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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ftmdpi:oai:mdpi.com:/2072-4292/11/12/1447/ 2023-08-20T04:05:56+02:00 Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds Frederic Brieger Ulrike Herzschuh Luidmila A. Pestryakova Bodo Bookhagen Evgenii S. Zakharov Stefan Kruse agris 2019-06-18 application/pdf https://doi.org/10.3390/rs11121447 EN eng Multidisciplinary Digital Publishing Institute Forest Remote Sensing https://dx.doi.org/10.3390/rs11121447 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 11; Issue 12; Pages: 1447 UAV photogrammetry remote sensing structure from motion tundra–taiga ecotone point cloud forest structure Text 2019 ftmdpi https://doi.org/10.3390/rs11121447 2023-07-31T22:22:04Z 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. Text Chukotka taiga Tundra Yakutia Siberia MDPI Open Access Publishing Remote Sensing 11 12 1447 |
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Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
UAV photogrammetry remote sensing structure from motion tundra–taiga ecotone point cloud forest structure |
spellingShingle |
UAV photogrammetry remote sensing structure from motion tundra–taiga ecotone point cloud forest structure Frederic Brieger Ulrike Herzschuh Luidmila A. Pestryakova Bodo Bookhagen Evgenii S. Zakharov Stefan Kruse Advances in the Derivation of Northeast Siberian Forest Metrics Using High-Resolution UAV-Based Photogrammetric Point Clouds |
topic_facet |
UAV photogrammetry remote sensing structure from motion tundra–taiga ecotone point cloud forest structure |
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 |
Text |
author |
Frederic Brieger Ulrike Herzschuh Luidmila A. Pestryakova Bodo Bookhagen Evgenii S. Zakharov Stefan Kruse |
author_facet |
Frederic Brieger Ulrike Herzschuh Luidmila A. Pestryakova Bodo Bookhagen Evgenii S. Zakharov Stefan Kruse |
author_sort |
Frederic Brieger |
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 |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/rs11121447 |
op_coverage |
agris |
genre |
Chukotka taiga Tundra Yakutia Siberia |
genre_facet |
Chukotka taiga Tundra Yakutia Siberia |
op_source |
Remote Sensing; Volume 11; Issue 12; Pages: 1447 |
op_relation |
Forest Remote Sensing https://dx.doi.org/10.3390/rs11121447 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs11121447 |
container_title |
Remote Sensing |
container_volume |
11 |
container_issue |
12 |
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1447 |
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1774716764574187520 |