Remote sensing techniques could enable remarkable advances in characterizing rivers hydromorphology by providing spatially and temporally explicit information. Remote mapping of hydromorphology can play a decisive role in a wide range of river science and management applications including habitat modeling and river restoration. High resolution satellite imagery (HRSI) has recently emerged as potentially powerful means of mapping riverine environments. This research aims to develop advanced methodologies for processing HRSI to map and quantify a set of key hydromorphological attributes including: (1) river boundaries, (2) bathymetry and (3) riverbed types and compositions. Boundary pixels of rivers are subject to spectral mixture that limits the accuracy of river areas extraction using conventional hard classifiers. To address this problem, unmixing and super resolution mapping (SRM) are focused as two steps, respectively, for estimation and then spatial allocation of water fractions within the mixed pixels. Optimal band analysis for NDWI (OBA-NDWI) is proposed to identify the pair of bands for which the NDWI values yield the highest correlation with water fractions. The OBA-NDWI then incorporates the optimal NDWI as a predictor of water fractions through a regression model. Water fractions obtained from the OBA-NDWI method are benchmarked against the results of simplex projection unmixing (SPU) algorithm. The pixel swapping (PS) and interpolation-based algorithms are applied on water fractions for SRM. In addition, a simple modified binary PS (MBPS) algorithm is proposed to reduce the computational time of the original PS method. Water fractions obtained from the proposed OBA-NDWI method are demonstrated to be in good agreement with those of SPU algorithm (R2=90%, RMSE=7% for WorldView-2 (WV-2) image and R2=87%, RMSE=9% for Geoeye image). The spectral bands of WV-2 provide a wealth of choices through the proposed OBA-NDWI to estimate water fractions. The interpolation-based and MBPS methods lead to sub-pixel maps comparable with those obtained using the PS algorithm, while they are computationally more effective. SRM algorithms improve user/producer accuracies of river areas about 10% with respect to conventional hard classification. This research introduces multiple optimal depth predictors analysis (MODPA) that combines previously developed depth predictors along with other measures such as the intensity components of HSI color space. To avoid over-fitting of the linear model, statistically optimal predictors are selected based on one of partial least square (PLS), stepwise and principal component (PC) regressions. The primary focus of this study is on shallow and clearly flowing streams where substrate variability could have pronounced effect on depth retrievals. Spectroscopic experiments are performed in controlled condition of a hydraulic laboratory to examine the robustness of bathymetry models with respect to changes in bottom types. Further, simulations from radiative transfer modeling are used to extend the analysis by isolating the effect of inherent optical properties (IOPs) and also by investigating the performance of bathymetry models in optically complex and also deeper streams. Bathymetry of Sarca, a shallow river in Italian Alps, is also mapped using a WorldView-2 (WV-2) image where the atmospheric compensation (AComp) product is evaluated for the first time. Results indicate the robustness of multiple-predictor models particularly MODPA rather than single-predictor models such as optimal band ratio analysis (OBRA) with respect to heterogeneity of bottom types, IOPs and atmospheric effects. This study suggests extra predictors when the multiple regression is assisted with an optimal predictors selection process (e.g. MODPA). The extra predictors enhance the accuracy of depth retrievals particularly in optically complex waters and also for low spectral resolution imagery (e.g. GeoEye). Further, enhanced spectral resolution of WV-2 compared to GeoEye improves the bathymetry retrievals. MODPA based on PLS regression provided improvements on the order of 0.05 R2 and 0.7 cm RMSE compared to multiple Lyzenga and 0.18 R2 and 2 cm RMSE compared to OBRA using AComp reflectances of WV-2 for Sarca River with a maximum 0.8 m depth. In addition, a theoretical approach namely hydraulically assisted bathymetry (HAB) is assessed and further modified for calibration of bathymetry models that provided comparable results with the empirical calibration approach. Substrate mapping in fluvial systems has not received as much attention as that in nearshore optically shallow waters of inland and coastal areas. The research to date has been primarily based on surface spectral reflectance data without accounting for water column attenuations. This study aims at retrieving the bottom reflectances in shallow rivers and then examining