Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline

22 páginas.- 10 figuras.- 96 referencias

Detalhes bibliográficos
Autores: Gutiérrez-Hernández, Oliver, García, Luis V.
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::06b4ae86f5cd8e40baecfd76c807df18
Acesso em linha:http://hdl.handle.net/10261/370974
Access Level:acceso abierto
Palavra-chave:Monotonic trends
Theil–Sen (TS)
Contextual Mann–Kendall (CMK)
False discovery rate (FDR)
Cork oak decline
Quercus suber
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spelling Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest DeclineGutiérrez-Hernández, OliverGarcía, Luis V.Monotonic trendsTheil–Sen (TS)Contextual Mann–Kendall (CMK)False discovery rate (FDR)Cork oak declineQuercus suber22 páginas.- 10 figuras.- 96 referenciasWe introduce a novel methodological framework for robust trend analysis (RTA) using remote sensing data to enhance the accuracy and reliability of detecting significant environmental trends. Our approach sequentially integrates the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test, and the false discovery rate (FDR) control. This comprehensive method addresses common challenges in trend analysis, such as handling small, noisy datasets with outliers and issues related to spatial autocorrelation, cross-correlation, and multiple testing. We applied this RTA workflow to study tree cover trends in Los Alcornocales Natural Park (Southern Spain), Europe’s largest cork oak forest, analysing interannual changes in tree cover from 2000 to 2022 using Terra MODIS MOD44B data. Our results reveal that the TS estimator provides a robust measure of trend direction and magnitude, but its effectiveness is dramatically enhanced when combined with the CMK test. This combination highlights significant trends and effectively corrects for spatial autocorrelation and cross-correlation, ensuring that genuine environmental signals are distinguished from statistical noise. Unlike previous workflows, our approach incorporates the FDR control, which successfully filtered out 29.6% of false discoveries in the case study, resulting in a more stringent assessment of true environmental trends captured by multi-temporal remotely sensed data. In the case study, we found that approximately one-third of the area exhibits significant and statistically robust declines in tree cover, with these declines being geographically clustered. Importantly, these trends correspond with relevant changes in tree cover, emphasising the ability of RTA to detect relevant environmental changes. Overall, our findings underscore the crucial importance of combining these methods, as their synergy is essential for accurately identifying and confirming robust environmental trends. The proposed RTA framework has significant implications for environmental monitoring, modelling, and management.Peer reviewedMultidisciplinary Digital Publishing InstituteGutiérrez-Hernández, Oliver [0000-0003-2580-5465]García, Luis V. [0000-0002-5514-2941]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/370974reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.3390/rs16203886Síinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::06b4ae86f5cd8e40baecfd76c807df182026-05-22T06:33:51Z
dc.title.none.fl_str_mv Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
title Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
spellingShingle Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
Gutiérrez-Hernández, Oliver
Monotonic trends
Theil–Sen (TS)
Contextual Mann–Kendall (CMK)
False discovery rate (FDR)
Cork oak decline
Quercus suber
title_short Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
title_full Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
title_fullStr Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
title_full_unstemmed Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
title_sort Robust Trend Analysis in Environmental Remote Sensing: A Case Study of Cork Oak Forest Decline
dc.creator.none.fl_str_mv Gutiérrez-Hernández, Oliver
García, Luis V.
author Gutiérrez-Hernández, Oliver
author_facet Gutiérrez-Hernández, Oliver
García, Luis V.
author_role author
author2 García, Luis V.
author2_role author
dc.contributor.none.fl_str_mv Gutiérrez-Hernández, Oliver [0000-0003-2580-5465]
García, Luis V. [0000-0002-5514-2941]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Monotonic trends
Theil–Sen (TS)
Contextual Mann–Kendall (CMK)
False discovery rate (FDR)
Cork oak decline
Quercus suber
topic Monotonic trends
Theil–Sen (TS)
Contextual Mann–Kendall (CMK)
False discovery rate (FDR)
Cork oak decline
Quercus suber
description 22 páginas.- 10 figuras.- 96 referencias
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/370974
url http://hdl.handle.net/10261/370974
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.3390/rs16203886
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812455

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