# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 5 Conclusions  
In this study, we conducted a comprehensive analysis of land cover dynamics in the Valencian Community, spanning from 1984 to 2023. Employing advanced algorithms, specifically RF and CART, we initially classified the land cover for the year 2023 using Landsat 9 imagery. The accuracy test results favoured RF, demonstrating superior OA (0.95) and Kappa coefficient (0.93) compared to CART. The subsequent validation process with in situ data highlighted the model's generally accurate reflection of real land cover, notwithstanding occasional misclassifications, particularly in Sparse Vegetation. This discrepancy was attributed to challenges in precisely assessing the vegetation density from ground-level perspectives, mitigated by the consistent identification of these areas as vegetation. Moreover, mapping extensive areas in complex landscapes proved challenging due to environmental gradients influencing spectral differentiability. Subsequent temporal analysis revealed a notable surge in urbanised surface area, affecting vegetation classes. Notably, the Built-up Areas class exhibited a substantial 95% increase from 1984 to 2022, displaying consistent urban expansion trends. Linear and polynomial adjustments further affirmed this growth pattern, with the Built-up Areas class demonstrating a polynomial regression fit (R\({}^{2}\) of 0.95). Regarding the other land cover classes, Water Bodies maintained relative stability, exhibiting minor fluctuations. Conversely, Dense and Sparse Vegetation classes collectively witnessed a cumulative decrease of 1.5% from 1984, indicative of the encroachment of urban areas over the years. Further analysis of the Other class unveiled a significant negative trend, underscoring the influence of urbanisation on areas once classified as bare soil and beaches. Holdout Validation, a pivotal step in assessing model performance, consistently showcased high accuracy, with OA ranging from 92% to 95% and Kappa coefficients between 89% and 92%. This method, involving data partitioning into training and testing sets, ensured the reliability and effectiveness of our model across diverse years. A comparison with external land covers revealed reasonable agreement, albeit with some discrepancies attributed to resolution differences and class definitions. The study's temporal focus on summer versus the yearly coverage of external products contributed to the observed variations. These findings not only elucidate the tangible consequences of urban growth in the Valencian Community but also offer valuable insights into broader environmental implications. The pronounced evolution from non-urban to urbanised areas over the years emphasises the transformative impact of human activities on the regional landscape. This dynamic urbanisation, as depicted in the land cover analysis, has implications beyond immediate spatial changes. In recent years, numerous studies have demonstrated the relationship between increased urban temperatures and specific surfaces or the lack of vegetation, among other factors [62]. Having a detailed classification map and correlating it with temperature maps, for instance, would allow for the development of mitigation strategies. These strategies could include the implementation of green spaces, thereby reducing urban temperatures and mitigating the Urban Heat Island effect. Such integrative approaches are crucial for creating sustainable and liveable urban environments [63; 64]. Moreover, this comprehensive view of land cover dynamics within the Valencian Community provides a valuable tool for anticipating and managing environmental challenges. The interplay between urbanisation, land cover changes, and environmental impacts underscores the need for sustainable urban planning and environmental management strategies. As urban areas continue to evolve, these insights become crucial for informed decision making, contributing to a more resilient and ecologically balanced future for the region.