Francesco Stecchi

Image classification methods applied to shoreline extraction on very high-resolution multispectral imagery

Comprehension of vulnerability to coastal erosion in dynamic coastal environments strongly depends on accurate and frequent detection of shoreline position. The monitoring of such environments could benefit from the semi-automatic shoreline delineation method, especially in terms of time, cost, and labour-intensiveness. This article explores the potential of using a semi-automatic approach in delineating a proxy-based shoreline by processing high-resolution multispectral WorldView-2 satellite imagery. We studied the potential and differences of basic and easily accessible standard classification methods for shoreline detection. In particular we explored the use of high spatial and spectral resolution satellite imagery for shoreline extraction. The case study was carried out on a 40 km coastal stretch facing the Northern Adriatic Sea (Italy) and belonging to the Municipality of Ravenna. In this area a frequent monitoring of shoreline position is required because of the extreme vulnerability to erosion phenomena that have resulted in a general trend of coastal retreat over recent decades. The wet/dry shorelines were delineated between the classes of wet and dry sand, resulting from different supervised (Parallelepiped, Gaussian Maximum Likelihood, Minimum-Distance-to-Means, and Mahalanobis distance) image classification techniques and the unsupervised Iterative Self-Organizing Data Analysis Technique (ISODATA). In order to assign reliability to outcomes, the extrapolated shorelines were compared to reference shorelines visually identified by an expert, by assessing the average mean distance between them. In addition, the correlation between offset rates and different types of coast was investigated to examine the influence of specific coastal features on shoreline extraction capability. The results highlighted a high level of compatibility. The average median distance between reference shorelines and those resulting from the classification methods was less than 5.6 m (Maximum likelihood), whereas a valuable distance of just 2.2 m was detected from ISODATA and Mahalanobis. Heterogeneous coastal stretches exhibited a larger offset between extracted and reference shorelines than the homogeneous ones. To finally evaluate the coastal evolution of the area, results from Mahalanobis classification were compared to a shoreline derived from airborne light detection and ranging (lidar) data. The fine spatial resolution provided by both methodologies allowed a detailed Digital Shoreline Analysis System (DSAS) comparison, detecting an erosive trend within a wide portion of the study area.

Land use and land cover change analysis in predominantly man-made coastal wetlands: towards a methodological framework

In areas with a long history of human occupation, coastal wetlands have undergone extensive modification to accommodate extractive activities as salt-extraction and aquaculture. These man-made wetlands maintain some of the ecological functions of natural wetlands in spite of their artificial character: their suitability as complimentary waterbird habitat is well documented. In cases of wetlands composed of mixed natural and man-made areas, similarities in substrate-vegetation-water compositions may pose challenges in the applicability of remote sensing and GIS techniques for the study of landscape changes, requiring tailor-made, case-specific methods. We explored this supposition by testing these techniques for the study of the Bahia de Cadiz Nature Park (Spain). Using Landsat imagery spanning the 1985–2011 period, natural and man-made marsh areas were classified separately and results merged to produce land cover classification maps. Different change dynamics were observed for the natural and man-made areas, the latter exhibiting prominent changes, including widespread vegetative succession. Further, through the overlay of ancillary land use data for 2011, an integrated land use and cover map was produced for this year. Different scenarios arising from the abandonment of extractive activity and structural negligence were highlighted. Furthermore, a methodological framework for the classification of predominantly man-made wetlands was designed. The method is cost-effective and open for integration of additional datasets, and is considered a beneficial input to conservation and land use management. Its applicability for monitoring of landscape change not only pertains to the study area, but also extends to other coastal wetland areas of a similar nature.

Application of SLEUTH Model to Predict Urbanization Along the Emilia-Romagna Coast (Italy): Considerations and Lessons Learned

Coastal zone of Emilia-Romagna region, Italy, has been significantly urbanized during the last decades, as a result of a tourism development. This was the main motivation to estimate future trajectories of urban growth in the area. Cellular automata (CA)-based SLEUTH model was applied for this purpose, by using quality geographical dataset combined with relevant information on environmental management policy. Three different scenarios of urban growth were employed: sprawled growth scenario, compact growth scenario and a scenario with business-as-usual pattern of development. The results showed the maximum increase in urbanization in the area would occur if urban areas continue to grow according to compact growth scenario, while minimum was observed in case of more sprawled-like type of growth. This research goes beyond the domain of the study site, providing future users of SLEUTH detailed discussion on considerations that need to be taken into account in its application.