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- A hybrid modelling approach for detecting seasonal variations in inland Green-Blue EcosystemsPublication . ALMEIDA, BRUNA; Cabral, PedroDeforestation, environmental pollution, and the overexploitation of resources, in addition to the Earth's natural cycles, are scaling up the impacts of climate change in the provision of Ecosystem Services (ES). Green-Blue Ecosystems (GBE) are impacted by climatic conditions, topography, and water presence. Data-driven modelling techniques may effectively capture the effects of seasonal variations while modelling natural ecosystems. This research proposes a hybrid modelling approach that combines Deep Learning and traditional Machine Learning, Sensitivity Analysis and Feature Importance Evaluation (FIE) to investigate seasonality effects on mapping GBE. The models, built using satellite imagery from the Spring and Summer seasons of the Mediterranean climate zone, included spectral indices, topography (DEM), and groundwater depth (GD). The model that best suited the analysis was selected using sensitivity tests and hyperparameter optimization. The study shows that land cover classes of transitional woodland shrubs, inland marshes, cultivated land parcels, and watercourses are better classified in the Spring, with an accuracy of 0.814. FIE indicates that spectral indices are the most important predictors for detecting green ecosystems in both seasons. Additionally, DEM and GD are the most relevant predictors to classify watercourses in the Summer. An analytical examination of the input data and hyperparameter settings facilitates understanding of models' behaviour while improving models' prediction. This research provides an advanced understanding of the effects of seasonal variations on the status of GBE and enhances understanding of modelling ES in areas with a growing need for changes in land use and high water supply demand.
- Satellite-based Machine Learning modelling of Ecosystem Services indicators: A review and meta-analysisPublication . ALMEIDA, BRUNA; David, João; S. Campos, Felipe; Cabral, PedroSatellite-based Machine Learning (ML) modelling has emerged as a powerful tool to understand and quantify spatial relationships between landscape dynamics, biophysical variables and natural stocks. Ecosystem Services indicators (ESi) provide qualitative and quantitative information aiding the assessment of ecosystems’ status. Through a systematic meta-analysis following the PRISMA guidelines, studies from one decade (2012–2022) were analyzed and synthesized. The results indicated that Random Forest emerged as the most frequently utilized ML algorithm, while Landsat missions stood out as the primary source of Satellite Earth Observation (SEO) data. Nonetheless, authors favoured Sentinel-2 due to its superior spatial, spectral, and temporal resolution. While 30% of the examined studies focused on modelling proxies of climate regulation services, assessments of natural stocks such as biomass, water, food production, and raw materials were also frequently applied. Meta-analysis illustrated the utilization of classification and regression tasks in estimating measurements of ecosystems' extent and conditions and findings underscored the connections between established methods and their replication. This study offers current perspectives on existing satellite-based approaches, contributing to the ongoing efforts to employ ML and artificial intelligence for unveiling the potential of SEO data and technologies in modelling ESi.