The city of Seville (Spain) is made up of a historical network of pre-existing city overlaps, which increase the economic and heritage value of certain urban areas. To date, green spaces are one of the most important variables in determining the economic value of housing. Thus, this paper uses the hedonic technique and geostatistical analysis with GIS as a methodological approach to infer the economic influence of urban green spaces on housing prices. Along with the traditional variables used to explain dwelling prices, the size of the green space has also been taken into account as an environmental variable affecting prices. The sample consists of 1000 observations collected from Seville. According to the findings, the most relevant variables depend on the hedonic model. Still, in general terms, a dwelling’s selling price is related to basic explanatory variables such as living area, number of rooms, age, and number of baths. The green area per inhabitant present in a dwelling’s district is also included as part of these basic explanatory variables. In conclusion, the hedonic linear model is the model that best fits housing prices where the values are similar to those obtained by kriging regardless of the district. Based on this research, each square meter of green space per inhabitant in a district raises the housing value by 120.19 €/m².

Located in the south of the Iberian Peninsula, the Doñana salt marshes occupy around half of Doñana National Park and are currently considered among the most important wetlands worldwide due to the importance of their ecosystem. In this research work, using a novel patented procedure, the effects of climate change on the study area between 2009 and 2020 were evaluated. For this reason, DEMs were downloaded from the 30-meter Shuttle Radar Topography Mission (SRTM). Furthermore, to check the depth of the flooded area, 792 satellite images (L5 TM, L7 ETM+, and L8 OLI) with a resolution of 30 m were analyzed. The results show how the combined use of geomatic techniques, such as radar, optical, and geographic information system (GIS) data, along with regression models and iterative processes, plays a key role in the prediction and analysis of the flooded area volume in the Doñana salt marshes. Another significant contribution of this work is the development of a new remote sensing index. In conclusion, given that the study area depends on its aquifers’ status, it would be advisable to implement policies aimed at eradicating illegal aquifer extraction, as well as recovery plans to avoid the complete clogging of this salt marsh.

This study investigates the use of high-resolution RGB aerial imagery from Spain’s National Aerial Orthophotography Plan (PNOA) for archeological feature detection through spectral index analysis and unsupervised clustering. Focusing on the Roman site of Munigua, eight orthophotographs acquired between 2014 and 2024 were analyzed to compute five RGB-based spectral indices: VARI, GLI, ExG, CSI, and BI. These indices were used to detect surface spectral anomalies potentially linked to buried archeological structures. A multi-temporal approach was employed, with Principal Component Analysis (PCA) and K-Means clustering applied independently to each image. This allowed for the identification of temporally persistent anomalies (areas that remained within the same spectral cluster across multiple years), suggesting the presence of underlying anthropogenic features. Despite the lack of near-infrared data, the combination of RGB-based indices and temporal clustering proved effective for non-invasive prospection. The methodology is scalable, repeatable, and relies entirely on open-access datasets, making it suitable for broader applications in heritage monitoring and landscape archeology. The results underscore the potential of RGB imagery and time-series clustering in detecting subtle archeological signals within complex vegetated environments.