the effectiveness of inferred bottom spectra in mapping of substrate types. Bathymetry and diffuse attenuation coefficient (kd) are derived from above-water reflectances for which some in-situ/known depths are required. Following the retrievals of depth and kd, bottom reflectances are estimated based on a water column correction method. Moreover, the efficacy of vegetation indices (VIs) is examined for making distinction among the densities of submerged aquatic vegetation (SAV) using either above-water or retrievals of bottom reflectances. This research benefits, for the first time, from three different approaches including controlled spectroscopic measurements in a hydraulic lab, simulations from radiative transfer modeling and an 8-band WordView-3 (WV-3) image. The results indicate the significant enhancements of streambed mapping using inferred bottom reflectances than using above-water spectra. This is evident, for instance, on clustering of three bottom types using simulated spectra with 20% enhancement of overall accuracy. Deep-water correction demonstrated to have most of an impact on retrievals of bottom reflectances only in NIR bands when the water column is relatively thick (> 0.5 m) and/or when the water is turbid. The red-edge (RE) band of WV-3/WV-2 improves remarkably the detection of SAV densities based on the VIs either using above-water or retrieved bottom spectra. Further, the simulated spectra suggest that enhanced spectral resolution of 8-band WV-3 leads to improvements in streambed mapping compared to traditional 4-band imagery. This study demonstrated the feasibility of retrieving bottom reflectances and mapping SAV densities from space in a shallow river using the WV-3 image (user and producer accuracies of 67% and 60% in average for three levels of SAV densities). Moreover, the feasibility of mapping grain size classes is assessed using spectral information based on laboratory experiments coupled with simulations. The changes in grain sizes affect the magnitude of reflectances while the shape of spectra remains almost identical. This characteristic feature demonstrated high potentials for mapping grain size classes by retrieving the bottom reflectances. In summary, HRSI provided promising results and effective means of mapping the selected hydromorphological attributes of shallow rivers in spatially continuous and in large extents.

Recent epidemiological studies have reinforced the link between short and long-term exposure to air pollutants and adverse effects on public health especially over the weaker part of the population, like children and older adults. The creation of simple tools to locate sensible areas as well as of dedicated Spatial Decision Support System (SDSS) to improve the management of pollution risk areas system is strongly advised. The aim of this work is to develop a SDSS methodology, based on easy to find data and usable by decision makers, to assess and reduce the impact of air pollutants in a urban context. To achieve this goals I tested the exploitability of a set of low-cost sensors for outdoor air quality monitoring, I characterized the urban micro-environments and the spatial variability of air pollutants using remote sensing compared to field data and eventually I developed a SDSS to improve the public health designing and comparing different scenarios. The city centre of Edinburgh has been used as study case for the purposed methodology. To test the reliability and applicability of low cost sensors as proxies for remote sensed data, we conducted a measurements campaign to compare the observed data between an official measurements station (OMS) in Trento (Italy) and electrochemical and thick film sensors respectively of Carbon Monoxide (CO) and Ozone ($O_3$). Due to data quality and availability we decided to characterize the urban micro-environments of Edinburgh (Scotland, UK) in eight main classes (water, grass, vegetation, road, car, bus, buildings and shadow) combining the Geographic Object-Based Image Analysis (GEOBIA) with Machine Learning algorithms to process the high resolution (0.25m x 0.25m) RGB aerial ortho-rectified images. This land-use characterization combined with other geographical informations, like the classification of the roads and the urban morphology, were compared with 37 Nitrogen Dioxide (NO2) concentration data, collected using passive tubes during a six week campaign of measurements conducted by the school of Chemistry of the University of Edinburgh. I developed a new open-source GIS python library (PyGRASS), integrated in the stable release of GRASS GIS, to speed-up the prototyping phase and to create and test new GIS tools and methodologies. Different studies on SDSS were carried out to implement procedures and models. Based on these models and data all the factors (land-use, roads and geo-morphological features) were ranked to identify