Eric Hardin

Modeling and analysis of landscape evolution using airborne, terrestrial, and laboratory laser scanning

Current laser scanning (Lidar, light detection and ranging) technologies span a wide range of survey extent and resolutions, from regional airborne Lidar mapping and terrestrial Lidar field surveys to laboratory systems utilizing indoor three-dimensional (3D) laser scanners. Proliferation in Lidar technology and data collection enables new approaches for monitoring and analysis of landscape evolution. For example, repeat Lidar surveys that generate a time series of point cloud data provide an opportunity to transition from traditional, static representations of topography to terrain abstraction as a 3D dynamic layer. Three case studies are presented to illustrate novel techniques for landscape evolution analysis based on time series of Lidar data: (1) application of multiyear airborne Lidar surveys to a study of a dynamic coastal region, where the change is driven by eolian sediment transport, wave-induced beach erosion, and human intervention; (2) monitoring of vegetation growth and the impact of landscape structure on overland flow in an agricultural field using terrestrial laser scanning; and (3) investigation of landscape design impacts on overland water flow and other physical processes using a tangible geospatial modeling system. The presented studies demonstrate new insights into landscape evolution in different environments that can be gained from Lidar scanning spanning 1.0–0.001 m resolutions with geographic information system analysis capabilities.

Least Cost Path Extraction of Topographic Features for Storm Impact Scale Mapping

Abstract Hardin, E.; Kurum, M.O.; Mitasova, H., and Overton, M.F., 2012. Least cost path extraction of topographic features for storm impact scale mapping. A raster-based, spatially distributed implementation of the storm impact scale, designed to assess barrier island vulnerability, is presented. The two core topographic parameters of the scale, dune ridge and dune toe elevation, are extracted from a high-resolution, light detection and ranging (LIDAR)-derived digital elevation model (DEM). In addition, the beach slope, necessary to compute wave run-up, is extracted from the beach face. Innovative implementation of least cost path analysis and a physics-based model of an elastic sheet are used to map the dune ridge and dune toe. The robustness and efficiency of the topographic feature extraction method is demonstrated along 4 km of shoreline in Pea Island, Outer Banks, North Carolina.

GIS-based Analysis of Coastal Lidar Time-Series

Introduction.- Processing coastal lidar time series.- Raster-based analysis.- Feature extraction and feature change metrics.- Volume analysis.- Visualizing coastal change.- Appendix.

New spatial measures of terrain dynamics derived from time series of lidar data

We anticipate that multiyear lidar surveys, currently focused on vulnerable coastal areas, will soon become a common resource for monitoring and analysis of various aspects of regional terrain change. We propose raster based measures for mapping and quantification of discrete and continuous terrain changes by introducing novel concepts, such as core and envelope surfaces, contour evolution band, and evolution regression slope map that can provide insights into the spatial aspects of terrain dynamics and changes in structures. The methodology is applied to a section of North Carolina coast where multiyear time series of lidar data is already available. Dynamics of bare dune and beach systems, changes in structures and vegetation growth are mapped and quantified to evaluate the proposed approach.

Visualizations of coastal terrain time series

In coastal regions, water, wind, gravitation, vegetation, and human activity continuously alter landscape surfaces. Visualizations are important for understanding coastal landscape evolution and its driving processes. Visualizing change in highly dynamic coastal terrain poses a formidable challenge; the combination of natural and anthropogenic forces leads to cycles of retreat and recovery and complex morphology of landforms. In recent years, repeated high-resolution laser terrain scans have generated a time series of point cloud data that represent landscapes at snapshots in time, including the impacts of major storms. In this article, we build on existing approaches for visualizing spatial–temporal data to create a collection of perceptual visualizations to support coastal terrain evolution analysis. We extract terrain features and track their migration; we derive temporal summary maps and heat graphs that quantify the pattern of elevation change and sediment redistribution and use the space–time cube concept to create visualizations of terrain evolution. The space–time cube approach allows us to represent shoreline evolution as an isosurface extracted from a voxel model created by stacking time series of digital elevation models. We illustrate our approach on a series of Light Detection and Ranging surveys of sandy North Carolina barrier islands. Our results reveal terrain changes of shoreline and dune ridge migration, dune breaches and overwash, the formation of new dune ridges, and the construction and destruction of homes, changes which are due to erosion and accretion, hurricanes, and human activities. These events are all visualized within their geographic and temporal contexts.

Raster-Based Analysis

Raster-based analysis on two or more DEMs can provide information about change patterns and trends. A common approach to mapping elevation change between two surveys is DEM differencing, performed by map algebra within GIS (r.mapcalc in GRASS). For a larger number of elevation data snapshots, per cell statistics can be applied to the raster DEMs to derive summary maps, which reveal the spatial patterns of stable and dynamic sites, the time periods when sites reach their highest or lowest elevations, and the trends in elevation change.

Processing Coastal Lidar Time Series

In this chapter, we analyze time series of lidar data point clouds to assess the point density, gaps in coverage, spatial extent and accuracy. Based on this analysis and a given application we select appropriate resolution and interpolation method for computation of raster-based digital elevation model (DEM). We explain computation of DEMs by per raster-cell averaging, two types of splines. Assessment of systematic error using geodetic benchmarks or other ground truth point data and correction of any shifted DEMs is the final step in creating a consistent DEM time series.

Visualizing Coastal Change

Scientific visualization provides a means for effective analysis and communication of complex information that may be otherwise difficult to explain and explore. This particularly applies to coastal geomorphology, where 3D spatial and temporal patterns and relationships are critical for capturing landscape features and their dynamics. In this chapter we present GIS-based techniques for visualizing dynamic coastal landscapes using 2D maps, 3D perspective views, animations, and the space-time cube approach.

Feature Extraction and Feature Change Metrics

Coastal change has been historically measured by metrics derived for specific coastal linear features such as shorelines. Linear features are also important for measuring sand dune migration based on the location of dune crests and slip faces and for prediction of coastal vulnerability. In this chapter we present methods for extracting shorelines, dune ridges, dune crests and building footprints from DEMs. Then we measure the change of these features and use them to map vulnerability to storms.