Francisco Salgado-Castillo

Vegetation phenology is a primary indicator of ecosystem functioning and climate sensitivity, yet robust monitoring at landscape scales remains challenging in land-use heterogeneous regions such as England. The Copernicus High-Resolution Vegetation Phenology and Productivity product (HR-VPP), derived from Sentinel-2, provides 10 m phenological metrics, including start of season (SOS), end of season (EOS), and length of season (LOS), enabling unprecedented fine-scale mapping of vegetation dynamics.In this contribution, we present a workflow to characterise phenological timing across England using HR-VPP time series from 2017 to 2024. We quantify spatial patterns and interannual variability in key phenometrics and summarise trends across major land cover types.This study provides: (i) an England-scale baseline of phenological timing at 10 m resolution; (ii) an assessment of recent anomalies and climate-driven variability within the HR-VPP record; and (iii) practical guidance for integrating high-resolution Copernicus products into national ecological monitoring. This work supports the broader application of HR-VPP for assessing vegetation resilience to climate and land-use pressures at the national level.

Space is one of the UK’s fastest-growing industry sectors of the last decade. Recognising this, in 2021 the UK Government’s first National Space Strategy established a new vision to make the nation one of the most innovative and attractive worldwide space economies. As part of the strategic funding programme, in 2023, the UK Space Agency (UKSA) funded a scoping study to assess the potential of satellite data to address issues that the three North East England Local Resilience Forums face at all stages of implementing the UK’s Integrated Emergency Management Framework (IEMF). Through dedicated workshops convened by two North East England universities, regional stakeholders from the emergency domain and related industries identified three case studies ripe for applying satellite data in support of multi-agency IEMF activities. Master’s students in the UK’s Centre for Doctoral Training in Geospatial Systems then undertook a month-long integrated group project to assess the potential for satellite imagery to be applied in the identified application areas. The research reported in this paper demonstrates how satellite imagery may be adopted to help address challenges posed during power outages, for mitigating illegal waste site fires, and during periods of snow and extreme cold. While the maturity levels of satellite applications vary in the three case studies due to data availability and image resolution, all three cases demonstrate that space data, particularly when augmented with additional geospatial information, help to enhance IEMF analysis. It is anticipated that the findings from the study will help stakeholders involved in IEMF management appreciate the added value of integrating satellite data into their current processes and analyses. By empowering key stakeholders to use satellite applications more effectively, it is predicted that better decisions will be achieved, thereby improving emergency risk management. A prototype dashboard, an output of the research to demonstrate the potential of space data for emergency management, is available online.

The increase in noise emissions is associated with an increase in the population, with anthropic activities, and it is in urban centers where a series of effects on both the population and the environment occur. This research The present study covered the evaluation of the noise behavior in the city of Cuenca during 2019, for which monitoring was carried out in 31 points distributed in the city based on the average daily traffic, the ordinance of use and occupation of the land and the dynamics of the population; the The national environmental standard TULSMA (Unified Text of Secondary Legislation of the Ministry of the Environment) was taken as a reference. To establish the comparison of the data collected with a (low-cost, noise level) sensor and a sound level meter, measurements were made in 6 monitoring stations established on the basis of noise complaints made by citizens to the autonomous decentralized municipal government of Cuenca (GAD) and the security conditions to place for the placement of the equipment. The results showed that noise, in the city of Cuenca during 2019, noise is above the TULSMA environmental standard in Zone EQ1 (100 % of the measurements), Zone R1 (96 % of the measurements), Zone CM (100 % of the measurements) and in Zone ID3 / ID4 (72 % of the measurements). There was a high correlation coefficient (r = 0.8) and determination coefficient (R2 >0.6), between the data collected with the sensor and the sound level meter. It is worth highlighting the use of the calibrated sound level meter when making sound measurements with various instruments, as it allows the data to be verified and validated.