which are driving forces for urban air quality and to help decision makers to develop new policies. The sensor tested in Trento revealed an evident drift in measurement residues for CO, furthermore the measurements were also quite sensitive to external factors such as temperature and humidity. Since these sensors required frequent recalibration in order to obtain reliable results, their use was not as low-cost as expected. The characterization of urban land-use in Edinburgh with GEOBIA and machine learning provided an overall accuracy of 93.71\% with a Cohen's k of 0.916 using a train/test dataset of 9301 objects. The $NO_2$ data confirm the assumption that air concentration is strongly dependent on geographical position and it is strongly influenced by the position of the pollutant's source. Using the results of the tests and remote sensing analysis, I developed an SDSS. Starting from the current situation, I designed three scenarios to assess the effect that different policies and actions could have on improving air quality at on the local and district level. The outcomes of this work can be used to define and compare different scenarios and develop effective policies to reduce the impact of air pollutants in an urban context using simple and easy to find data. The GIS-based tool can help to identify critical areas before deploying sensors and splitting the study area in homogeneous micro-environments clusters. The model is easy to expand following different procedures.

Access to geographic information has radically changed in the past decade. Previously, it was a specific task, for which complex desktop Geographic Information Systems (GISs) were built, and geographic data was maintained locally, managed by a restricted number of technicians. With the significant impact of the world-wide-web, an increasing number of different geographic services became available from heterogeneous sources. To support interoperability among different providers and users, GIS agencies have started to adopt Spatial Data Infrastructure (SDI) models. Usually, each SDI service provider publishes and gathers geographic information based on its background knowledge. Hence, discovering, chaining, and using services require a semantic interoperability level between different providers. This problem is typically referred as the need for 'semantic interoperability among autonomous and heterogeneous systems' and it is a challenge for current SDIs, due to their distributed architecture. This thesis provides a framework to approach the semantic heterogeneity problem in the field of geo-services - services that deal with the generation and management of geographical information - among distributed SDIs. The framework is based on: (i) a peer-to-peer (P2P) view of the semantics of web service coordination, implemented by using the OpenKnowledge system and (ii) the use of a specific semantic matching solution called Structure Preserving Semantic Matching (SPSM). SPSM is a basic module of OpenKnowledge as it enables web service discovery and integration by using semantic matching between invocations of web services and web service descriptions. We applied the OpenKnowledge system on a realistic emergency response scenario and selected SDI services. We modeled an emergency response scenario, i.e., a potential flooding event in the area of Trento. The scenario is based on the past experience and actual emergency plans as collected from interviews with personnel of the involved institutions and from related documents. Within this emergency response scenario a detailed implementation of selected SDI services is presented, namely the gazetteer, map and download services. The SPSM solution has been assessed on a set of GIS ESRI ArcWeb services. Two kinds of experiments have been conducted: the first experiment includes matching of original web service signatures with synthetically altered ones. In the second experiment a manual classification of the GIS dataset has been compared to the unsupervised one produced by SPSM. The evaluation results demonstrate robustness and good performance of the SPSM approach on a large (ca. 700.000) number of matching tasks. In the first experiment a high overall matching relevance quality (F-measure) was obtained (over 55%). In the second experiment the best F-measure value exceeded 50% for the given GIS operations set. SPSM performance is good, since the average execution time per matching task was 43 ms. This suggests that SPSM could be employed to find similar web service implementations at runtime. The aforementioned results suggest the practical real time application of the SPSM approach to: (i) discovering geo-services from specific geographic information catalogs, (ii) composing specific geo-processing services, (iii) supporting coordination of geo-sensor networks, and (iv) supporting geo-data discovering and integration.