Andrea Taramelli

Spectral characterization of coastal sediments using Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR Data (FHyL)

Abstract Beach dune systems are important for coastal zone ecosystems as they provide natural sea defences that dissipate wave energy. Geomorphological models of this near-shore topography require site-specific sediment composition, grain size and moisture content as inputs. Hyperspectral, field radiometry and LiDAR remote sensing can be used as tools by providing synoptic maps of these properties. However, multi-remote sensing of near-shore beach images can only be interpreted if there are adequate bio-geophysical or empirical models for information extraction. Our aim was thus to model the effects of varying sediment properties on the reflectance in both field and laboratory conditions within the FHyL (Field Spectral Libraries, Airborne Hyperspectral Images and Topographic LiDAR) procedure, using a multisource dataset (airborne Hyperspectral – MIVIS and topographic LiDAR – Hawk-eye II and field radiometry). The methodology consisted of (i) acquisition of simultaneous multi-source datasets (airborne Hyperspectral – MIVIS and topographic LiDAR – Hawk-eye) (ii) hyperspectral measurements of sediment mixtures with varying physical characteristics (moisture, grain size and minerals) in field and laboratory conditions, (iii) determination and quantification of specific absorption features, and (iv) correlation between the absorption features and physical parameters cited above. Results showed the potential of hyperspectral signals to assess the effect of moisture, grain-size and mineral composition on sediment properties.

Ten-years sediment dynamics in Northern Adriatic sea investigated through optical remote sensing observations

Understanding the factors influencing sediment fluxes is a key issue to interpret the evolution of coastal sedimentation under natural and human impact and relevant for the natural resources management. Despite river plumes represent one of the major gain in sedimentary budget of littoral cells, complex behavior of coastal plumes, like river discharge characteristics, wind stress and hydro-climatic variables, has not been yet fully investigated. Use of Earth Observation data allows the identification of spatial and temporal variations of suspended sediments related to river runoff, seafloor erosion, sediment transport and deposition processes. The objective of this study is to investigate superficial processes in sedimentary depositional marine environment integrating in-situ data and remote sensing data. The developed innovative approach allow quantitative evaluation of sediment dynamics using Earth Observation data, by relating spatial and temporal patterns of sediment dispersal with climatic forcings.

Detection of Natural and Anthropic Features on Small Islands

ABSTRACT Cappucci, S.; Valentini, E.; Del Monte, M.; Paci, M.; Filipponi, F., and Taramelli, A., 2017. Detection of natural and anthropic features on small islands. In: Martinez, M.L.; Taramelli, A., and Silva, R. (eds.), Coastal Resilience: Exploring the Many Challenges from Different Viewpoints. Journal of Coastal Research, Special Issue No. 77, pp. 73–87. Coconut Creek (Florida), ISSN 0749-0208. Mapping the distribution of seabed habitats, and estimating the spatial distribution of features and biocenosis over land and the seafloor, is particularly important for the analysis of human impacts. The present paper uses an innovative image analysis method that integrates different data sources from airborne remote sensing and in situ measurements for different features, allowing the detection of ecological ‘tipping points’ both in emerged and submerged coastal environments. Results show that it is possible to differentiate between the respective roles of: first, the internal variability of the natural morphological system and second, of external forcing factors. The final evidence, however, identifies a clear signature of external forcing, but whether of anthropogenic or natural origin, is unclear. The spatial pattern of the response to anthropogenic forcing may be indistinguishable from patterns of natural variability. It is argued that this novel approach to define tipping points following anthropogenic impacts could be most valuable in the management of natural resources and the economic development of coastal areas worldwide.

Modelling coastal processes by means of innovative integration of remote sensing time series analysis

Coastal marine and inland landforms are dynamic systems undergoing adjustments in form at different time and space scales in response to varying conditions external to the system. Coastal emerged and nearshore areas, affected by short-term perturbations, return to their pre-disturbance morphology and generally reach a dynamic equilibrium. The objective of this research is to propose innovative remote sensing applications for monitoring and/or combination of existing ones to monitor specific coastal processes in order to quantify and model their time evolution. In particular, it shows which properties are the best proxy for remote sensing characterisation of nearshore coastal areas both emerged and submerged environments by combining multi-sensor spaceborne remote sensing (SAR and OPTICAL) to a) produce deformation and spatiotemporal variations maps in coastal morphology with a special focus to point out the temporal subsidence evolution, b) integrate inter and intra-annual change detection maps of vegetation into coastal morphology.

A Hybrid Power Law Approach for Spatial and Temporal Pattern Analysis of Salt Marsh Evolution

ABSTRACT Taramelli, A.; Valentini, E.; Cornacchia, L., and Bozzeda, F., 2017. A hybrid power law approach for spatial and temporal pattern analysis of salt marsh evolution. In: Martinez, M.L.; Taramelli, A., and Silva, R. (eds.), Coastal Resilience: Exploring the Many Challenges from Different Viewpoints. Journal of Coastal Research, Special Issue No. 77, pp. 62–72. Coconut Creek (Florida), ISSN 0749-0208. A striking feature in salt marshes is vegetation distribution, which can self-organize in patterns over time and space. Self-organized patchiness of vegetation can often give rise to power law relationships in the frequency distribution of vegetation patch sizes. In cases where the whole distribution does not follow a power law, the variance of scale in its tail may often be disregarded. The deviation from power laws represents stochastic effect that can be hybridized on the basis of a Fuzzy Bayesian (FB) generative algorithm, to emphasize the influence of different physical and climatic variables on the patch size distribution to detect tipping point of the ephemeral life of a salt marsh under frequent disturbance events. Using remote sensing observations, we investigate the statistical distribution of spatial vegetation patterns controlled by changes in environmental variables acting on salt marshes, and we speculate the conditions under which a shift from a scale-invariant (power law) distribution to patterns characterized by a dominant patch size could be expected using channel sinuosity as a parameter. Our results show that the hybrid model without considering channel sinuosity can only detect the pure power law exponent, while considering channel sinuosity detects the exponent in the tail of the patch size distribution. Thus the evolution of vegetation patches (under power law) detected by the hybrid model considering channel sinuosity can then be used to forecast potential deviation from steady states in intertidal systems, taking into account the climatic and hydrological regimes. The research shows how numerical thresholds can describe the influence of changes in the non-linearity of patch size frequency distribution and how these thresholds can be reflected as attributes for the resilience capacity measurements in estuarine salt marshes.

