Evelyn Uuemaa,
Aveliina Helm,
Christian Röger,
Sabine Timpf
S. 63 - 72 doi:10.1553/giscience2020_01_s63 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2020_01_s63
Abstract: Calcareous alvar grasslands are one of the most species-rich habitats in Estonia. Land-use change and cessation of traditional agricultural practices have led to a decrease of the area of these valuable grasslands during the past century. Therefore, their conservation and restoration are becoming increasingly important. Efforts to restore these habitats have already been made in recent years. Land suitability analysis for potential restoration sites, using the machine learning technique Random Forest (RF), was performed for the first time in this study, which aimed to assess the use of RF for a suitability analysis of alvar grassland. RF predicted 610.91 km2 of areas suitable for restoring alvar grasslands or for creating alvar-like habitats in Estonia. These areas include all existing alvar areas as well an additional 140.91 km2 suitable for establishing new habitat similar to calcareous alvar grasslands. We discuss suitability analysis to help with restoration planning and find it to be a reasonable and efficient tool that has potential to provide relevant information. The quality of the prediction could be improved by including additional data relevant for alvar grasslands, such as soil depth, but such data was unfortunately unavailable. Keywords: alvar grasslands, restoration, land suitability, machine learning, Random Forest Published Online: 2020/06/25 07:51:50 Object Identifier: 0xc1aa5576 0x003b9d7a Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |