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

Manzo, Ciro; Valentini, Emiliana; Taramelli, Andrea; Filipponi, Federico; Disperati, Leonardo

doi:10.1016/j.jag.2014.11.003

Cited by 24 publications

(27 citation statements)

References 31 publications

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“…We then built and implemented a decision tree classification algorithm that classified each pixel based on the abundance categories from fractions of cover typologies and named each pixel based on the strongest Pearson correlation between the selected spectral profiles and the field spectral library (Manzo et al, ; Valentini et al, ). Every pixel in the image was assigned to a certain class using a threshold value in fractional cover (i.e., ≥ 0.5552 for the wet class; ≥ 0.5950 for the soil class; ≥ 0.4992 for P. australis ; ≥ 0.5507 for E. athericus ; and ≥ 0.8945 for pioneer vegetation).…”

Section: Methodsmentioning

confidence: 99%

“…However, the use of spatial variables derived from RS imagery offers an ideal tool to study the relationships among vegetation type, patch-size distribution, and channel sinuosity (Jefferies et al, 2006;Méléder et al, 2010;Moffett et al, 2015;Vande Castle, 1998;Wang et al, 2007). In fact, to capture smaller-scale variation, linear spectral mixture analysis (LSMA) allows the consideration of subpixel variation (Manzo et al, 2014;Valentini et al, 2014) and provides a validated tool that can be used to study temporal multiscale channel and vegetation variability in tidal ecosystems (Taramelli et al, 2017;Taramelli, Valentini, et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

et al. 2018

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Salt marshes are important coastal areas that consist of a vegetated intertidal marsh platform and a drainage network of tidal channels. How salt marshes and their drainage networks develop is not fully understood, but it has been shown that the biogeomorphic interactions and feedbacks between vegetation development and channel formation play an important role. We examined the relationships among tidal channel sinuosity, marsh roughness, vegetation type (pioneer, Elymus athericus or Phragmites australis), and patch size at different spatial scales using a high‐resolution vegetation map (derived from aerial photography) and lower‐resolution satellite imagery processed with linear spectral mixture analysis. The patch‐size distribution in all vegetation types corresponded to a power law, suggesting the presence of self‐organizational processes. While small vegetation patches are more dominant in pioneer vegetation, they were present in all vegetation types. The largest patch size is restricted to E. athericus. We observed an inverse logarithmic relationship between channel sinuosity and vegetation patch size in all vegetation types. The fact that this relationship is observed in both pioneer and later successional stages suggests that after the establishment of a drainage network in the dynamic pioneer stages of salt marsh development, the later stages of salt marsh succession largely inherit the meandering pattern of the early successional stages. Our study confirms recent evidence that no significant changes in the specific features of tidal channel networks (e.g., channel width, drainage density, and efficiency) take place during the later stages of salt marsh development.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

et al. 2018

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“…Remote sensing data are essential to provide synoptic and extensive maps of biological and physical properties of the oceans (Schofield et al, 2002). Recently, Earth observation (EO) data have also been used to investigate the dynamic processes at high spatial resolution along the Italian coasts Manzo et al, 2015). A few studies, among them Cristina et al (2015), demonstrated the usefulness of remote sensing for supporting the MSFD, using MEdium Resolution Imaging Spectrometer (MERIS) sensor products.…”

Section: Components Of the C-cemsmentioning

confidence: 99%

The Civitavecchia Coastal Environment Monitoring System (C-CEMS): a new tool to analyze the conflicts between coastal pressures and sensitivity areas

et al. 2016

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Abstract. The understanding of the coastal environment is fundamental for efficiently and effectively facing the pollution phenomena as expected by the Marine Strategy Framework Directive, and for limiting the conflicts between anthropic activities and sensitivity areas, as stated by Maritime Spatial Planning Directive. To address this, the Laboratory of Experimental Oceanology and Marine Ecology developed a multi-platform observing network that has been in operation since 2005 in the coastal marine area of Civitavecchia (Latium, Italy) where multiple uses and high ecological values closely coexist. The Civitavecchia Coastal Environment Monitoring System (C-CEMS), implemented in the current configuration, includes various components allowing one to analyze the coastal conflicts by an ecosystem-based approach. The long-term observations acquired by the fixed stations are integrated with in situ data collected for the analysis of the physical, chemical and biological parameters of the water column, sea bottom and pollution sources detected along the coast. The in situ data, integrated with satellite observations (e.g., temperature, chlorophyll a and TSM), are used to feed and validate the numerical models, which allow the analysis and forecasting of the dynamics of pollutant dispersion under different conditions. To test the potential capabilities of C-CEMS, two case studies are reported here:(1) the analysis of fecal bacteria dispersion for bathing water quality assessment, and (2) the evaluation of the effects of the dredged activities on Posidonia meadows, which make up most of the two sites of community importance located along the Civitavecchia coastal zone. The simulation outputs are overlapped by the thematic maps showing bathing areas and Posidonia oceanica distribution, thus giving a first practical tool that could improve the resolution of the conflicts between coastal uses (in terms of stress produced by anthropic activities) and sensitivity areas.

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“…Coastal morphology has been studied using Lidar and other remotely sensed data [1,2,8,[15][16][17][18][19][20]; however, these studies have mapped individual features such as shorelines, dunes, and marshes. To our knowledge, there has been little research addressing a comprehensive mapping method for classifying all geomorphic features on a barrier island, from the ocean to the sound.…”

Section: Introductionmentioning

confidence: 99%

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

2018

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Abstract:Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island's future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions.

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

Cited by 24 publications

References 31 publications

Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

Indications of Dynamic Effects on Scaling Relationships Between Channel Sinuosity and Vegetation Patch Size Across a Salt Marsh Platform

The Civitavecchia Coastal Environment Monitoring System (C-CEMS): a new tool to analyze the conflicts between coastal pressures and sensitivity areas

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

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