the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Nov 2024
Global basic landform units derived from multi-source digital elevation models at 1 arc-second resolution
Abstract. Landforms are fundamental components of the Earth surface, providing the base on which surface processes operate. Understanding and classifying global landforms, which record the internal and external dynamics of the planet's evolution, constitutes a critical aspect of Earth system science. Advances in Earth observation technologies have enabled access to higher resolution data, for example remote sensing imagery and digital elevation models (DEMs). However, landform data with a resolution of approximately 1 arc-second (approximately 30 m) are lacking at the global scale, which limits the progress of geomorphologic studies at finer scales. Here, we propose a novel framework for global landform classification and release a unique dataset called Global Basic Landform Units (GBLU), which incorporates a comprehensive set of objects that constitute the range of landforms on Earth. Constructed from multiple 1 arc-second DEMs, GBLU ranks among the highest-resolution global geomorphology datasets to date. Its development integrates geomorphological ontologies and key derivatives to strike a balance between mitigating local noise and preserving valuable landform details. GBLU categorizes the Earth's landforms into three levels with 26 classes, yielding discrete vector units that record landform type and distribution. Comparative analyses with previous datasets reveal that GBLU enhances capture of landform details, enabling more precise depiction of geomorphological boundaries. This refinement facilitates the identification of novel spatial disparities in landform patterns, exemplified by marked contrasts between Asia and other continents, and highlights the distinct prominence of China in terms of landform diversity. Given that the fundamental data resolution of GBLU accords well with available remote sensing datasets, it is readily incorporated into analytical workflows, exploring the relationship between landforms, climate and land cover. The full data set is available on the Deep-time Digital Earth Geomorphology platform and Zenodo (Yang et al., 2024; https://doi.org/10.5281/zenodo.13187969).
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RC1: 'Comment on essd-2024-401', Anonymous Referee #1, 30 Dec 2024
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The authors present a global dataset of landforms derived from a high resolution DEM. They propose new ways to identify plain areas and their transition to hilly and mountainous terrain. The novel way to do this by identifying core areas and including transition areas through a cost distance analysis yields results that seem visually quite accurate when the map is overlayed onto a relief background. Plain and higher relief areas are neatly differentiated. This type of information can be quite useful for geographical and ecological macro studies. The precise workflow does miss details to be reproducible. It is a pity that proprietary software was used and the workflow described in general terms only, which makes replication more difficult. The choice for some cut off values or thresholds (slope, elevation, accumulated cost) is not always clearly explained or motivated.
To classify the hilly and mountainous areas the authors propose a new approach as an alternative to a moving windows analysis that has documented limitations. Landform relief is not calculated with reference to the nearest elevation data within a (small) window, but expressed with reference to a regional baseline calculated by creating a TIN on the basis of the elevation at the border of a mountain range (i.e. where it transitions to plain). In addition, the baseline elevation takes into account the elevation of points along water courses within the mountain to create a baseline surface to act as reference for the roughness calculations. Thresholds are applied to the elevation differences calculated by subtracting the baseline elevation from the actual surface elevation. The lowest elevation differences are labelled hills, followed by low relief mountain up to highest relief mountain. This leads to a conceptual problem. In my opinion, when one talks about a mountain or mountain range such as the Himalaya as a landform, one considers the mountain as a whole, from the foothills to the highest summits as the landform "highest relief mountain". Similarly when talking about the Jura or the Vosges mountains, one would talk about low relief mountains, but not consider only the mountain summits to be low, but the whole landform down to the foot slopes as being the low mountain.
In some cases the transitions from different categories of mountain to hilly land is well captured in this approach, typically in ancient eroded landscapes with remnants of higher mountains. The dissected rolling hill landscape gets the label hills, while the remaining inselbergs are classified as mountain.
However, the story is very different in younger mountain areas such as the European Alps or Himalayas. If one looks at the GBLU map without legend overlaid onto a relief map, valley-like shapes appear very distinctly that follow  the actual valleys of these mountains. When looking at the legend, one sees that these are actually classified as hills. The same holds for flat valley bottoms inside the mountains, these are classified plains, even if they are long, narrow and sinuous.
