# Ecosystem Services Assessment for Their Integration in the Analysis of Landslide Risk
## 2 Materials and Methods
### Materials Used for the Present Paper  
For risk assessment, the components of hazard and vulnerability are necessary. In terms of landslide risk, the hazard is characterised by susceptibility maps. The susceptibility to landslides map of the Guerrero area was obtained from [PERSON] et al. [27] based on the combination of different factors and landslide inventory through machine learning. In this map, the susceptibility values were established through 12 various conditioning factors (slope, orientation, distance to the drainage network, density of the drainage network, normal curvature, accumulated annual precipitation, lithology, distance to lineaments, density of lineaments, distance to road, density of roads, NDVI and land cover) and a trigger (precipitation). The landslide reference data were provided from an inventory made from three orthophotos taken on 12 August 2014, showing the consequences of the landslide period due to the Ingrid and Manuel meteorological phenomena. The vulnerability to landslide map was provided by [PERSON] et al. [28] based on the combination of ecological and socio-economic values. Ecological values were characterised by biodiversity, conservation status, habitat fragmentation, and regeneration delay (composed of erosivity of precipitation, soil erosivity, slope, and soil protection). Socio-economic values were estimated through marginalisation indices, population density, and building density. Typically, the vulnerability assessment is based on ecological and socio-economic values. However, it is necessary to characterise the ecosystem services to have a holistic view of the values of the specific area [29; 30]. Thus, these ecosystem services intend to provide a tool for unionising ecological and socio-economic values [31]. Artificial Intelligence for Ecosystem Services (ARIES) is the dynamic tool used to integrate and evaluate ecosystem services based on the software k.LAB through different values of fluxes, productivity, and societal uses [32]. Moreover, it provides models of supply and demand ecosystem services using empirical data and models to create flows in greater detail. The different ecosystem services used are from provisioning (crop production, value of water from forests, value of non-wood forest products), regulation (organic carbon mass, the occurrence of pollinator insects, potential removed soil mass, and retained soil mass caused by vegetation), and cultural (potential value of outdoor recreation). Previously, a selection of ecosystem services affected by landslides was carried out (Table 1). The value of water from forests is extracted through the data of the models of the ARIES platform ([[https://aries.integratedmodelling.org/](https://aries.integratedmodelling.org/)]([https://aries.integratedmodelling.org/](https://aries.integratedmodelling.org/)), accessed on 15 October 2022). Regarding the regulation of water flows, the ecosystem service represents the ability of vegetation to intercept the rainfall, contributing to surface runoff. The regulation of these water flows on the earth's surface depends on the components that store water in the surface layers and are influenced above all by the vegetation and physical properties of the soil [33]. In order to understand this service, ARIES simulates both surface and groundwater flows and groundwater extraction through wells, which connect human beneficiaries (agriculture, industry, domestic use) with upstream sources and sinks throughout the area thanks to high-resolution slope and elevation data [34]. The value of non-timber forest products was extracted from the ARIES platform ([[https://aries.integratedmodelling.org/](https://aries.integratedmodelling.org/)]([https://aries.integratedmodelling.org/](https://aries.integratedmodelling.org/)), accessed on 15 October 2022). Regression models must be implemented to quantify the value of non-wood forest products (excluding firewood) for their monetary value.