A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data

In the context of geospatial studies, meaningful information may be hidden in the aspects of form and connectivity inscribed in the measurements. Therefore, here is proposed the use of H0 Persistent Homology (PH), a Topological Data Analysis tool to automatically summarize and quantify relevant spat...

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Autores principales:	Caio Átila Pereira Sena, João Antônio Recio da Paixão, José Ricardo de Almeida França
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Persistent homology Local climate zone EVI Bottleneck distance Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/0d488014ed1e4ee2b60936969367a494
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id	oai:doaj.org-article:0d488014ed1e4ee2b60936969367a494
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spelling	oai:doaj.org-article:0d488014ed1e4ee2b60936969367a4942021-11-16T04:11:38ZA Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data2667-010010.1016/j.envc.2021.100359https://doaj.org/article/0d488014ed1e4ee2b60936969367a4942021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2667010021003334https://doaj.org/toc/2667-0100In the context of geospatial studies, meaningful information may be hidden in the aspects of form and connectivity inscribed in the measurements. Therefore, here is proposed the use of H0 Persistent Homology (PH), a Topological Data Analysis tool to automatically summarize and quantify relevant spatial features in satellite data. With that aim, we extend the algebraic concepts of cubical complexes to the satellite data perspective and describe homology groups portrayal. As a proof by example, we present an inter-site comparison of Enhanced Vegetation Index from MODerate-resolution Imaging Spectroradiometer over fifteen regions worldwide. There, the Local Climate Zone (LCZ) framework is used to examine the outcomes of the PH filtration. Then, the features from every region that were encapsulated by the PH were compared against each other with the aid of the Bottleneck Distance metric. After that, it was performed a dimensionality reduction with a multi-dimensional scaling to build a 2-D geometry of the level of similarity among them. Thereby, the common aspects of the regions became explicit by their coordinates’ proximity in space. Then, with the use of the K-means algorithm, we were able to cluster those areas belonging to the same LCZ class. The results indicate that the proposed methods are robust to missing data in the satellite data and insensitive to a certain level of inhomogeneity in the spatial subsetting of data. Furthermore, the outcomes provide insights on several viable applications for future research.Caio Átila Pereira SenaJoão Antônio Recio da PaixãoJosé Ricardo de Almeida FrançaElsevierarticlePersistent homologyLocal climate zoneEVIBottleneck distanceEnvironmental sciencesGE1-350ENEnvironmental Challenges, Vol 5, Iss , Pp 100359- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Persistent homology Local climate zone EVI Bottleneck distance Environmental sciences GE1-350
spellingShingle	Persistent homology Local climate zone EVI Bottleneck distance Environmental sciences GE1-350 Caio Átila Pereira Sena João Antônio Recio da Paixão José Ricardo de Almeida França A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data
description	In the context of geospatial studies, meaningful information may be hidden in the aspects of form and connectivity inscribed in the measurements. Therefore, here is proposed the use of H0 Persistent Homology (PH), a Topological Data Analysis tool to automatically summarize and quantify relevant spatial features in satellite data. With that aim, we extend the algebraic concepts of cubical complexes to the satellite data perspective and describe homology groups portrayal. As a proof by example, we present an inter-site comparison of Enhanced Vegetation Index from MODerate-resolution Imaging Spectroradiometer over fifteen regions worldwide. There, the Local Climate Zone (LCZ) framework is used to examine the outcomes of the PH filtration. Then, the features from every region that were encapsulated by the PH were compared against each other with the aid of the Bottleneck Distance metric. After that, it was performed a dimensionality reduction with a multi-dimensional scaling to build a 2-D geometry of the level of similarity among them. Thereby, the common aspects of the regions became explicit by their coordinates’ proximity in space. Then, with the use of the K-means algorithm, we were able to cluster those areas belonging to the same LCZ class. The results indicate that the proposed methods are robust to missing data in the satellite data and insensitive to a certain level of inhomogeneity in the spatial subsetting of data. Furthermore, the outcomes provide insights on several viable applications for future research.
format	article
author	Caio Átila Pereira Sena João Antônio Recio da Paixão José Ricardo de Almeida França
author_facet	Caio Átila Pereira Sena João Antônio Recio da Paixão José Ricardo de Almeida França
author_sort	Caio Átila Pereira Sena
title	A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data
title_short	A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data
title_full	A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data
title_fullStr	A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data
title_full_unstemmed	A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data
title_sort	topological data analysis approach for retrieving local climate zones patterns in satellite data
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0d488014ed1e4ee2b60936969367a494
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A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data

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