UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017 http://hdl.handle.net/11449/228411 |
Resumo: | Biodiversity is commonly referred to as species diversity but in forest ecosystems variability in structural and functional characteristics can also be treated as measures of biodiversity. Small unmanned aerial vehicles (UAVs) provide a means for characterizing forest ecosystem with high spatial resolution, permitting measuring physical characteristics of a forest ecosystem from a viewpoint of biodiversity. The objective of this study is to examine the applicability of photogrammetric point clouds and hyperspectral imaging acquired with a small UAV helicopter in mapping biodiversity indicators, such as structural complexity as well as the amount of deciduous and dead trees at plot level in southern boreal forests. Standard deviation of tree heights within a sample plot, used as a proxy for structural complexity, was the most accurately derived biodiversity indicator resulting in a mean error of 0.5 m, with a standard deviation of 0.9 m. The volume predictions for deciduous and dead trees were underestimated by 32.4 m3/ha and 1.7 m3/ha, respectively, with standard deviation of 50.2 m3/ha for deciduous and 3.2 m3/ha for dead trees. The spectral features describing brightness (i.e. higher reflectance values) were prevailing in feature selection but several wavelengths were represented. Thus, it can be concluded that structural complexity can be predicted reliably but at the same time can be expected to be underestimated with photogrammetric point clouds obtained with a small UAV. Additionally, plot-level volume of dead trees can be predicted with small mean error whereas identifying deciduous species was more challenging at plot level. |
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UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forestsForest EcologyForest InventoryForest MensurationPhotogrammetryRemote SensingSpectral ImagingUASBiodiversity is commonly referred to as species diversity but in forest ecosystems variability in structural and functional characteristics can also be treated as measures of biodiversity. Small unmanned aerial vehicles (UAVs) provide a means for characterizing forest ecosystem with high spatial resolution, permitting measuring physical characteristics of a forest ecosystem from a viewpoint of biodiversity. The objective of this study is to examine the applicability of photogrammetric point clouds and hyperspectral imaging acquired with a small UAV helicopter in mapping biodiversity indicators, such as structural complexity as well as the amount of deciduous and dead trees at plot level in southern boreal forests. Standard deviation of tree heights within a sample plot, used as a proxy for structural complexity, was the most accurately derived biodiversity indicator resulting in a mean error of 0.5 m, with a standard deviation of 0.9 m. The volume predictions for deciduous and dead trees were underestimated by 32.4 m3/ha and 1.7 m3/ha, respectively, with standard deviation of 50.2 m3/ha for deciduous and 3.2 m3/ha for dead trees. The spectral features describing brightness (i.e. higher reflectance values) were prevailing in feature selection but several wavelengths were represented. Thus, it can be concluded that structural complexity can be predicted reliably but at the same time can be expected to be underestimated with photogrammetric point clouds obtained with a small UAV. Additionally, plot-level volume of dead trees can be predicted with small mean error whereas identifying deciduous species was more challenging at plot level.Dept. of Forest Sciences University of Helsinki, P.O. Box 27Dept. of Remote Sensing and Photogrammetry Finnish Geospatial Research Institute FGI National Land Survey, Geodeetinrinne 2Pacific Forestry Centre National Resources Canada, 506 West Burnside RoadDept. of Cartography São Paulo State University, Roberto Simonsen 305Catarinense Federal Institute, Rodovia Duque de Caxias - km 6 - s/nDept. of Geography São Paulo State University, Roberto Simonsen 305Centre of Excellence in Laser Scanning Research Finnish Geospatial Research Institute FGI National Land Survey of FinlandDept. of Cartography São Paulo State University, Roberto Simonsen 305Dept. of Geography São Paulo State University, Roberto Simonsen 305University of HelsinkiNational Land SurveyNational Resources CanadaUniversidade Estadual Paulista (UNESP)Catarinense Federal InstituteNational Land Survey of FinlandSaarinen, N.Vastaranta, M.Näsi, R.Rosnell, T.Hakala, T.Honkavaara, E.Wulder, M. A.Luoma, V.Tommaselli, A. M.G. [UNESP]Imai, N. N. [UNESP]Ribeiro, E. A.W.Guimarães, R. B. [UNESP]Holopainen, M.Hyyppä, J.2022-04-29T08:14:40Z2022-04-29T08:14:40Z2017-10-19info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject171-175http://dx.doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v. 42, n. 3W3, p. 171-175, 2017.1682-1750http://hdl.handle.net/11449/22841110.5194/isprs-archives-XLII-3-W3-171-20172-s2.0-85033667269Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archivesinfo:eu-repo/semantics/openAccess2024-06-18T15:02:08Zoai:repositorio.unesp.br:11449/228411Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:31:41.086490Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
