Articles | Volume 7, issue 2
https://doi.org/10.5194/esurf-7-475-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esurf-7-475-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
Aljoscha Rheinwalt
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
Bodo Bookhagen
Institute of Geosciences, Universität Potsdam, Potsdam, Germany
Related authors
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Taylor Smith, Bodo Bookhagen, and Aljoscha Rheinwalt
The Cryosphere, 11, 2329–2343, https://doi.org/10.5194/tc-11-2329-2017, https://doi.org/10.5194/tc-11-2329-2017, 2017
Short summary
Short summary
High Mountain Asia’s rivers, which serve more than a billion people, receive a significant portion of their water budget in the form of snow. We develop an algorithm to track timing of the snowmelt season using passive microwave data from 1987 to 2016. We find that most of High Mountain Asia has experienced shorter melt seasons, earlier snow clearance, and earlier snowmelt onset, but that these changes are highly spatially and temporally heterogeneous.
T. Smith, B. Bookhagen, and F. Cannon
The Cryosphere, 9, 1747–1759, https://doi.org/10.5194/tc-9-1747-2015, https://doi.org/10.5194/tc-9-1747-2015, 2015
Short summary
Short summary
We describe and apply a newly developed glacial mapping algorithm which uses spectral, topographic, velocity, and spatial data to quickly and accurately map glacial extents over a wide area. This method maps both clean glacier ice and debris-covered glacier tongues across diverse topographic, land cover, and spectral settings using primarily open-source tools.
Taylor Smith, Ruxandra-Maria Zotta, Chris A. Boulton, Timothy M. Lenton, Wouter Dorigo, and Niklas Boers
Earth Syst. Dynam., 14, 173–183, https://doi.org/10.5194/esd-14-173-2023, https://doi.org/10.5194/esd-14-173-2023, 2023
Short summary
Short summary
Multi-instrument records with varying signal-to-noise ratios are becoming increasingly common as legacy sensors are upgraded, and data sets are modernized. Induced changes in higher-order statistics such as the autocorrelation and variance are not always well captured by cross-calibration schemes. Here we investigate using synthetic examples how strong resulting biases can be and how they can be avoided in order to make reliable statements about changes in the resilience of a system.
Taylor Smith, Bodo Bookhagen, and Aljoscha Rheinwalt
The Cryosphere, 11, 2329–2343, https://doi.org/10.5194/tc-11-2329-2017, https://doi.org/10.5194/tc-11-2329-2017, 2017
Short summary
Short summary
High Mountain Asia’s rivers, which serve more than a billion people, receive a significant portion of their water budget in the form of snow. We develop an algorithm to track timing of the snowmelt season using passive microwave data from 1987 to 2016. We find that most of High Mountain Asia has experienced shorter melt seasons, earlier snow clearance, and earlier snowmelt onset, but that these changes are highly spatially and temporally heterogeneous.
T. Smith, B. Bookhagen, and F. Cannon
The Cryosphere, 9, 1747–1759, https://doi.org/10.5194/tc-9-1747-2015, https://doi.org/10.5194/tc-9-1747-2015, 2015
Short summary
Short summary
We describe and apply a newly developed glacial mapping algorithm which uses spectral, topographic, velocity, and spatial data to quickly and accurately map glacial extents over a wide area. This method maps both clean glacier ice and debris-covered glacier tongues across diverse topographic, land cover, and spectral settings using primarily open-source tools.
