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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. [ABSTRACT FROM AUTHOR]

Global warming is increasing the vulnerability of ecosystems, especially in peninsular Spain. Biosphere Reserves are internationally protected areas that seek to protect biodiversity and, at the same time, promote sustainable development. Evaluating these protected areas is essential to verify environmental changes and establish priorities in their management. In this work, we have studied the time trends of NDVI in the high mountain Biosphere Reserves of Spain from 2001 to 2016 to check if the trend patterns are associated with some environmental variables. Significant differences were found between NDVI trends and high mountain Biosphere Reserves. Firstly, significant positive trends in NDVI were observed when analysing both reserves together. However, significant differences were found between the two reserves. The Ordesa-Viñamala Reserve shows higher positive NDVI trends and lower negative trends, while this pattern is reversed in Sierra Nevada. We observed how the fluctuations in temperature and drought due to climate change have already negatively affected the Mediterranean reserve (Sierra Nevada). In contrast, the alpine reserve (Ordesa-Viñamala) maintains positive NDVI trends. This study helps to close the gap in information related to Biosphere Reserves, which gives value to the work that is being carried out by the local communities that make up them, generating statistically significant results that Biosphere Reserves are protected areas that help us understand how to manage and govern socioecological systems sustainably. • The study assesses NDVI trend in the Spanish high mountain Biosphere Reserves. • We analysis the association between NDVI trends and environmental variables. • Significant differences in NDVI trend frequency found between the reserves. • Climate change is having effects on reserves NDVI trends. • Trend data is key to ensuring reserves take into account in political decisions. [ABSTRACT FROM AUTHOR]