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Over 150 years of change: object-oriented analysis of historical land cover in the Main river catchment, Bavaria/Germany

Please always quote using this URN: urn:nbn:de:bvb:20-opus-220029
  • The monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use changeThe monitoring of land cover and land use change is critical for assessing the provision of ecosystem services. One of the sources for long-term land cover change quantification is through the classification of historical and/or current maps. Little research has been done on historical maps using Object-Based Image Analysis (OBIA). This study applied an object-based classification using eCognition tool for analyzing the land cover based on historical maps in the Main river catchment, Upper Franconia, Germany. This allowed land use change analysis between the 1850s and 2015, a time span which covers the phase of industrialization of landscapes in central Europe. The results show a strong increase in urban area by 2600%, a severe loss of cropland (−24%), a moderate reduction in meadows (−4%), and a small gain in forests (+4%). The method proved useful for the application on historical maps due to the ability of the software to create semantic objects. The confusion matrix shows an overall accuracy of 82% for the automatic classification compared to manual reclassification considering all 17 sample tiles. The minimum overall accuracy was 65% for historical maps of poor quality and the maximum was 91% for very high-quality ones. Although accuracy is between high and moderate, coarse land cover patterns in the past and trends in land cover change can be analyzed. We conclude that such long-term analysis of land cover is a prerequisite for quantifying long-term changes in ecosystem services.show moreshow less

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Metadaten
Author: Yrneh Ulloa-Torrealba, Reinhold Stahlmann, Martin Wegmann, Thomas Koellner
URN:urn:nbn:de:bvb:20-opus-220029
Document Type:Journal article
Faculties:Philosophische Fakultät (Histor., philolog., Kultur- und geograph. Wissensch.) / Institut für Geographie und Geologie
Language:English
Parent Title (English):Remote Sensing
ISSN:2072-4292
Year of Completion:2020
Volume:12
Issue:24
Article Number:4048
Source:Remote Sensing (2020) 12:24, 4048. https://doi.org/10.3390/rs12244048
DOI:https://doi.org/10.3390/rs12244048
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 52 Astronomie / 526 Mathematische Geografie
5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften
Tag:eCognition; historical; land cover change; object-based classification
Release Date:2022/07/07
Date of first Publication:2020/12/10
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International