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• 1. [Article] Where's the ground surface? Elevation bias in LIDAR-derived digital elevation models due to dense vegetation in Oregon tidal marshes

  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 ...

  Citation × Citation Citation Abstract Copy Title: Where's the ground surface? Elevation bias in LIDAR-derived digital elevation models due to dense vegetation in Oregon tidal marshes Author: Ewald, Michael J. 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 largely unknown. The minimum-bin LIDAR gridding technique has been proposed as a way to mitigate dense vegetation interference to generate LIDAR-derived digital elevation models (DEM). Past research has focused on the ability to resolve the marsh plain elevation and only limited research has investigated the overall DEM accuracy across the landscape using a wide range of cell sizes and land cover classes. I compared LIDAR-derived DEM accuracy across a 174 ha tidal wetland restoration site with a mix of native wetland and non-native agricultural pasture species. I found an optimum cell size of 1.4 m (1.96 m²) with a mean positive bias of 4.5 cm and a mean absolute error of 24.3 cm. At cell sizes smaller than the optimum, vegetation interferes with the LIDAR sensor and positively biases DEM models. At cell sizes larger than 1.4 m, the DEM captures and favors low features within the landscape, such as channels and ditches, which thereby degrade overall DEM performance. In addition, I investigated LIDAR interference by twelve common tidal wetland vegetation associations across six Oregon estuaries, using survey-grade Global Positioning System (GPS) measurements of the wetland surface and quantitative vegetation data (percent cover by species) for each measurement location. The fundamental vertical accuracy (FVA) of the LIDAR datasets was 4.5 cm root mean square error (RMSE) and had no consistent positive or negative bias in open landcover. Within wetland vegetation communities, my results suggest that LIDAR estimates of the ground surface in tidal wetlands are typically 10 cm to 30 cm above GPS measurements. Plant associations dominated by Carex obnupta and Carex lyngbyei exhibited the largest discrepancy between LIDAR and GPS measurements (mean discrepancies 36.6 cm and 48.8 cm respectively). The smallest errors observed in the study were about 10 cm to 11 cm and occurred in several different plant associations, including two low tidal marsh associations dominated by a mixture of Deschampsia cespitosa, Distichlis spicata, Sarcocornia perennis and Jaumea carnosa. These results suggest that the minimum-bin gridding technique for LIDAR may mitigate vegetation interference by densely vegetated land covers within LIDAR-derived DEM. However, care should be taken to select an appropriate cell size and validate the results before relying on the DEM for analysis. My research yields new information for coastal LIDAR users and increases our understanding of uncertainty in LIDAR-derived datasets to improve the ability to accurately evaluate and manage coastal environments. Title: Where's the ground surface? Elevation bias in LIDAR-derived digital elevation models due to dense vegetation in Oregon tidal marshes Author: Ewald, Michael J. 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 largely unknown. The minimum-bin LIDAR gridding technique has been proposed as a way to mitigate dense vegetation interference to generate LIDAR-derived digital elevation models (DEM). Past research has focused on the ability to resolve the marsh plain elevation and only limited research has investigated the overall DEM accuracy across the landscape using a wide range of cell sizes and land cover classes. I compared LIDAR-derived DEM accuracy across a 174 ha tidal wetland restoration site with a mix of native wetland and non-native agricultural pasture species. I found an optimum cell size of 1.4 m (1.96 m²) with a mean positive bias of 4.5 cm and a mean absolute error of 24.3 cm. At cell sizes smaller than the optimum, vegetation interferes with the LIDAR sensor and positively biases DEM models. At cell sizes larger than 1.4 m, the DEM captures and favors low features within the landscape, such as channels and ditches, which thereby degrade overall DEM performance. In addition, I investigated LIDAR interference by twelve common tidal wetland vegetation associations across six Oregon estuaries, using survey-grade Global Positioning System (GPS) measurements of the wetland surface and quantitative vegetation data (percent cover by species) for each measurement location. The fundamental vertical accuracy (FVA) of the LIDAR datasets was 4.5 cm root mean square error (RMSE) and had no consistent positive or negative bias in open landcover. Within wetland vegetation communities, my results suggest that LIDAR estimates of the ground surface in tidal wetlands are typically 10 cm to 30 cm above GPS measurements. Plant associations dominated by Carex obnupta and Carex lyngbyei exhibited the largest discrepancy between LIDAR and GPS measurements (mean discrepancies 36.6 cm and 48.8 cm respectively). The smallest errors observed in the study were about 10 cm to 11 cm and occurred in several different plant associations, including two low tidal marsh associations dominated by a mixture of Deschampsia cespitosa, Distichlis spicata, Sarcocornia perennis and Jaumea carnosa. These results suggest that the minimum-bin gridding technique for LIDAR may mitigate vegetation interference by densely vegetated land covers within LIDAR-derived DEM. However, care should be taken to select an appropriate cell size and validate the results before relying on the DEM for analysis. My research yields new information for coastal LIDAR users and increases our understanding of uncertainty in LIDAR-derived datasets to improve the ability to accurately evaluate and manage coastal environments. Close

