Worldwide, urbanization constitutes a major and growing driver of global change and a distinctive feature of the Anthropocene. Thus, urban development paths present opportunities for technological and societal transformations towards energy efficiency and decarbonization, with benefits for both greenhouse gas (GHG) and air pollution mitigation. This requires a better understanding of the intertwined dynamics of urban energy and land use, emissions, demographics, governance, and societal and biophysical processes. In this study, we address several characteristics of urbanization in Santiago (33.5°S, 70.5°W, 500 m a.s.l.), the capital city of Chile. Specifically, we focus on the multiple links between mobility and air quality, describe the evolution of these two aspects over the past 30 years, and review the role scientific knowledge has played in policy-making. We show evidence of how technological measures (e.g., fuel quality, three-way catalytic converters, diesel particle filters) have been successful in decreasing coarse mode aerosol (PM10) concentrations in Santiago despite increasing urbanization (e.g., population, motorization, urban sprawl). However, we also show that such measures will likely be insufficient if behavioral changes do not achieve an increase in the use of public transportation. Our investigation seeks to inform urban development in the Anthropocene, and our results may be useful for other developing countries, particularly in Latin America and the Caribbean where more than 80% of the population is urban.

The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m² each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R² = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R² = 0.571, RMSE = 5.05). Selected variables were statistically significant: catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.