Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City
Rapid urbanisation impacts land use and land cover (LULC), contributing to environmental challenges such as urban heat islands (UHIs) and air pollution. This study investigates the morphological changes in Sofia’s LULC from 1990 to 2018, predicts future LULC patterns, and evaluates the eff...
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| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1159/2025/isprs-archives-XLVIII-G-2025-1159-2025.pdf |
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| author | D. Pavlov D. Pavlov L. L. Vitanova D. Petrova-Antonova |
| author_facet | D. Pavlov D. Pavlov L. L. Vitanova D. Petrova-Antonova |
| author_sort | D. Pavlov |
| collection | DOAJ |
| description | Rapid urbanisation impacts land use and land cover (LULC), contributing to environmental challenges such as urban heat islands (UHIs) and air pollution. This study investigates the morphological changes in Sofia’s LULC from 1990 to 2018, predicts future LULC patterns, and evaluates the effectiveness of Cellular Automata-Artificial Neural Network (CA-ANN) models for LULC forecasting. Historical LULC data from Urban Atlas (UA) and Corine Land Cover (CLC) and predictive variable data such as population density, road networks, water bodies, and elevation are used to model transition potential and simulate future land cover. The analysis revealed a slight increase in urban areas, primarily at the expense of cropland, between 1990–2018. Simulations for 2074 suggest a continued urban expansion, with a significant cropland decline. Validation of CA-ANN models showed high accuracy but limited ability to predict small-scale transitions due to low transition potential. This study highlights the importance of input data quality and temporal range in predictive accuracy. Furthermore, it provides valuable insights for urban planning, sustainable development, and climate adaptation strategies by offering a data-driven approach to forecasting LULC changes. Future research should integrate additional socio-economic factors and alternative approaches to enhance prediction reliability. |
| format | Article |
| id | doaj-art-6bc20f975e6c4eb88d0afab472ef4926 |
| institution | Kabale University |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-6bc20f975e6c4eb88d0afab472ef49262025-08-20T03:34:53ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-07-01XLVIII-G-20251159116610.5194/isprs-archives-XLVIII-G-2025-1159-2025Past Expansion and Future Prediction of Land Use and Land Cover of Sofia CityD. Pavlov0D. Pavlov1L. L. Vitanova2D. Petrova-Antonova3GATE Institute, Sofia University “St. Kliment Ohridski”, 1164 Sofia, BulgariaFaculty of Technology, Policy and Management, TU Delft, Delft, the NetherlandsGATE Institute, Sofia University “St. Kliment Ohridski”, 1164 Sofia, BulgariaGATE Institute, Sofia University “St. Kliment Ohridski”, 1164 Sofia, BulgariaRapid urbanisation impacts land use and land cover (LULC), contributing to environmental challenges such as urban heat islands (UHIs) and air pollution. This study investigates the morphological changes in Sofia’s LULC from 1990 to 2018, predicts future LULC patterns, and evaluates the effectiveness of Cellular Automata-Artificial Neural Network (CA-ANN) models for LULC forecasting. Historical LULC data from Urban Atlas (UA) and Corine Land Cover (CLC) and predictive variable data such as population density, road networks, water bodies, and elevation are used to model transition potential and simulate future land cover. The analysis revealed a slight increase in urban areas, primarily at the expense of cropland, between 1990–2018. Simulations for 2074 suggest a continued urban expansion, with a significant cropland decline. Validation of CA-ANN models showed high accuracy but limited ability to predict small-scale transitions due to low transition potential. This study highlights the importance of input data quality and temporal range in predictive accuracy. Furthermore, it provides valuable insights for urban planning, sustainable development, and climate adaptation strategies by offering a data-driven approach to forecasting LULC changes. Future research should integrate additional socio-economic factors and alternative approaches to enhance prediction reliability.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1159/2025/isprs-archives-XLVIII-G-2025-1159-2025.pdf |
| spellingShingle | D. Pavlov D. Pavlov L. L. Vitanova D. Petrova-Antonova Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City |
| title_full | Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City |
| title_fullStr | Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City |
| title_full_unstemmed | Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City |
| title_short | Past Expansion and Future Prediction of Land Use and Land Cover of Sofia City |
| title_sort | past expansion and future prediction of land use and land cover of sofia city |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1159/2025/isprs-archives-XLVIII-G-2025-1159-2025.pdf |
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