Future scenarios impact on land use change and habitat quality in Lithuania

Anticipating future land use and land cover (LULC) changes can improve our knowledge of the complexity of human-environment interactions that lead to transformations in the landscape. Therefore, it is key to understand these LULC changes under different scenarios and how they affect habitat quality (HQ) a key indicator for ecosystem services (ES) supply quality. This work aims to study the impacts of LULC changes under different scenarios: business as usual (A0), urbanisation (A1), land abandonment and afforestation (A2) and agriculture intensification (A3) in 2050. To simulate future LULC changes we applied the Cellular Automata (CA) method, and to assess HQ, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model was used. Spatial autocorrelation was assessed with a Moran's I index and the Getis Ord* hotspot analysis. The result showed that the LULC model calibration and validation were accurate (80%). Between 1990 and 2018 there was an increase in urban areas and forest and woodlands, which was reflected in the A0 scenario in 2050. Under the A1 scenario there was an increase in the urban area (4628 ha) compared to 2018, and in the most important cities (e.g., Vilnius, Kaunas, Klaipeda) in the scenario A2 there was an increase of 375,820 ha of woodland and forest. Finally, under the scenario A3, a large growth in cropland area (884,030 ha) was identified. HQ model had a better validation using three cover density data (r2 = 0.67), than with imperviousness (r2 = 0.26). A2 scenario showed the highest HQ and A3 scenario have the lowest HQ. The land uses of 1990, 2018, and A3 scenario had a clustered distribution while A0, A1 and A2 showed a random pattern. The results can support policy-makers by assessing the impact of future LULC changes in Lithuania.

Automated summary

This study reveals the trends of landscape changes in Lithuania in 2050 by simulating future land use and cover changes under different scenarios and assessing the impacts on habitat quality. Using Cellular Automata method and InVEST model, the analysis shows significant differences in habitat quality under different scenarios, providing important insights for policymakers.