Remote Sensing Solutions to Monitor Biotic and Abiotic Dynamics in Coastal Ecosystems

Land- and seascapes are complex systems heterogeneous in space and variable in time. Land- and seascapes can be characterized by attributes and exchanges of energy and mass between these attributes. Under such conditions the most realistic interpretation of nearshore land- and seascapes is as open systems, with multiple interactions between attributes and environment.

Global MODIS Fraction of Green Vegetation Cover for Monitoring Abrupt and Gradual Vegetation Changes

The presence and distribution of green vegetation cover in the biosphere are of paramount importance in investigating cause-effect phenomena at the land/atmosphere interface, estimating primary production rates as part of global carbon and water cycle assessments and evaluating soil protection and land use change over time. The fraction of green vegetation cover (FCover) as estimated from satellite observations has already been demonstrated to be an extraordinarily useful product for understanding vegetation cover changes, for supporting ecosystem service assessments over areas with variable extents and for processes spanning a variable period of time (abrupt events or long-term processes). This study describes a methodology implemented to estimate global FCover (from 2001 to 2015) by applying a linear spectral mixture analysis with global endmembers to an entire temporal series of MODIS satellite observations and gap-filling missing FCover observations in temporal series using the DINEOF algorithm. The resulting global MODV1 FCover product was validated with two global validation datasets and showed an overall good thematic absolute accuracy (RMSE = 0.146) consistent with the validation performance of other FCover global products. Basic statistics performed on the product show changes in average and trend values and allow for the quantification of gross vegetation loss and gain over different temporal scales. To demonstrate the capacity of this global product to monitor specific dynamics, a multitemporal analysis was performed on selected sites and vegetation responses (i.e., cover changes), and specific dynamics resulting from cause-effect phenomena are briefly discussed. The product is intended to be used for monitoring vegetation dynamics, but it also has the potential to be integrated in other modeling frameworks (e.g., the carbon cycle, primary production, and soil erosion) in conjunction with other spatial datasets such as those on climate and soil type.

Figure 6-7_Patch size vs Sinuosity.xlsx

Figure 6 – Relationships betweennvegetation patch size (m 2 , log. scale) and maximum sinuosity of the adjacentnchannel, on both the high-resolution RWS vegetation map from 2010 and the twonSPOT remote sensing images from 1999 and 2012. The relationships are shown forneach of the three main vegetation typologies (e.g., pioneer, Elymus and reed vegetation).nnFigure 7 – Boxplot of the distribution ofnchannel sinuosity values within each vegetation typology, on the RWS vegetationnmap and on the SPOT satellite images from 1999 and 2012. Different lettersndenote significant differences among the three vegetation classes (ANOVA, p =n0.05).

Sea Surface Temperature changes analysis, an Essential Climate Variable for Ecosystem Services provisioning

Sea Surface Temperature is an Essential Climate Variable (ECV) that allows an effective quantitative estimation of recent changes in large marine ecosystem like the Mediterranean Sea. Sea Surface Temperature (SST) continues to rise, threatening marine ecosystem status and Ecosystem Services (ESS) provisioning. One of the questions that multitemporal analysis of Earth Observation (EO) time series should address is the response to variations in SST spatial distribution due to climate change. Ecosystem indicators like the fish growth rates across Mediterranean regions reveal temporal trends and regional variability. This study addresses the changes of the SST of the Mediterranean Sea “Large Marine Ecosystem” over the last three decades in order to evaluate trends, identify spatial and temporal patterns of SST variability from multitemporal analysis of EO products. Time series of daily SST estimated for the period 1982–2016 from multi-sensor satellite data were collected from operational Copernicus Marine Environment Monitoring Service (CMEMS). A wide range of statistical approaches are considered, like Seasonal Trend decomposition, Empirical Orthogonal Function, Self-Organizing Maps. Focusing on the thermal habitat of fish species, a fish growth model is used to reveal different scenarios in the potential growth of fish populations under past and current conditions as well as future climate projections. Results indicate that in the past three decades the eastern part of the Mediterranean Sea experienced greater SST increase than the western part, producing different scenarios of fish growth rates across the Mediterranean regions.

Integrating Fuzzy Logic and GIS Analysis to Assess Sediment Characterization within a Confined Harbour

Characterization of marine sediments in areas heavily impacted by human activities is a good example for situations where high complexity of physical and chemical processes can lead to an incomplete understanding of the configuration and distribution of pollutants material. These processes are often very complex for a direct prediction from a mathematical theory, making necessary that the process of identifying areas of contaminated sediment is mainly based on defined empirical models of concentrations distribution. In this paper an ontological fuzzy approach in GIS serves as a framework to define a specific scenario grounded on the abilities from an existing dataset. The final model is based on a large number of known concentrations (samples for characterization), which are considered sufficiently similar in terms of features. Therefore the model is working as guides (description of the model) for the identification of areas of same type.