Master Thesis

The automation of biological data collection and analysis processes has a huge potential to improve the efficiency and quality control of ecological studies. This work focuses on the data pipeline automation in a biodiversity consultancy company. The primary objective of the work was to develop an integrated methodology to automate the entire process from data collection to statistical analysis, thereby improving the quality and speed of analyses and contributing to more informed decisions in ecological studies.

Given Portugal’s initiative to establish a land cover monitoring system delivering annual products for its mainland territory, it becomes crucial to ensure an adequate representation of areas experiencing vegetation loss. The problem at hand consists of creating and updating, using Sentinel-2 imagery, a vegetation loss mask. Towards that end, the study aims to build a robust forest clear-cut reference database that will serve as a training data for a change detection algorithm, automating the creation of these maps.

This dissertation presents a methodology for monitoring photosynthetic activity and carbon sequestration in cork oak woodlands, known in Portugal as Montado, by utilizing Solar-Induced Fluorescence (SIF) data. The study was conducted in the Companhia das Lezírias, Portugal, where the SIF data was collected using the Fluorescence Box (FloX), a cutting-edge hyperspectral field instrument designed for the passive measurement of SIF.

With global population growth and climate change, agriculture in the 21st century is facing significant and complex challenges. Increasing productivity has become a requirement, as has ensuring the sustainability of available resources, putting positive pressure on changes in production methods and the efficient management of these resources. Irrigation is one of the cultural practices with the greatest responsibility for vegetative growth, operating in parallel with sustainability.