title |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
spellingShingle |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests Saarinen, N. Forest Ecology Forest Inventory Forest Mensuration Photogrammetry Remote Sensing Spectral Imaging UAS |
title_short |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
title_full |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
title_fullStr |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
title_full_unstemmed |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
title_sort |
UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests |
author |
Saarinen, N. |
author_facet |
Saarinen, N. Vastaranta, M. Näsi, R. Rosnell, T. Hakala, T. Honkavaara, E. Wulder, M. A. Luoma, V. Tommaselli, A. M.G. [UNESP] Imai, N. N. [UNESP] Ribeiro, E. A.W. Guimarães, R. B. [UNESP] Holopainen, M. Hyyppä, J. |
author_role |
author |
author2 |
Vastaranta, M. Näsi, R. Rosnell, T. Hakala, T. Honkavaara, E. Wulder, M. A. Luoma, V. Tommaselli, A. M.G. [UNESP] Imai, N. N. [UNESP] Ribeiro, E. A.W. Guimarães, R. B. [UNESP] Holopainen, M. Hyyppä, J. |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
University of Helsinki National Land Survey National Resources Canada Universidade Estadual Paulista (UNESP) Catarinense Federal Institute National Land Survey of Finland |
dc.contributor.author.fl_str_mv |
Saarinen, N. Vastaranta, M. Näsi, R. Rosnell, T. Hakala, T. Honkavaara, E. Wulder, M. A. Luoma, V. Tommaselli, A. M.G. [UNESP] Imai, N. N. [UNESP] Ribeiro, E. A.W. Guimarães, R. B. [UNESP] Holopainen, M. Hyyppä, J. |
dc.subject.por.fl_str_mv |
Forest Ecology Forest Inventory Forest Mensuration Photogrammetry Remote Sensing Spectral Imaging UAS |
topic |
Forest Ecology Forest Inventory Forest Mensuration Photogrammetry Remote Sensing Spectral Imaging UAS |
description |
Biodiversity is commonly referred to as species diversity but in forest ecosystems variability in structural and functional characteristics can also be treated as measures of biodiversity. Small unmanned aerial vehicles (UAVs) provide a means for characterizing forest ecosystem with high spatial resolution, permitting measuring physical characteristics of a forest ecosystem from a viewpoint of biodiversity. The objective of this study is to examine the applicability of photogrammetric point clouds and hyperspectral imaging acquired with a small UAV helicopter in mapping biodiversity indicators, such as structural complexity as well as the amount of deciduous and dead trees at plot level in southern boreal forests. Standard deviation of tree heights within a sample plot, used as a proxy for structural complexity, was the most accurately derived biodiversity indicator resulting in a mean error of 0.5 m, with a standard deviation of 0.9 m. The volume predictions for deciduous and dead trees were underestimated by 32.4 m3/ha and 1.7 m3/ha, respectively, with standard deviation of 50.2 m3/ha for deciduous and 3.2 m3/ha for dead trees. The spectral features describing brightness (i.e. higher reflectance values) were prevailing in feature selection but several wavelengths were represented. Thus, it can be concluded that structural complexity can be predicted reliably but at the same time can be expected to be underestimated with photogrammetric point clouds obtained with a small UAV. Additionally, plot-level volume of dead trees can be predicted with small mean error whereas identifying deciduous species was more challenging at plot level. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-10-19 2022-04-29T08:14:40Z 2022-04-29T08:14:40Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017 International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v. 42, n. 3W3, p. 171-175, 2017. 1682-1750 http://hdl.handle.net/11449/228411 10.5194/isprs-archives-XLII-3-W3-171-2017 2-s2.0-85033667269 |
url |
http://dx.doi.org/10.5194/isprs-archives-XLII-3-W3-171-2017 http://hdl.handle.net/11449/228411 |
identifier_str_mv |
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, v. 42, n. 3W3, p. 171-175, 2017. 1682-1750 10.5194/isprs-archives-XLII-3-W3-171-2017 2-s2.0-85033667269 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
171-175 |
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Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
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UNESP |
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UNESP |
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Repositório Institucional da UNESP |
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Repositório Institucional da UNESP |
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Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
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1808128526673510400 |