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Hedgerow Map of Bavaria, Germany, based on Orthophotos and Convolutional Neural Networks

Huber Garcia, Verena und Kriese, Jennifer und Asam, Sarah und Dirscherl, Mariel und Stellmach, Michael und Buchner, Johanna und Kerler, Kristel und Gessner, Ursula (2025) Hedgerow Map of Bavaria, Germany, based on Orthophotos and Convolutional Neural Networks. Remote Sensing Applications: Society and Environment, 27, Seiten 1-23. Elsevier. doi: 10.1016/j.rsase.2025.101451. ISSN 2352-9385.

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Kurzfassung

Hedgerows play a significant role in biodiversity preservation, carbon sequestration, soil stability and the ecological integrity of rural landscapes. Understanding their current condition and future development is therefore crucial for a range of stakeholders such as municipalities, state agencies or environmental organizations. The wall-to-wall mapping and characterization of hedgerows in-situ is, however, very labour-, time- and cost-intensive. This impedes a regular monitoring at adequate intervals. In the Federal State of Bavaria, Germany, the hedgerow biotope mapping is repeated every 20-30 years for each district. State-wide consistent and up-to-date data are hence not available. In this study we present an approach for mapping all hedgerows in Bavaria using orthophotos and deep learning. We used hedgerow polygons of the federal in-situ biotope mapping from 5 focus districts as well as additional manually digitized polygons as training and test data and orthophotos as input in a DeepLabV3 Convolutional Neural Network (CNN). The CNN has a Resnet50 Backbone and was optimized using the Dice loss as cost function. The orthophotos were acquired in 2019 – 2021. They have a spatial resolution of 20 cm and were fed to the CNN at tiles of 125 x 125 m. The generated hedgerow probability tiles were post-processed through merging and averaging the overlapping tile boarders, shape simplification and filtering. The resulting hedgerow vector data set achieved medium overall accuracies (precision = 0.43, recall = 0.53, F1-score = 0.48). The model generally overestimated the number of hedgerows, and hedgerows were often confused with riparian as well as urban vegetation. Looking at each hedgerow polygon individually, the mapping accuracy varied considerably, with a median F1-score of 0.51 for all detected objects. In addition, we found differences in accuracies among districts in different landscapes. For example, the Hassberge district, a landscape rich of hedgerows, reached a F1-score of 0.61. A comprehensive comparison with the Copernicus High Resolution Layer (HRL) Small Woody Features (SWF) revealed significant differences between the datasets. About 43 % of the hedgerows in our data set were not present in the SWF layer. Especially narrow, elongated vegetated structures are not captured in the SWF product. This highlights the potential to use our state-wide hedgerow map of Bavaria in combination with the SWF dataset, but also by itself, for a range of administrative, statistical and nature conservation applications.

elib-URL des Eintrags:https://elib.dlr.de/212234/
Dokumentart:Zeitschriftenbeitrag
Titel:Hedgerow Map of Bavaria, Germany, based on Orthophotos and Convolutional Neural Networks
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Huber Garcia, Verenaverena.hubergarcia (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Kriese, JenniferJennifer.Kriese (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Asam, Sarahsarah.asam (at) dlr.dehttps://orcid.org/0000-0002-7302-6813NICHT SPEZIFIZIERT
Dirscherl, MarielMariel.Dirscherl (at) dlr.dehttps://orcid.org/0000-0002-3324-7646NICHT SPEZIFIZIERT
Stellmach, MichaelMichael.Stellmach (at) lfu.bayern.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Buchner, JohannaJohanna.Buchner (at) lfu.bayern.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Kerler, KristelKristel.Kerler (at) lfu.bayern.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Gessner, Ursulaursula.gessner (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:10 Januar 2025
Erschienen in:Remote Sensing Applications: Society and Environment
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:27
DOI:10.1016/j.rsase.2025.101451
Seitenbereich:Seiten 1-23
Verlag:Elsevier
ISSN:2352-9385
Status:veröffentlicht
Stichwörter:linear woody vegetationbiotopesaerial imagesCNNDeepLabV3Small Woody Features
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche
Hinterlegt von: Asam, Dr. Sarah
Hinterlegt am:30 Jan 2025 11:56
Letzte Änderung:30 Jan 2025 11:56

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