**6. Conclusions**

By using Remote Sensing techniques at the visible and microwave frequencies of EM waves this research did relate EO measurements with ground physical parameters such as scattering mechanisms, topography, land cover type and surface deformation patterns. All of the above in relationship with landslides inventory of the study area.

This research did implement unsupervised and supervised classification methods. The first to understand the pattern of LSI clustering and the second to classify the LSI with multidimensional variables derived from EO data and RS techniques.

All of the EO data collected and generated by RS techniques during this research was stored in appropriate containers of data.

This research used errors' theory, ANOVA, TUKEY and cross-validation techniques to determine the internal and external precision of the method generated for landslides detection.

**5. Discussion**

*Importance of the variables in decreasing order.*

**Figure 9.**

**134**

**Figure 8.**

*Slope Engineering*

*Detection model of landslides by the Random Forest method.*

This study confirmed that the slope angle is a key classification factor in landslide detection in a similar way reported by Donnarumffia et al. [23]. So as land use

is the most influencing factor to the occurrence of landslides [24].

*Slope Engineering*
