Affiliation:
1. Department of the “Oceanology”, Southern Federal University, Zorge, 40, 340015 Rostov-on-Don, Russia
Abstract
This study presents an in-depth analysis of land use and land cover change on the Kerch Peninsula over a period spanning three decades. Convolutional neural networks were employed in conjunction with satellite imagery analysis to map and quantify the changes in land use and cover. This revealed significant trends and transformations within the peninsula’s landscape. The analysis revealed a notable increase in urban expansion, particularly at the expense of natural ecosystems. Furthermore, there was a notable reversion of agricultural lands to grasslands, driven by economic downturns and reduced agricultural activity. These land cover changes underscore the urgency of implementing sustainable land management policies. The study recommends the establishment of conservation easements to protect remaining natural ecosystems, the initiation of reforestation programs to restore degraded lands, and the development of comprehensive water management strategies to address the peninsula’s hydrological challenges. Furthermore, the study underscores the pivotal importance of integrating change analysis and predictive modeling to anticipate future land cover scenarios and inform effective land management strategies. The model developed through this research, which employs advanced remote sensing and GIS technologies, provides a robust framework for understanding and managing land use and land cover change. This model can serve as a reference for similar regions globally, offering insights that can inform sustainable land use practices and policy decisions. The findings of this study have implications that extend beyond the Kerch Peninsula. They provide insights that can inform the management of land use changes and the conservation of natural landscapes in regions facing comparable socio-economic and environmental challenges.
Reference77 articles.
1. Analysis of Land Use/Land Cover Change (LULCC) and Debris Flow Risks in Adama District, Ethiopia, Aided by Numerical Simulation and Deep Learning-Based Remote Sensing;Bojer;Stoch. Environ. Res. Risk Assess.,2023
2. Spatially Explicit Simulation of Land Use/Land Cover Changes: Current Coverage and Future Prospects;Ren;Earth-Sci. Rev.,2019
3. Gaur, S., and Singh, R. (2023). A Comprehensive Review on Land Use/Land Cover (LULC) Change Modeling for Urban Development: Current Status and Future Prospects. Sustainability, 15.
4. A Review on Change Detection Method and Accuracy Assessment for Land Use Land Cover;Chughtai;Remote Sens. Appl. Soc. Environ.,2021
5. Current Challenges of Implementing Anthropogenic Land-Use and Land-Cover Change in Models Contributing to Climate Change Assessments;Prestele;Earth Syst. Dyn. Discuss.,2017