Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset

Smith, Taylor; Rheinwalt, Aljoscha; Bookhagen, Bodo

doi:10.5194/esurf-7-475-2019

Cited by 16 publications

(20 citation statements)

References 61 publications

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“…Our method development is motivated by a number of factors building on our previous work (Purinton and Bookhagen, 2017;Purinton and Bookhagen, 2018;Smith et al, 2019). Firstly, we wish to quantify the inter-pixel consistency (non-topographic variability, sometimes referred to as relative DEM error; e.g., Rizzoli et al (2017)) and not point-based vertical accuracy using reference data (e.g., derived from kinematic and static dGNSS, ICESat, ICESat-2).…”

Section: Motivationmentioning

confidence: 99%

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Purinton

Bookhagen

2021

Front. Earth Sci.

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Quantitative geomorphic research depends on accurate topographic data often collected via remote sensing. Lidar, and photogrammetric methods like structure-from-motion, provide the highest quality data for generating digital elevation models (DEMs). Unfortunately, these data are restricted to relatively small areas, and may be expensive or time-consuming to collect. Global and near-global DEMs with 1 arcsec (∼30 m) ground sampling from spaceborne radar and optical sensors offer an alternative gridded, continuous surface at the cost of resolution and accuracy. Accuracy is typically defined with respect to external datasets, often, but not always, in the form of point or profile measurements from sources like differential Global Navigation Satellite System (GNSS), spaceborne lidar (e.g., ICESat), and other geodetic measurements. Vertical point or profile accuracy metrics can miss the pixel-to-pixel variability (sometimes called DEM noise) that is unrelated to true topographic signal, but rather sensor-, orbital-, and/or processing-related artifacts. This is most concerning in selecting a DEM for geomorphic analysis, as this variability can affect derivatives of elevation (e.g., slope and curvature) and impact flow routing. We use (near) global DEMs at 1 arcsec resolution (SRTM, ASTER, ALOS, TanDEM-X, and the recently released Copernicus) and develop new internal accuracy metrics to assess inter-pixel variability without reference data. Our study area is in the arid, steep Central Andes, and is nearly vegetation-free, creating ideal conditions for remote sensing of the bare-earth surface. We use a novel hillshade-filtering approach to detrend long-wavelength topographic signals and accentuate short-wavelength variability. Fourier transformations of the spatial signal to the frequency domain allows us to quantify: 1) artifacts in the un-projected 1 arcsec DEMs at wavelengths greater than the Nyquist (twice the nominal resolution, so > 2 arcsec); and 2) the relative variance of adjacent pixels in DEMs resampled to 30-m resolution (UTM projected). We translate results into their impact on hillslope and channel slope calculations, and we highlight the quality of the five DEMs. We find that the Copernicus DEM, which is based on a carefully edited commercial version of the TanDEM-X, provides the highest quality landscape representation, and should become the preferred DEM for topographic analysis in areas without sufficient coverage of higher-quality local DEMs.

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Section: Motivationmentioning

confidence: 99%

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Purinton

Bookhagen

2021

Front. Earth Sci.

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“…IDW/IDP methods result in few artifacts near holes commonly present in terrestrial laser scanning (TLS) data. The DEM spatial resolution is typically dictated by the point cloud resolution (e.g., Smith et al, 2019). DEM uncertainties represent grid resolution, terrain variability, and acquisition and processing errors (e.g., Smith et al, 2019).…”

Section: Dem Generation and Uncertaintymentioning

confidence: 99%

Measuring change at Earth’s surface: On-demand vertical and three-dimensional topographic differencing implemented in OpenTopography

et al. 2021

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Topographic differencing measures landscape change by comparing multitemporal high-resolution topography data sets. Here, we focused on two types of topographic differencing: (1) Vertical differencing is the subtraction of digital elevation models (DEMs) that span an event of interest. (2) Three-dimensional (3-D) differencing measures surface change by registering point clouds with a rigid deformation. We recently released topographic differencing in OpenTopography where users perform on-demand vertical and 3-D differencing via an online interface. OpenTopography is a U.S. National Science Foundation–funded facility that provides access to topographic data and processing tools. While topographic differencing has been applied in numerous research studies, the lack of standardization, particularly of 3-D differencing, requires the customization of processing for individual data sets and hinders the community’s ability to efficiently perform differencing on the growing archive of topography data. Our paper focuses on streamlined techniques with which to efficiently difference data sets with varying spatial resolution and sensor type (i.e., optical vs. light detection and ranging [lidar]) and over variable landscapes. To optimize on-demand differencing, we considered algorithm choice and displacement resolution. The optimal resolution is controlled by point density, landscape characteristics (e.g., leaf-on vs. leaf-off), and data set quality. We provide processing options derived from metadata that allow users to produce optimal high-quality results, while experienced users can fine tune the parameters to suit their needs. We anticipate that the differencing tool will expand access to this state-of-the-art technology, will be a valuable educational tool, and will serve as a template for differencing the growing number of multitemporal topography data sets.