• 2. [Article] Assessment of the potential for conflict between existing ocean space use and renewable energy development off the coast of Oregon

  Oregon's ocean waters are a potential source of wind, wave, and tidal energy; of interest to renewable energy entrepreneurs and to the U.S. government as it seeks to bolster energy security. In order to ...

  Citation × Citation Citation Abstract Copy Title: Assessment of the potential for conflict between existing ocean space use and renewable energy development off the coast of Oregon Author: Sullivan, Colleen M. (Colleen Marie) Oregon's ocean waters are a potential source of wind, wave, and tidal energy; of interest to renewable energy entrepreneurs and to the U.S. government as it seeks to bolster energy security. In order to install technology to capture this energy, however, it may be necessary to mitigate conflict with existing ocean space users. The objective of this research was to construct a conflict analysis model in a GIS to answer the following research questions: (1) Within the study area off the coast of Oregon, where are stakeholders currently using ocean space and how many uses overlap? (2) To what extent might existing ocean space use present potential for conflict with renewable energy development? (3) How do various types of uncertainty affect analysis results? (4) What are the implications of these findings for ecosystem based management of the ocean? All available spatial information on ocean space usage by commercial fishing, commercial non-fishing, recreational, Native American, and scientific communities was gathered. Stakeholder outreach with these communities was used to vet the collected data and allow each to contribute knowledge not previously available through GIS data clearinghouses maintained by government or interest groups. The resulting data were used as inputs to a conflict visualization model written in Python and imported to an ArcGIS tool. Results showed extensive coverage and overlap of existing ocean space uses; specifically that 99.7% of the 1-nm² grid cells of the study area are occupied by at least 6 different categories of ocean space use. The six uses with the greatest coverage were: Fishing – Trolling, Habitat, Military, Fishing - Closure Areas, Protected, and Marine Transportation - Low Intensity. An uncertainty analysis was also completed to illustrate the margin for error and therefore the necessity of appropriate stakeholder outreach during the renewable energy siting process, as opposed to relying only on a GIS. Ranking of each category by its potential for conflict with renewable energy development demonstrated which areas of the ocean may be particularly contentious. Because rankings are subjective, a tool was created to allow users to input their own rankings. For the purpose of this report, default rankings were assigned to each as justified by the literature. Results under these assumptions showed that space use and potential for conflict were highest between the coast and approximately 30 nm at sea. This is likely because certain space use is limited by depth (e.g., recreational use); there is increased shipping density as vessels approach and depart major ports; and increased fuel costs associated with traveling further from shore. Two potential applications of model results were demonstrated. First, comparison with existing wave energy permit sites highlighted relative potential for conflict among the sites and the input data detailed the specific uses present. Second, comparison with areas determined most suitable for development by the wave energy industry illustrated that areas of high suitability often also had high rankings for potential for conflict. It appeared that the factors that determined development suitability were often the same factors that drew current ocean space users to those locations. Current support at the state, regional and federal level under the National Ocean Policy for the use of marine spatial planning as a tool to implement ecosystem based management of the oceans requires that tools such as the one developed in this research are used, to ensure that all components of the marine ecosystem are considered prior to implementation of a management plan. The addition of renewable energy to the current social landscape of the ocean will reduce the resource base for many categories of ocean space use. Model results demonstrated that mitigation of conflict between development and existing space use is not merely a best practice supported by current policy, but a