# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 4 Discussion  
Regarding the validation of the algorithm, the confusion matrix (Figure 5) reveals a generally accurate reflection of real land cover. There is an exception in the classification of sparse vegetation, occasionally misidentified as dense vegetation. This discrepancy may arise from the challenge of precisely assessing vegetation density from ground-level perspectives, where the angle of vision varies, making it difficult to discern the intensity/density of vegetation. However, it is important to highlight that the classification consistently identifies these areas as vegetation, mitigating the impact of the error. It is worth remembering that mapping extensive areas in complex landscapes proves challenging due to abrupt changes in environmental gradients such as humidity, altitude, and temperature. These diverse landscapes exhibit land cover categories that are challenging to differentiate spectrally, given the low separability between classes and high intra-class variability [24]. Anyway, the metrics obtained collectively affirm the robust performance of the RF model, characterised by a commendable classification accuracy and a substantial concordance between predictions and ground truth values. Moving on to the analysis of change at a temporal level, the steady growth of the Built-up Areas class can be attributed to significant urban expansion during the 1980s/1990s, which has continued, albeit to a lesser extent over the decades, leading to a corresponding decline in vegetation. Figure 14: Confusion matrix of the 2023 land cover map calculated with Random Forest algorithm and the one provided by ESA. Class 80 corresponds to Water Bodies class; class 50 represents Built-up Areas class; classes 10, 20 and 90 pertain to Dense Vegetation; classes 30, 40, and 60 indicate Sparse Vegetation; and None represents the Other class. The oscillation in the ratio between Dense and Sparse Vegetation is attributed to annual variations in the rainfall regime, which influence plant growth, and to shifts in crop types, resulting in alternating vegetation patterns. The years 1990 and 2020, identified by the national Spanish meteorological agency (Agencia Estatal de Meteorologia, AEMET) as concluding years of periods marked by substantial rainfall, exhibit the highest percentages of Dense Vegetation. In contrast, the years 1985, 1995, and 2005, corresponding to periods of pronounced aridity, display lower percentages of Dense Vegetation and higher proportions of Sparse Vegetation. This pattern underscores the impact of reduced precipitation during these years, contributing to diminished plant growth and a shift toward a prevalence of less densely vegetated areas. Finally, the decline in the Other class indicates that areas previously classified as bare soil, beaches, etc., have likely undergone urbanisation and now form part of Built-up Areas. Analysing the land covers in greater detail (Figure 10), it is notable how urban expansion in rural areas began around the years 2000/2005, while the most substantial city expansion occurred from 1985 to 1990. Numerous areas classified as non-urban in 1985 began urbanised zones over the subsequent years. These regions, initially characterised as rural landscapes, have undergone substantial development, evolving into small towns or cities. The spatial distribution highlights the concentration of increased urbanisation along key transportation arteries and adjoining the pre-established urban core. These transformations underscore the dynamic and evolving nature of land cover in the Valencian Community, portraying the intricate interplay between urban expansion and the transformation of rural landscapes. The validation process emphasised the accuracy and precision of the maps produced. In particular, the high OA and Kappa values of the holdout validation demonstrate the strength and reliability of the data.