Essential climate variables (ECVs) have been recognized as crucial information for achieving Sustainable Development Goals (SDGs). There is an agreement on 54 ECVs to understand climate evolution, and multiple rely on satellite Earth observation (abbreviated as s-ECVs). Despite the efforts to encourage s-ECV use for SDGs, there is still a need to further integrate them into the indicator calculations. Therefore, we conducted a systematic literature review to identify s-ECVs used in SDG monitoring. Results showed the use of 14 s-ECVs, the most frequent being land cover, ozone, precursors for aerosols and ozone, precipitation, land surface temperature, soil moisture, soil carbon, lakes, and leaf area index. They were related to 16 SDGs (mainly SDGs 3, 6, 11, 14, and 15), 33 targets, and 23 indicators. However, only 10 indicators (belonging to SDGs 6, 11, and 15) were calculated using s-ECVs. This review raises research opportunities by identifying s-ECVs yet to be used in the indicator calculations. Therefore, indicators supporting SDGs must be updated to use this valuable source of information which, in turn, allows a worldwide indicator comparison. Additionally, this review is relevant for scientists and policymakers for future actions and policies to better integrate s-ECVs into the Agenda 2030.

El análisis y comprensión de la incidencia de los factores socioeconómicos, de percepción y de mesoescala urbana sobre los modos de movilidad de los niños y niñas en edad escolar es fundamental para fomentar el uso de modos más sustentables. Este estudio explora la relación entre factores socioeconómicos, de percepción y de mesoescala urbana sobre los modos de movilidad diaria de niños y niñas en edad escolar (6 a 12 años) en la ciudad intermedia de Cuenca (Ecuador, América Latina). Se utilizó la herramienta Random Forest, como método de aprendizaje automático supervisado, para clasificar los modos de movilidad en: “caminar”, “autobús” y “automóvil”, y para identificar la importancia de los factores en cada modo. Los datos se obtuvieron de una encuesta de movilidad realizada en hogares de Cuenca en el 2019. A pesar de que buseta es un modo de movilidad usual para escolares, no pudo ser considerado en este estudio ya que no se contemplaba en la encuesta original utilizada. Los resultados mostraron que el mejor modelo para los modos de movilidad “caminar” y “autobús” fue con todos los grupos de factores (socioeconómicos, de percepción y de mesoescala urbana), mientras que para “automóvil”, como se esperaba, fue el modelo con factores socioeconómicos el más relevante. Si bien los factores más importantes fueron el número de vehículos por familia y nivel socioeconómico, también encontramos que los factores de percepción son relevantes para incentivar el caminar como un modo de movilidad cotidiana . Del mismo modo, para fomentar el uso del autobús, deben tenerse en cuenta los factores urbanos de mesoescala. Este estudio aporta datos y un enfoque metodológico para contribuir a la política pública en materia de movilidad activa en edad escolar. Palabras clave: Modos de movilidad, niños y niñas de 6 a 12 años, factores de mesoescala urbana, Random Forest. AbstractAnalyzing and understanding the incidence of socioeconomic, perception and urban mesoscale factors on mobility modes of school-age children is essential to motivate a more sustainable mobility. This study explores the relationship between socioeconomic, perception and urban mesoscale factors on the daily mobility modes of school-age boys and girls (6 to 12 years old) in the intermediatesized city of Cuenca (Ecuador, Latin America). Random Forest, as a classifcation machine learning method, was used to classify the mobility modes into walk, bus and car, and to identify factor importance in each mode. The data were obtained from a mobility survey carried out on Cuenca households in 2019. Even if school bus is a usual mobility mode for schoolchildren, it could not be accounted in this study because it was not contained in the original survey. The results showed that the best model for walk and bus mobility modes was with all the factor groups, while for Car, as expected, was the socioeconomic model. Even if the most important factors were cars´ number per family and socioeconomic level, we also found that, in order to encourage walking as the mobility mode, the perception factors are relevant. Similarly, in order to encourage bus mobility mode, the urban mesoscale factors should be accounted for. This study contributes with data and a methodological approach that could infuence public policy regarding scholar-aged active mobility. Keywords: Mobility Modes, children 6 to 12 years old, urban mesoscale factors, Random Forest