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- 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)Publication . Tiengo, Rafaela; Merino De Miguel, Silvia; Uchôa, Jéssica; Gil, Artur José FreireSmall 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.
- Burned Areas Mapping Using Sentinel-2 Data and a Rao’s Q Index-Based Change Detection Approach: A Case Study in Three Mediterranean Islands’ Wildfires (2019–2022)Publication . Tiengo, Rafaela; Merino de Miguel, Silvia; Uchôa, Jéssica; Guiomar, Nuno; Freire Gil, Artur José; Sprintsin, Michael; Huang, WenjiangABSTRACT: This study explores the application of remote sensing-based land cover change detection techniques to identify and map areas affected by three distinct wildfire events that occurred in Mediterranean islands between 2019 and 2022, namely Sardinia (2019, Italy), Thassos (2022, Greece), and Pantelleria (2022, Italy). Applying Rao’s Q Index-based change detection approach to Sentinel-2 spectral data and derived indices, we evaluate their effectiveness and accuracy in identifying and mapping burned areas affected by wildfires. Our methodological approach implies the processing and analysis of pre- and post-fire Sentinel-2 imagery to extract relevant indices such as the Normalized Burn Ratio (NBR), Mid-infrared Burn Index (MIRBI), Normalized Difference Vegetation Index (NDVI), and Burned area Index for Sentinel-2 (BAIS2) and then use (the classic approach) or combine them (multidimensional approach) to detect and map burned areas by using a Rao’s Q Index-based change detection technique. The Copernicus Emergency Management System (CEMS) data were used to assess and validate all the results. The lowest overall accuracy (OA) in the classical mode was 52%, using the BAIS2 index, while in the multidimensional mode, it was 73%, combining NBR and NDVI. The highest result in the classical mode reached 72% with the MIRBI index, and in the multidimensional mode, 96%, combining MIRBI and NBR. The MIRBI and NBR combination consistently achieved the highest accuracy across all study areas, demonstrating its effectiveness in improving classification accuracy regardless of area characteristics.