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Durum wheat (Triticum durum Desf.) is one of the most widely cultivated cereal species in the Mediterranean basin, supporting pasta, bread and other typical food productions. Considering its importance for the nutrition of a large population and production of high economic value, its supply is of strategic significance. Therefore, an early and accurate crop yield estimation may be fundamental to planning the purchase, storage, and sale of this commodity on a large scale. Multispectral (MS) remote sensing (RS) of crops using unpiloted aerial vehicles (UAVs) is a powerful tool to assess crop status and productivity with a high spatial–temporal resolution and accuracy level. The object of this study was to monitor the behaviour of thirty different durum wheat varieties commonly cultivated in Italy, taking into account their spectral response to different vegetation indices (VIs) and assessing the reliability of this information to estimate their yields by Pearson's correlation and different machine learning (ML) approaches. VIs allowed us to separate the tested wheat varieties into different groups, especially when surveyed in April. Pearson's correlations between VIs and grain yield were good (R2 > 0.7) for a third of the varieties tested; the VIs that best correlated with grain yield were CVI, GNDVI, MTVI, MTVI2, NDRE, and SR RE. Implementing ML approaches with VIs data highlighted higher performance than Pearson's correlations, with the best results observed by random forest (RF) and support vector machine (SVM) models. [ABSTRACT FROM AUTHOR]

• Joint use of Corine Land Cover and Urban Atlas allowed building ecological network. • 10 species have been considered to build the components of the ecological network. • Graph theory has proven to be a valuable tool for ecological connectivity. • Defragmentation scenario improved connectivity of the identified ecological network. Ecological sustainability has recently risen to prominence in scientific research and management applications. Approaches to measuring ecological connectivity and their application to optimize ecological network (EN) design are powerful tools against landscape fragmentation and biodiversity loss. We focused on building an EN by identifying the most sensitive areas for ecological connectivity within the Reggio Calabria (Italy) metropolitan area. We also proposed a defragmentation scenario to improve the obtained EN. The CORINE Land Cover and the Urban Atlas 2018 were used to obtain a fine-scale representation of the study area. Ten terrestrial mammal species were used to model connectivity following a multi-species approach. Dispersal distance, patch size, and resistance to species movement were used to identify patches and corridors. Vegetational fractional coverage based on three years time series of Sentinel-2 red-edge normalized difference vegetation index was used to discriminate areas with higher naturalness. We used graph theory and connectivity metrics to test the EN's robustness and identify locations for restoration in a defragmentation scenario. The obtained EN, formed by three separate components, was composed of 724 arcs and 300 nodes with an average patch area of 27.04 ha. After the defragmentation hypothesis, the EN, formed by only one component, was composed of 771 arcs and 328 nodes with an average patch area of 26.82 ha. It was possible to analyze an EN's connectivity and evaluate the impact of a scenario intended to enhance multi-species connectivity. By comparing several connectivity metrics, we highlighted the potential of land interventions as a planning tool to enhance future ecological sustainability and biodiversity conservation. [ABSTRACT FROM AUTHOR]