Ricerc@Sapienza

Socotra, the largest of four islands in the Socotra archipelago (Yemen), is characterized by high geo-morphological and biological diversity. Traditional management of land resources has been impacted by cultural shifts, globalization and urban expansion. In addition, the island has been affected by climate impacts, such as droughts and recent cyclones causing significant economic and environmental damage. Our study presents an analysis of the dynamics of the vegetation on the island for the period 2001–2019 and highlights the status of different landscapes.

Fire ignitions tend to be aggregated in time and space creating a clustered spatio-temporal pattern that is mainly
driven by climatic factors and the availability of ignition sources. The aim of this work is to identify the spatiotemporal
distribution of wildfires hotspots in Sardinia (Italy) during 2000–2013 and to relate their dynamics
with remotely-sensed NDVI-based fuel phenology patterns. We considered eleven bi-weekly time frames (TFs)
and used kernel density (KD) estimation to spatialize the corresponding fire ignitions. Then, to identify zones of

Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources-particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park'smarsh vegetation.

Although there is a general consensus on the distribution and ecological features of terrestrial biomes, the allocation of alpine ecosystems in the global biogeographic system is still unclear. Here, we delineate a global map of alpine areas above the treeline by modelling regional treeline elevation at 30 m resolution, using global forest cover data and quantile regression.