Remote sensing liana infestation in an aseasonal tropical forest

Detalhes bibliográficos
Autor(a) principal: Chandler, Chris J.
Data de Publicação: 2021
Outros Autores: van der Heijden, Geertje M.F., Boyd, Doreen S., Cutler, Mark E.J., Costa, Hugo, Nilus, Reuben, Foody, Giles M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/112876
Resumo: Chandler, C. J., van der Heijden, G. M. F., Boyd, D. S., Cutler, M. E. J., Costa, H., Nilus, R., & Foody, G. M. (2021). Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation, 7(3), 397-410. https://doi.org/10.1002/rse2.197 ---------------------------------------------------------------- The authors also thank the Natural Environment Research Council [NE/P004806/1 to MEJC, DSB, GMF, GMFvdH; NE/I528477/1 (ARSF MA14/11) to MEJC, DSB, GMF and NE/L002604/1 to CJC, GMF, GMFvdH]. The FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) to HC. The authors also thank the University of Nottingham for an Anne McLaren Research Fellowship to GMFvdH which funded the collection of the ground data.
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spelling Remote sensing liana infestation in an aseasonal tropical forestaddressing mismatch in spatial units of analysesHyperspectral imagingliana infestationLiDARneural networkpixel-based soft classificationsegmentationEcology, Evolution, Behavior and SystematicsEcologyComputers in Earth SciencesNature and Landscape ConservationChandler, C. J., van der Heijden, G. M. F., Boyd, D. S., Cutler, M. E. J., Costa, H., Nilus, R., & Foody, G. M. (2021). Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation, 7(3), 397-410. https://doi.org/10.1002/rse2.197 ---------------------------------------------------------------- The authors also thank the Natural Environment Research Council [NE/P004806/1 to MEJC, DSB, GMF, GMFvdH; NE/I528477/1 (ARSF MA14/11) to MEJC, DSB, GMF and NE/L002604/1 to CJC, GMF, GMFvdH]. The FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) to HC. The authors also thank the University of Nottingham for an Anne McLaren Research Fellowship to GMFvdH which funded the collection of the ground data.The ability to accurately assess liana (woody vine) infestation at the landscape level is essential to quantify their impact on carbon dynamics and help inform targeted forest management and conservation action. Remote sensing techniques provide potential solutions for assessing liana infestation at broader spatial scales. However, their use so far has been limited to seasonal forests, where there is a high spectral contrast between lianas and trees. Additionally, the ability to align the spatial units of remotely sensed data with canopy observations of liana infestation requires further attention. We combined airborne hyperspectral and LiDAR data with a neural network machine learning classification to assess the distribution of liana infestation at the landscape-level across an aseasonal primary forest in Sabah, Malaysia. We tested whether an object-based classification was more effective at predicting liana infestation when compared to a pixel-based classification. We found a stronger relationship between predicted and observed liana infestation when using a pixel-based approach (RMSD = 27.0% ± 0.80) in comparison to an object-based approach (RMSD = 32.6% ± 4.84). However, there was no significant difference in accuracy for object- versus pixel-based classifications when liana infestation was grouped into three classes; Low [0–30%], Medium [31–69%] and High [70–100%] (McNemar’s χ2 = 0.211, P = 0.65). We demonstrate, for the first time, that remote sensing approaches are effective in accurately assessing liana infestation at a landscape scale in an aseasonal tropical forest. Our results indicate potential limitations in object-based approaches which require refinement in order to accurately segment imagery across contiguous closed-canopy forests. We conclude that the decision on whether to use a pixel- or object-based approach may depend on the structure of the forest and the ultimate application of the resulting output. Both approaches will provide a valuable tool to inform effective conservation and forest management.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNChandler, Chris J.van der Heijden, Geertje M.F.Boyd, Doreen S.Cutler, Mark E.J.Costa, HugoNilus, ReubenFoody, Giles M.2021-03-01T23:59:01Z2021-09-222021-09-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article14application/pdfhttp://hdl.handle.net/10362/112876eng2056-3485PURE: 28357986https://doi.org/10.1002/rse2.197info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:56:10Zoai:run.unl.pt:10362/112876Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:13.682638Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Remote sensing liana infestation in an aseasonal tropical forest
addressing mismatch in spatial units of analyses
title Remote sensing liana infestation in an aseasonal tropical forest
spellingShingle Remote sensing liana infestation in an aseasonal tropical forest
Chandler, Chris J.
Hyperspectral imaging
liana infestation
LiDAR
neural network
pixel-based soft classification
segmentation
Ecology, Evolution, Behavior and Systematics
Ecology
Computers in Earth Sciences
Nature and Landscape Conservation
title_short Remote sensing liana infestation in an aseasonal tropical forest
title_full Remote sensing liana infestation in an aseasonal tropical forest
title_fullStr Remote sensing liana infestation in an aseasonal tropical forest
title_full_unstemmed Remote sensing liana infestation in an aseasonal tropical forest
title_sort Remote sensing liana infestation in an aseasonal tropical forest
author Chandler, Chris J.
author_facet Chandler, Chris J.
van der Heijden, Geertje M.F.
Boyd, Doreen S.
Cutler, Mark E.J.
Costa, Hugo
Nilus, Reuben
Foody, Giles M.
author_role author
author2 van der Heijden, Geertje M.F.
Boyd, Doreen S.
Cutler, Mark E.J.
Costa, Hugo
Nilus, Reuben
Foody, Giles M.
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Chandler, Chris J.
van der Heijden, Geertje M.F.
Boyd, Doreen S.
Cutler, Mark E.J.
Costa, Hugo
Nilus, Reuben
Foody, Giles M.
dc.subject.por.fl_str_mv Hyperspectral imaging
liana infestation
LiDAR
neural network
pixel-based soft classification
segmentation
Ecology, Evolution, Behavior and Systematics
Ecology
Computers in Earth Sciences
Nature and Landscape Conservation
topic Hyperspectral imaging
liana infestation
LiDAR
neural network
pixel-based soft classification
segmentation
Ecology, Evolution, Behavior and Systematics
Ecology
Computers in Earth Sciences
Nature and Landscape Conservation
description Chandler, C. J., van der Heijden, G. M. F., Boyd, D. S., Cutler, M. E. J., Costa, H., Nilus, R., & Foody, G. M. (2021). Remote sensing liana infestation in an aseasonal tropical forest: addressing mismatch in spatial units of analyses. Remote Sensing in Ecology and Conservation, 7(3), 397-410. https://doi.org/10.1002/rse2.197 ---------------------------------------------------------------- The authors also thank the Natural Environment Research Council [NE/P004806/1 to MEJC, DSB, GMF, GMFvdH; NE/I528477/1 (ARSF MA14/11) to MEJC, DSB, GMF and NE/L002604/1 to CJC, GMF, GMFvdH]. The FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC) to HC. The authors also thank the University of Nottingham for an Anne McLaren Research Fellowship to GMFvdH which funded the collection of the ground data.
publishDate 2021
dc.date.none.fl_str_mv 2021-03-01T23:59:01Z
2021-09-22
2021-09-22T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/112876
url http://hdl.handle.net/10362/112876
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2056-3485
PURE: 28357986
https://doi.org/10.1002/rse2.197
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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