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Makowski, C.; Finkl, C.W., and Vollmer, H.M., 2017. Geoform and landform classification of continental shelves using geospatially integrated IKONOS satellite imagery. Geomorphological characterization of coastal environments along continental shelves depends on accurate interpretation of mesoscale lithic and clastic benthic geoforms and landforms. Using the Geospatially Integrated Seafloor Classification Scheme (G-ISCS), cognitive visual interpretations of seafloor geoforms and their associated landforms were conducted along a diverse segment of the southeast (SE) Florida continental shelf. GeoEye IKONOS-2 satellite imagery provided the remotely sensed visual medium on which interpretations were based. With ESRI ArcGIS® ArcMap software, classification maps were created from the cognitive interpretations to show spatial distribution results of geoform and landform features throughout the study area. Additionally, smaller-scale 'call-out figures' documented specific geomorphological associations among the classified units. Analysis attribute tables compared and contrasted the abundance ( i.e. number of classifying vector polygons) and calculated areas for each geoform and landform classified. It was determined that classification of geoform and landform benthic features along continental shelves can be achieved where water clarity conditions allow for the cognitive visual interpretation of such seafloor formations. Future studies may build upon the classification of continental shelf geoforms and landforms to integrate more transient biogeomorphological features of the marine environment ( e.g., sediment distribution, biological species identification, density of flora and fauna present), thus creating a more detailed and inclusive classification of a selected coastal region. [ABSTRACT FROM AUTHOR]

Makowski, C.; Finkl, C.W., and Vollmer, H.M., 2016. Classification of continental shelves in terms of geospatially integrated physiographic realms and morphodynamic zones. The continental shelf off southeast Florida contains a range of benthic environments that are discernible via remote sensing platforms because of low turbidity in the water column. Using the Geospatially Integrated Seafloor Classification Scheme (G-ISCS), physiographic realms and associated morphodynamic zones were cognitively interpreted and classified at a nominal scale of 1:6000 across four remote sensing platforms ( i.e. GeoEye IKONOS-2, Landsat-5 Thematic Mapper [TM], Landsat-7 Enhanced Thematic Mapper [ETM], and National Agriculture Imagery Program [NAIP] high-resolution aerial orthoimagery). Attribute tables were created in conjunction with interpretations to quantify and compare spatial relationships between classificatory units and the different remote sensing platforms. Resultant maps exported from ESRI ArcGIS® ArcMap software showed that while IKONOS-2 satellite imagery and NAIP aerial orthoimagery provided the greatest detail, classification of physiographic realms and morphodynamic zones was still possible using TM and ETM satellite images. Overall, it was determined that IKONOS-2 provided the most beneficial imagery when applying such a classification of coastal and seafloor features. It is postulated that accurate delineation of physiographic realms and morphodynamic zones can provide a foundation for more in-depth biogeomorpholocial classification ( e.g., further subdivision into benthic geoforms, coastal landforms, biological cover) along continental shelves. [ABSTRACT FROM AUTHOR]

Makowski, C.; Finkl, C.W., and Vollmer, H.M., 2015. Geospatially integrated seafloor classification scheme (G-ISCS): A new method for cognitively interpreting benthic biogeomorphological features. Because benthic environments show ecotonal successions that biophysically fuse geomorphological units with biological coverages, there is a need for cognitive biogeomorphological classification methods. This study introduces a new geospatially integrated seafloor classification scheme (G-ISCS) methodology for cognitively interpreting geomorphological components and dominant biological covers from a variety of remotely sensed imagery within a geographic information system interface. An ordered, hierarchical classification scheme was thus developed to discriminate various categories such as physiographic realm, morphodynamic zone, geoform, landform, dominant surface sediment, dominant biological cover, and density of dominant biological cover. When applying the G-ISCS method to multispectral satellite imagery ( e.g., GeoEye IKONOS-2), individual raw scenes were imported into IDRISI© Taiga to enhance frequency distributions of spectral reflectance through contrast stretching. Image enhancement was applied to blue, green, and red wavelength bands and combined to create 24-bit color composite images that were suitable for interpretation. Using ESRI ArcGIS® ArcMap software, raster catalogs were created for enhanced imagery data sets and projected on an interactive SmartBoard® overlay system for real-time, on-screen digitizing of seafloor feature boundaries at a nominal scale of 1:6000. Differentiation of marine environments was based on spectral reflectance characteristics of color, tone, saturation, pattern, and texture of the seafloor topology. Individual thematic subset maps were then created by hierarchical category and amalgamated in ArcMap to create interactive benthic environment maps. Attribute tables were compiled in conjugation with thematic map products to quantify relationships between mapping units, as well as to generate accurate biogeomorphological spatial statistics. Overall, the new G-ISCS method can be applied across multiple remote sensing platforms to cognitively delineate and interpret benthic biogeomorphological features along shelf environments. [ABSTRACT FROM AUTHOR]

Beach hydromorphological classification is a complex subject. Different beach classification models were presented by several authors. However, fundamental parameters are usually unavailable. Therefore, a morphological analysis using remotely sensed data and image processing techniques is a good approach to identify and to classify beach hydromorphologies. Remote sensing data is an increasingly important component of natural resources monitoring programs. Its usefulness can be maximized by understanding the constraints and capabilities of the imagery and change detection techniques, related to the monitoring objectives. The aim of this study was to explore different remotely sensed data (aerial photographs and a satellite image) and different image processing algorithms in order to identify coastal forms/patterns and further classify beach hydromorphological stage. To achieve that, different image processing techniques were applied to remotely sensed data: pixel and object-based classification algorithms and a pattern recognition approach using artificial neural networks. A stretch of the northwest coast of Portugal was chosen as the study area. The data used in this study consisted in aerial photographs and an IKONOS-2 image. Based on the obtained results two main conclusions could be taken: the pixel-based classification (supervised classification algorithms) showed better results than the object-based classification algorithms; and the pattern recognition approach is the most effective and accurate methodology. Therefore, the association of remote sensing data and image processing techniques is very useful in identifying coastal forms/patterns regarding the classification of beach morphological stage. [ABSTRACT FROM AUTHOR]