Analysis of Global Change in Macaronesian National Parks Using Multi-Platform Remote Sensing and New Data Processing Methodologies

The main objective of this project is to analyze the impact of global change in the Macaronesian National Parks through the development and validation of new multi-platform remote sensing data processing methodologies. The project focuses on generating detailed vegetation cover maps using advanced strategies for analyzing satellite and drone images, applied to all Macaronesian National Parks in the Canary Islands.

Project Objectives:

Objective 1: Development of species classification methodologies:

• Develop advanced methodologies for the classification of plant species using multiplatform data processing and machine learning techniques, including deep learning
• Approaches include the classification of plant species, their abundance, and spatial distribution, as well as determining the phenological state of species of interest.

Objective 2: Development of change detection methodologies:

• Develop methodologies for processing and analyzing images to detect changes in ecosystems by integrating multi-platform and multitemporal data.
• Approaches involve the analysis of long data series to detect changes such as snow presence, and the analysis of annual series to monitor seasonal processes, such as the flowering of species.

Objective 3: Generation of maps for studying vulnerability to climate change:

• Generate maps to study vulnerability to climate change by applying the methodologies developed in the previous objectives.
• Products will be generated to determine the conservation status of areas, detect threats, and analyze their possible relationships with anthropogenic factors or those associated with climate change.

Additional Information:

• The project aligns with biodiversity conservation, natural process monitoring, and adaptation to global change in Macaronesian national parks, with special attention to invasive species.
• The use of remote sensing data and processing techniques contributes to biodiversity conservation by generating detailed maps of natural areas and allowing the recording of zones that are difficult or costly to access.

Duration:  2023 – 2025 (36 months)

Funding source (Code): 

Participant entities: Universidad de las Palmas de Gran Canarias, Universidad Politécnica de Madrid, Universidad Politécnica de Cataluña, Universidad de Concepción (Chile) y Università di Pavia (Italia)

Official webpage: https://tara.ulpgc.es/

Laboratory/internal web: N/A

Responsible: Dionisio Rodríguez Esparragón