Optimizing aerial imagery collection and processing parameters for drone‐based individual tree mapping in structurally complex conifer forests
Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost equipment and minimal ground‐based data collection. One such method involves collecting many partially overlapping aerial photos, processing...
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| Vydáno v: | Methods in ecology and evolution Ročník 13; číslo 7; s. 1447 - 1463 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London
John Wiley & Sons, Inc
01.07.2022
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| ISSN: | 2041-210X, 2041-210X |
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| Abstract | Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost equipment and minimal ground‐based data collection. One such method involves collecting many partially overlapping aerial photos, processing them using ‘structure from motion’ (SfM) photogrammetry to create a digital 3D representation and using the 3D model to detect individual trees. SfM‐based forest mapping involves myriad decisions surrounding methods and parameters for imagery acquisition and processing, but it is unclear how these individual decisions or their combinations impact the quality of the resulting forest inventories.
We collected and processed drone imagery of a moderate‐density, structurally complex mixed‐conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch and image overlap), 12 imagery processing parameterizations (image resolutions and depth map filtering intensities) and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23‐ha ground reference map of 1,775 trees >5 m tall that we created using traditional field survey methods.
The accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image‐to‐image overlap, photogrammetrically processing images into a canopy height model (CHM) with a twofold upscaling (coarsening) step and detecting trees from the CHM using a variable window filter after applying a moving window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy exceeding expectations for structurally complex forests (for canopy‐dominant trees >10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely measured tree heights corresponded to ground‐measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understorey trees and would likely be higher in less dense and less structurally complex stands.
Our results may guide others wishing to efficiently produce broad‐extent individual tree maps of conifer forests without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and informing forest management. |
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| AbstractList | Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost equipment and minimal ground‐based data collection. One such method involves collecting many partially overlapping aerial photos, processing them using ‘structure from motion’ (SfM) photogrammetry to create a digital 3D representation and using the 3D model to detect individual trees. SfM‐based forest mapping involves myriad decisions surrounding methods and parameters for imagery acquisition and processing, but it is unclear how these individual decisions or their combinations impact the quality of the resulting forest inventories.We collected and processed drone imagery of a moderate‐density, structurally complex mixed‐conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch and image overlap), 12 imagery processing parameterizations (image resolutions and depth map filtering intensities) and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23‐ha ground reference map of 1,775 trees >5 m tall that we created using traditional field survey methods.The accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image‐to‐image overlap, photogrammetrically processing images into a canopy height model (CHM) with a twofold upscaling (coarsening) step and detecting trees from the CHM using a variable window filter after applying a moving window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy exceeding expectations for structurally complex forests (for canopy‐dominant trees >10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely measured tree heights corresponded to ground‐measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understorey trees and would likely be higher in less dense and less structurally complex stands.Our results may guide others wishing to efficiently produce broad‐extent individual tree maps of conifer forests without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and informing forest management. Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost equipment and minimal ground‐based data collection. One such method involves collecting many partially overlapping aerial photos, processing them using ‘structure from motion’ (SfM) photogrammetry to create a digital 3D representation and using the 3D model to detect individual trees. SfM‐based forest mapping involves myriad decisions surrounding methods and parameters for imagery acquisition and processing, but it is unclear how these individual decisions or their combinations impact the quality of the resulting forest inventories. We collected and processed drone imagery of a moderate‐density, structurally complex mixed‐conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch and image overlap), 12 imagery processing parameterizations (image resolutions and depth map filtering intensities) and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23‐ha ground reference map of 1,775 trees >5 m tall that we created using traditional field survey methods. The accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image‐to‐image overlap, photogrammetrically processing images into a canopy height model (CHM) with a twofold upscaling (coarsening) step and detecting trees from the CHM using a variable window filter after applying a moving window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy exceeding expectations for structurally complex forests (for canopy‐dominant trees >10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely measured tree heights corresponded to ground‐measured heights with R2 = 0.95. Accuracy was higher for taller trees and lower for understorey trees and would likely be higher in less dense and less structurally complex stands. Our results may guide others wishing to efficiently produce broad‐extent individual tree maps of conifer forests without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and informing forest management. Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost equipment and minimal ground‐based data collection. One such method involves collecting many partially overlapping aerial photos, processing them using ‘structure from motion’ (SfM) photogrammetry to create a digital 3D representation and using the 3D model to detect individual trees. SfM‐based forest mapping involves myriad decisions surrounding methods and parameters for imagery acquisition and processing, but it is unclear how these individual decisions or their combinations impact the quality of the resulting forest inventories. We collected and processed drone imagery of a moderate‐density, structurally complex mixed‐conifer stand. We tested 22 imagery collection methods (altering flight altitude, camera pitch and image overlap), 12 imagery processing parameterizations (image resolutions and depth map filtering intensities) and 286 tree detection methods (algorithms and their parameterizations) to create 7,568 tree maps. We compared these maps to a 3.23‐ha ground reference map of 1,775 trees >5 m tall that we created using traditional field survey methods. The accuracy of individual tree detection (ITD) and the resulting tree maps was generally maximized by collecting imagery at high altitude (120 m) with at least 90% image‐to‐image overlap, photogrammetrically processing images into a canopy height model (CHM) with a twofold upscaling (coarsening) step and detecting trees from the CHM using a variable window filter after applying a moving window mean smooth to the CHM. Using this combination of methods, we mapped trees with an accuracy exceeding expectations for structurally complex forests (for canopy‐dominant trees >10 m tall, sensitivity = 0.69 and precision = 0.90). Remotely measured tree heights corresponded to ground‐measured heights with R 2 = 0.95. Accuracy was higher for taller trees and lower for understorey trees and would likely be higher in less dense and less structurally complex stands. Our results may guide others wishing to efficiently produce broad‐extent individual tree maps of conifer forests without investing substantial time tailoring imagery acquisition and processing parameters. The resulting tree maps create opportunities for addressing previously intractable ecological questions and informing forest management. |
