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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape. [ABSTRACT FROM AUTHOR]