Related subject area
Cross-cutting themes: Quantitative and statistical methods in Earth surface dynamics
Introducing standardized field methods for fracture-focused surface process research
Full four-dimensional change analysis of topographic point cloud time series using Kalman filtering
Comparison of rainfall generators with regionalisation for the estimation of rainfall erosivity at ungauged sites
Inverse modeling of turbidity currents using an artificial neural network approach: verification for field application
Automated quantification of floating wood pieces in rivers from video monitoring: a new software tool and validation
Particle size dynamics in abrading pebble populations
Computing water flow through complex landscapes – Part 3: Fill–Spill–Merge: flow routing in depression hierarchies
A photogrammetry-based approach for soil bulk density measurements with an emphasis on applications to cosmogenic nuclide analysis
Dominant process zones in a mixed fluvial–tidal delta are morphologically distinct
Identifying sediment transport mechanisms from grain size–shape distributions, applied to aeolian sediments
Systematic identification of external influences in multi-year microseismic recordings using convolutional neural networks
Earth's surface mass transport derived from GRACE, evaluated by GPS, ICESat, hydrological modeling and altimetry satellite orbits
The R package “eseis” – a software toolbox for environmental seismology
Bayesian inversion of a CRN depth profile to infer Quaternary erosion of the northwestern Campine Plateau (NE Belgium)
A new CT scan methodology to characterize a small aggregation gravel clast contained in a soft sediment matrix
Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics
An introduction to learning algorithms and potential applications in geomorphometry and Earth surface dynamics
Sensitivity analysis and implications for surface processes from a hydrological modelling approach in the Gunt catchment, high Pamir Mountains
Constraining the stream power law: a novel approach combining a landscape evolution model and an inversion method
Martha Cary Eppes, Alex Rinehart, Jennifer Aldred, Samantha Berberich, Maxwell P. Dahlquist, Sarah G. Evans, Russell Keanini, Stephen E. Laubach, Faye Moser, Mehdi Morovati, Steven Porson, Monica Rasmussen, and Uri Shaanan
Earth Surf. Dynam., 12, 35–66, https://doi.org/10.5194/esurf-12-35-2024, https://doi.org/10.5194/esurf-12-35-2024, 2024
Short summary
Short summary
All rocks have fractures (cracks) that can influence virtually every process acting on Earth's surface where humans live. Yet, scientists have not standardized their methods for collecting fracture data. Here we draw on past work across geo-disciplines and propose a list of baseline data for fracture-focused surface process research. We detail the rationale and methods for collecting them. We hope their wide adoption will improve future methods and knowledge of rock fracture overall.
Lukas Winiwarter, Katharina Anders, Daniel Czerwonka-Schröder, and Bernhard Höfle
Earth Surf. Dynam., 11, 593–613, https://doi.org/10.5194/esurf-11-593-2023, https://doi.org/10.5194/esurf-11-593-2023, 2023
Short summary
Short summary
We present a method to extract surface change information from 4D time series of topographic point clouds recorded with a terrestrial laser scanner. The method uses sensor information to spatially and temporally smooth the data, reducing uncertainties. The Kalman filter used for the temporal smoothing also allows us to interpolate over data gaps or extrapolate into the future. Clustering areas where change histories are similar allows us to identify processes that may have the same causes.
Ross Pidoto, Nejc Bezak, Hannes Müller-Thomy, Bora Shehu, Ana Claudia Callau-Beyer, Katarina Zabret, and Uwe Haberlandt
Earth Surf. Dynam., 10, 851–863, https://doi.org/10.5194/esurf-10-851-2022, https://doi.org/10.5194/esurf-10-851-2022, 2022
Short summary
Short summary
Erosion is a threat for soils with rainfall as the driving force. The annual rainfall erosivity factor quantifies rainfall impact by analysing high-resolution rainfall time series (~ 5 min). Due to a lack of measuring stations, alternatives for its estimation are analysed in this study. The best results are obtained for regionalisation of the erosivity factor itself. However, the identified minimum of 60-year time series length suggests using rainfall generators as in this study as well.
Hajime Naruse and Kento Nakao
Earth Surf. Dynam., 9, 1091–1109, https://doi.org/10.5194/esurf-9-1091-2021, https://doi.org/10.5194/esurf-9-1091-2021, 2021
Short summary
Short summary
This paper proposes a method to reconstruct the hydraulic conditions of turbidity currents from turbidites. We investigated the validity and problems of this method in application to actual field datasets using artificial data. Once this method is established, it is expected that the method will elucidate the generation process of turbidity currents and will help to predict the geometry of resultant turbidites in deep-sea environments.
