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...

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Autor principal:	Maan Habib
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/d5bf2fc8c02d4170b51678678fdbfa8c
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id	oai:doaj.org-article:d5bf2fc8c02d4170b51678678fdbfa8c
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spelling	oai:doaj.org-article:d5bf2fc8c02d4170b51678678fdbfa8c2021-11-15T01:20:04ZQuantifying Topographic Ruggedness Using Principal Component Analysis1687-809410.1155/2021/3311912https://doaj.org/article/d5bf2fc8c02d4170b51678678fdbfa8c2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3311912https://doaj.org/toc/1687-8094The 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.Maan HabibHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040ENAdvances in Civil Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Maan Habib Quantifying Topographic Ruggedness Using Principal Component Analysis
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
author	Maan Habib
author_facet	Maan Habib
author_sort	Maan Habib
title	Quantifying Topographic Ruggedness Using Principal Component Analysis
title_short	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_sort	quantifying topographic ruggedness using principal component analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/d5bf2fc8c02d4170b51678678fdbfa8c
work_keys_str_mv	AT maanhabib quantifyingtopographicruggednessusingprincipalcomponentanalysis
_version_	1718428951400939520

Quantifying Topographic Ruggedness Using Principal Component Analysis

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