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Abstract
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.
Highlights
Marshes have long been recognized as integral systems containing high biological productivity and diversity within the global ecosystem [1,2,3,4]
Clustering Results Using the data measured within a 170 m radius circle around the terrestrial laser scanning (TLS) scanner, K-meanUssirnugntshwe dearetapmeerfaosrumreeddwtiothginenae1r7a0temcrluadstieursscibraclseedaroounnadllthperTevLiSosucsalnynmere(nsetieoSneecdtiopno2in),tKa-nd voxmeelafnesatruurness.wDearevipeesr-fBoormuleddinto(DgeBn)eirnadteexcelusswteersrebcaosemdpounteadll wprheivleioiunsclryemaseinngtiotnheednpuominbtearnodf vclouxsetlers
A three-dimensional point cloud of a marsh measured with TLS was partitioned using unsupervised clustering methods
Summary
Marshes have long been recognized as integral systems containing high biological productivity and diversity within the global ecosystem [1,2,3,4]. In many coastal areas such as the Northwest Gulf of Mexico, vertical land motion contributes a substantial component to the overall relative sea level rise (rslr). Due to their low elevations above mean sea level, the frequency of inundation of marshes associated with rslr will increase at a much higher rate than inundations associated to storm events [11]. For a marsh to sustain its ecosystem function, its accretion rate must be at least equal to the pace of the rslr at its location as the vertical elevation of the marsh relative to mean sea level is critical for its productivity and stability [12]. It is important to accurately characterize salt marsh surface elevation, landcover, and their changes to be able to predict whether salt marsh wetlands and the ecosystem function they support can withstand an accelerated rise in sea level
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