In my conceptualization of a mountain, the mid slopes of high mountains do not pertain to the landform class middle relief mountain. They are mid slopes of a high mountain. Similarly, mountain valleys are not hills, just because the local surface elevation is below a certain threshold.
When I look at the methods and results of this paper, I think of the product as something like "Map of relief classes and relative (or regional) elevation zones", and I am convinced that this classification is useful for different scientific applications. Ontologically I don't think that the presented map units should be thought of as representations of landforms.
In summary, I commend the authors for what seems to be a very detailed and precise work and the product and the work that has gone into its production. Also, the results seem to be useful for certain research applications. I do not however agree with the authors that what is represented here are landforms, ontologically speaking.
The distinction I make here is further illustrated in the figure.
Figure: Upper transect: how I understand the current version of the GLBU. Lower transect: how I think landforms should be conceptualized in this context.
Regarding the data availability, the authors have presented the resources they developed on Zenodo. The files are easily accessible and useable in open source software. Files are presented in folders by 10 degree latitudinal bands, and it is quite easy to find a region of interest. All terrestrial areas of the world seem to be included in the data. There is a possible issue for global level use of the data in that it consists of many different tiles that need to be mosaiced, but this can be coded.
The validation is done against a number of similar products where one of the main differences is the resolution of the source layers (DEM), this product being based on very high resolution sources (~30 m at the equator.) The identification of plains seems to be more accurate than in any of the products with which it is compared.
Overall the manuscript is sufficiently concise, the language clear, although it could benefit from some minor edits here and there (see below). On several points the methods section should be developed a bit further to allow full replication of the work flow.
Overall the language is clear and very understandable, but some suggestions for minor improvements are given.
As said, in my opinion the layers presented in this work do not represent landforms. However I think that the classification of relief in plains and mountains with different values of elevation and relief intensity (roughness) can be quite useful for a series of environmental applications.
My recommendation would therefore to revise the title and some sections of the text where the product is labelled as a map of landforms and replace this with formulations that more accurately reflect what is shown, that is, not to speak of landforms but about a map of relief (roughness) and elevation classes (or something similar) instead. This would require rather limited changes to the text and figures.
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Specific comments
35-36: I would add evolution or genesis to this list of research subfields of geomorphology
43: I would add that field work is an essential component of landform mapping (geomorphology)
46-47: there is a more recent product produced by Amatulli et al. that might be useful to refer to here: Amatulli, G., McInerney, D., Sethi, T., Strobl, P., & Domisch, S. (2020). Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Scientific Data, 7(1), 162. https://doi.org/10.1038/s41597-020-0479-6
56-58: However, as the authors stated, unsupervised classification based methods to perform higher-resolution global landform classification require an international team with knowledge of geomorphological development in a variety of climatic and physiographic settings. > do you address this?
69-70: not clear if this paper only object is to classify the shape or also something about the material (lithology) and / or genesis, / evolution. Methods and final product seem to be focusing on shape irrespective of material / genesis.
80: objective:Â "to construct a global classification system for landforms that integrates geomorphological knowledge," : not clear where the geomorphological knowledge comes in in the method
82: typo: "high-resolutiojn" > high-resolution
99-100 "The first-level (L1) types are defined as ‘plain’ and ‘mountain’, reflecting the most fundamental morphological characteristics of landforms." If I understand it well, the first level distinguishes between plain and non-plain (i.e. hills and mountains), as all that is not plain is later subdivided into several classes of hills and mountains, not mountains alone.
102:Â "This classification perspective aids researchers in conducting macro-scale studies"Â This is indeed a valuable distinction
113:Â "the area the missing from FABDEM"Â >Â the area missing from FABDEM
120:Â "The following sections provide details that should allow users to reproduce our results." Â : some more details would be needed to achieve this I think
123: Fig 1: Â "accumulate slope " > accumulated slope?
