Abstract
Mapping and monitoring of urban land cover are becoming increasingly important in the face of ongoing climate change. The recently launched pair of Sentinel-2 satellites regularly delivers images with the highest spectral and spatial resolution among the freely available images. Based on these data, we proposed an object-based classification method for urban land-cover mapping at two basic scale levels. The proposed set of classification rules is transferable to different urban areas without the need to collect training samples. The inevitable problem of different spectral characteristics of vegetation in individual areas is solved by computing the area-specific thresholds using the central values of forest stands and grasslands read out of the histogram. Special features summarising the normalised difference vegetation index (NDVI) time-series using the Google Earth Engine platform were designed to distinguish cropland from other classes. The transferability of the rule sets was verified in six Central European cities with different climatic conditions—Bratislava, Nitra, and Žilina (Slovakia), Zakopane (Poland), and Kaposvár and Orosháza (Hungary). The overall classification accuracy reached 78–90% in each tested area at the first hierarchical level, and 76–89% on the second level, respectively. The performance of the methodology was compared with the random forest (RF) machine learning method with training samples collected in Bratislava. The results confirmed that without area-specific training samples, the accuracy of the RF method is 5–35 percentage point (p.p.) lower than the accuracy of the proposed rule-based method. In addition, without the NDVI summary indices, the accuracy of the RF classifier decreased by another 10–30 p.p.
Zusammenfassung
Hierarchische objektbasierte Kartierung städtischer Bodenbedeckungen mit Sentinel-2 Data: Eine Fallstudie anhand von sechs Städten Mitteleuropas. Die Kartierung und Überwachung der städtischen Bodenbedeckung wird in Hinblick auf den Klimawandel immer wichtiger. Das vor einiger Zeit gestartete Sentinel-2-Satellitenpaar liefert regelmäßig Bilder, die unter den frei verfügbaren die höchste spektrale und räumliche Auflösung bieten. Basierend auf diesen Daten schlagen wir eine objektorientierte Klassifizierungsmethode für städtische Bodenbedeckungen vor und orientieren uns dabei an zwei Maßstäben. Die vorgeschlagenen Klassifizierungsregeln lassen sich auf unterschiedliche Stadtgebiete ohne neue Trainingsdaten übertragen. Das unvermeidliche Problem der unterschiedlichen spektralen Merkmale der Vegetation auf unterschiedlichen Flächen wird durch Umwandlung der aus dem Histogramm abgelesenen Zentralwerte in flächenspezifische Schwellenwerte bei der Entscheidungsfindung gelöst. Spezielle Algorithmen wurden zur Auswertung der NDVI (Normalised Differenence Vegetation Index)- Zeitreihe auf Basis von Google Earth entwickelt, um Ackerland von anderen Klassen zu unterscheiden. Die Anwendbarkeit der Regeln wurde an sechs mitteleuropäischen Städten mit unterschiedlichen klimatischen Bedingungen verifiziert, und zwar in Bratislava, Nitra und Žilina (alle Slowakei) und Zakopane (Polen) sowie Kaposvár und Orosháza (beide Ungarn). Die Gesamtklassifizierungsgenauigkeit erreichte überall 78% – 90% auf der ersten Hierarchieebene und 76% – 89% auf der zweiten. Die Leistungsfähigkeit unserer Methode wurde mit der Random Forest Methode (RF) und Trainingsgebieten in Bratislava verglichen. Die Ergebnisse zeigten, dass ohne flächenspezifische Trainingsgebiete die Genauigkeit der RF-Methode 5 bis 35 Prozentpunkte geringer als die Genauigkeit unserer vorgeschlagenen regelbasierten Methode ist. Ohne die Zusammenfassung der NDVI-Indizes ist die Genauigkeit der RF-Klassifikation noch einmal um 10 bis 30 Prozentpunkte geringer.
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Acknowledgements
This work was supported by the Research and Development Operational Programme funded by the ERDF under Grant ITMS 26240220086 (Comenius University in Bratislava Science Park) and the Slovak Research and Development Agency under Grant APVV-15-0136. The authors would like to thank the anonymous reviewers for their valuable feedback and comments on the manuscript that improved it significantly.
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Bobáľová, H., Benová, A. & Kožuch, M. Hierarchical Object-Based Mapping of Urban Land Cover Using Sentinel-2 Data: A Case Study of Six Cities in Central Europe. PFG 89, 15–31 (2021). https://doi.org/10.1007/s41064-020-00135-8
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DOI: https://doi.org/10.1007/s41064-020-00135-8