The paramo, referred to as a variety of tundra ecosystems, are considered one of the most essential biomes in South America, as they are primary sources of fresh water for many countries. However, páramos are increasingly threatened by agricultural expansion, livestock grazing, mining activities that cause deforestation and pollution, urbanization and infrastructure development that fragment and destroy habitats, and climate change, disrupting the delicate balance of these ecosystems. While many approaches have been proposed to monitor forest, grassland, mountain, and other ecosystems, páramo monitoring remains overlooked in the literature. In this work, we propose an end-to-end framework for monitoring páramos ecosystems in Colombia by leveraging deep neural networks and cloud-based technologies. We develop a U-Net convolutional neural network architecture to capture spatiotemporal features about the presence or absence of páramos from Satellite images across different periods. We propose a replicable cloud-based API with highly accurate monitoring.