| Author | Weeks, JonahMaria Koontz, Michael J. Young, Derek J. N. |
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| Cites_doi | 10.1080/01431161.2019.1706200 10.1007/s10712‐019‐09529‐9 10.3390/f12020250 10.2737/PSW-GTR-256 10.1016/j.jag.2018.08.017 10.1016/j.rse.2018.06.023 10.1007/s40725‐019‐00094‐3 10.7809/b‐e.00079 10.1109/LGRS.2010.2079913 10.1002/eap.2002 10.5194/isprs‐archives‐XLII‐2‐W13‐657‐2019 10.1016/j.rse.2013.04.005 10.1080/01431161.2017.1338839 10.3390/drones4020010 10.1111/ele.12322 10.3390/rs11101238 10.3390/rs11030233 10.1016/j.rse.2021.112540 10.1016/j.foreco.2017.09.019 10.1126/science.283.5401.554 10.1109/JSTARS.2018.2867945 10.7554/eLife.62922 10.1093/jofore/fvy023 10.14358/PERS.78.1.75 10.3390/f4040922 10.1139/cjfr‐2020‐0433 10.3390/rs71013895 10.1016/j.rse.2017.04.007 10.1016/j.ecoinf.2020.101061 10.1029/2005RG000183 10.32942/OSF.IO/P7YGU 10.3133/ofr20211039 10.1080/07038992.2016.1196582 10.3390/rs11030239 10.1016/j.geomorph.2012.08.021 10.3390/f8090340 10.1038/s41467‐020‐20455‐y 10.2307/1943577 10.1093/jofore/fvab026 10.3390/f8030068 10.14358/PERS.70.5.589 10.1016/j.rse.2012.01.020 10.1007/s11056‐019‐09754‐5 10.1002/rse2.137 10.1002/ecs2.2594 10.3390/rs13214292 10.1007/s11119‐017‐9502‐0 10.3390/rs10081266 10.1109/JSTARS.2019.2942811 10.3390/rs10060912 10.1038/s41586‐020‐2824‐5 10.3390/rs11111263 10.1890/09‐2335.1 |
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| References | 2017; 8 2012; 121 2013; 4 2019; 11 2019; 10 2019; 12 1999; 283 2017; 195 2020; 56 2017; 405 2020; 6 2020; 4 2004; 70 2019; XLII‐2/W13 2012; 179 2018; 215 2020; 51 2017; 38 2021; 119 2016; 42 2018; 73 2014; 17 2019; 5 2020; 41 2021b 2021a 2021; 263 2020; 1–5 2012; 78 2021; 51 2015; 7 2011; 8 2021; 13 2018; 19 2021; 10 2019; 40 2021; 12 2018; 116 2020; 30 2021 2020 2013; 136 2018 2017 1956; 26 2016 2018; 11 2010; 91 2018; 10 2012; 4 2007; 45 e_1_2_9_31_1 e_1_2_9_52_1 e_1_2_9_10_1 e_1_2_9_35_1 e_1_2_9_56_1 e_1_2_9_12_1 e_1_2_9_33_1 e_1_2_9_54_1 e_1_2_9_14_1 e_1_2_9_39_1 e_1_2_9_16_1 e_1_2_9_37_1 e_1_2_9_58_1 USGS (e_1_2_9_50_1) 2017 e_1_2_9_18_1 e_1_2_9_41_1 e_1_2_9_20_1 e_1_2_9_62_1 e_1_2_9_22_1 e_1_2_9_45_1 e_1_2_9_24_1 e_1_2_9_43_1 e_1_2_9_8_1 e_1_2_9_6_1 e_1_2_9_4_1 e_1_2_9_60_1 e_1_2_9_2_1 e_1_2_9_26_1 e_1_2_9_49_1 e_1_2_9_28_1 e_1_2_9_47_1 e_1_2_9_30_1 e_1_2_9_53_1 e_1_2_9_51_1 e_1_2_9_11_1 e_1_2_9_34_1 e_1_2_9_57_1 e_1_2_9_13_1 e_1_2_9_32_1 e_1_2_9_55_1 e_1_2_9_15_1 e_1_2_9_38_1 e_1_2_9_17_1 e_1_2_9_36_1 e_1_2_9_59_1 e_1_2_9_19_1 e_1_2_9_42_1 e_1_2_9_40_1 e_1_2_9_61_1 e_1_2_9_21_1 e_1_2_9_46_1 e_1_2_9_23_1 e_1_2_9_44_1 e_1_2_9_7_1 e_1_2_9_5_1 e_1_2_9_3_1 e_1_2_9_9_1 e_1_2_9_25_1 e_1_2_9_27_1 e_1_2_9_48_1 e_1_2_9_29_1 |
| References_xml | – volume: 11 start-page: 1238 issue: 10 year: 2019 article-title: Object‐based land cover classification of Cork oak woodlands using UAV imagery and Orfeo ToolBox publication-title: Remote Sensing – volume: 10 year: 2021 article-title: A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network publication-title: eLife – volume: XLII‐2/W13 start-page: 657 year: 2019 end-page: 663 article-title: Individual tree detection from UAV LiDAR data in a mixed species woodland publication-title: ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences – volume: 13 start-page: 4292 issue: 21 year: 2021 article-title: Comparison of low‐cost commercial unpiloted digital aerial photogrammetry to airborne laser scanning across multiple Forest types in California, USA publication-title: Remote Sensing – volume: 5 start-page: 155 issue: 3 year: 2019 end-page: 168 article-title: Structure from motion photogrammetry in forestry: A review publication-title: Current Forestry Reports – volume: 11 start-page: 3578 issue: 10 year: 2018 end-page: 3589 article-title: Mapping three‐dimensional structures of Forest canopy using UAV stereo imagery: Evaluating impacts of forward overlaps and image resolutions with LiDAR data as reference publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing – year: 