# Assessing Ecosystem and Urban Services for Landscape Suitability Mapping
## 3 Results and Discussion  
### Landscape Suitability Assessment  
The model application results for the suitability assessment are shown in Figures 4 and 5 (baseline and the five scenarios). Supply-based suitability identifies the landscape areas where there is a relatively higher presence of services. Demand-based suitability shows, in percentage, how the different locations of the landscape fulfil the potential demand of services. The application of the model in the baseline scenario resulted in non-uniform levels of suitability across space. These results are driven by the higher presence of services in the more urbanized areas, representing the benefiting areas of many of the services included in the classification. In the first case, when supply-based indicators are used, suitability is comparably higher in natural landscapes, where many cultural services are provided in relatively high quantities. When the demand-based scaling is used to calculate the results, suitability is generally higher than in the first case in the whole study area. In contrast, the highest levels are clearly shown in the more urbanized landscapes, where urban services are provided. In both cases, landscapes dominated by agricultural features such as olive yards (OMI and OHI), arable lands (AHI and AMI), or vineyards (VHI and VMI) are suffering from a lack of suitability due to the low presence of different categories of services, mainly cultural. Figure 4: _Cont_. The explained suitability, representing the percentage of suitability for which indicators are available, is generally high, ranging from 71% to 96% in the cases under analysis, since we spatialized 49 on 53 services included in the classification. The application of SUSAM in the selected scenarios shows that the highest value of suitability usually characterizes the urbanized landscapes (UHI and UMI). Results from the supply-based scaling show that values of suitability for agricultural activities (FARM) are higher in the agri-natural areas. In contrast, the demand-based suitability is higher in urban and peri-urban areas, showing high possibilities for the whole area to perform urban agriculture. In fact, the land and environmental conditions are also generally good in the city centre, while materials are generally well available. The housing scenario (HOUSE) shows, as expected, its highest values in the UHI landscape, with both types of scaling. UMI and CMI landscapes also show relatively high levels of suitability for housing if compared to other landscapes, meaning that the urban component is highly benefiting this group of activities. Inversely, suitability to housing is considerably low in other landscapes. The supply-based and demand-based values of suitability for the commercial activity scenario (COMM) are also increased by the landscape's urban component, particularly by the main roads and fast internet connection. The highest levels of this kind of suitability appear in the UHI landscape, where the demand-based SI reaches 0.66, which is the  
Figure 4: Maps from SUSAM application in the different scenarios. (**A**): BASE, (**B**): FARM, (**C**): HOUS, (**D**): COMM, (**E**): INF, (**F**): NINF, EX: explained suitability (scenario codes are described in Table 2). maximum value reached by the indicator in the whole study area. The explained suitability is high in each case, ranging between 0.81 and 0.96. When SUSAM is applied to produce maps for identifying the areas that need infrastructures (INF), results show that the more urbanized areas (UHI, UMI, and CMI) typically need roads and transport infrastructures. This tendency appears more evident when demand-based scaling is applied. Similarly, the suitability of natural landscapes (NHI and NMI) to roads and transport infrastructures is also high, since they deliver services that can only be benefited in loco. On the other side, when the areas with no need for infrastructures are highlighted (NINF), the suitability shows higher values in all the landscapes.