Hossein Ghaffarian, Pierre Lemaire, Zhang Zhi, Laure Tougne, Bruce MacVicar, and Hervé Piégay
Earth Surf. Dynam., 9, 519–537, https://doi.org/10.5194/esurf-9-519-2021, https://doi.org/10.5194/esurf-9-519-2021, 2021
Short summary
Short summary
Quantifying wood fluxes in rivers would improve our understanding of the key processes in river ecology and morphology. In this work, we introduce new software for the automatic detection of wood pieces in rivers. The results show 93.5 % and 86.5 % accuracy for piece number and volume, respectively.
András A. Sipos, Gábor Domokos, and János Török
Earth Surf. Dynam., 9, 235–251, https://doi.org/10.5194/esurf-9-235-2021, https://doi.org/10.5194/esurf-9-235-2021, 2021
Short summary
Short summary
Abrasion of sedimentary particles is widely associated with mutual collisions. Utilizing results of individual, geometric abrasion theory and techniques adopted in statistical physics, a new model for predicting the collective mass evolution of large numbers of particles is introduced. Our model uncovers a startling fundamental feature of collective particle dynamics: collisional abrasion may either focus size distributions or it may act in the opposite direction by dispersing the distribution.
Richard Barnes, Kerry L. Callaghan, and Andrew D. Wickert
Earth Surf. Dynam., 9, 105–121, https://doi.org/10.5194/esurf-9-105-2021, https://doi.org/10.5194/esurf-9-105-2021, 2021
Short summary
Short summary
Existing ways of modeling the flow of water amongst landscape depressions such as swamps and lakes take a long time to run. However, as our previous work explains, depressions can be quickly organized into a data structure – the depression hierarchy. This paper explains how the depression hierarchy can be used to quickly simulate the realistic filling of depressions including how they spill over into each other and, if they become full enough, how they merge into one another.
Joel Mohren, Steven A. Binnie, Gregor M. Rink, Katharina Knödgen, Carlos Miranda, Nora Tilly, and Tibor J. Dunai
Earth Surf. Dynam., 8, 995–1020, https://doi.org/10.5194/esurf-8-995-2020, https://doi.org/10.5194/esurf-8-995-2020, 2020
Short summary
Short summary
In this study, we comprehensively test a method to derive soil densities under fieldwork conditions. The method is mainly based on images taken from consumer-grade cameras. The obtained soil/sediment densities reflect
truevalues by generally > 95 %, even if a smartphone is used for imaging. All computing steps can be conducted using freeware programs. Soil density is an important variable in the analysis of terrestrial cosmogenic nuclides, for example to infer long-term soil production rates.
Mariela Perignon, Jordan Adams, Irina Overeem, and Paola Passalacqua
Earth Surf. Dynam., 8, 809–824, https://doi.org/10.5194/esurf-8-809-2020, https://doi.org/10.5194/esurf-8-809-2020, 2020
Short summary
Short summary
We propose a machine learning approach for the classification and analysis of large delta systems. The approach uses remotely sensed data, channel network extraction, and the analysis of 10 metrics to identify clusters of islands with similar characteristics. The 12 clusters are grouped in six main classes related to morphological processes acting on the system. The approach allows us to identify spatial patterns in large river deltas to inform modeling and the collection of field observations.
Johannes Albert van Hateren, Unze van Buuren, Sebastiaan Martinus Arens, Ronald Theodorus van Balen, and Maarten Arnoud Prins
Earth Surf. Dynam., 8, 527–553, https://doi.org/10.5194/esurf-8-527-2020, https://doi.org/10.5194/esurf-8-527-2020, 2020
Short summary
Short summary
In this paper, we introduce a new technique that can be used to identify how sediments were transported to their place of deposition (transport mode). The traditional method is based on the size of sediment grains, ours on the size and the shape. A test of the method on windblown sediments indicates that it can be used to identify the transport mode with less ambiguity, and therefore it improves our ability to extract information, such as climate from the past, from sediment deposits.
Matthias Meyer, Samuel Weber, Jan Beutel, and Lothar Thiele
Earth Surf. Dynam., 7, 171–190, https://doi.org/10.5194/esurf-7-171-2019, https://doi.org/10.5194/esurf-7-171-2019, 2019
Short summary
Short summary
Monitoring rock slopes for a long time helps to understand the impact of climate change on the alpine environment. Measurements of seismic signals are often affected by external influences, e.g., unwanted anthropogenic noise. In the presented work, these influences are automatically identified and removed to enable proper geoscientific analysis. The methods presented are based on machine learning and intentionally kept generic so that they can be equally applied in other (more generic) settings.
Christian Gruber, Sergei Rudenko, Andreas Groh, Dimitrios Ampatzidis, and Elisa Fagiolini
Earth Surf. Dynam., 6, 1203–1218, https://doi.org/10.5194/esurf-6-1203-2018, https://doi.org/10.5194/esurf-6-1203-2018, 2018
Short summary
Short summary
By using a set of evaluation methods involving GPS, ICESat, hydrological modelling and altimetry satellite orbits, we show that the novel radial basis function (RBF) processing technique can be used for processing the Gravity Recovery and Climate Experiment (GRACE) data yielding global gravity field models which fit independent reference values at the same level as commonly accepted global geopotential models based on spherical harmonics.
Michael Dietze
Earth Surf. Dynam., 6, 669–686, https://doi.org/10.5194/esurf-6-669-2018, https://doi.org/10.5194/esurf-6-669-2018, 2018
Short summary
Short summary
Environmental seismology is the study of the seismic signals emitted by Earth surface processes. This emerging research field is at the intersection of many Earth science disciplines. The overarching scope requires free integrative software that is accepted across scientific disciplines, such as R. The article introduces the R package "eseis" and illustrates its conceptual structure, available functions, and worked examples.
Eric Laloy, Koen Beerten, Veerle Vanacker, Marcus Christl, Bart Rogiers, and Laurent Wouters
Earth Surf. Dynam., 5, 331–345, https://doi.org/10.5194/esurf-5-331-2017, https://doi.org/10.5194/esurf-5-331-2017, 2017
Short summary
Short summary
Over very long timescales, 100 000 years or more, landscapes may drastically change. Sediments preserved in these landscapes have a cosmogenic radionuclide inventory that tell us when and how fast such changes took place. In this paper, we provide first evidence of an elevated long-term erosion rate of the northwestern Campine Plateau (lowland Europe), which can be explained by the loose nature of the subsoil.
Laurent Fouinat, Pierre Sabatier, Jérôme Poulenard, Jean-Louis Reyss, Xavier Montet, and Fabien Arnaud
Earth Surf. Dynam., 5, 199–209, https://doi.org/10.5194/esurf-5-199-2017, https://doi.org/10.5194/esurf-5-199-2017, 2017
Short summary
Short summary
This study focuses on the creation of a novel CT scan methodology at the crossroads between medical imagery and earth sciences. Using specific density signatures, pebbles and/or organic matter characterizing wet avalanche deposits can be quantified in lake sediments. Starting from AD 1880, we were able to identify eight periods of higher avalanche activity from sediment cores. The use of CT scans, alongside existing approaches, opens up new possibilities in a wide variety of geoscience studies.
Daniel E. J. Hobley, Jordan M. Adams, Sai Siddhartha Nudurupati, Eric W. H. Hutton, Nicole M. Gasparini, Erkan Istanbulluoglu, and Gregory E. Tucker
Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, https://doi.org/10.5194/esurf-5-21-2017, 2017
Short summary
Short summary
Many geoscientists use computer models to understand changes in the Earth's system. However, typically each scientist will build their own model from scratch. This paper describes Landlab, a new piece of open-source software designed to simplify creation and use of models of the Earth's surface. It provides off-the-shelf tools to work with models more efficiently, with less duplication of effort. The paper explains and justifies how Landlab works, and describes some models built with it.
Andrew Valentine and Lara Kalnins
Earth Surf. Dynam., 4, 445–460, https://doi.org/10.5194/esurf-4-445-2016, https://doi.org/10.5194/esurf-4-445-2016, 2016
Short summary
Short summary
Learning algorithms are powerful tools for understanding and working with large data sets, particularly in situations where any underlying physical models may be complex and poorly understood. Such situations are common in geomorphology. We provide an accessible overview of the various approaches that fall under the umbrella of "learning algorithms", discuss some potential applications within geomorphometry and/or geomorphology, and offer advice on practical considerations.
E. Pohl, M. Knoche, R. Gloaguen, C. Andermann, and P. Krause
Earth Surf. Dynam., 3, 333–362, https://doi.org/10.5194/esurf-3-333-2015, https://doi.org/10.5194/esurf-3-333-2015, 2015
Short summary
Short summary
A semi-distributed hydrological model is used to analyse the hydrological cycle of a glaciated high-mountain catchment in the Pamirs.
We overcome data scarcity by utilising various raster data sets as meteorological input. Temperature in combination with the amount of snow provided in winter play the key role in the annual cycle.
This implies that expected Earth surface processes along precipitation and altitude gradients differ substantially.
T. Croissant and J. Braun
Earth Surf. Dynam., 2, 155–166, https://doi.org/10.5194/esurf-2-155-2014, https://doi.org/10.5194/esurf-2-155-2014, 2014
Cited articles
Ayalew, L. and Yamagishi, H.: The application of GIS-based logistic
regression
for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central
Japan, Geomorphology, 65, 15–31, 2005. a
Baguskas, S. A., Peterson, S. H., Bookhagen, B., and Still, C. J.: Evaluating
spatial patterns of drought-induced tree mortality in a coastal California
pine forest, Forest Ecol. Manag., 315, 43–53, 2014. a
Band, L. E.: Topographic partition of watersheds with digital elevation
models, Water Resour. Res., 22, 15–24, 1986. a
Bolstad, P. V. and Stowe, T.: An evaluation of DEM accuracy: elevation,
slope,
and aspect, Photogramm. Eng. Rem. S., 60, 7327–7332,
1994. a
Carlisle, B. H.: Modelling the spatial distribution of DEM error, T. GIS, 9,
521–540, 2005. a
Dibblee, T. W.: Geologic Map of Western Santa Cruz Island, Dibblee Geological
Foundation, Santa Barbara, California, USA, 2001. a
Dietrich, W. E., Bellugi, D. G., Sklar, L. S., Stock, J. D., Heimsath, A. M.,
and Roering, J. J.: Geomorphic transport laws for predicting landscape form
and dynamics, Prediction in geomorphology, 135, 103–132, 2003. a
Durran, D. R.: Numerical methods for wave equations in geophysical fluid
dynamics, vol. 32, Springer Science & Business Media, New York, USA, 1999. a
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The shuttle radar
topography mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007. a
Fisher, P. F.: Models of uncertainty in spatial data, Geographical
information systems, 1, 191–205, 1999. a
Fisher, P. F. and Tate, N. J.: Causes and consequences of error in digital
elevation models, Prog. Phys. Geog., 30, 467–489, 2006. a
Fornberg, B.: Generation of finite difference formulas on arbitrarily spaced
grids, Math. Comput., 51, 699–706, 1988. a
Franklin, J.: Predictive vegetation mapping: geographic modelling of
biospatial patterns in relation to environmental gradients, Prog. Phys.
Geog., 19, 474–499, 1995. a
Grieve, S. W. D., Mudd, S. M., Milodowski, D. T., Clubb, F. J., and Furbish,
D. J.: How does grid-resolution modulate the topographic expression of
geomorphic processes?, Earth Surf. Dynam., 4, 627–653,
https://doi.org/10.5194/esurf-4-627-2016, 2016. a
Guisan, A. and Zimmermann, N. E.: Predictive habitat distribution models in
ecology, Ecol. Model., 135, 147–186, 2000. a
Guzzetti, F., Carrara, A., Cardinali, M., and Reichenbach, P.: Landslide
hazard
evaluation: a review of current techniques and their application in a
multi-scale study, Central Italy, Geomorphology, 31, 181–216, 1999. a
Holmes, K., Chadwick, O., and Kyriakidis, P. C.: Error in a USGS 30-meter
digital elevation model and its impact on terrain modeling, J.
Hydrol., 233, 154–173, 2000. a
Kent, M.: Vegetation description and data analysis: a practical approach,
John Wiley & Sons, New York, USA, 2011. a
Kirby, E. and Whipple, K. X.: Expression of active tectonics in erosional
landscapes, J. Struct. Geol., 44, 54–75, 2012. a
Kraus, K. and Pfeifer, N.: Determination of terrain models in wooded areas
with airborne laser scanner data, ISPRS J. Photogramm., 53, 193–203, 1998. a
Kyriakidis, P. C., Shortridge, A. M., and Goodchild, M. F.: Geostatistics for
conflation and accuracy assessment of digital elevation models, Int.
J. Geog. Inf. Sci., 13, 677–707, 1999. a
Lague, D.: The stream power river incision model: evidence, theory and
beyond,
Earth Surf. Proc. Land., 39, 38–61, 2014. a
LAStools: Efficient LiDAR Processing Software (version 180831, academic),
available at: http://rapidlasso.com/LAStools (last access:
12 January 2019), 2017. a
Lee, J., Fisher, P., Snyder, P., et al.: Modeling the effect of data errors
on feature extraction from digital elevation models, Photogramm. Eng. Rem.
S., 58, 1461–1461, 1992. a
Montgomery, D. R. and Dietrich, W. E.: A physically based model for the
topographic control on shallow landsliding, Water Resour. Res., 30,
1153–1171, 1994. a
Mukul, M., Srivastava, V., Jade, S., and Mukul, M.: Uncertainties in the
Shuttle Radar Topography Mission (SRTM) Heights: Insights from the Indian
Himalaya and Peninsula, Sci. Rep., 7, 41672, https://doi.org/10.1038/srep41672, 2017. a
Neely, A., Bookhagen, B., and Burbank, D.: An automated knickzone selection
algorithm (KZ-Picker) to analyze transient landscapes: Calibration and
validation, J. Geophys. Res.-Earth, 122, 1236–1261,
2017. a
Oksanen, J. and Sarjakoski, T.: Uncovering the statistical and spatial
characteristics of fine toposcale DEM error, Int. J.
Geog. Inf. Sci., 20, 345–369, 2006. a
OpenTopography: 2010 channel islands lidar collection,
https://doi.org/10.5069/G95D8PS7, 2012. a
Ouma, Y. O. and Tateishi, R.: Urban flood vulnerability and risk mapping
using
integrated multi-parametric AHP and GIS: methodological overview and case
study assessment, Water, 6, 1515–1545, 2014. a
Pelletier, J. D.: Quantitative modeling of earth surface processes, Cambridge
University Press, Cambridge, UK, https://doi.org/10.1017/CBO9780511813849, 2008. a
Pelletier, J. D.: Minimizing the grid-resolution dependence of flow-routing
algorithms for geomorphic applications, Geomorphology, 122, 91–98, 2010. a
Pelletier, J. D.: A robust, two-parameter method for the extraction of
drainage
networks from high-resolution digital elevation models (DEMs): Evaluation
using synthetic and real-world DEMs, Water Resour. Res., 49, 75–89,
2013. a
Perroy, R. L., Bookhagen, B., Asner, G. P., and Chadwick, O. A.: Comparison
of
gully erosion estimates using airborne and ground-based LiDAR on Santa Cruz
Island, California, Geomorphology, 118, 288–300, 2010. a
Perroy, R. L., Bookhagen, B., Chadwick, O. A., and Howarth, J. T.: Holocene
and
Anthropocene landscape change: arroyo formation on Santa Cruz Island,
California, Ann. Assoc. Am. Geogr., 102,
1229–1250, 2012. a
Pierce, K. B., Lookingbill, T., and Urban, D.: A simple method for estimating
potential relative radiation (PRR) for landscape-scale vegetation analysis,
Landscape Ecol., 20, 137–147, 2005. a
Purinton, B. and Bookhagen, B.: Validation of digital elevation models (DEMs)
and comparison of geomorphic metrics on the southern Central Andean Plateau,
Earth Surf. Dynam., 5, 211–237, https://doi.org/10.5194/esurf-5-211-2017,
2017. a, b
Purinton, B. and Bookhagen, B.: Measuring decadal vertical land-level changes
from SRTM-C (2000) and TanDEM-X (∼2015) in the south-central Andes,
Earth Surf. Dynam., 6, 971–987, https://doi.org/10.5194/esurf-6-971-2018,
2018. a, b
Rodriguez, E., Morris, C. S., and Belz, J. E.: A global assessment of the
SRTM performance, Photogramm. Eng. Rem. S., 72, 249–260, 2006. a
Roering, J. J., Kirchner, J. W., and Dietrich, W. E.: Evidence for nonlinear,
diffusive sediment transport on hillslopes and implications for landscape
morphology, Water Resour. Res., 35, 853–870, 1999. a
Roering, J. J., Marshall, J., Booth, A. M., Mort, M., and Jin, Q.: Evidence
for
biotic controls on topography and soil production, Earth Planet.
Sc. Lett., 298, 183–190, 2010. a
Shortridge, A. and Messina, J.: Spatial structure and landscape associations
of SRTM error, Remote Sens. Environ., 115, 1576–1587, 2011. a
Smith, B. and Sandwell, D.: Accuracy and resolution of shuttle radar
topography mission data, Geophys. Res. Lett., 30, 1467, https://doi.org/10.1029/2002GL016643, 2003. a, b
Smith, T., Rheinwalt, A., and Bookhagen, B.: TopoMetricUncertainty –
Calculating Topographic Metric Uncertainty and Optimal Grid Resolution. V.
1.0, GFZ Data Services, https://doi.org/10.5880/fidgeo.2019.017, 2019. a
Snyder, N. P., Whipple, K. X., Tucker, G. E., and Merritts, D. J.: Landscape
response to tectonic forcing: Digital elevation model analysis of stream
profiles in the Mendocino triple junction region, northern California,
Geol. Soc. Am. Bull., 112, 1250–1263, 2000. a
Thompson, J. A., Bell, J. C., and Butler, C. A.: Digital elevation model
resolution: effects on terrain attribute calculation and quantitative
soil-landscape modeling, Geoderma, 100, 67–89, 2001. a
Tucker, G. E. and Bras, R. L.: Hillslope processes, drainage density, and
landscape morphology, Water Resour. Res., 34, 2751–2764, 1998. a
Tucker, G. E. and Hancock, G. R.: Modelling landscape evolution, Earth Surf.
Proc. Land., 35, 28–50, 2010. a
Wechsler, S. P.: Uncertainties associated with digital elevation models for
hydrologic applications: a review, Hydrol. Earth Syst. Sci., 11, 1481–1500,
https://doi.org/10.5194/hess-11-1481-2007, 2007. a, b
Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., and Roth,
A.: Accuracy assessment of the global TanDEM-X Digital Elevation Model with
GPS data, ISPRS J. Photogramm., 139, 171–182, 2018. a
Whipple, K. X. and Tucker, G. E.: Dynamics of the stream-power river incision
model: Implications for height limits of mountain ranges, landscape response
timescales, and research needs, J. Geophys. Res.-Sol. Ea.,
104, 17661–17674, 1999. a
Zhang, J. and Goodchild, M. F.: Uncertainty in geographical information, CRC
press, London, UK, https://doi.org/10.1201/b12624, 2002. a
Zhang, W. and Montgomery, D. R.: Digital elevation model grid size, landscape
representation, and hydrologic simulations, Water Resour. Res., 30,
1019–1028, 1994. a
Short summary
Representing the surface of the Earth on an equally spaced grid leads to errors and uncertainties in derived slope and aspect. Using synthetic data, we develop a quality metric that can be used to compare the uncertainties in different datasets. We then apply this method to a real-world lidar dataset, and find that 1 m data have larger error bounds than lower-resolution data. The highest data resolution is not always the best choice – it is important to consider the quality of the data.
Representing the surface of the Earth on an equally spaced grid leads to errors and...