Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds
<i>Research Highlights:</i> This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenol...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/11/9/924 |
| _version_ | 1797555959992680448 |
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| author | Guillermo Castilla Michelle Filiatrault Gregory J. McDermid Michael Gartrell |
| author_facet | Guillermo Castilla Michelle Filiatrault Gregory J. McDermid Michael Gartrell |
| author_sort | Guillermo Castilla |
| collection | DOAJ |
| description | <i>Research Highlights:</i> This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. <i>Background and Objectives:</i> Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. <i>Materials and Methods:</i> We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. <i>Results:</i> The best result (root mean square error (RMSE) = 24 cm; bias = −11 cm; <i>R</i><sup>2</sup> = 0.63; <i>n</i> = 48) was achieved for seedlings >30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings ≤30 cm were unreliable (nil <i>R</i><sup>2</sup>). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. <i>Conclusions:</i> Millimetric (GSD <1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm. |
| first_indexed | 2024-03-10T16:55:05Z |
| format | Article |
| id | doaj.art-8a8b191415e54c81b8c575320d2b4260 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T16:55:05Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-8a8b191415e54c81b8c575320d2b42602023-11-20T11:10:41ZengMDPI AGForests1999-49072020-08-0111992410.3390/f11090924Estimating Individual Conifer Seedling Height Using Drone-Based Image Point CloudsGuillermo Castilla0Michelle Filiatrault1Gregory J. McDermid2Michael Gartrell3Canadian Forest Service, Natural Resources Canada, 5320 122 Street Northwest, Edmonton, AB T6H 3S5, CanadaCanadian Forest Service, Natural Resources Canada, 5320 122 Street Northwest, Edmonton, AB T6H 3S5, CanadaDepartment of Geography, University of Calgary, Calgary, AB T2N 1N4, CanadaCanadian Forest Service, Natural Resources Canada, 5320 122 Street Northwest, Edmonton, AB T6H 3S5, Canada<i>Research Highlights:</i> This is the most comprehensive analysis to date of the accuracy of height estimates for individual conifer seedlings derived from drone-based image point clouds (DIPCs). We provide insights into the effects on accuracy of ground sampling distance (GSD), phenology, ground determination method, seedling size, and more. <i>Background and Objectives:</i> Regeneration success in disturbed forests involves costly ground surveys of tree seedlings exceeding a minimum height. Here we assess the accuracy with which conifer seedling height can be estimated using drones, and how height errors translate into counting errors in stocking surveys. <i>Materials and Methods:</i> We compared height estimates derived from DIPCs of different GSD (0.35 cm, 0.75 cm, and 3 cm), phenological state (leaf-on and leaf-off), and ground determination method (based on either the DIPC itself or an ancillary digital terrain model). Each set of height estimates came from data acquired in up to three linear disturbances in the boreal forest of Alberta, Canada, and included 22 to 189 surveyed seedlings, which were split into two height strata to assess two survey scenarios. <i>Results:</i> The best result (root mean square error (RMSE) = 24 cm; bias = −11 cm; <i>R</i><sup>2</sup> = 0.63; <i>n</i> = 48) was achieved for seedlings >30 cm with 0.35 cm GSD in leaf-off conditions and ground elevation from the DIPC. The second-best result had the same GSD and ground method but was leaf-on and not significantly different from the first. Results for seedlings ≤30 cm were unreliable (nil <i>R</i><sup>2</sup>). Height estimates derived from manual softcopy interpretation were similar to the corresponding DIPC results. Height estimation errors hardly affected seedling counting errors (best balance was 8% omission and 6% commission). Accuracy and correlation were stronger at finer GSDs and improved with seedling size. <i>Conclusions:</i> Millimetric (GSD <1 cm) DIPC can be used for estimating the height of individual conifer seedlings taller than 30 cm.https://www.mdpi.com/1999-4907/11/9/924drone-based image point clouds (DIPC)Unmanned Aerial Vehichles (UAV)photogrammetryforest monitoringforest inventoryrestoration |
| spellingShingle | Guillermo Castilla Michelle Filiatrault Gregory J. McDermid Michael Gartrell Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds Forests drone-based image point clouds (DIPC) Unmanned Aerial Vehichles (UAV) photogrammetry forest monitoring forest inventory restoration |
| title | Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds |
| title_full | Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds |
| title_fullStr | Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds |
| title_full_unstemmed | Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds |
| title_short | Estimating Individual Conifer Seedling Height Using Drone-Based Image Point Clouds |
| title_sort | estimating individual conifer seedling height using drone based image point clouds |
| topic | drone-based image point clouds (DIPC) Unmanned Aerial Vehichles (UAV) photogrammetry forest monitoring forest inventory restoration |
| url | https://www.mdpi.com/1999-4907/11/9/924 |
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