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“…We consider this as yet another computational artifact since generally, when two locations are closer to each other-i.e., in terms of number of cells (or more correctly, with respect to the cell size as the unit of length), a leastcost path between them comprises fewer cells and its costweighted length is thus more affected by the error associated with a single cell. This error propagation mechanism is similar to that of topographic characterization with a raster DEM, in which a terrain attribute (e.g., slope and aspect) of each cell is derived by combining elevation values within its immediate neighborhood (typically limited to nine cells including itself) (see Zhang et al 1999;Deng et al 2007, andSmith et al 2019).…”

Section: Variationmentioning

confidence: 99%

“…Effective distance is scale dependent, as is the case with other natural phenomena on the Earth's surface (Wiens 1989, Wu et al 2002, Liu et al 2007, Cushman and Landguth 2010 for examples in ecology; Deng et al 2007, Smith et al 2019 for examples in geomorphology; Ghaffari 2011, Goulden et al 2014, Buakhao and Kangrang 2016, Thomas et al 2007 for examples of hydrology). It is generally known that the cost-weighted length of a least-cost path (Rae et al 2007;Etherington 2016) as well as its geometric length (Broquet et al 2006) are affected by the spatial resolution of the input cost surface partly because some landscape elements are too small to be detected at low (or coarse) resolutions.…”

Section: Introductionmentioning

confidence: 99%

On the effects of spatial resolution on effective distance measurement in digital landscapes

Murekatete

Shirabe

2021

Ecol Process

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Background Connectivity is an important landscape attribute in ecological studies and conservation practices and is often expressed in terms of effective distance. If the cost of movement of an organism over a landscape is effectively represented by a raster surface, effective distances can be equated with the cost-weighted distance of least-cost paths. It is generally recognized that this measure is sensitive to the grid’s cell size, but little is known if it is always sensitive in the same way and to the same degree and if not, what makes it more (or less) sensitive. We conducted computational experiments with both synthetic and real landscape data, in which we generated and analyzed large samples of effective distances measured on cost surfaces of varying cell sizes derived from those data. The particular focus was on the statistical behavior of the ratio—referred to as ‘accuracy indicator’—of the effective distance measured on a lower-resolution cost surface to that measured on a higher-resolution cost surface. Results In the experiment with synthetic cost surfaces, the sample values of the accuracy indicator were generally clustered around 1, but slightly greater with the absence of linear sequences (or barriers) of high-cost or inadmissible cells and smaller with the presence of such sequences. The latter tendency was more dominant, and both tendencies became more pronounced as the difference between the spatial resolutions of the associated cost surfaces increased. When two real satellite images (of different resolutions with fairly large discrepancies) were used as the basis of cost estimation, the variation of the accuracy indicator was found to be substantially large in the vicinity (1500 m) of the source but decreases quickly with an increase in distance from it. Conclusions Effective distances measured on lower-resolution cost surfaces are generally highly correlated with—and useful predictors of—effective distances measured on higher-resolution cost surfaces. This relationship tends to be weakened when linear barriers to dispersal (e.g., roads and rivers) exist, but strengthened when moving away from sources of dispersal and/or when linear barriers (if any) are detected by other presumably more accessible and affordable sources such as vector line data. Thus, if benefits of high-resolution data are not likely to substantially outweigh their costs, the use of lower resolution data is worth considering as a cost-effective alternative in the application of least-cost path modeling to landscape connectivity analysis.

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Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset

Cited by 16 publications

References 61 publications

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes

Measuring change at Earth’s surface: On-demand vertical and three-dimensional topographic differencing implemented in OpenTopography

On the effects of spatial resolution on effective distance measurement in digital landscapes

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