# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 1 Introduction  
Enhancing our comprehension of the Earth's surface coverage is pivotal for a wide range of activities and applications. These encompass urban and regional planning [1], environmental impact assessment [2], global warming mitigation [3; 4], and the monitoring of natural phenomena [2; 5; 6]. This type of research facilitates improved management and understanding of the ongoing transformations [7], aiming at their preservation. These changes exert a substantial impact not only on the environment and climate [8; 9] but also on human society. This impact manifests in the depletion of ecosystem services [10]; heightened susceptibility to extreme events like floods, landslides, and droughts [11]; and environmental degradation [12], among other consequences. The generation of land cover maps using satellite images offers significant advantages due to their economic efficiency, rapidity, and extensive coverage in both time and space [13]. Consequently, the creation of this type of product stands out among the fields of application of Remote Sensing (RS) technology [14; 15; 16]. In recent years, the application of machine learning algorithms has garnered considerable attention in ecological research [17; 18; 19]. The use of this technique spans numerous geoscience topics, including extreme weather pattern analysis, climate change projections, precipitation nowcasting, and carbon flux prediction. Additionally, it plays a significantrole in image fusion, registration, change detection, image segmentation, and drought forecasting [20]. These algorithms facilitate the classification of satellite-acquired information based on clearly identifiable characteristics, resulting in a product that identifies the different land cover types designed for specific end uses [21]. Among the array of algorithms available, Random Forest (RF) is one of the most widely employed [10; 22; 23; 24]. Renowned for its robust classification methodology [25], RF excels in producing accurate maps for complex land cover categories [26], handling noisy data [24; 27; 28] at relatively modest computational costs [29; 30]. Moreover, its nonparametric nature and effectiveness with limited datasets compared to study area size make it an ideal candidate for this type of analysis [17; 23; 26; 31]. The Google Earth Engine (GEE) platform, launched by Google in 2010, enables the analysis of a huge amount of data in a short time [32], allowing the general public to process large quantities of satellite information. With its impressive computational capabilities and a broad range of available RS products encompassing both raw and processed data, GEE has swiftly emerged as one of the most utilised platforms in this field [33]. This research presents a quantitative analysis of land cover change from 1984 to 2022, using Landsat, one of the most widely used satellite constellations in this area [34; 35] due to its spatiotemporal resolution, suitable for studying regional land transformations. By selecting the entire Landsat series, we can effectively study the evolution of land cover change over an extended period, spanning nearly 40 years from the earliest to the most recent date. This long-term perspective allows for a comprehensive analysis of trends and patterns, providing valuable insights into the dynamics of land cover transformation. We choose to conduct the study in the Valencian Community, a region with unique ecosystems of strong environmental importance. We ensured the accuracy of our classification results by comparing them with field measurements and existing datasets from the Copernicus service and European Space Agency (ESA). The detailed account of land cover changes presented here provides essential insights for policymaking, delivering a comprehensive overview of land cover alterations within the Valencian Community over the past four decades.