2021 – volume: 19 start-page: 115 issue: 1 year: 2018 end-page: 133 article-title: Assessing UAV‐collected image overlap influence on computation time and digital surface model accuracy in olive orchards publication-title: Precision Agriculture – volume: 12 start-page: 4131 issue: 10 year: 2019 end-page: 4148 article-title: Fine‐scale spatial and spectral clustering of UAV‐Acquired Digital Aerial Photogrammetric (DAP) point clouds for individual tree crown detection and segmentation publication-title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing – volume: 215 start-page: 330 year: 2018 end-page: 342 article-title: Quantifying understory vegetation density using small‐footprint airborne lidar publication-title: Remote Sensing of Environment – volume: 7 start-page: 13895 issue: 10 year: 2015 end-page: 13920 article-title: Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure publication-title: Remote Sensing – year: 2018 – volume: 4 start-page: 10 issue: 2 year: 2020 article-title: Individual tree crown segmentation in two‐layered dense mixed forests from UAV LiDAR data publication-title: Drones – volume: 51 start-page: 573 issue: 4 year: 2020 end-page: 596 article-title: Monitoring forest structure to guide adaptive management of forest restoration: A review of remote sensing approaches publication-title: New Forests – volume: 40 start-page: 959 issue: 4 year: 2019 end-page: 977 article-title: New opportunities for forest remote sensing through ultra‐high‐density drone lidar publication-title: Surveys in Geophysics – volume: 4 start-page: 225 year: 2012 end-page: 231 article-title: Forest inventory and analysis database of the United States of America (FIA) publication-title: Biodiversity and Ecology – volume: 17 start-page: 1158 issue: 9 year: 2014 end-page: 1167 article-title: The relationship between tree biodiversity and biomass dynamics changes with tropical forest succession publication-title: Ecology Letters – volume: 51 start-page: 1093 year: 2021 end-page: 1105 article-title: Potential for individual tree monitoring in ponderosa pine‐dominated forests using unmanned aerial system structure from motion point clouds publication-title: Canadian Journal of Forest Research – volume: 116 start-page: 336 issue: 4 year: 2018 end-page: 346 article-title: Applying LiDAR individual tree detection to management of structurally diverse forest landscapes publication-title: Journal of Forestry – volume: 11 start-page: 239 issue: 3 year: 2019 article-title: Enhancing UAV–SfM 3D model accuracy in high‐relief landscapes by incorporating oblique images publication-title: Remote Sensing – volume: 10 issue: 2 year: 2019 article-title: Wildfire activity and land use drove 20th‐century changes in forest cover in the Colorado front range publication-title: Ecosphere – volume: 38 start-page: 5310 issue: 19 year: 2017 end-page: 5337 article-title: Comparison of UAS photogrammetric products for tree detection and characterization of coniferous stands publication-title: International Journal of Remote Sensing – volume: 8 start-page: 340 issue: 9 year: 2017 article-title: Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest publication-title: Forests – volume: 8 start-page: 426 issue: 3 year: 2011 end-page: 430 article-title: Mini‐UAV‐borne LIDAR for fine‐scale mapping publication-title: IEEE Geoscience and Remote Sensing Letters – year: 2021a – volume: 91 start-page: 3664 issue: 12 year: 2010 end-page: 3674 article-title: Functional traits and the growth–mortality trade‐off in tropical trees publication-title: Ecology – volume: 10 start-page: 912 issue: 6 year: 2018 article-title: UAV photogrammetry of forests as a vulnerable process. A sensitivity analysis for a structure from motion RGB‐image pipeline publication-title: Remote Sensing – volume: 12 start-page: 250 issue: 2 year: 2021 article-title: Influence of Agisoft Metashape parameters on UAS structure from motion individual tree detection from canopy height models publication-title: Forests – volume: 136 start-page: 259 year: 2013 end-page: 276 article-title: High spatial resolution three‐dimensional mapping of vegetation spectral dynamics using computer vision publication-title: Remote Sensing of Environment – volume: 263 year: 2021 article-title: Influence of flight parameters