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As part of a collaborative effort among researchers of several institutions and organizations, this project takes advantage of the Google Earth Engine (GEE) cloud computing environment to map algae bloom over the main water bodies and reservoirs of Latin America using Sentinel-2 imagery (2015 to present). The methodology based on the Normalized Difference Chlorophyll Index (NDCI) for chlorophyll-a and Trophic State Index (TSI) detection provided promising results. NDCI responds well to high levels of chlorophyll-a and, therefore, can be used as an indicator for algae blooms. The image processing as well as the display of maps and charts are being implemented into a GEE App to be freely available for general public use.

Mapping Human Exposure to Risky Environmental Conditions is key to quantify the vulnerability of population and economic assets. In order to develop novel tools, implementing the use of information layers from current and future Earth Observation (EO) missions is necessary. The EOxposure project that has been created and funded by the European Commission's Horizon 2020 research and innovation program covers this porpoise. Several topics involving human exposure to environmental risks are being studied, including water quality and pollution, which is addressed in this paper. The high productivity of biomass registered in eutrophic water bodies, leads to qualitative and quantitative changes in the phytoplankton community, resulting in massive algae blooms. This research work proposes a methodology that takes advantage of the temporal and spectral resolution of Sentinel-2 (S2) for monitoring eutrophic reservoir. Specifically, it uses large temporal series of S2 images and advanced data mining techniques to study the turbid and productive water of San Roque reservoir, Argentina. The spatial patterns and the temporal tendencies of these aquatic indicators are analysed and evaluated in order to assess their contribution to water quality models and a local water management program.&nbsp

Winter snow packs in the Argentinean Andes are the primary source of surface runoff and water supply in the adjacent lowlands. The socioeconomic, environmental, and recreational benefits provided by the mountain snow packs are enjoyed by over 10 million people. Monitoring the spatial and temporal changes of seasonal snow cover helps to predict and mitigate floodings, avalanches, frost damage, among other hazards. It can also help estimating the supply for human consumption fresh water, irrigation and hydropower stations, and may contribute to the improvement of weather forecasts and the understanding of the climate system. This study is a first approach for evaluating and detecting changes in Argentinean Andes snow packs through temporal series of Sentinel-1 SAR images using the data available in Google Earth Engine (GEE). Additionally, the products of applying a wet snow mapping algorithm with images obtained in GEE, and the same images but pre-processed in SNAP coregistrating the images, were compared in order to assess the commission and omission classification errors.

Throughout the world, bees fulfill a vital ecosystem function, as well as other insects that ensure the delicate balance of natural ecosystems. In fact, biodiversity and production of food depend largely on pollination carried out by bees. At present, there is a widespread concern due to the decrease in the number of hives, a strange phenomenon that occurs worldwide and until today little is known about its causes. Hence the importance to design beekeeping environmental policies to strengthen the activity and preserve bees. San Javier, Cordoba, Argentina presents an important beekeeping activity being leader as honey exporter in the region due to its favorable environmental conditions, constituting itself as a source rich in biodiversity. In order to carry out an optimal territorial order, it is necessary to consider environmental and legal aspects to lineout good apicultural practices. In this work we implement for the first time in Argentina geographic information systems (GIS) as a management tool for data collection, analysis and modelling in order to generate an aptitude map to carry out this activity. Based on georeferenced information collected by satellite sensors, field surveys and official institutions, new spatial data were generated through the elaboration of thematic maps that show the current situation of apicultural territorial order in the study zone. Particularly, distance of apiary sites to water bodies, to native forest, to urban areas, to cultivated areas and to roads are taken into account to make a decision tree model which generates an aptitude map to carry out beekeeping activity. Finally, it is contrasted against apiaries distribution in the study zone in order to provide support to the sector by showing number apiaries that should be relocated and best zones to settle new ones.

Global climate change has different demonstrable effects on ecosystem structures and socio-economic factors throughout the entire planet. 90 percent of last decade natural hazards around the world were attributed to water-related disasters. Forest-land use change, deforestation, unplanned urbanization, localization of marginal population groups in vulnerable areas, wild fires, high temperatures, unusual precipitations, among others are some of the variables that increase the probability of a water-related catastrophe. Comprehensive hazard assessments and appropriated environmental policies orientated towards risk management complemented with planning and designing of hydraulic infrastructures may contribute to decrease flash flooding risk a well as the magnitude of the potential damages. The aim of this study is to develop a consistent methodology to determine the vulnerability, threat and risk of mountain urban areas that are particularly vulnerable to the effects of heavy, rapid rains and represent the obtained results in a risk map. Identifying risk prone areas can be achieved by adapting geological-geotechnical charts, hydrological models, field inventories and official census data from the town of Unquillo, in the province of Cordoba, Argentina. The analytic hierarchy process was used in order to determine the individual contribution of each vulnerability factor, together with geographical information systems used to combine the obtained information trough algebra and transformation of satellite imagery bands. The case of study show that 59% of the total flooding vulnerability relates to the buildings infrastructure, followed by the social factors which represent 33% of the total vulnerability equation. The threat map was validated using landmarks and remains obtained as a consequence of the flooding that occurred on February 15th 2015 in the study area.

Eutrophication is a phenomenon that affects many water bodies around the world. San Roque Dam is not an exception since it has been going through a huge increase in algae concentration. In this framework, its monitoring is a key matter, as is the source of water supply most important of Cordoba city. Remote sensing is a fundamental tool to complement traditional in situ monitoring and understand processes that occur in the reservoir. This study presents a semi-empirical algorithm obtained from MODIS data and in situ chlorophyll-a measurements, provided by the Ministry of Water Resources of Cordoba Province. From this model, a daily time chlorophyll-a time series was obtained from 2001 to 2014. Statistical analysis of the series indicate that water quality of the reservoir has worsened when comparing sets of last (2014–2010) and first (2010–2014) five years.