necessity. Results presented a visualization of the social landscape of the ocean that could help managers determine which stakeholders to engage during the initial stage of choosing a site for development. Title: Assessment of the potential for conflict between existing ocean space use and renewable energy development off the coast of Oregon Author: Sullivan, Colleen M. (Colleen Marie) Oregon's ocean waters are a potential source of wind, wave, and tidal energy; of interest to renewable energy entrepreneurs and to the U.S. government as it seeks to bolster energy security. In order to install technology to capture this energy, however, it may be necessary to mitigate conflict with existing ocean space users. The objective of this research was to construct a conflict analysis model in a GIS to answer the following research questions: (1) Within the study area off the coast of Oregon, where are stakeholders currently using ocean space and how many uses overlap? (2) To what extent might existing ocean space use present potential for conflict with renewable energy development? (3) How do various types of uncertainty affect analysis results? (4) What are the implications of these findings for ecosystem based management of the ocean? All available spatial information on ocean space usage by commercial fishing, commercial non-fishing, recreational, Native American, and scientific communities was gathered. Stakeholder outreach with these communities was used to vet the collected data and allow each to contribute knowledge not previously available through GIS data clearinghouses maintained by government or interest groups. The resulting data were used as inputs to a conflict visualization model written in Python and imported to an ArcGIS tool. Results showed extensive coverage and overlap of existing ocean space uses; specifically that 99.7% of the 1-nm² grid cells of the study area are occupied by at least 6 different categories of ocean space use. The six uses with the greatest coverage were: Fishing – Trolling, Habitat, Military, Fishing - Closure Areas, Protected, and Marine Transportation - Low Intensity. An uncertainty analysis was also completed to illustrate the margin for error and therefore the necessity of appropriate stakeholder outreach during the renewable energy siting process, as opposed to relying only on a GIS. Ranking of each category by its potential for conflict with renewable energy development demonstrated which areas of the ocean may be particularly contentious. Because rankings are subjective, a tool was created to allow users to input their own rankings. For the purpose of this report, default rankings were assigned to each as justified by the literature. Results under these assumptions showed that space use and potential for conflict were highest between the coast and approximately 30 nm at sea. This is likely because certain space use is limited by depth (e.g., recreational use); there is increased shipping density as vessels approach and depart major ports; and increased fuel costs associated with traveling further from shore. Two potential applications of model results were demonstrated. First, comparison with existing wave energy permit sites highlighted relative potential for conflict among the sites and the input data detailed the specific uses present. Second, comparison with areas determined most suitable for development by the wave energy industry illustrated that areas of high suitability often also had high rankings for potential for conflict. It appeared that the factors that determined development suitability were often the same factors that drew current ocean space users to those locations. Current support at the state, regional and federal level under the National Ocean Policy for the use of marine spatial planning as a tool to implement ecosystem based management of the oceans requires that tools such as the one developed in this research are used, to ensure that all components of the marine ecosystem are considered prior to implementation of a management plan. The addition of renewable energy to the current social landscape of the ocean will reduce the resource base for many categories of ocean space use. Model results demonstrated that mitigation of conflict between development and existing space use is not merely a best practice supported by current policy, but a necessity. Results presented a visualization of the social landscape of the ocean that could help managers determine which stakeholders to engage during the initial stage of choosing a site for development. Close