Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation

Raber, George T.; Jensen, John R.; Hodgson, Michael E.; Tullis, Jason A.; Davis, Bruce A.; Berglund, Judith

doi:10.14358/pers.73.7.793

Cited by 96 publications

(57 citation statements)

References 19 publications

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“…The TLS survey was conducted during a spring low tide to minimize the area of water cover on the marsh surface. The 1550 nm near-infrared laser pulse used by this system is heavily absorbed by water or scattered (due to specular reflectance) [33] and hence information cannot be reliably captured from inundated areas. Furthermore, the scanner utilizes online waveform processing to enable multi-return echo detection with up to 10+ echoes per an emitted laser pulse, although such a high number of returns is not expected in a marsh environment.…”

Section: Datasetmentioning

confidence: 99%

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

et al. 2018

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Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the "k" clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point-and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.

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Section: Datasetmentioning

confidence: 99%

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

et al. 2018

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“…LiDAR data were decimated according to the methodology described by Raber et al [41]. A decimation level 0 (D0) represented the original dataset characterized by a point density of approximately 3.2 pulses m −2 .…”

Section: Data Processingmentioning

confidence: 99%

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

et al. 2012

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Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m −2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m −2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m −2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario.

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“…Although this approach does not fully mimic the actual reduction that would be obtained with different flights, it allows study of the most important factor in canopy height modelling. For this purpose, several alternatives have been proposed, as follows: (i) a random reduction over the entire dataset (Anderson et al, 2006;Liu and Zhang, 2008;Puetz et al, 2009), (ii) a systematic reduction in each scan line (Gueudet, 2004;Raber et al, 2007;Magnusson et al, 2007;Treitz et al, 2010), and (iii) a reduction by randomly maintaining one return within a grid cell of a specific size (Magnusson, 2006;Gobakken and Naesset, 2007). The first alternative may not preserve the order and regularity of the original LiDAR data.…”

Section: Preparation Of Lidar Datamentioning

confidence: 99%

Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities

et al. 2013

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Aims of study: To evaluate the potential use of canopy height and intensity distributions, determined by airborne LiDAR, for the estimation of crown, stem and aboveground biomass fractions.To assess the effects of a reduction in LiDAR pulse densities on model precision.Area of study: The study area is located in Galicia, NW Spain. The forests are representative of Eucalyptus globules stands in NW Spain, characterized by low-intensity silvicultural treatments and by the presence of tall shrub.Material and methods: Linear, multiplicative power and exponential models were used to establish empirical relationships between field measurements and LiDAR metrics.A random selection of LiDAR returns and a comparison of the prediction errors by LiDAR pulse density factor were performed to study a possible loss of fit in these models.Main results: Models showed similar goodness-of-fit statistics to those reported in the international literature. R2 ranged from 0.52 to 0.75 for stand crown biomass, from 0.64 to 0.87 for stand stem biomass, and from 0.63 to 0.86 for stand aboveground biomass. The RMSE/MEAN · 100 of the set of fitted models ranged from 17.4% to 28.4%.Models precision was essentially maintained when 87.5% of the original point cloud was reduced, i.e. a reduction from 4 pulses m–2 to 0.5 pulses m–2.Research highlights: Considering the results of this study, the low-density LiDAR data that are released by the Spanish National Geographic Institute will be an excellent source of information for reducing the cost of forest inventories.Key words: Eucalypt plantations; airborne laser scanning; aboveground biomass; carbon stocks; remote sensing; forest inventory.

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Impact of Lidar Nominal Post-spacing on DEM Accuracy and Flood Zone Delineation

Cited by 96 publications

References 19 publications

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada

Modelling stand biomass fractions in Galician Eucalyptus globulus plantations by use of different LiDAR pulse densities

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