Detecting northern peatland vegetation patterns at ultra‐high spatial resolution
Abstract Within northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra‐high (centimeter level) to coarse scale. In addition to within‐site spatial heterogeneity, there is evident between‐site heterogeneity, but there is a lack of studies assessi...
Published in: | Remote Sensing in Ecology and Conservation |
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crwiley:10.1002/rse2.140 2024-09-30T14:40:08+00:00 Detecting northern peatland vegetation patterns at ultra‐high spatial resolution Räsänen, Aleksi Aurela, Mika Juutinen, Sari Kumpula, Timo Lohila, Annalea Penttilä, Timo Virtanen, Tarmo Horning, Ned Zhang, Jian Academy of Finland 2019 http://dx.doi.org/10.1002/rse2.140 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Frse2.140 https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.140 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/rse2.140 https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.140 en eng Wiley http://creativecommons.org/licenses/by-nc/4.0/ Remote Sensing in Ecology and Conservation volume 6, issue 4, page 457-471 ISSN 2056-3485 2056-3485 journal-article 2019 crwiley https://doi.org/10.1002/rse2.140 2024-09-17T04:46:56Z Abstract Within northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra‐high (centimeter level) to coarse scale. In addition to within‐site spatial heterogeneity, there is evident between‐site heterogeneity, but there is a lack of studies assessing whether different combinations of remotely sensed features and mapping approaches are needed in different types of landscapes. We evaluated the value of different mapping methods and remote sensing datasets and analyzed the kinds of differences present in vegetation patterns and their mappability between three northern boreal peatland landscapes in northern Finland. We utilized field‐inventoried vegetation plots together with spectral, textural, topography and vegetation height remote sensing data from 0.02‐ to 3‐m pixel size. Remote sensing data included true‐color unmanned aerial vehicle images, aerial images with four spectral bands, aerial lidar data and multiple PlanetScope satellite images. We used random forest regressions for tracking plant functional type ( PFT ) coverage, non‐metric multidimensional scaling ordination axes and fuzzy k‐medoid plant community clusters. PFT regressions had variable performance for different study sites ( R 2 −0.03 to 0.69). Spatial patterns of some spectrally or structurally distinctive PFT s could be predicted relatively well. The first ordination axis represented wetness gradient and was well predicted using remotely sensed data ( R 2 0.64 to 0.82), but the other three axes had a less straightforward explanation and lower mapping performance ( R 2 −0.09 to 0.53). Plant community clusters were predicted most accurately in the sites with clear string‐flark topography but less accurately in the flatter site ( R 2 0.16–0.82). The most important remote sensing features differed between dependent variables and study sites: different topographic, spectral and textural features; and coarse‐scale and fine‐scale datasets were the most important in different tasks. We suggest that ... Article in Journal/Newspaper Northern Finland Wiley Online Library Remote Sensing in Ecology and Conservation 6 4 457 471 |
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Open Polar |
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Wiley Online Library |
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crwiley |
language |
English |
description |
Abstract Within northern peatlands, landscape elements such as vegetation and topography are spatially heterogenic from ultra‐high (centimeter level) to coarse scale. In addition to within‐site spatial heterogeneity, there is evident between‐site heterogeneity, but there is a lack of studies assessing whether different combinations of remotely sensed features and mapping approaches are needed in different types of landscapes. We evaluated the value of different mapping methods and remote sensing datasets and analyzed the kinds of differences present in vegetation patterns and their mappability between three northern boreal peatland landscapes in northern Finland. We utilized field‐inventoried vegetation plots together with spectral, textural, topography and vegetation height remote sensing data from 0.02‐ to 3‐m pixel size. Remote sensing data included true‐color unmanned aerial vehicle images, aerial images with four spectral bands, aerial lidar data and multiple PlanetScope satellite images. We used random forest regressions for tracking plant functional type ( PFT ) coverage, non‐metric multidimensional scaling ordination axes and fuzzy k‐medoid plant community clusters. PFT regressions had variable performance for different study sites ( R 2 −0.03 to 0.69). Spatial patterns of some spectrally or structurally distinctive PFT s could be predicted relatively well. The first ordination axis represented wetness gradient and was well predicted using remotely sensed data ( R 2 0.64 to 0.82), but the other three axes had a less straightforward explanation and lower mapping performance ( R 2 −0.09 to 0.53). Plant community clusters were predicted most accurately in the sites with clear string‐flark topography but less accurately in the flatter site ( R 2 0.16–0.82). The most important remote sensing features differed between dependent variables and study sites: different topographic, spectral and textural features; and coarse‐scale and fine‐scale datasets were the most important in different tasks. We suggest that ... |
author2 |
Horning, Ned Zhang, Jian Academy of Finland |
format |
Article in Journal/Newspaper |
author |
Räsänen, Aleksi Aurela, Mika Juutinen, Sari Kumpula, Timo Lohila, Annalea Penttilä, Timo Virtanen, Tarmo |
spellingShingle |
Räsänen, Aleksi Aurela, Mika Juutinen, Sari Kumpula, Timo Lohila, Annalea Penttilä, Timo Virtanen, Tarmo Detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
author_facet |
Räsänen, Aleksi Aurela, Mika Juutinen, Sari Kumpula, Timo Lohila, Annalea Penttilä, Timo Virtanen, Tarmo |
author_sort |
Räsänen, Aleksi |
title |
Detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
title_short |
Detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
title_full |
Detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
title_fullStr |
Detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
title_full_unstemmed |
Detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
title_sort |
detecting northern peatland vegetation patterns at ultra‐high spatial resolution |
publisher |
Wiley |
publishDate |
2019 |
url |
http://dx.doi.org/10.1002/rse2.140 https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1002%2Frse2.140 https://onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.140 https://onlinelibrary.wiley.com/doi/full-xml/10.1002/rse2.140 https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.140 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_source |
Remote Sensing in Ecology and Conservation volume 6, issue 4, page 457-471 ISSN 2056-3485 2056-3485 |
op_rights |
http://creativecommons.org/licenses/by-nc/4.0/ |
op_doi |
https://doi.org/10.1002/rse2.140 |
container_title |
Remote Sensing in Ecology and Conservation |
container_volume |
6 |
container_issue |
4 |
container_start_page |
457 |
op_container_end_page |
471 |
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1811642665059483648 |