@article {5707, title = {Improving Salt Marsh Digital Elevation Model Accuracy with Full-Waveform Lldar and Nonparametric Predictive Modeling}, volume = {202}, year = {2018}, month = {March 5}, pages = {193-211}, publisher = {Elsevier}, abstract = {

Salt marsh vegetation tends to increase vertical uncertainty in light detection and ranging (lidar) derived elevation data, often causing the data to become ineffective for analysis of topographic features governing tidal inundation or vegetation zonation. Previous attempts at improving lidar data collected in salt marsh environments range from simply computing and subtracting the global elevation bias to more complex methods such as computing vegetation-specific, constant correction factors. The vegetation specific corrections can be used along with an existing habitat map to apply separate corrections to different areas within a study site. It is hypothesized here that correcting salt marsh lidar data by applying location-specific, point-by-point corrections, which are computed from lidar waveform-derived features, tidal-datum based elevation, distance from shoreline and other lidar digital elevation model based variables, using nonparametric regression will produce better results. The methods were developed and tested using full-waveform lidar and ground truth for three marshes in Cape Cod, Massachusetts, U.S.A. Five different model algorithms for nonparametric regression were evaluated, with TreeNet\&$\#$39;s stochastic gradient boosting algorithm consistently producing better regression and classification results. Additionally, models were constructed to predict the vegetative zone (high marsh and low marsh). The predictive modeling methods used in this study estimated ground elevation with a mean bias of 0.00 m and a standard deviation of 0.07 m (0.07 m root mean square error). These methods appear very promising for correction of salt marsh lidar data and, importantly, do not require an existing habitat map, biomass measurements, or image based remote sensing data such as multi/hyperspectral imagery.

}, keywords = {cart, dem correction, regression trees, Spartina alterniflora; random forests, treenet stocgastuc graduebt boosting}, doi = {https://doi.org/10.1016/j.ecss.2017.11.034}, url = {https://www.sciencedirect.com/science/article/pii/S0272771417307849}, author = {J Rogers and Christopher E Parrish and Larry G Ward and Burdick, David M} } @article {5503, title = {Assessment of Elevation Uncertainty in Salt Marsh Environments Using Discrete-Return and Full-Waveform Lidar}, volume = {SI 76}, year = {2016}, pages = {107-122}, publisher = {Coastal Education \& Research Foundation [CERF]}, address = {Coconut Creek, FL}, abstract = {

Lidar data can serve as an important source of elevation information for studying, monitoring and managing salt marshes. However, previous studies have shown that lidar data tend to have greater vertical uncertainty in salt marshes than in other environments, hindering the ability to resolve small elevation differences that can be ecologically significant in marshes. For coastal scientists and managers to effectively collect, evaluate, and/or use lidar data in salt marshes, factors affecting elevation uncertainty (e.g., plant species, season, and lidar processing methods) must be well understood. This study addresses this need using discrete-return (DRL) and full-waveform lidar, along with field-surveyed reference data, for four marshes on Cape Cod, Massachusetts (USA). The lidar bias and standard deviation were computed across all four marsh systems and four major taxa using varying interpolation and filtering methods. The effects of seasonality were also investigated using lidar data acquired in the summer and the following spring. Relative uncertainty surfaces (RUS) were computed from lidar waveform-derived metrics and examined for their utility and correlation with individual lidar errors. The results clearly illustrate the importance of seasonality, species, and lidar interpolation and filtering methods on elevation uncertainty in salt marshes. Results also demonstrate that RUS generated from lidar waveform features are useful in qualitative assessments of lidar elevation uncertainty and correlate well with vegetation height (r = 0.85; n = 268). Knowledge of where DRL uncertainty persists within salt marshes and the factors influencing the higher uncertainty should facilitate the development of better correction methods.

}, keywords = {bare earth filtering, DEM, interpolation, inverse distance weighting, salt marsh vegetation, Spartina alterniflora, uncertainty}, doi = {10.2112/SI76-010}, url = {http://www.jcronline.org/doi/full/10.2112/SI76-010}, author = {J Rogers and Christopher E Parrish and Larry G Ward and Burdick, David M} } @article {5278, title = {Evaluation of Field-Measured Vertical Obscuration and Full Waveform Lidar to Assess Salt Marsh Vegetation Biophysical Parameters}, volume = {156}, year = {2015}, pages = {264-275}, abstract = {

Local, high-resolution, accurate data sets are needed to support restoration and other management initiatives in coastal salt marshes, yet field collections of site-specific vegetation data is often impractical. In this study, a novel combination of full-waveform light detection and ranging (lidar) and field techniques for assessing the distribution of aboveground biomass throughout its height and its light blocking properties were investigated. Using new field methods, strong correlations were observed (r \> 0.9) between subsamples\&$\#$39; vertical biomass (VB), the distribution of vegetation biomass by height, and vertical obscuration (VO), the measure of the vertical distribution of the ratio of vegetation to airspace, for Spartina alterniflora. Also, it was found that simple metrics derived from the lidar waveforms, such as waveform width, can provide new information to estimate salt marsh vegetation parameters. The strong correlations between field-collected biophysical parameters and metrics derived from lidar data suggest that remote sensing methods can be used to estimate some vegetation biophysical parameters such as plant height and proportion of vegetation area (PVA) using smaller, more targeted field surveys. Future work will be needed to verify the extensibility of the methods to other sites and vegetation types.

}, keywords = {biomass, coastal vegetation, LIDAR, Remote Sensing, salt march, Spartina alterniflora}, url = {http://dx.doi.org/10.1016/j.rse.2014.09.035}, author = {J Rogers and Christopher E Parrish and Larry G Ward and Burdick, David M} } @article {5179, title = {Assessment of Waveform Shape Features for Lidar Uncertainty Modeling in a Coastal Salt Marsh Environment}, volume = {Vol. 11, No. 2}, year = {2014}, pages = {569 - 573}, publisher = {IEEE}, abstract = {

There is currently great interest in lidar surveys of salt marshes to support coastal management and decision making. However, vertical uncertainty of lidar elevations is generally higher in salt marshes than in upland areas, and it can be difficult to empirically quantify due to the challenges of obtaining ground control in marshes. Assuming that most of the component uncertainties in the lidar geolocation equation will remain essentially constant over a relatively small location, it is posited that vertical uncertainty in a marsh will vary mostly as a function of surface and cover characteristics. These, in turn, should affect lidar waveforms recorded during the survey, and therefore, analysis of the waveform shapes may allow for prediction of vertical uncertainty variation. Waveforms at three test sites were used to compute 16 computationally efficient features that describe the shapes; and simple, multilinear, and principal component regressions were used to evaluate their ability to predict elevation differences between lidar and Global Positioning System ground control. The results show that a simple estimate of waveform width can explain over 50\% of the total variability in elevation differences but that multilinear regression does not significantly improve the performance. Somewhat surprisingly, skewness of the waveform does not appear to be a good predictor of elevation differences in these cases.

}, keywords = {Geospatial Analysis, Lasers, LIDAR, Ranging, Sea coast}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6600874}, author = {Christopher E Parrish and J Rogers and Brian R Calder} } @article {5383, title = {Enhanced coastal mapping using lidar waveform features}, year = {2014}, month = {10-12 June 2014}, address = {Mobile, AL}, author = {Christopher E Parrish and J Rogers and Larry G Ward and Jennifer A. Dijkstra} }