Remote sensing and geomorphological data in support of precision agriculture and forestry

Precision agriculture and forestry require a great deal of spatial data, in order to study the heterogeneity in vegetation conditions. The acquisition of this information can help decision-making and site-specific operations. The final aim of these land management procedures is to meet production demand, reducing waste and environmental impacts. The amount of data requested is huge and their collection could be considerably expensive in time and money. However, some of this information can be extracted from ready available data source, which are Digital Terrain Models (DTMs) and spaceborne Remote Sensing. The aim of this work is to analyze these data and to study how they can be processed in order to obtain useful information for precision land management. The potentialities of these data sources were tested on the Rio Sinigo catchment. This basin is located near the town of Merano, in the autonomous province of Bolzano, South Tyroly. The Rio Sinigo watershed has an extension of 35 km2 and it presents a variety of morphological features, that make it suitable for the study. The DTM was processed, using different FOSS GIS packages, to extract primary and secondary topographic indexes, and to perform hydrological analysis. Particular attention has been paid to the extraction of the secondary topographic attributes (TWI and Potential Incoming Solar Radiation), which operating principles were discussed in detail, highlighting their limitations and potentialities. Satellite images from Sentinel-1 and Sentinel-2 were used to obtain information on the soil moisture spatial and temporal variations, and to identify different land covers and possible major changes in time of the basin vegetation. The conducted study confirmed the wide range of applications and the potentiality that these data sources have. The obtained products represent good basic data to perform preliminary investigations, to plan targeted data collection and to start a multi-criteria analysis.