Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns
We propose an approach to texture characterization and comparison that directly uses the information of digital images of the earth surface without requesting a prior distinction of structural 'patches'. Digital images are partitioned into square 'windows' that define the scale of the analysis and which are submitted to the two-dimensional Fourier transform for extraction of a simplified textural characterization (in terms of coarseness) via the computation of a 'radial' power spectrum. Spectra computed from many images of the same size are systematically compared by means of a principal component analysis (PCA), which provides an ordination along a limited number of coarseness vs. fineness gradients. As an illustration, we applied this approach to digitized panchromatic air photos depicting various types of land cover in a semiarid landscape of northern Cameroon. We performed 'textural ordinations' at several scales by using square windows with sides ranging from 120 m to 1 km. At all scales, we found two coarseness gradients (PCA axes) based on the relative importance in the spectrum of large (> 50 km [exponent) -1), intermediate (30-50 km [exponent)-1), small (10-25 km [exponent) -1) and very small (<10 km [exponent) -1) spatial frequencies. Textural ordination based on Fourier spectra provides a powerful and consistent framework to identifying prominent scales of landscape patterns and to compare scaling properties across landscapes.
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Subjects: | U30 - Méthodes de recherche, U10 - Informatique, mathématiques et statistiques, P31 - Levés et cartographie des sols, modèle mathématique, télédétection, cartographie, analyse d'image, paysage, http://aims.fao.org/aos/agrovoc/c_24199, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_1344, http://aims.fao.org/aos/agrovoc/c_36762, http://aims.fao.org/aos/agrovoc/c_4185, http://aims.fao.org/aos/agrovoc/c_6734, http://aims.fao.org/aos/agrovoc/c_1229, |
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dig-cirad-fr-5601482024-01-28T19:15:19Z http://agritrop.cirad.fr/560148/ http://agritrop.cirad.fr/560148/ Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns. Couteron Pierre, Barbier Nicolas, Gautier Denis. 2006. Landscape Ecology, 21 (4) : 555-567.https://doi.org/10.1007/s10980-005-2166-6 <https://doi.org/10.1007/s10980-005-2166-6> Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns Couteron, Pierre Barbier, Nicolas Gautier, Denis eng 2006 Landscape Ecology U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols modèle mathématique télédétection cartographie analyse d'image paysage http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 Sahel Cameroun http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_1229 We propose an approach to texture characterization and comparison that directly uses the information of digital images of the earth surface without requesting a prior distinction of structural 'patches'. Digital images are partitioned into square 'windows' that define the scale of the analysis and which are submitted to the two-dimensional Fourier transform for extraction of a simplified textural characterization (in terms of coarseness) via the computation of a 'radial' power spectrum. Spectra computed from many images of the same size are systematically compared by means of a principal component analysis (PCA), which provides an ordination along a limited number of coarseness vs. fineness gradients. As an illustration, we applied this approach to digitized panchromatic air photos depicting various types of land cover in a semiarid landscape of northern Cameroon. We performed 'textural ordinations' at several scales by using square windows with sides ranging from 120 m to 1 km. At all scales, we found two coarseness gradients (PCA axes) based on the relative importance in the spectrum of large (> 50 km [exponent) -1), intermediate (30-50 km [exponent)-1), small (10-25 km [exponent) -1) and very small (<10 km [exponent) -1) spatial frequencies. Textural ordination based on Fourier spectra provides a powerful and consistent framework to identifying prominent scales of landscape patterns and to compare scaling properties across landscapes. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/560148/1/document_560148.pdf application/pdf Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1007/s10980-005-2166-6 10.1007/s10980-005-2166-6 http://catalogue-bibliotheques.cirad.fr/cgi-bin/koha/opac-detail.pl?biblionumber=211411 info:eu-repo/semantics/altIdentifier/doi/10.1007/s10980-005-2166-6 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1007/s10980-005-2166-6 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols modèle mathématique télédétection cartographie analyse d'image paysage http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_1229 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols modèle mathématique télédétection cartographie analyse d'image paysage http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_1229 |
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U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols modèle mathématique télédétection cartographie analyse d'image paysage http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_1229 U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols modèle mathématique télédétection cartographie analyse d'image paysage http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_1229 Couteron, Pierre Barbier, Nicolas Gautier, Denis Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns |
description |
We propose an approach to texture characterization and comparison that directly uses the information of digital images of the earth surface without requesting a prior distinction of structural 'patches'. Digital images are partitioned into square 'windows' that define the scale of the analysis and which are submitted to the two-dimensional Fourier transform for extraction of a simplified textural characterization (in terms of coarseness) via the computation of a 'radial' power spectrum. Spectra computed from many images of the same size are systematically compared by means of a principal component analysis (PCA), which provides an ordination along a limited number of coarseness vs. fineness gradients. As an illustration, we applied this approach to digitized panchromatic air photos depicting various types of land cover in a semiarid landscape of northern Cameroon. We performed 'textural ordinations' at several scales by using square windows with sides ranging from 120 m to 1 km. At all scales, we found two coarseness gradients (PCA axes) based on the relative importance in the spectrum of large (> 50 km [exponent) -1), intermediate (30-50 km [exponent)-1), small (10-25 km [exponent) -1) and very small (<10 km [exponent) -1) spatial frequencies. Textural ordination based on Fourier spectra provides a powerful and consistent framework to identifying prominent scales of landscape patterns and to compare scaling properties across landscapes. |
format |
article |
topic_facet |
U30 - Méthodes de recherche U10 - Informatique, mathématiques et statistiques P31 - Levés et cartographie des sols modèle mathématique télédétection cartographie analyse d'image paysage http://aims.fao.org/aos/agrovoc/c_24199 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_1344 http://aims.fao.org/aos/agrovoc/c_36762 http://aims.fao.org/aos/agrovoc/c_4185 http://aims.fao.org/aos/agrovoc/c_6734 http://aims.fao.org/aos/agrovoc/c_1229 |
author |
Couteron, Pierre Barbier, Nicolas Gautier, Denis |
author_facet |
Couteron, Pierre Barbier, Nicolas Gautier, Denis |
author_sort |
Couteron, Pierre |
title |
Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns |
title_short |
Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns |
title_full |
Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns |
title_fullStr |
Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns |
title_full_unstemmed |
Textural ordination based on Fourier spectral decomposition: a method to analyze and compare landscape patterns |
title_sort |
textural ordination based on fourier spectral decomposition: a method to analyze and compare landscape patterns |
url |
http://agritrop.cirad.fr/560148/ http://agritrop.cirad.fr/560148/1/document_560148.pdf |
work_keys_str_mv |
AT couteronpierre texturalordinationbasedonfourierspectraldecompositionamethodtoanalyzeandcomparelandscapepatterns AT barbiernicolas texturalordinationbasedonfourierspectraldecompositionamethodtoanalyzeandcomparelandscapepatterns AT gautierdenis texturalordinationbasedonfourierspectraldecompositionamethodtoanalyzeandcomparelandscapepatterns |
_version_ |
1792497831645282304 |