on UAS‐based monitoring of tree height, diameter, and density publication-title: Remote Sensing of Environment – volume: 11 start-page: 1263 issue: 11 year: 2019 article-title: Mean shift segmentation assessment for individual Forest tree delineation from airborne LiDAR data publication-title: Remote Sensing – volume: 70 start-page: 589 issue: 5 year: 2004 end-page: 604 article-title: Seeing the trees in the Forest publication-title: Photogrammetric Engineering & Remote Sensing – volume: 119 start-page: 520 issue: 5 year: 2021 end-page: 544 article-title: Pyrosilviculture needed for landscape resilience of dry Western United States forests publication-title: Journal of Forestry – volume: 8 start-page: 68 issue: 3 year: 2017 article-title: Structure from motion (SfM) photogrammetry with drone data: A low cost method for monitoring greenhouse gas emissions from forests in developing countries publication-title: Forests – volume: 56 year: 2020 article-title: Cross‐site learning in deep learning RGB tree crown detection publication-title: Ecological Informatics – volume: 41 start-page: 3480 issue: 9 year: 2020 end-page: 3510 article-title: Customizing unmanned aircraft systems to reduce forest inventory costs: Can oblique images substantially improve the 3D reconstruction of the canopy? publication-title: International Journal of Remote Sensing – volume: 30 issue: 1 year: 2020 article-title: Forest recovery following extreme drought in California, USA: Natural patterns and effects of pre‐drought management publication-title: Ecological Applications – year: 2016 – volume: 121 start-page: 210 year: 2012 end-page: 223 article-title: 3‐D mapping of a multi‐layered Mediterranean forest using ALS data publication-title: Remote Sensing of Environment – volume: 11 start-page: 233 issue: 3 year: 2019 article-title: Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration publication-title: Remote Sensing – volume: 12 start-page: 129 issue: 1 year: 2021 article-title: Cross‐scale interaction of host tree size and climatic water deficit governs bark beetle‐induced tree mortality publication-title: Nature Communications – volume: 195 start-page: 30 year: 2017 end-page: 43 article-title: UAV lidar and hyperspectral fusion for forest monitoring in the southwestern USA publication-title: Remote Sensing of Environment – year: 2021b – volume: 283 start-page: 554 issue: 5401 year: 1999 end-page: 557 article-title: Light‐gap disturbances, recruitment limitation, and tree diversity in a neotropical Forest publication-title: Science – year: 2020 – volume: 4 start-page: 922 issue: 4 year: 2013 end-page: 944 article-title: A photogrammetric workflow for the creation of a Forest canopy height model from Small unmanned aerial system imagery publication-title: Forests – volume: 73 start-page: 767 year: 2018 end-page: 777 article-title: The use of fixed–wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer–broadleaf forest publication-title: International Journal of Applied Earth Observation and Geoinformation – volume: 45 start-page: 1 issue: 2 year: 2007 end-page: 33 article-title: The shuttle radar topography Mission publication-title: Reviews of Geophysics – volume: 405 start-page: 166 year: 2017 end-page: 178 article-title: Cover of tall trees best predicts California spotted owl habitat publication-title: Forest Ecology and Management – volume: 6 start-page: 181 issue: 2 year: 2020 end-page: 197 article-title: UAV‐derived estimates of forest structure to inform ponderosa pine forest restoration publication-title: Remote Sensing in Ecology and Conservation – volume: 10 start-page: 1266 issue: 8 year: 2018 article-title: Evaluating unmanned aerial vehicle images for estimating Forest canopy fuels in a ponderosa pine stand publication-title: Remote Sensing – year: 2017 – volume: 26 start-page: 1 issue: 1 year: 1956 end-page: 80 article-title: Vegetation of the Great Smoky Mountains publication-title: Ecological Monographs – volume: 179 start-page: 300 year: 2012 end-page: 314 article-title: ‘Structure‐from‐motion’ photogrammetry: A low‐cost, effective tool for geoscience applications publication-title: Geomorphology – volume: 42 start-page: 554 issue: 5 year: 2016 end-page: 573 article-title: Imputation of individual longleaf pine ( Mill.) tree