In a context where several sectors of society compete for space, land use types must be carefully designed and spatially allocated to guarantee a sufficient level of relevant ecosystem services (ES) in a territory of interest. In this respect, contemporary land use planning involves multiple, often conflicting objectives and criteria. Consequently, major benefits can be expected from spatial decision support systems (sDSS) designed to deal with complex spatial allocation problems.

Renewable energy sources (RES) can undoubtedly contribute to protecting the environment and conserving fossil fuels, as well as enhancing regional and rural development opportunities. However, every energy production process affects the environment and involves the use of land resources. The risks linked to intensified RES use should be adequately taken into consideration in any planning process, as ill-conceived energy policies may adversely impact land and local ecosystems, and lead to increases in public spending.

Precise soil quality assessment is critical for designing sustainable agriculture policies, restoring degraded soils, carbon (C) modeling, and improving environmental quality. Although the consequences of soil quality reduction are generally recognized, the spatial extent of soil degradation is difficult to determine, because no universal equation or soil quality prediction model exists that fits all ecoregions. Furthermore, existing soil organic C (SOC) models generate estimates with uncertainties that may exceed 50%.