**2.2 Landslide inventory**

about the vector representation such as point objects, date, municipality, mass distribution, type, sub-type, material and origin contributing to landslide detection [1]. At regional scale, landslide detection and landslide distribution analysis allows to

Remote sensing techniques have been used for landslides detection [4]. One of them is morphometry based on Digital Elevation Models (DEMs), for a quantitative analysis of the land surface topography. In this way, the geomorphometry analysis derives land surface parameters such as slope, aspect, curvature or basic local descriptors, regional parameters as catchment area, parameters connected with hydrology like topographic wetness index and so on [5]. Terrain parameters can be related to landslides to build detection models [6]. Change detection technique [7] based on the sudden disappearance of vegetation by NDVI difference computation can serve to landslide detection. Also, SAR-based techniques in rural areas can help

Interferometric spaceborne radar to measure linear deformation rates has been implemented on several studies to landslides detection [9]. PS-InSAR (persistent scatterers) allows measurements with millimetre accuracy of individual features. PS-InSAR is differential measurements with respect to a reference point that is assumed to be stable [10]. PolSAR imagery allows to characterise objects on the ground based on that different structures and geometries show different backscatter values at different SAR polarisations. Quad polarimetric SAR data content information to landslide detection in forested areas under the assumption that the

dominant mechanism is surface scattering with high homogeneity [11].

**2. Landslide inventory and earth observation data**

48<sup>0</sup>

This way Earth Observation (EO) data encompasses sensors like SAR, optical images and GPS on board of platforms either satellite-based, aircraft-based or ground-based provide high spatial, temporal, and spectral resolution to geohazard studies [12]. The above combined with machine learning (ML) techniques like Random Forest, allow the mapping, monitoring and modelling of landslides occurrences.

In this section we describe the study area, the landslide inventory, taken the from the Colombian Geologic Service (CGS), and the Earth Observation data.

The study area is located at the southwest of Colombia and covers the inter and central Andes Mountains in a rectangle within the following WGS84 system coor-

West. In here, we found elevations between 848 and 4932 m.a.s.l. The area covers the inter-Andean valleys of Cauca river and the central mountain range of the Andes in the southwestern of Colombia. Former is to comprise Tertiary and Quaternary formations and above there are volcanic ash depositions. The Central Mountain Range of the Andes in Cauca state, have quaternary deposits at the summit and its western slope it is found diabase rock. **Figure 1** shows the study area

49.71″ West and 02<sup>∘</sup>

34<sup>0</sup>

27.81″ North, 76<sup>∘</sup>

24<sup>0</sup> 32.25″

analyse the distribution and classification of landslides [2]. These analysis use univariate and multivariate statistical methods, obtaining weights by the correlation between landslides occurrences and conditioning factors. Weight of Evidence (WofE) method is a bivariate approach where the landslides inventory is used to calculate weights of conditioning factors in order to delineate potential areas of landslides. Logistic regression (LR) is one of the statistical methods more used to

evaluate the relationship between landslides and related factors [3].

to landslide detection [8].

*Slope Engineering*

**2.1 Study area**

dinates: 02<sup>∘</sup>

**124**

06<sup>0</sup>

53.50″ North, 76<sup>∘</sup>

The CGS-SIMMA geo-service allowed to build the landslide inventory database for training the detection model. Landslide database contains an inventory of

#### **Figure 1.**

*National and regional location of the study area.*


#### **Table 1.**

*CGS-SIMMA landslide frequency distribution.*

geomorphologic and catalogue type. The former contained more precise information in its thematic attributes. The overall distribution of SIMMA landslide inventory was 77.4% of slide type, 16.5% of fall type, and 6.1% of flow and creep type for a total of 230 registered events (**Table 1**). The spatial information contained only allowed mapping in a shapefile of point type.