attributes from field and LiDAR data publication-title: Canadian Journal of Remote Sensing – volume: 1–5 start-page: 78 year: 2020 end-page: 82 article-title: An unexpectedly large count of trees in the West African Sahara and Sahel publication-title: Nature – volume: 78 start-page: 75 issue: 1 year: 2012 end-page: 84 article-title: A new method for segmenting individual trees from the lidar point cloud publication-title: Photogrammetric Engineering & Remote Sensing – ident: e_1_2_9_12_1 doi: 10.1080/01431161.2019.1706200 – ident: e_1_2_9_21_1 doi: 10.1007/s10712‐019‐09529‐9 – ident: e_1_2_9_46_1 doi: 10.3390/f12020250 – ident: e_1_2_9_41_1 doi: 10.2737/PSW-GTR-256 – ident: e_1_2_9_19_1 doi: 10.1016/j.jag.2018.08.017 – ident: e_1_2_9_7_1 doi: 10.1016/j.rse.2018.06.023 – ident: e_1_2_9_18_1 doi: 10.1007/s40725‐019‐00094‐3 – ident: e_1_2_9_16_1 doi: 10.7809/b‐e.00079 – ident: e_1_2_9_26_1 doi: 10.1109/LGRS.2010.2079913 – ident: e_1_2_9_39_1 – ident: e_1_2_9_51_1 – ident: e_1_2_9_60_1 doi: 10.1002/eap.2002 – ident: e_1_2_9_62_1 doi: 10.5194/isprs‐archives‐XLII‐2‐W13‐657‐2019 – ident: e_1_2_9_9_1 doi: 10.1016/j.rse.2013.04.005 – ident: e_1_2_9_2_1 – ident: e_1_2_9_4_1 doi: 10.1080/01431161.2017.1338839 – ident: e_1_2_9_47_1 doi: 10.3390/drones4020010 – ident: e_1_2_9_24_1 doi: 10.1111/ele.12322 – ident: e_1_2_9_35_1 – ident: e_1_2_9_11_1 doi: 10.3390/rs11101238 – ident: e_1_2_9_37_1 doi: 10.3390/rs11030233 – ident: e_1_2_9_45_1 doi: 10.1016/j.rse.2021.112540 – ident: e_1_2_9_32_1 doi: 10.1016/j.foreco.2017.09.019 – ident: e_1_2_9_49_1 – ident: e_1_2_9_17_1 doi: 10.1126/science.283.5401.554 – ident: e_1_2_9_31_1 doi: 10.1109/JSTARS.2018.2867945 – ident: e_1_2_9_52_1 doi: 10.7554/eLife.62922 – ident: e_1_2_9_20_1 doi: 10.1093/jofore/fvy023 – ident: e_1_2_9_25_1 doi: 10.14358/PERS.78.1.75 – ident: e_1_2_9_59_1 – ident: e_1_2_9_27_1 doi: 10.3390/f4040922 – ident: e_1_2_9_8_1 doi: 10.1139/cjfr‐2020‐0433 – ident: e_1_2_9_10_1 doi: 10.3390/rs71013895 – ident: e_1_2_9_42_1 doi: 10.1016/j.rse.2017.04.007 – ident: e_1_2_9_53_1 doi: 10.1016/j.ecoinf.2020.101061 – ident: e_1_2_9_13_1 doi: 10.1029/2005RG000183 – ident: e_1_2_9_61_1 doi: 10.32942/OSF.IO/P7YGU – ident: e_1_2_9_34_1 doi: 10.3133/ofr20211039 – ident: e_1_2_9_44_1 doi: 10.1080/07038992.2016.1196582 – ident: e_1_2_9_30_1 doi: 10.3390/rs11030239 – ident: e_1_2_9_40_1 – ident: e_1_2_9_54_1 doi: 10.1016/j.geomorph.2012.08.021 – ident: e_1_2_9_29_1 doi: 10.3390/f8090340 – ident: e_1_2_9_22_1 doi: 10.1038/s41467‐020‐20455‐y – ident: e_1_2_9_55_1 doi: 10.2307/1943577 – ident: e_1_2_9_33_1 doi: 10.1093/jofore/fvab026 – ident: e_1_2_9_28_1 doi: 10.3390/f8030068 – volume-title: Unmanned aircraft systems data post‐processing: Structure‐from‐motion photogrammetry year: 2017 ident: e_1_2_9_50_1 – ident: e_1_2_9_36_1 doi: 10.14358/PERS.70.5.589 – ident: e_1_2_9_14_1 doi: 10.1016/j.rse.2012.01.020 – ident: e_1_2_9_6_1 doi: 10.1007/s11056‐019‐09754‐5 – ident: e_1_2_9_3_1 doi: 10.1002/rse2.137 – ident: e_1_2_9_38_1 doi: 10.1002/ecs2.2594 – ident: e_1_2_9_23_1 doi: 10.3390/rs13214292 – ident: e_1_2_9_48_1 doi: 10.1007/s11119‐017‐9502‐0 – ident: e_1_2_9_43_1 doi: 10.3390/rs10081266 – ident: e_1_2_9_58_1 doi: 10.1109/JSTARS.2019.2942811 – ident: e_1_2_9_15_1 doi: 10.3390/rs10060912 – ident: e_1_2_9_5_1 doi: 10.1038/s41586‐020‐2824‐5 – ident: e_1_2_9_57_1 doi: 10.3390/rs11111263 – ident: e_1_2_9_56_1 doi: 10.1890/09‐2335.1 |
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| Snippet | Recent advances in remotely piloted aerial systems (‘drones’) and imagery processing enable individual tree mapping in forests across broad areas with low‐cost... |
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| SubjectTerms | Accuracy Algorithms Altitude Canopies Coniferous forests Coniferous trees Conifers Data collection Decisions drone Drone aircraft Ecological effects Equipment costs Flight altitude Forest management Forests High altitude Image acquisition inventory Mapping Photogrammetry Process parameters remote sensing Stand structure structure from motion Three dimensional models tree Trees UAV |
| Title | Optimizing aerial imagery collection and processing parameters for drone‐based individual tree mapping in structurally complex conifer forests |
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