Vertical Accuracy and Use of Topographic LIDAR Data in Coastal Marshes

Schmid, Keil; Hadley, Brian; Wijekoon, Nishanthi

doi:10.2112/jcoastres-d-10-00188.1

Cited by 116 publications

(121 citation statements)

References 13 publications

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“…Our results confirm findings of others, which suggest that lidar DEMs can have a substantial level of vertical uncertainty in intertidal areas [17][18][19], and this uncertainty should be accounted for if data are directly used in classification algorithms for habitat mapping or for use in sea-level rise modeling efforts [42,43]. Our findings highlighted that optimal results with regards to the maximum identification of actual intertidal areas (i.e., highest producer's accuracy) are likely produced when site-specific RTK GPS data are used.…”

Section: Discussionsupporting

confidence: 91%

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

et al. 2017

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While airborne lidar data have revolutionized the spatial resolution that elevations can be realized, data limitations are often magnified in coastal settings. Researchers have found that airborne lidar can have a vertical error as high as 60 cm in densely vegetated intertidal areas. The uncertainty of digital elevation models is often left unaddressed; however, in low-relief environments, such as barrier islands, centimeter differences in elevation can affect exposure to physically demanding abiotic conditions, which greatly influence ecosystem structure and function. In this study, we used airborne lidar elevation data, in situ elevation observations, lidar metadata, and tide gauge information to delineate low-lying lands and the intertidal wetlands on Dauphin Island, a barrier island along the coast of Alabama, USA. We compared three different elevation error treatments, which included leaving error untreated and treatments that used Monte Carlo simulations to incorporate elevation vertical uncertainty using general information from lidar metadata and site-specific Real-Time Kinematic Global Position System data, respectively. To aid researchers in instances where limited information is available for error propagation, we conducted a sensitivity test to assess the effect of minor changes to error and bias. Treatment of error with site-specific observations produced the fewest omission errors, although the treatment using the lidar metadata had the most well-balanced results. The percent coverage of intertidal wetlands was increased by up to 80% when treating the vertical error of the digital elevation models. Based on the results from the sensitivity analysis, it could be reasonable to use error and positive bias values from literature for similar environments, conditions, and lidar acquisition characteristics in the event that collection of site-specific data is not feasible and information in the lidar metadata is insufficient. The methodology presented in this study should increase efficiency and enhance results for habitat mapping and analyses in dynamic, low-relief coastal environments.

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Section: Discussionsupporting

confidence: 91%

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

et al. 2017

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“…To create the ground elevation raster, we selected only those lidar returns representing the minimum height value in each 5 m × 5 m cell. This method is known as "minimum bin-gridding" and has been used to reduce the vertical errors in digital elevation model (DEM) rasters created from lidar data acquired over areas of dense vegetation, such as salt marshes [32]. For the canopy elevation raster, we selected the returns representing the maximum elevation in each 5 m × 5 m cell.…”

Section: Remote Sensing Datamentioning

confidence: 99%

Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

et al. 2015

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Digital elevation models (DEMs) derived from airborne lidar are traditionally unreliable in coastal salt marshes due to the inability of the laser to penetrate the dense grasses and reach the underlying soil. To that end, we present a novel processing methodology that uses ASTER Band 2 (visible red), an interferometric SAR (IfSAR) digital surface model, and lidar-derived canopy height to classify biomass density using both a three-class scheme (high, medium and low) and a two-class scheme (high and low). Elevation adjustments associated with these classes using both median and quartile approaches were applied to adjust lidar-derived elevation values closer to true bare earth elevation. The performance of the method was tested on 229 elevation points in the lower Apalachicola River Marsh. The two-class quartile-based adjusted DEM produced the best results, reducing the RMS error in elevation from 0.65 m to 0.40 m, a 38% improvement. The raw mean errors for the lidar DEM and the adjusted DEM were 0.61 ± 0.24 m and 0.32 ± 0.24 m, respectively, thereby reducing the high bias by approximately 49%.

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“…The CHM was created by interpolating 2014 LIDAR points using LasTools and ArcGIS [57,58]. While a LIDAR-based CHM can be helpful in identifying vegetation heights in certain terrains, coastal wetland vegetation can cause errors and positive bias caused by litter and certain vegetation structures that can conceal the ground elevation [59]. As a result we only used the CHM to identify tall wetland vegetation, dead trees within the restored marsh, and forests.…”

Section: Classification Methods 2015mentioning

confidence: 99%

Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

et al. 2017

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Coastal wetlands are important ecosystems for carbon storage and coastal resilience to climate change and sea-level rise. As such, changes in wetland habitat types can also impact ecosystem functions. Our goal was to quantify historical vegetation change within the Nisqually River watershed relevant to carbon storage, wildlife habitat, and wetland sustainability, and identify watershed-scale anthropogenic and hydrodynamic drivers of these changes. To achieve this, we produced time-series classifications of habitat, photosynthetic pathway functional types and species in the Nisqually River Delta for the years 1957, 1980, and 2015. Using an object-oriented approach, we performed a hierarchical classification on historical and current imagery to identify change within the watershed and wetland ecosystems. We found a 188.4 ha (79%) increase in emergent marsh wetland within the Nisqually River Delta between 1957 and 2015 as a result of restoration efforts that occurred in several phases through 2009. Despite these wetland gains, a total of 83.1 ha (35%) of marsh was lost between 1957 and 2015, particularly in areas near the Nisqually River mouth due to erosion and shifting river channels, resulting in a net wetland gain of 105.4 ha (44%). We found the trajectory of wetland recovery coincided with previous studies, demonstrating the role of remote sensing for historical wetland change detection as well as future coastal wetland monitoring.

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Vertical Accuracy and Use of Topographic LIDAR Data in Coastal Marshes

Cited by 116 publications

References 13 publications

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

The Impact of Lidar Elevation Uncertainty on Mapping Intertidal Habitats on Barrier Islands

Adjusting Lidar-Derived Digital Terrain Models in Coastal Marshes Based on Estimated Aboveground Biomass Density

Remote Sensing for Wetland Mapping and Historical Change Detection at the Nisqually River Delta

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