Land suitability analysis of Alvar grassland vegetation in Estonia using Random Forest

  • 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 itCalcareous 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.show moreshow less

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Metadaten
Author:Irada IsmayilovaGND, Evelyn Uuemaa, Aveliina Helm, Christian RögerGND, Sabine TimpfORCiDGND
URN:urn:nbn:de:bvb:384-opus4-870590
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/87059
ISBN:978-3-7001-8740-0OPAC
Parent Title (English):GI_Forum: Journal for Geographic Information Science
Publisher:Verlag der Österreichischen Akademie der Wissenschaften
Place of publication:Wien
Type:Article
Language:English
Year of first Publication:2020
Publishing Institution:Universität Augsburg
Release Date:2021/05/27
Volume:8
Issue:1
First Page:63
Last Page:72
DOI:https://doi.org/10.1553/giscience2020_01_s63
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Geographie
Fakultät für Angewandte Informatik / Institut für Geographie / Professur für Angewandte Geoinformatik
Fakultät für Angewandte Informatik / Institut für Geographie / Professur für Geoinformatik
Dewey Decimal Classification:9 Geschichte und Geografie / 91 Geografie, Reisen / 910 Geografie, Reisen
Licence (German):CC-BY-ND 4.0: Creative Commons: Namensnennung - Keine Bearbeitung (mit Print on Demand)