The accurate detection and characterization of vegetation covers are essential to support monitoring and prevention actions in different fields of application, ranging from agricultural management to environmental conservation. Polarimetric decompositions over L-band SAR data (SAOCOM) were evaluated here to enable it. Cameron [1], Van zyl [2], Generalyzed Freeman Durden [2], Cloud-Pottier [3], Yamaguchi [4], GRVI [5] and Cloude [2] decompositions were explored in two contrasting seasons (winter and summer, dry and rainy seasons in the study area, respectively) to differentiate vegetation cover types in Chaco serrano (Córdoba, Argentina). Differences between vegetation covers were found, particularly between grasslands, shrublands and forests. Specifically, the volume components, which exhibited significant differences between seasons for all vegetation covers, would be suitable for studying vegetation phenology from time series data.

(1) Background: Land-use change threatens biodiversity globally, making it essential to anticipate future impacts. (2) Methods: We assess future land-use change scenarios as a tool for analyzing the taxonomic and functional richness of birds in the Serrano forest. We developed two change scenarios: The “Business as usual” scenario assumes that the trend of land-use change observed between 2004 and 2019 will continue without modifications by 2035 and 2050. The “Sustainable” scenario seeks to achieve a sustainable relationship between anthropogenic land-use activities and ecosystem conservation. We created distribution models and derived the potential distribution of the taxonomic and functional richness of forest and understory specialist birds in the change scenarios. (3) Results: The taxonomic and functional richness of both bird groups was strongly affected in the “Business as usual” change scenario, which presented extreme deforestation events, while the “Sustainable” change scenario tended to maintain bird richness over time. We detected areas with a reduction in richness greater than 20% and areas where richness increased due to being distant from urbanization and exotic forests.