Assessment of the regeneration of landslides areas using unsupervised and supervised methods and explainable machine learning models

Regeneration after landslides is how an ecosystem recovers itself following a landslide event. Natural systems can recover and regenerate over time. The main objective of the current work is to identify and characterise the regeneration pattern after a great landslide event in 2013 in the Guerrero state (Mexico) using remote sensing, geographic information system (GIS), and machine learning techniques. Remarkably, the authors consider normalised difference vegetation index (NDVI) as a proxy of part of this “regeneration” in the present work. A first methodology attempt presented here was to monitor and characterise the “regeneration” after severe landslide events. First, the authors calculated and identified the (1) losses, (2) speed and (3) recovery time through a continuous change detection and classification algorithm (CCDC) in Google Earth Engine (GEE) from a NDVI-Landsat time series (from 1984 to 2021). Second, these three factors were introduced as variables in a not supervised machine-learning model to get 5 clusters that characterise the different regeneration patterns followed from 2013 to 2021 in the landslide zones. Finally, we studied 16 variables like elevation, slope, or soil pH. The authors included those variables in a supervised machine learning classification to find the most important main drivers related to the NDVI regeneration of the landslide areas. The results showed that the cluster called Group 0 had a low loss of NDVI value, speed and medium recovery time. Group 1 showed a medium–high loss, high speed and low recovery time. Group 2 reached a high loss, a low speed, and an increased recovery time. Group 3 with a medium loss, a medium speed, and a low recovery time. Finally, Group 4 with a high loss of NDVI, a medium speed, and a medium recovery time. Distance to the edge of the landslide, precipitation, land cover type and lithology are highlighted as the main drivers in landslide regeneration.