Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds
Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness...
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Online Access: | https://epic.awi.de/id/eprint/52084/ https://epic.awi.de/id/eprint/52084/2/Briegeretal2019AdvancesDerivationNortheastSiberianForestMetricsUsingUAVPhotogrammetric.pdf https://doi.org/10.3390/rs11121447 https://hdl.handle.net/10013/epic.7aa1967c-d366-4f8e-b601-9215d659606c |
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ftawi:oai:epic.awi.de:52084 2024-09-15T18:02:04+00:00 Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds Brieger, Frederic Herzschuh, Ulrike Pestryakova, Luidmila A. Bookhagen, Bodo Zakharov, Evgenii S. Kruse, Stefan 2019 application/pdf https://epic.awi.de/id/eprint/52084/ https://epic.awi.de/id/eprint/52084/2/Briegeretal2019AdvancesDerivationNortheastSiberianForestMetricsUsingUAVPhotogrammetric.pdf https://doi.org/10.3390/rs11121447 https://hdl.handle.net/10013/epic.7aa1967c-d366-4f8e-b601-9215d659606c unknown https://epic.awi.de/id/eprint/52084/2/Briegeretal2019AdvancesDerivationNortheastSiberianForestMetricsUsingUAVPhotogrammetric.pdf Brieger, F. , Herzschuh, U. orcid:0000-0003-0999-1261 , Pestryakova, L. A. , Bookhagen, B. , Zakharov, E. S. and Kruse, S. orcid:0000-0003-1107-1958 (2019) Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds , Remote Sensing, 11 (12), p. 1447 . doi:10.3390/rs11121447 <https://doi.org/10.3390/rs11121447> , hdl:10013/epic.7aa1967c-d366-4f8e-b601-9215d659606c info:eu-repo/semantics/openAccess EPIC3Remote Sensing, 11(12), pp. 1447, ISSN: 2072-4292 Article isiRev info:eu-repo/semantics/article 2019 ftawi https://doi.org/10.3390/rs11121447 2024-06-24T04:24:41Z Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure. Article in Journal/Newspaper Chukotka taiga Tundra Yakutia Siberia Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Remote Sensing 11 12 1447 |
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Open Polar |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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ftawi |
language |
unknown |
description |
Forest structure is a crucial component in the assessment of whether a forest is likely to act as a carbon sink under changing climate. Detailed 3D structural information about the tundra–taiga ecotone of Siberia is mostly missing and still underrepresented in current research due to the remoteness and restricted accessibility. Field based, high-resolution remote sensing can provide important knowledge for the understanding of vegetation properties and dynamics. In this study, we test the applicability of consumer-grade Unmanned Aerial Vehicles (UAVs) for rapid calculation of stand metrics in treeline forests. We reconstructed high-resolution photogrammetric point clouds and derived canopy height models for 10 study sites from NE Chukotka and SW Yakutia. Subsequently, we detected individual tree tops using a variable-window size local maximum filter and applied a marker-controlled watershed segmentation for the delineation of tree crowns. With this, we successfully detected 67.1% of the validation individuals. Simple linear regressions of observed and detected metrics show a better correlation (R2) and lower relative root mean square percentage error (RMSE%) for tree heights (mean R2 = 0.77, mean RMSE% = 18.46%) than for crown diameters (mean R2 = 0.46, mean RMSE% = 24.9%). The comparison between detected and observed tree height distributions revealed that our tree detection method was unable to representatively identify trees <2 m. Our results show that plot sizes for vegetation surveys in the tundra–taiga ecotone should be adapted to the forest structure and have a radius of >15–20 m to capture homogeneous and representative forest stands. Additionally, we identify sources of omission and commission errors and give recommendations for their mitigation. In summary, the efficiency of the used method depends on the complexity of the forest’s stand structure. |
format |
Article in Journal/Newspaper |
author |
Brieger, Frederic Herzschuh, Ulrike Pestryakova, Luidmila A. Bookhagen, Bodo Zakharov, Evgenii S. Kruse, Stefan |
spellingShingle |
Brieger, Frederic Herzschuh, Ulrike Pestryakova, Luidmila A. Bookhagen, Bodo Zakharov, Evgenii S. Kruse, Stefan Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
author_facet |
Brieger, Frederic Herzschuh, Ulrike Pestryakova, Luidmila A. Bookhagen, Bodo Zakharov, Evgenii S. Kruse, Stefan |
author_sort |
Brieger, Frederic |
title |
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
title_short |
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
title_full |
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
title_fullStr |
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
title_full_unstemmed |
Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
title_sort |
advances in the derivation of northeast siberian forest metrics using high-resolution uav-based photogrammetric point clouds |
publishDate |
2019 |
url |
https://epic.awi.de/id/eprint/52084/ https://epic.awi.de/id/eprint/52084/2/Briegeretal2019AdvancesDerivationNortheastSiberianForestMetricsUsingUAVPhotogrammetric.pdf https://doi.org/10.3390/rs11121447 https://hdl.handle.net/10013/epic.7aa1967c-d366-4f8e-b601-9215d659606c |
genre |
Chukotka taiga Tundra Yakutia Siberia |
genre_facet |
Chukotka taiga Tundra Yakutia Siberia |
op_source |
EPIC3Remote Sensing, 11(12), pp. 1447, ISSN: 2072-4292 |
op_relation |
https://epic.awi.de/id/eprint/52084/2/Briegeretal2019AdvancesDerivationNortheastSiberianForestMetricsUsingUAVPhotogrammetric.pdf Brieger, F. , Herzschuh, U. orcid:0000-0003-0999-1261 , Pestryakova, L. A. , Bookhagen, B. , Zakharov, E. S. and Kruse, S. orcid:0000-0003-1107-1958 (2019) Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds , Remote Sensing, 11 (12), p. 1447 . doi:10.3390/rs11121447 <https://doi.org/10.3390/rs11121447> , hdl:10013/epic.7aa1967c-d366-4f8e-b601-9215d659606c |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.3390/rs11121447 |
container_title |
Remote Sensing |
container_volume |
11 |
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12 |
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1447 |
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1810439173438439424 |