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Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds

Bibliographic Details
Main Author: Teixeira, Zara
Publication Date: 2020
Other Authors: Vital, Saulo Roberto de Oliveira, Vendel, Ana Lúcia, Mendonça, Juan Diego Lourenço de, Patrício, Joana
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/10316/96197
https://doi.org/10.1016/j.ocecoaman.2019.104903
Summary: In land cover maps, categories represent a continuum of variation and for this reason, fuzzy set theory, which accepts degrees of membership, has been suggested for land classification. Nevertheless, classical set theory, which only assumes single map categories, is still widely used. The purpose of this study is to develop a methodology to reduce the weakness of land cover maps in which classical theory has been applied. To do so, we propose adding an error relevance step after accuracy assessment, which evaluates how relevant are the classification errors to selected land applications. First, a membership matrix is built based on a linguistic scale associated to land cover rates obtained from literature. Then, two fuzzy measures are calculated and the frequency of categories, that do not pose a problem to the user in light of the land application, is determined. The methodology is demonstrated using two Brazilian tropical coastal regions and two land applications relevant for coastal watershed management. The study presents land cover maps of the Mamanguape and the Paraíba estuarine regions, their full accuracy assessment, and the relevance of the classification errors to the land applications. The accuracy assessment step has demonstrated that the land cover maps are reliable. The error relevance step has shown that the map weakness can be reduced. Both steps show that the land cover maps produced are suitable for further land mapping applications. The results on land cover composition point to the importance of future work focused on the environmental sustainability of the studied regions. The new procedure has proven useful to decrease the degree of distrust with which land cover maps are regarded. The framework provided is suitable for virtually any land mapping application.
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spelling Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watershedsAccuracy assessmentClassificationClassical set theoryError relevanceFuzzy set theoryUncertaintyParaíba stateBrazilIn land cover maps, categories represent a continuum of variation and for this reason, fuzzy set theory, which accepts degrees of membership, has been suggested for land classification. Nevertheless, classical set theory, which only assumes single map categories, is still widely used. The purpose of this study is to develop a methodology to reduce the weakness of land cover maps in which classical theory has been applied. To do so, we propose adding an error relevance step after accuracy assessment, which evaluates how relevant are the classification errors to selected land applications. First, a membership matrix is built based on a linguistic scale associated to land cover rates obtained from literature. Then, two fuzzy measures are calculated and the frequency of categories, that do not pose a problem to the user in light of the land application, is determined. The methodology is demonstrated using two Brazilian tropical coastal regions and two land applications relevant for coastal watershed management. The study presents land cover maps of the Mamanguape and the Paraíba estuarine regions, their full accuracy assessment, and the relevance of the classification errors to the land applications. The accuracy assessment step has demonstrated that the land cover maps are reliable. The error relevance step has shown that the map weakness can be reduced. Both steps show that the land cover maps produced are suitable for further land mapping applications. The results on land cover composition point to the importance of future work focused on the environmental sustainability of the studied regions. The new procedure has proven useful to decrease the degree of distrust with which land cover maps are regarded. The framework provided is suitable for virtually any land mapping application.AD1D-1E23-B6C4 | Zara Fani Gonçalves TeixeiraN/A2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://hdl.handle.net/10316/96197https://hdl.handle.net/10316/96197https://doi.org/10.1016/j.ocecoaman.2019.104903eng0964-5691cv-prod-726402Teixeira, ZaraVital, Saulo Roberto de OliveiraVendel, Ana LúciaMendonça, Juan Diego Lourenço dePatrício, Joanainfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-06-07T09:26:09Zoai:estudogeral.uc.pt:10316/96197Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T05:44:33.199175Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
title Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
spellingShingle Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
Teixeira, Zara
Accuracy assessment
Classification
Classical set theory
Error relevance
Fuzzy set theory
Uncertainty
Paraíba state
Brazil
title_short Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
title_full Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
title_fullStr Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
title_full_unstemmed Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
title_sort Introducing fuzzy set theory to evaluate risk of misclassification of land cover maps to land mapping applications: Testing on coastal watersheds
author Teixeira, Zara
author_facet Teixeira, Zara
Vital, Saulo Roberto de Oliveira
Vendel, Ana Lúcia
Mendonça, Juan Diego Lourenço de
Patrício, Joana
author_role author
author2 Vital, Saulo Roberto de Oliveira
Vendel, Ana Lúcia
Mendonça, Juan Diego Lourenço de
Patrício, Joana
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Teixeira, Zara
Vital, Saulo Roberto de Oliveira
Vendel, Ana Lúcia
Mendonça, Juan Diego Lourenço de
Patrício, Joana
dc.subject.por.fl_str_mv Accuracy assessment
Classification
Classical set theory
Error relevance
Fuzzy set theory
Uncertainty
Paraíba state
Brazil
topic Accuracy assessment
Classification
Classical set theory
Error relevance
Fuzzy set theory
Uncertainty
Paraíba state
Brazil
description In land cover maps, categories represent a continuum of variation and for this reason, fuzzy set theory, which accepts degrees of membership, has been suggested for land classification. Nevertheless, classical set theory, which only assumes single map categories, is still widely used. The purpose of this study is to develop a methodology to reduce the weakness of land cover maps in which classical theory has been applied. To do so, we propose adding an error relevance step after accuracy assessment, which evaluates how relevant are the classification errors to selected land applications. First, a membership matrix is built based on a linguistic scale associated to land cover rates obtained from literature. Then, two fuzzy measures are calculated and the frequency of categories, that do not pose a problem to the user in light of the land application, is determined. The methodology is demonstrated using two Brazilian tropical coastal regions and two land applications relevant for coastal watershed management. The study presents land cover maps of the Mamanguape and the Paraíba estuarine regions, their full accuracy assessment, and the relevance of the classification errors to the land applications. The accuracy assessment step has demonstrated that the land cover maps are reliable. The error relevance step has shown that the map weakness can be reduced. Both steps show that the land cover maps produced are suitable for further land mapping applications. The results on land cover composition point to the importance of future work focused on the environmental sustainability of the studied regions. The new procedure has proven useful to decrease the degree of distrust with which land cover maps are regarded. The framework provided is suitable for virtually any land mapping application.
publishDate 2020
dc.date.none.fl_str_mv 2020
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https://hdl.handle.net/10316/96197
https://doi.org/10.1016/j.ocecoaman.2019.104903
url https://hdl.handle.net/10316/96197
https://doi.org/10.1016/j.ocecoaman.2019.104903
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