**Abstract**

This landslide detection research applied remote sensing techniques. Morphometry to derive both DEM terrain parameters and land use variables. SAR interferometry (InSAR) for showing that InSAR coherence and InSAR displacement obtained with SRTM DEM 30 m resolution were strongly related to landslides. InSAR coherence values from 0.43 to 0.66 had a high association with landslides. PS-InSAR allowed to estimate terrain velocities in the satellite line-of-sight (LOS) in the range 10 to 10 mm/year concerning extremely slow landslide displacement rates. SAR polarimetry (PolSAR) was used over L-band UAVSAR quad-pol data, obtaining the scattering mechanism of volume and surface retrodispersion more associated with landslides. The optical remote sensing with a multitemporal approach for change detection by multi-year Landsat (5, 7 and 8)-NDVI, showed that NDVI related to landslides had values between 0.42 and 0.72. All the information was combined into a multidimensional grid product and crossed with training data containing a Colombian Geologic Service (CGS) landslide inventory. A detection model was implemented using the Random Forest supervised method relating the training sample of landslides with multidimensional explanatory variables. A test sample with a proportion of 70:30 allowed to find the accuracy of detection of about 70.8% for slides type.

**Keywords:** landslide detection, morphometry, multi-InSAR, pol-SAR, NDVI, Random Forest