 "Interecting with flat landforms"  > Intersecting with flat landforms
 "Eliminating fragement blocks"  > Eliminating fragment blocks
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125: data preprocessing or data pre-processing (see figure, perhaps harmonize?)
130: "data from latitudes below 70° are transposed onto the Behrmann projection, and the remaining data are transported onto the Lambert azimuth equal-area projection. " : suggested edit: Tiles between 70° N/S are reprojected to the equal area Behrmann projection, and the tiles polewards of 70° N/S to Lambert azimuthal equal-area.
132-133: this first sentence is more of a statement that would perhaps be better in the introduction. Starting this section with the second sentence works quite well.
140: Fig 2b typo: "variant" > variant
147: how large must the continuous area of plain be to be considered a core area? I.e. how many contiguous pixels constitute a plain core area? Do you also apply a shape criterion, or can a very long area of contiguous plain pixels also constitute a core plain area?
148-150: it is not clear to me what the cost layer is in this calculation: elevation, slope, or something else? Same holds for 'cost' in Fig 2a.
149:Â "The AS is calculated as the minimum cumulative cost of each position to the nearest landform core along a specific path"Â Would it not be more precise to say: The AS is calculated as the minimum cumulative cost of each position to the nearest plain core along a specific path.
155-156: not clear to me how such an algorithm  achieves the most direct integration of geomorphological knowledge and expertise
160: does T2 have a dimension and a unit? 1500-2000, is that length in meters, or slope in degrees or something else?
161-162: "but needs to be determined by integration with expert knowledge within different geomorphic regions". Not clear if you state that this should be done or that it has been done, and if so how?
162: "In some cases, it may exceed the recommended threshold range."  – not clear where and when
165-167:Â "This novel method avoids the negative effect of local window analysis and is beneficial for maintaining the landform semantics for each block."Â Visual inspection of a number of tiles indeed shows a neat identification of the borders of plains and their transition to hilly or mountainous terrain.
176-177:Â "a method that fails to account for geomorphological semantics, and which therefore disregards the integrity of a mountain. " I would argue that the classification of L2 landforms proposed in this paper does just that. I do not see any landform concept reflected in the classes, and even less so in the map units corresponding to these classes. See general comments above
192: Â "on basis of the plain boundary" Â > on the basis of the plain boundary
192-193:Â "To refine the representation of surface relief, we also take into account linear features representing the rivers. " I suppose you do not consider all rivers and streams to construct your TIN of mountain base. Rivers and streams go up to great altitudes. Which sections of mountain rivers did you consider to construct the TIN?
206: was there any reasoning behind the selection of these elevation bands? 0-1000, 1000-3500, 3500-5000 and >5000?
207-208: idem
277: Figure 7. Comparison between the GBLU and the Global Mountain Biodiversity Assessment (GMBA) projects. > Figure 7. Comparison between the GBLU and three mountain definitions presented on the Global Mountain Explorer (https://rmgsc.cr.usgs.gov/gme/)
278-279: this does not seem to be entirely accurate: Â "We conducted a more detailed comparison for mountain regions using the Global Mountain Biodiversity Assessment (GMBA) (Snethlage et al., 2022) as reference data."Â The three definitions are from three different institutions (WCMC, GMBA and USGS) but have conveniently been presented together on the Global Mountain Explorer (https://rmgsc.cr.usgs.gov/gme/). The latest mountain definition is the one by Snethlage et al (2002) which can be obtained from https://www.earthenv.org/mountains (scroll down to: Download the GMBA Mountain Definition v.2 here.)
337:Â "fundamental role in supporting the identification of landforms that incorporates complex semantics."Â > not clear what semantics means in this context
344Â "influencinge community structure and function,"Â > influencing community structure and function,
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Citation: https://doi.org/10.5194/essd-2024-401-RC1
Data sets
Global Basic Landform Units (GBLU) datasets v1.0 Xin Yang, Sijin Li, Junfei Ma, Yang Chen, Xingyu Zhou, Fayuan Li, Liyang Xiong, Chenghu Zhou, Guoan Tang, and Michael E. Meadows https://doi.org/10.5281/zenodo.13187969
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