Interacting river discharge, tidal oscillation, and tropical rainfall across the 22,000 km 2 Orinoco delta plain support diverse fresh and brackish water ecosystems. To develop environmental baseline information for this largely unpopulated region, we evaluate major coastal plain, shallow marine, and river systems of northeastern South America, which serves to identify principal sources and controls of water and sediment flow into, through, and out of the Orinoco Delta. The regional analysis includes a summary of the geology, hydrodynamics, sediment dynamics, and geomorphic characteristics of the Orinoco drainage basin, river, and delta system. Because the Amazon River is a major source of sediment deposited along the Orinoco coast, we summarize Amazon water and sediment input to the northeastern South American littoral zone. We investigate sediment dynamics and geomorphology of the Guiana coast, where marine processes and Holocene history are similar to the Orinoco coast. Major factors controlling Orinoco Delta water and sediment dynamics include the pronounced annual flood discharge; the uneven distribution of water and sediment discharge across the delta plain; discharge of large volumes of water with low sediment concentrations through the Río Grande and Araguao distributaries; water and sediment dynamics associated with the Guayana littoral current along the northeastern South American coast; inflow of large volumes of Amazon sediment to the Orinoco coast; development of a fresh water plume seaward of Boca Grande; disruption of the Guayana Current by Trinidad, Boca de Serpientes, and Gulf of Paria; and the constriction at Boca de Serpientes. D

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Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the "k" clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point-and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.

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Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land, dry sand or debris, wet sand, and water. Unmanned aircraft system (UAS) remote sensing that can acquire imagery with sub-decimeter pixel size provides opportunities to map these four beach zones. This paper attempts to delineate four beach zones based on UAS hyperspatial RGB (Red, Green, and Blue) imagery, namely imagery of sub-decimeter pixel size, and feature textures. Besides the RGB images, this paper also uses USGS (the United States Geological Survey) Munsell HSV (Hue, Saturation, and Value) and CIELUV (the CIE 1976 (L*, u*, v*) color space) images transformed from an RGB image. The four beach zones are identified based on the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) textures. Experiments were conducted with South Padre Island photos acquired by a Nikon D80 camera mounted on the US-16 UAS during March 2014. The results show that USGS Munsell hue can separate land and water reliably. GLCM and LBP textures can slightly improve classification accuracies by both unsupervised and supervised classification techniques. The experiments also indicate that we could reach acceptable results on different photos while using training data from another photo for site-specific UAS remote sensing. The findings imply that parallel processing of classification is feasible.

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