Benthic habitat characterization

We present the first digital seafloor geomorphic features map (GSFM) of the global ocean.  The GSFM includes 131,192 separate polygons in 29 geomorphic feature categories, used here to assess differences between passive and active continental margins as well as between 8 major ocean regions (the Arctic, Indian, North Atlantic, North Pacific, South Atlantic, South Pacific and the Southern Oceans and the Mediterranean and Black Seas).  The GSFM provides quantitative assessments of differences between passive and active margins: continental shelf width of passive margins (88 km) is nearly three times that of active margins (31 km); the average width of active slopes (36 km) is less than the average width of passive margin slopes (46 km); active margin slopes contain an area of 3.4 million km2 where the gradient exceeds 5o, compared with 1.3 million km2 on passive margin slopes; the continental rise covers 27 million km2 adjacent to passive margins and less than 2.3 million km2 adjacent to active margins.  Examples of specific applications of the GSFM are presented to show that: 1) larger rift valley segments are generally associated with slow-spreading rates and smaller rift valley segments are associated with fast spreading; 2) polar submarine canyons are twice the average size of non-polar canyons and abyssal polar regions exhibit lower seafloor roughness than non-polar regions, expressed as spatially extensive fan, rise and abyssal plain sediment deposits – all of which are attributed here to the effects of continental glaciations; and 3) recognition of seamounts as a separate category of feature from ridges results in a lower estimate of seamount number compared with estimates of previous workers.

An integrated cartographic approach has been used to summarize different data (geomorphological, sedimentological, hydrodynamic, ecological and anthropic) from an urban microtidal, wave-dominated beach and adjacent inner shelf in a comprehensive and easily readable mapping output. The study area is located in S Sardinia (Italy,  Mediterranean Sea) and focuses on Poetto beach. All the data in this study were processed to produce a Main Map (1:6400 scale) showing the key characteristics of the entire area and three detailed secondary maps (1:56,000 and 1:59,000 scale) that include topographic and eco-graphic profiles, the distribution of sedimentary facies and the main anthropic impact. This map, providing detailed information on the beach dynamics, human impact and the marine ecological status of the Poetto urban beach, represents a useful new tool to facilitate environmental conservation and beach management.

An integrated cartographic approach has been used to summarize different data (geomorphological, sedimentological, hydrodynamic, ecological and anthropic) from an urban microtidal, wave-dominated beach and adjacent inner shelf in a comprehensive and easily readable mapping output. The study area is located in S Sardinia (Italy, Mediterranean Sea) and focuses on Poetto beach. All the data in this study were processed to produce a Main Map (1:6400 scale) showing the key characteristics of the entire area and three detailed secondary maps (1:56,000 and 1:59,000 scale) that include topographic and eco-graphic profiles, the distribution of sedimentary facies and the main anthropic impact. This map, providing detailed information on the beach dynamics, human impact and the marine ecological status of the Poetto urban beach, represents a useful new tool to facilitate environmental conservation and beach management.

This study demonstrates the application of CIELAB, Color intensity, and One Dimensional Scalar Constancy as features for image recognition and classifying benthic habitats in an image with the coastal areas of Hinatuan, Surigao Del Sur, Philippines as the study area. The study area is composed of four datasets, namely: (a) Blk66L005, (b) Blk66L021, (c) Blk66L024, and (d) Blk66L0114. SVM optimization was performed in Matlab ® software with the help of Parallel Computing Toolbox to hasten the SVM computing speed. The image used for collecting samples for SVM procedure was Blk66L0114 in which a total of 134,516 sample objects of mangrove, possible coral existence with rocks, sand, sea, fish pens and sea grasses were collected and processed. The collected samples were then used as training sets for the supervised learning algorithm and for the creation of class definitions. The learned hyper-planes separating one class from another in the multi-dimensional feature space can be thought of as a super feature which will then be used in developing the C (classifier) rule set in eCognition ® software. The classification results of the sampling site yielded an accuracy of 98.85% which confirms the reliability of remote sensing techniques and analysis employed to orthophotos like the CIELAB, Color Intensity and One dimensional scalar constancy and the use of SVM classification algorithm in classifying benthic habitats.