Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing
There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hy...
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ftdatacite:10.6084/m9.figshare.13110169 2023-05-15T17:42:57+02:00 Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing Räsänen, Aleksi Juutinen, Sari Kalacska, Margaret Aurela, Mika Heikkinen, Pauli Mäenpää, Kari Rimali, Aleksi Virtanen, Tarmo 2020 https://dx.doi.org/10.6084/m9.figshare.13110169 https://tandf.figshare.com/articles/journal_contribution/Peatland_leaf-area_index_and_biomass_estimation_with_ultra-high_resolution_remote_sensing/13110169 unknown Taylor & Francis https://dx.doi.org/10.1080/15481603.2020.1829377 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Ecology FOS Biological sciences 20199 Astronomical and Space Sciences not elsewhere classified FOS Physical sciences 69999 Biological Sciences not elsewhere classified 80699 Information Systems not elsewhere classified FOS Computer and information sciences Inorganic Chemistry Plant Biology Text article-journal Journal contribution ScholarlyArticle 2020 ftdatacite https://doi.org/10.6084/m9.figshare.13110169 https://doi.org/10.1080/15481603.2020.1829377 2021-11-05T12:55:41Z There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500–900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3–61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between −14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits. Text Northern Finland DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
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unknown |
topic |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Ecology FOS Biological sciences 20199 Astronomical and Space Sciences not elsewhere classified FOS Physical sciences 69999 Biological Sciences not elsewhere classified 80699 Information Systems not elsewhere classified FOS Computer and information sciences Inorganic Chemistry Plant Biology |
spellingShingle |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Ecology FOS Biological sciences 20199 Astronomical and Space Sciences not elsewhere classified FOS Physical sciences 69999 Biological Sciences not elsewhere classified 80699 Information Systems not elsewhere classified FOS Computer and information sciences Inorganic Chemistry Plant Biology Räsänen, Aleksi Juutinen, Sari Kalacska, Margaret Aurela, Mika Heikkinen, Pauli Mäenpää, Kari Rimali, Aleksi Virtanen, Tarmo Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
topic_facet |
59999 Environmental Sciences not elsewhere classified FOS Earth and related environmental sciences 39999 Chemical Sciences not elsewhere classified FOS Chemical sciences Ecology FOS Biological sciences 20199 Astronomical and Space Sciences not elsewhere classified FOS Physical sciences 69999 Biological Sciences not elsewhere classified 80699 Information Systems not elsewhere classified FOS Computer and information sciences Inorganic Chemistry Plant Biology |
description |
There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500–900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3–61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between −14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits. |
format |
Text |
author |
Räsänen, Aleksi Juutinen, Sari Kalacska, Margaret Aurela, Mika Heikkinen, Pauli Mäenpää, Kari Rimali, Aleksi Virtanen, Tarmo |
author_facet |
Räsänen, Aleksi Juutinen, Sari Kalacska, Margaret Aurela, Mika Heikkinen, Pauli Mäenpää, Kari Rimali, Aleksi Virtanen, Tarmo |
author_sort |
Räsänen, Aleksi |
title |
Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
title_short |
Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
title_full |
Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
title_fullStr |
Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
title_full_unstemmed |
Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
title_sort |
peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing |
publisher |
Taylor & Francis |
publishDate |
2020 |
url |
https://dx.doi.org/10.6084/m9.figshare.13110169 https://tandf.figshare.com/articles/journal_contribution/Peatland_leaf-area_index_and_biomass_estimation_with_ultra-high_resolution_remote_sensing/13110169 |
genre |
Northern Finland |
genre_facet |
Northern Finland |
op_relation |
https://dx.doi.org/10.1080/15481603.2020.1829377 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.13110169 https://doi.org/10.1080/15481603.2020.1829377 |
_version_ |
1766144898146238464 |