An automated approach for updating land cover change maps using satellite imagery

• Autorzy: Bartold Maciej , Kluczek Marcin , Dąbrowska-Zielińska Katarzyna

Identyfikatory

DOI

10.34659/eis.2025.92.1.810

Warianty tytułu

PL

Zautomatyzowana metoda aktualizacji map zmian pokrycia terenu na podstawie zobrazowań satelitarnych

• Języki publikacji: EN

Abstrakty

EN

Land cover change is a critical aspect of global environmental dynamics, influencing ecosystems, biodiversity, and climate change. This study presents an automated approach for updating land cover maps across Europe, combining Sentinel-1 and Sentinel-2 satellite imagery within the Copernicus framework. The application utilises machine learning algorithms to categorise land cover changes into classes such as no change, retained/reclassified, deurbanisation, afforestation, and urbanisation. Case studies in Poland, Greece, and Italy demonstrate the application's effectiveness, revealing the impact of motorway construction, afforestation efforts, and rapid urbanisation. Overall accuracy rates ranged from 68% to 95%, emphasising the reliability of the methodology. The open-source application, implemented in Python Jupyter and Voila, provides a user-friendly platform for researchers and stakeholders to monitor and analyse land cover changes, supporting informed decision-making for sustainable land management and conservation efforts. This study contributes valuable insights to understanding and addressing the environmental consequences of land cover changes in diverse geographical contexts.

PL

Zmiany w pokryciu terenu stanowią istotne zagadnienie w badaniach globalnych procesów środowiskowych, wpływając na ekosystemy, bioróżnorodność oraz zmiany klimatu. W niniejszej pracy przedstawiono zautomatyzowaną metodę aktualizacji map pokrycia terenu w Europie, wykorzystującą zobrazowania z misji satelitów Sentinel-1 oraz Sentinel-2 w ramach programu Copernicus. Algorytmy uczenia maszynowego posłużyły do klasyfikacji zmian pokrycia terenu w kategoriach takich jak brak zmiany, zachowane-przeklasyfikowane, dezurbanizacja, zalesianie oraz urbanizacja. Skuteczność rozwiązania została potwierdzona w studiach przypadków przeprowadzonych w Polsce, Grecji i Włoszech, gdzie zidentyfikowano wpływ budowy autostrad, działań zalesieniowych oraz intensywnej urbanizacji. Dokładność klasyfikacji wynosiła od 68% do 95%, co świadczy o wysokiej jakości zastosowanej metodyki. Aplikacja, opracowana w otwartym środowisku Python Jupyter i Voila, zapewnia intuicyjną platformę dla naukowców i decydentów, umożliwiającą monitorowanie oraz analizę zmian pokrycia terenu. Narzędzie to wspiera podejmowanie świadomych decyzji dotyczących zrównoważonego zarządzania gruntami i ochrony środowiska. Niniejsze badanie dostarcza cennych informacji na temat konsekwencji zmian pokrycia terenu w różnych kontekstach geograficznych, przyczyniając się do lepszego zrozumienia i skuteczniejszego zarządzania tymi procesami.

Słowa kluczowe

EN

application; land use; land cover; machine learning; satellite images

PL

aplikacja; pokrycie terenu; użytkowanie ziemi; uczenie maszynowe; obrazy satelitarne

• Wydawca: Polskie Stowarzyszenie Ekonomistów Środowiska i Zasobów Naturalnych
• Czasopismo: Economics and Environment
• Rocznik: 2025
• Tom: No. 1
• Strony: art. no. 810
• Opis fizyczny: Bibliogr. 41 poz., rys.

Twórcy

autor

Bartold Maciej

• Remote Sensing Centre, Institute of Geodesy and Cartography, Modzelewskiego Street 27, 02-679 Warsaw, Poland

autor

Kluczek Marcin

• Remote Sensing Centre, Institute of Geodesy and Cartography

• https://orcid.org/0000-0003-2133-0984

autor

Dąbrowska-Zielińska Katarzyna

• Remote Sensing Centre, Institute of Geodesy and Cartography

• https://orcid.org/0000-0001-8928-1942

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