Next Article in Journal
Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI
Previous Article in Journal
The Analysis of Experimental Deployment of IGLUNA 2019 Trans-Ice Longwave System
Article

Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany

1
Professorship of Ecological Services (PES), BayCEER, University of Bayreuth, 95440 Bayreuth, Germany
2
Faculty of Geoinformatics, Munich University of Applied Sciences, 80333 Munich, Germany
3
Department of Biogeography, BayCEER, University of Bayreuth, 95440 Bayreuth, Germany
4
Department of Remote Sensing, Institute of Geography, University of Wuerzburg, 97074 Wuerzburg, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(24), 4048; https://doi.org/10.3390/rs12244048
Received: 12 October 2020 / Revised: 4 December 2020 / Accepted: 7 December 2020 / Published: 10 December 2020
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 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. View Full-Text
Keywords: historical; land cover change; object-based classification; eCognition historical; land cover change; object-based classification; eCognition
Show Figures

Graphical abstract

MDPI and ACS Style

Ulloa-Torrealba, Y.; Stahlmann, R.; Wegmann, M.; Koellner, T. Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany. Remote Sens. 2020, 12, 4048. https://doi.org/10.3390/rs12244048

AMA Style

Ulloa-Torrealba Y, Stahlmann R, Wegmann M, Koellner T. Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany. Remote Sensing. 2020; 12(24):4048. https://doi.org/10.3390/rs12244048

Chicago/Turabian Style

Ulloa-Torrealba, Yrneh, Reinhold Stahlmann, Martin Wegmann, and Thomas Koellner. 2020. "Over 150 Years of Change: Object-Oriented Analysis of Historical Land Cover in the Main River Catchment, Bavaria/Germany" Remote Sensing 12, no. 24: 4048. https://doi.org/10.3390/rs12244048

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop