Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis?
Physical processes, including anthropogenic feedbacks, sculpt planetary surfaces (e.g. Earth's). A fundamental tenet of geomorphology is that the shapes created, when combined with other measurements, can be used to understand those processes. Artificial or synthetic digital elevation models (D...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-12-01
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| Series: | Earth Surface Dynamics |
| Online Access: | http://www.earth-surf-dynam.net/3/587/2015/esurf-3-587-2015.pdf |
| _version_ | 1811201167133245440 |
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| author | J. K. Hillier G. Sofia S. J. Conway |
| author_facet | J. K. Hillier G. Sofia S. J. Conway |
| author_sort | J. K. Hillier |
| collection | DOAJ |
| description | Physical processes, including anthropogenic feedbacks, sculpt planetary
surfaces (e.g. Earth's). A fundamental tenet of geomorphology is that the
shapes created, when combined with other measurements, can be used to
understand those processes. Artificial or synthetic digital elevation models
(DEMs) might be vital in progressing further with this endeavour in two
ways. First, synthetic DEMs can be built (e.g. by directly using governing
equations) to encapsulate the processes, making predictions from theory. A
second, arguably underutilised, role is to perform checks on accuracy and
robustness that we dub "synthetic tests". Specifically, synthetic DEMs can
contain a priori known, idealised morphologies that numerical landscape evolution
models, DEM-analysis algorithms, and even manual mapping can be assessed
against. Some such tests, for instance examining inaccuracies caused by
noise, are moderately commonly employed, whilst others are much less so.
Derived morphological properties, including metrics and mapping (manual and
automated), are required to establish whether or not conceptual models
represent reality well, but at present their quality is typically weakly
constrained (e.g. by mapper inter-comparison). Relatively rare examples
illustrate how synthetic tests can make strong "absolute" statements about
landform detection and quantification; for example, 84 % of valley heads in the
real landscape are identified correctly. From our perspective, it is vital
to verify such statistics quantifying the properties of landscapes as
ultimately this is the link between physics-driven models of processes and
morphological observations that allows quantitative hypotheses to be tested.
As such the additional rigour possible with this second usage of synthetic
DEMs feeds directly into a problem central to the validity of much of
geomorphology. Thus, this note introduces synthetic tests and DEMs and then
outlines a typology of synthetic DEMs along with their benefits, challenges,
and future potential to provide constraints and insights. The aim is to
discuss how we best proceed with uncertainty-aware landscape analysis to
examine physical processes. |
| first_indexed | 2024-04-12T02:16:19Z |
| format | Article |
| id | doaj.art-03bc0f4657d14504b7cd96263c27c35f |
| institution | Directory Open Access Journal |
| issn | 2196-6311 2196-632X |
| language | English |
| last_indexed | 2024-04-12T02:16:19Z |
| publishDate | 2015-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth Surface Dynamics |
| spelling | doaj.art-03bc0f4657d14504b7cd96263c27c35f2022-12-22T03:52:14ZengCopernicus PublicationsEarth Surface Dynamics2196-63112196-632X2015-12-013458759810.5194/esurf-3-587-2015Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis?J. K. Hillier0G. Sofia1S. J. Conway2Dept. Geography, Loughborough University, Loughborough, LE11 3TU, UKDept. Land, Environment, Agriculture and Forestry, University of Padova, Agripolis, viale dell'Università 16, 35020 Legnaro (PD), ItalyDept. Physical Sciences, The Open University, Milton Keynes, MK7 6AA, UKPhysical processes, including anthropogenic feedbacks, sculpt planetary surfaces (e.g. Earth's). A fundamental tenet of geomorphology is that the shapes created, when combined with other measurements, can be used to understand those processes. Artificial or synthetic digital elevation models (DEMs) might be vital in progressing further with this endeavour in two ways. First, synthetic DEMs can be built (e.g. by directly using governing equations) to encapsulate the processes, making predictions from theory. A second, arguably underutilised, role is to perform checks on accuracy and robustness that we dub "synthetic tests". Specifically, synthetic DEMs can contain a priori known, idealised morphologies that numerical landscape evolution models, DEM-analysis algorithms, and even manual mapping can be assessed against. Some such tests, for instance examining inaccuracies caused by noise, are moderately commonly employed, whilst others are much less so. Derived morphological properties, including metrics and mapping (manual and automated), are required to establish whether or not conceptual models represent reality well, but at present their quality is typically weakly constrained (e.g. by mapper inter-comparison). Relatively rare examples illustrate how synthetic tests can make strong "absolute" statements about landform detection and quantification; for example, 84 % of valley heads in the real landscape are identified correctly. From our perspective, it is vital to verify such statistics quantifying the properties of landscapes as ultimately this is the link between physics-driven models of processes and morphological observations that allows quantitative hypotheses to be tested. As such the additional rigour possible with this second usage of synthetic DEMs feeds directly into a problem central to the validity of much of geomorphology. Thus, this note introduces synthetic tests and DEMs and then outlines a typology of synthetic DEMs along with their benefits, challenges, and future potential to provide constraints and insights. The aim is to discuss how we best proceed with uncertainty-aware landscape analysis to examine physical processes.http://www.earth-surf-dynam.net/3/587/2015/esurf-3-587-2015.pdf |
| spellingShingle | J. K. Hillier G. Sofia S. J. Conway Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? Earth Surface Dynamics |
| title | Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? |
| title_full | Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? |
| title_fullStr | Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? |
| title_full_unstemmed | Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? |
| title_short | Perspective – synthetic DEMs: A vital underpinning for the quantitative future of landform analysis? |
| title_sort | perspective synthetic dems a vital underpinning for the quantitative future of landform analysis |
| url | http://www.earth-surf-dynam.net/3/587/2015/esurf-3-587-2015.pdf |
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