Quantifying Topographic Ruggedness Using Principal Component Analysis
The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/3311912 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849696134609502208 |
|---|---|
| author | Maan Habib |
| author_facet | Maan Habib |
| author_sort | Maan Habib |
| collection | DOAJ |
| description | The development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones. |
| format | Article |
| id | doaj-art-d5bf2fc8c02d4170b51678678fdbfa8c |
| institution | DOAJ |
| issn | 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-d5bf2fc8c02d4170b51678678fdbfa8c2025-08-20T03:19:33ZengWileyAdvances in Civil Engineering1687-80942021-01-01202110.1155/2021/3311912Quantifying Topographic Ruggedness Using Principal Component AnalysisMaan Habib0Faculty of Civil EngineeringThe development of geospatial technologies has opened a new era in terms of data collection techniques and analysis procedures. Digital elevation models as 3D visualization of the Earth’s surface have many mapping and spatial analysis applications. The primary terrain factors derived from the raster dataset are usually less critical than secondary ones, e.g., ruggedness index, which plays a vital role in engineering, hydrological information derivation, and geomorphological processes. Surface ruggedness is a significant predictor of topographic heterogeneity by calculating the absolute value of elevation differences within a specified neighborhood surrounding a central pixel. The current study investigates the impacts of various topographic metrics obtained from a digital elevation model on characterizing terrain ruggedness utilizing stepwise principal component analysis. This popular multivariate statistical technique is applied to conduct a comprehensive assessment and treat the information redundancy of terrain parameters. Simultaneously, the standard deviation of elevation is also proposed as an alternative approach to quantifying topographic ruggedness. Besides, quantitative and qualitative method is espoused to validate the algorithms and compare their capabilities to the previously introduced models in the literature. The findings have shown that principal component analysis provides superior performance against other models. Furthermore, they indicated that the standard deviation of elevation could be used instead of the available ones.http://dx.doi.org/10.1155/2021/3311912 |
| spellingShingle | Maan Habib Quantifying Topographic Ruggedness Using Principal Component Analysis Advances in Civil Engineering |
| title | Quantifying Topographic Ruggedness Using Principal Component Analysis |
| title_full | Quantifying Topographic Ruggedness Using Principal Component Analysis |
| title_fullStr | Quantifying Topographic Ruggedness Using Principal Component Analysis |
| title_full_unstemmed | Quantifying Topographic Ruggedness Using Principal Component Analysis |
| title_short | Quantifying Topographic Ruggedness Using Principal Component Analysis |
| title_sort | quantifying topographic ruggedness using principal component analysis |
| url | http://dx.doi.org/10.1155/2021/3311912 |
| work_keys_str_mv | AT maanhabib quantifyingtopographicruggednessusingprincipalcomponentanalysis |