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- Detection of Geothermal Anomalies in Hydrothermal Systems Using ASTER Data: The Caldeiras da Ribeira Grande Case Study (Azores, Portugal)Publication . Uchôa, Jéssica; Viveiros, Fátima; Tiengo, Rafaela; Gil, Artur José FreireABSTRACT: Current-day volcanic activity in the Azores archipelago is characterized by seismic events and secondary manifestations of volcanism. Remote sensing techniques have been widely employed to monitor deformation in volcanic systems, map lava flows, or detect high-temperature gas emissions. However, using satellite imagery, it is still challenging to identify low-magnitude thermal changes in a volcanic system. In 2010, after drilling a well for geothermal exploration on the northern flank of Fogo Volcano on São Miguel Island, a new degassing and thermal area emerged with maximum temperatures of 100 °C. In the present paper, using the ASTER sensor, we observed changes in the near-infrared signals (15 m spatial resolution) six months after the anomaly emerged. In contrast, the thermal signal (90 m spatial resolution) only changed its threshold value one and a half years after the anomaly was recognized. The results show that wavelength and spatial resolution can influence the response time in detecting changes in a system. This paper reiterates the importance of using thermal imaging and high spatial resolution images to monitor and map thermal anomalies in hydrothermal systems such as those found in the Azores.
- 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.
- Informing implementation of Nature-based Solutions in marine and coastal environments: the MaCoBioS Blue NBS ToolboxPublication . Casal, Gema; Fonseca, Catarina; Allegri, Elena; Bianconi, Angelica; Boyd, Emily; CORNET, Cindy; Juan, Silvia; Córdova, Fabiola; Furlan, Elisa; Freire Gil, Artur José; Krausen, Torsten; Maréchal, Jena-Philippe; McCarthy, Tim; Özkiper, Ozan; Pérez, Géraldine; Pham, Hung; Roberts, Callum; Simide, Rémy; Simeoni, Christian; Taylor, Daisy; Tiengo, Rafaela; Trégarot, Ewan; Uchôa, Jéssica; O'Leary, Bethan C; Geneletti, DavideABSTRACT: Interconnected societal challenges such as climate change, biodiversity loss and food security demand immediate and coordinated action across local to global scales, guided by coherent policies and management mechanisms. Reflecting on the critical need to address societal challenges, Nature-based Solutions in marine and coastal environments, known as blue NBS, have emerged as an important part of the response strategy. Blue NBS integrate actions to protect and restore marine and coastal ecosystems while managing human impacts, embedding nature and people into decision-making through multifaceted approaches. However, blue NBS implementation trails terrestrial NBS. To effectively inform blue NBS implementation, research must produce actionable science that is relevant, timely and usable, requiring collaboration and active knowledge exchange across the science-policy-practitioner interface. Working with stakeholders, we developed the MaCoBioS Blue NBS Toolbox to begin addressing some of the barriers facing blue NBS implementation. Containing a collection of multi-disciplinary, scientifically-grounded and stakeholder-informed tools and products, the toolbox guides practitioners through different stages of blue NBS implementation. This toolbox provides an important initial set of resources to support the design and implementation of effective blue NBS and pave the way for further collaborative work to operationalise these tools in different social-ecological contexts.
- Spatially explicit assessment of carbon storage and sequestration in forest ecosystemsPublication . almeida, bruna; Monteiro, Luís; Tiengo, Rafaela; Freire Gil, Artur José; Cabral, PedroABSTRACT: Forests play an important role in the global carbon cycle, making accurate assessments of carbon dynamics essential for effective forest management and climate change mitigation strategies. This research examines the spatiotemporal patterns of carbon storage and sequestration (CSS) in forests' aboveground biomass using satellite data, machine learning (Support Vector Machines), carbon modelling and spatial statistics. The methodology follows a two-step classification process: (i) binary forest classification and (ii) forest type classification, mapping seven forest types within two main categories - Broadleaves (Quercus suber, Quercus ilex, Eucalyptus sp., and other species) and Coniferous (Pinus pinaster, Pinus pinea, and other species). We analyzed the relationship between forest type and CSS at the Nomenclature of Territorial Units for Statistics (NUTS) III level and identified spatial clusters, outliers, and hot and cold spots of carbon sequestration at the municipal level across mainland Portugal. The broadleaved category demonstrated the highest classification accuracy in both years, decreasing slightly from 90.3 % in 2018 to 89 % in 2022, while the Coniferous group had the lowest accuracy, declining from 84.1 % in 2018 to 83.6 % in 2022. Anselin's Local Moran's I identified clusters of carbon sequestration, while the Getis-Ord Gi analysis confirmed these findings, revealing statistically significant hotspots of carbon sequestration in the northern and central regions and cold spots in the southern region. By providing insights at the sub-regional and municipal levels, this study offers a robust framework to support sustainable forest management and climate change mitigation strategies. Moreover, it can assist decision-makers in prioritizing natural capital, and developing nature-based solutions to tackle climate change and biodiversity loss.