Uncovering NDVI time trends in Spanish high mountain biosphere reserves: A detailed study.

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.

Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment.

Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socio-economic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters.

Landslide Susceptibility Assessment Using an AutoML Framework.

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides.