**1. Introduction**

This research aimed to design a model for the detection of landslides at the regional scale using Earth Observation data and remote sensing techniques. The following techniques were used to achieve this goal.

Landslide inventory (LI), for example the CGS-SIMMA1 as the base for the evaluation of the multivariate data generated by new technologies. It provides information

<sup>1</sup> Sistema de Información de Movimientos en Masa del Servicio Geológico de Colombia; in English: Mass Movement Information System from the Colombian Geologic Service.

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

on Alos Pasar elevation data as background and the sheets of the topo-map provided

*The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection…*

The CGS-SIMMA geo-service allowed to build the landslide inventory database

**LS Type Detritus Rock Earth Other Total %** Slides 77 14 78 9 178 77.4 Falls 15 8 14 1 38 16.5 Creep 0 2 4 1 7 3.0 Flows 0 0 3 3 6 2.6 Lateral Spreads 0 0 1 0 1 0.4 Total 92 24 100 14 230 100

% 40.0 10.4 43.4 6.1 100

for training the detection model. Landslide database contains an inventory of

by the National Geographical Institute 'Agustin Codazzi' (IGAC).

**2.2 Landslide inventory**

*DOI: http://dx.doi.org/10.5772/intechopen.94251*

**Figure 1.**

**Table 1.**

**125**

*National and regional location of the study area.*

*CGS-SIMMA landslide frequency distribution.*

At regional scale, landslide detection and landslide distribution analysis allows to 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].

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 to landslide detection [8].

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].

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.

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

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

#### **2.1 Study area**

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 coordinates: 02<sup>∘</sup> 06<sup>0</sup> 53.50″ North, 76<sup>∘</sup> 48<sup>0</sup> 49.71″ West and 02<sup>∘</sup> 34<sup>0</sup> 27.81″ North, 76<sup>∘</sup> 24<sup>0</sup> 32.25″ 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 *The Potential of Remote Sensing to Assess Conditioning Factors for Landslide Detection… DOI: http://dx.doi.org/10.5772/intechopen.94251*

on Alos Pasar elevation data as background and the sheets of the topo-map provided by the National Geographical Institute 'Agustin Codazzi' (IGAC).
