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Cited by 105Soil Geostatistics and MappingGeography and Environmental StudiesLandslides and related hazards

The DOI matches its Crossref record; 194 of the 196 references it cites were found in a registry or an index.

Land-surface parameters for spatial predictive mapping and modeling

Open access
Maxwell, Aaron E., Shobe, Charles M.·2022·Earth-Science Reviews
DOI 10.1016/j.earscirev.2022.103944
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Abstract

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks.

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Citations

Cites

Show all 196 references

Research works printed but not linked: 10

  1. Eisank, 2011, A generic procedure for semantics-oriented landform classification using object-based image analysis, Geomorphometry, 2011, 125
  2. Gessler, 1995, Soil-landscape modelling and spatial prediction of soil attributes, Int. J. Geogr. Inf. Syst., 9, 421, 10.1080/02693799508902047 · DOI
  3. Goldberg, 2006, Genetic algorithms, Pearson Education India
  4. Jasiewicz, 2013, Content-based landscape retrieval using geomorphons, Geomorphometry, 2013, 52
  5. Kai, 2013, Research on the classification of terrain texture from DEMs based on BP neural network, Geomorphometry, 2013, 1
  6. Lopez, 2002, Use surface area for realistic calculations, GeoWorld, 15, 25
  7. Maxwell, 2019, Is high spatial resolution DEM data necessary for mapping palustrine wetlands?, Int. J. Remote Sens., 40, 118, 10.1080/01431161.2018.1506184 · DOI
  8. Riley, 1999, Index that quantifies topographic heterogeneity, Intermount. J. Sci., 5, 23
  9. Stepinski, 2011, Geomorphons-a new approach to classification of landforms, Proceedings of geomorphometry, 109
  10. Tarboton, 2005, 3012, 2018

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Metadata

Authors
Maxwell, A. E., & Shobe, C. M.
Affiliations
West Virginia University (US)
Corresponding author
Maxwell, Aaron E.
Venue
Earth-Science Reviews
Publisher
Elsevier BV
Published
2022-03
Volume
226
Pages
103944
ISSN
0012-8252 (ISSN-L)
DOI
10.1016/j.earscirev.2022.103944
OpenAlex
W4210565847
Cited by
105
References
196
Citations by year
2026: 21 · 2025: 33 · 2024: 22 · 2023: 22 · 2022: 7
FWCI
10.54
Type
journal article
Language
en
Open access
Licence
Locations
  • Earth-Science Reviews (publishedVersion, cc-by)
  • The Research Repository @ WVU (West Virginia University) (submittedVersion)
Funders
  • NASA West Virginia Space Grant Consortium
  • National Science Foundation (2046059)
  • National Science Foundation Directorate for Geosciences
Topics
  • Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Geostatistics and Mapping
  • Physical Sciences › Environmental Science › Water Science and Technology › Geography and Environmental Studies
  • Physical Sciences › Environmental Science › Management, Monitoring, Policy and Law › Landslides and related hazards
Keywords
terrain variables, land surface parameters, predictive mapping, feature selection, feature space reduction, sliding window analysis, landscape metrics, slope, local relief, rugosity, solar irradiation, geomorphological processes, explainable machine learning, convolutional neural networks

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