Spatio-Temporal Assessment and Future Projection of Land Cover Dynamics in Savanna Woodlands of Sudan Using Machine Learning and CA–ANN Modeling

Yasin, Emad Hassan Elawad és Koreň, Milan és Czimber, Kornél (2026) Spatio-Temporal Assessment and Future Projection of Land Cover Dynamics in Savanna Woodlands of Sudan Using Machine Learning and CA–ANN Modeling. REMOTE SENSING, 18 (7). ISSN 2072-4292

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Absztrakt (kivonat)

Spatio-temporal analysis of land cover (LC) dynamics is essential for understanding landscape transformation in semi-arid woodland ecosystems. This study assessed historical and projected land cover changes in the Elnour Natural Forest Reserve (ENFR), Sudan, from 1995 to 2060. Historical maps for 1995, 2008, and 2021 were generated using a Random Forest classifier, while future scenarios for 2034, 2047, and 2060 were simulated using a Cellular Automata–Artificial Neural Network (CA–ANN) model. The results show that semi-bare land expanded from 23.1% in 1995 to 40.0% in 2021, while dense woodland declined from 26.7% to 15.7%, indicating substantial structural transformation of the landscape. Open woodland exhibited partial recovery, increasing to 39.9% in 2021. Future projections indicate a moderate increase in dense woodland to 23.8% by 2060; however, semi-bare land remains the dominant class, reflecting persistent landscape instability. These findings demonstrate the coexistence of degradation and localized regeneration processes in ENFR and highlight the importance of long-term monitoring of land cover dynamics in dryland environments. The study further shows that integrating machine learning classification with spatially explicit CA–ANN modeling provides an effective framework for analyzing historical trends and exploring potential future trajectories of land cover change in data-limited semi-arid regions.

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agrártudományok > erdészeti és vadgazdálkodási tudományok
műszaki tudományok > agrárműszaki tudományok

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Mű tipusa:	Cikk
SWORD Depositor:	Teszt Sword
Felhasználó:	Csaba Horváth
A mű MTMT azonosítója:	MTMT:37079329
Dátum:	17 Ápr 2026 10:53
Utolsó módosítás:	17 Ápr 2026 10:53
URI:	http://publicatio.uni-sopron.hu/id/eprint/3980

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