Geostatistical modelling of PM10 mass concentrations with satellite imagery from MODIS sensor

Several epidemiological studies suggested that there is an association between incidence and exacerbation of adverse respiratory and cardiovascular health effects and air pollution. Accurate, high resolution maps of ground-level Particulate Matter (PM) are highly awaited for environmental policies and future monitoring stations design. Though the measurements made by the ground stations can ensure a high level of reliability, still they cannot provide full spatial coverage over an area, giving rise among other things to misclassified epidemiological studies. Fine particles are usually categorized by size distribution, known as fractions: PM10 represents the particles with aerodynamic diameter smaller than 10 µm and comprises the thoracic (or coarse) fraction – with diameter in the range 2.5-10 µm – and the smaller inhalable (or fine) fraction. Although including the less dangerous thoracic particles, PM10 measurements are usually far more available and hence lend themselves better for modelling. Spaceborne aerosols products like the ones offered by the polar-orbiting MODerate resolution Imaging Spectrometer (MODIS) are successfully finding practical applications for scientific research studies and, though not previously intended, the Aerosol Optical Thickness (AOT, or simply τ ) from MODIS revealed to have a leading role in the evaluation of surface air quality due to its full spatial (clear-sky constrained) coverage and daily overpasses almost throughout the globe. Despite the “promised land” has not been reached yet, researchers have verified an existing correlation between aerosols and particulate concentrations, rising expectation of air quality models for high-scale environmental characterization. Air quality modelling is generally a challenging application, due to the wide range of sources affecting this variable and the high spatial and temporal variability of the particles, especially over high populated areas with rugged topography and complex meteorological profiles. In this thesis, different variogram-based geostatistical techniques are evaluated to predict the concentrations of PM10, with a focus on the effective advantages brought by AOT from satellites. This work is meant as a guide for students and researchers who are taking their first steps in this specific application, as well as to experts of the field who want to overview geostatistical filling of PM concentrations, and weigh up the usefulness of MODIS imagery. Different areas of study and temporal resolutions will be considered, so as to propose directions and outline conclusions on how this task – still far from being definitively ruled out – should be approached. Aside from modelling, the interactive visualization, extraction and analysis of the model-based predicted maps are also covered, cutting-edge Web-based software architectures based on the Open Geospatial Consortium (OGC) standard services are proposed, giving rise to increased capabilities in the spatio-temporal elaboration of the model results. The availability of spaceborne maps of AOT at an increased nominal resolution of 1×1 km2 has been a unique occasion to experiment their role for air quality issues; the latest algorithmics from leading FOSS-like (Free and Open Source Software) modelling software where learned and used, resulting in several new testing results in a field where variogram-based geostatistics were lacking. Solutions for novel online analysis and visualization capabilities were explored, in order to approach an open and interconnected uncertainty-enabled Web

Aerosol optical thickness retrieval from satellite observation using support vector regression

Processing of data recorded by the MODIS sensors on board the Terra and Aqua satellites has provided AOT maps that in some cases show low correlations with ground-based data recorded by the AERONET. Application of SVR techniques to MODIS data is a promising, though yet poorly explored, method of enhancing the correlations between satellite data and ground measurements. The article explains how satellite data recorded over three years on central Europe are correlated in space and time with ground based data and then shows results of the application of the SVR technique which somewhat improves previously computed correlations. Hints about future work in testing different SVR variants and methodologies are inferred from the analysis of the results thus far obtained. © 2010 Springer-Verlag

Multitemporal data management and exploitation infrastructure

The development of new technologies and tools for as-much-as-possible automatic multi-temporal data analysis has been a goal for most of the institutions that aim at promoting the use of satellite data in different application domains. In the framework of the Support by Pre-classification to specific Applications Project, started in 2008, the European Space Agency has requested the development of a specific platform, named Multi-sensor Evolution Analysis (MEA), with the scope of demonstrating that long term satellite datasets coming from different sensors can be accessed and exploited in almost real time (few seconds) from a web application as user interface. The MEA system has been implemented based on 15 years of global (A)ATSR data (1 km resolution), together with 5 years of regional AVNIR-2 data (10 m resolution), with the final aim of permitting on-the-fly Land Use / Land Cover Change analysis. Moreover, a modified version of MEA has been set-up to permit the multi-temporal analysis of pollution maps coming from satellite observations and ground measurements, demonstrating the generality of the pursued approach. The present work aims at introducing the basis of the MEA system, describing the two implementations for land cover and pollution multi-temporal analysis, including external validation activities being performed for the first application by third parties

A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy)

When and where shallow landslides occur depends on an interplay of predisposing, preparatory, and triggering factors. At a regional scale, data-driven analyses are extensively used to assess landslide susceptibility based on “static” maps of predisposing conditions. In contrast, data-driven analyses focusing on landslide triggering factors often rely on non-spatially explicit approaches to derive empirical rainfall thresholds. So far, few attempts have been made to integrate the spatial and temporal analysis domains beyond a posterior combination of separately derived susceptibility models and rainfall thresholds. This work focuses on the mountainous Italian province of South Tyrol (7400 km²) and proposes a novel data-driven landslide prediction model that jointly considers landslide predisposition and dynamic preparatory and triggering factors. The approach builds on a hierarchical generalized additive model, multi-temporal shallow landslide data from 2000 to 2020 and a range of environmental variables (e.g., daily rainfall, topography, lithology, forest cover). The model produces maps that portray the relative probability of landslide occurrence. These spatially explicit predictions change dynamically as a function of local predisposition, seasonality, and observed (or hypothesized) dynamic preparatory and triggering rainfall (i.e. cumulative rainfall amounts based on varying day-windows). Linking the model output to known measures of model performance, such as hit rate and false alarm rate, enables the creation of dynamic classified maps that can be interpreted in analogy to commonly used empirical rainfall thresholds. The approach also accounts for potential spatial and temporal biases in the landslide inventory by restricting the underlying data sampling to effectively surveyed areas and time periods and by including (and averaging out) bias-describing random effect variables. Our validation confirms the model's high generalizability and predictive power while providing insights into the interplay of predisposing, preparatory and triggering factors for shallow landslide occurrence in South Tyrol. Application possibilities of this novel approach are discussed. The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige