We use geographic information systems and remote sensing such as satellite- and unmanned aerial vehicle (UAV) imaging to improve the management of natural assets from a sustainability point of view.
This work aims to evaluate the phytoremediation potential of hemp strains and other fibrous crops in gold mining soils contaminated with mercury and other heavy metals in the Bajo Cauca region of Antioquia, Colombia.
As the main precursors of current and future Mediterranean forests, shrublands offer a unique opportunity to explore the influence of topography and climate on their distribution and potential for ecological succession.
Remote sensing data obtained from drones and satellites aid hemp cultivators to increase crop quality, diminish costs and be environment-friendly. We track biochemical and physical parameters from aerial images and build models to increase crop productivity.
Paramo ecosystems are areas of high natural interest as they contribute to water regulation, carbon retention, and possess significant biodiversity. The objective of our study is to develop an effective, economical, and easy to update methodology that allows simple monitoring of its conservation status, rate of degradation, and changes in land use at all times with the aim of, helping to take adequate measures to protect the high Andean tropical moors.
This work provides visual information regarding the generation and management of the industrial waste to facilitate the application of policies or business models that contribute to a better management of such waste. The data used are part of the 2018 Annual Industrial Waste Declaration, provided by the Agència de Residus de Catalunya.
Drones and other unmanned aerial vehicles (UAVs) can measure stress on plants, irrigation water management and plant population counts, etc. In this study we identify heterogeneous areas of irrigated olive groves and vineyard crops using a commercial multispectral camera mounted on an UAV, which allows farmers to put hydric resources where they are needed most.