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Where’s the Ground Surface? – Elevation Bias in LIDAR-derived Digital Elevation Models Due to Dense Vegetation in Oregon Tidal Marshes
Citation:
Ewald, M., L. Brophy, AND Chris Janousek. Where’s the Ground Surface? – Elevation Bias in LIDAR-derived Digital Elevation Models Due to Dense Vegetation in Oregon Tidal Marshes. Presented at Coastal and Estuarine Research Federation, San Diego, CA, November 03 - 07, 2013.
Impact/Purpose:
LIDAR (Light Detection and Ranging) is a powerful remote sensing technique used to assess ground elevation but it can be subject to error due to the presence of vegetation in natural ecosystems. In this research we assessed the accuracy of coastal wetland LIDAR data in different types of plant assemblages by comparing digital ground surface elevation models with on-the-ground GPS measurements. Our data show that LIDAR overestimates ground elevation on average by 15-30 cm, with greater errors in specific vegetation types. Our results can be used to improve LIDAR-derived digital elevation models where vegetation data are available and will better inform research and management efforts to understand sea-level rise effects on tidal wetland ecosystems in the Pacific Northwest.
Description:
Light Detection and Ranging (LIDAR) is a powerful resource for coastal and wetland managers and its use is increasing. Vegetation density and other land cover characteristics influence the accuracy of LIDAR-derived ground surface digital elevation models; however the degree to which wetland land cover biases LIDAR estimates of the ground surface is unknown. We investigated LIDAR interference by tidal vegetation across seven Oregon estuaries and twelve common wetland types using survey-grade Global Positioning System (GPS) measurements of the wetland surface and quantitative vegetation data (percent cover by species) for each measurement location. Our results suggest that LIDAR estimates of the ground surface in tidal wetlands are typically 15 – 30 cm above GPS measurements of the wetland surface. Plant associations dominated by Carex obnupta and Carex lyngbyei exhibited the largest discrepancy between GPS and LIDAR measurements (mean discrepancies 54 cm and 36 cm respectively). The smallest errors observed in the study were about 9 cm and occurred in several different plant associations including a low to middle salt marsh association dominated by a mixture of Deschampsia cespitosa, Distichlis spicata, Sarcocornia perennis and Jaumea carnosa. Our research yields new information for coastal LIDAR users and increases our understanding of uncertainty in LIDAR-derived datasets, improving our ability to accurately evaluate and manage coastal environments.