A Land Cover Change Detection Approach to Assess the Effectiveness of Conservation Projects: A Study Case on the EU-Funded LIFE Projects in São Miguel Island, Azores (2002–2021)

Rafaela Tiengo, Silvia Merino- De-Miguel, Jéssica Uchôa and Artur Gil ()
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Rafaela Tiengo: Departamento de Ingeniería y Gestión Forestal y Ambiental, ETSIMFMN, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Silvia Merino- De-Miguel: Departamento de Ingeniería y Gestión Forestal y Ambiental, ETSIMFMN, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Jéssica Uchôa: cE3c—Centre for Ecology, Evolution and Environmental Changes, Azorean Biodiversity Group, CHANGE—Global Change and Sustainability Institute, University of the Azores, 9500-321 Ponta Delgada, Portugal
Artur Gil: IVAR—Research Institute for Volcanology and Risk Assessment, University of the Azores, 9500-321 Ponta Delgada, Portugal

Land, 2024, vol. 13, issue 5, 1-18

Abstract: Small oceanic islands, such as São Miguel Island in the Azores (Portugal), face heightened susceptibility to the adverse impacts of climate change, biological invasions, and land cover changes, posing threats to biodiversity and ecosystem functions and services. Over the years, persistent conservation endeavors, notably those supported by the EU LIFE Programme since 2003, have played a pivotal role in alleviating biodiversity decline, particularly in the eastern region of São Miguel Island. This study advocates the application of remote sensing data and techniques to support the management and effective monitoring of LIFE Nature projects with land cover impacts. A land cover change detection approach utilizing Rao’s Q diversity index identified and assessed changes from 2002 to 2021 in intervention areas. The study analyzed the changes in LIFE project areas using ASTER, Landsat 8, and Sentinel 2 data through Google Earth Engine on Google Colab (with Python). This methodological approach identified and assessed land cover changes in project intervention areas within defined timelines. This technological integration enhances the potential of remote sensing for near-real-time monitoring of conservation projects, making it possible to assess their land cover impacts and intervention achievements.

Keywords: remote sensing; oceanic islands; Python; Rao’s Q; land use; land cover; open data; Google Earth Engine; Google Colab (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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