Articles | Volume 14, issue 6
https://doi.org/10.5194/tc-14-1919-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-14-1919-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Jun 2020
Research article |
| 15 Jun 2020
| 15 Jun 2020
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques
Centre for Hydrology, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
John W. Pomeroy
Centre for Hydrology, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
Warren D. Helgason
Centre for Hydrology, University of Saskatchewan, Saskatoon,
Saskatchewan, Canada
Department of Civil, Geological, and Environmental Engineering,
University of Saskatchewan, Saskatoon, Saskatchewan, Canada
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Cited
68 citations as recorded by crossref.
- Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion G. Gunn et al. https://doi.org/10.3389/frsen.2021.675846
- Snow Depth Retrieval With an Autonomous UAV-Mounted Software-Defined Radar S. Prager et al. https://doi.org/10.1109/TGRS.2021.3117509
- Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. https://doi.org/10.1029/2021WR029925
- Review article: using spaceborne lidar for snow depth retrievals: recent findings and utility for hydrologic applications Z. Fair et al. https://doi.org/10.5194/tc-19-5671-2025
- Mountain Roads Across the Globe Significantly Alter Local Soil Thermal Microclimates R. Lejeune et al. https://doi.org/10.1111/geb.70237
- Location Dictates Snow Aerodynamic Roughness S. Fassnacht et al. https://doi.org/10.3390/glacies1010001
- Measurement of snow depth and avalanche accumulation distribution in avalanche zones using unmanned aerial vehicle photogrammetry with post-processing kinematic positioning S. ADACHI et al. https://doi.org/10.5331/seppyo.84.5_439
- Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States J. Jacobs et al. https://doi.org/10.5194/tc-15-1485-2021
- Snow Surface Roughness across Spatio-Temporal Scales S. Fassnacht et al. https://doi.org/10.3390/w15122196
- Analyzing vegetation effects on snow depth variability in Alaska's boreal forests with airborne lidar L. May et al. https://doi.org/10.5194/tc-19-3477-2025
- Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion J. Meyer et al. https://doi.org/10.3389/feart.2022.989792
- Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions L. Meriö et al. https://doi.org/10.5194/tc-17-4363-2023
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. https://doi.org/10.5194/tc-17-3383-2023
- Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain J. Revuelto et al. https://doi.org/10.1016/j.coldregions.2021.103344
- Winters are changing: snow effects on Arctic and alpine tundra ecosystems C. Rixen et al. https://doi.org/10.1139/as-2020-0058
- An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar V. Dharmadasa et al. https://doi.org/10.3390/rs14071649
- Drone-based ground-penetrating radar (GPR) application to snow hydrology E. Valence et al. https://doi.org/10.5194/tc-16-3843-2022
- Fine-scale Antarctic grounded ice cliff 3D calving monitoring based on multi-temporal UAV photogrammetry without ground control S. Zhang et al. https://doi.org/10.1016/j.jag.2025.104620
- Mapping snow depth and spatial variability using SFM photogrammetry of UAV images over rugged mountainous regions of the Western Himalaya S. Dewali et al. https://doi.org/10.1080/10106049.2022.2127923
- Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach J. Zhang et al. https://doi.org/10.1088/1748-9326/abfe8d
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. https://doi.org/10.5194/tc-16-3269-2022
- The Role of Basin Geometry in Mountain Snowpack Responses to Climate Change J. Shea et al. https://doi.org/10.3389/frwa.2021.604275
- UAV-borne LiDAR revolutionizing groundwater level mapping S. García-López et al. https://doi.org/10.1016/j.scitotenv.2022.160272
- Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region C. Wang et al. https://doi.org/10.1088/1748-9326/ad27bb
- Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling T. Çitgez et al. https://doi.org/10.1002/hyp.15260
- Assessing UAV-based laser scanning for monitoring glacial processes and interactions at high spatial and temporal resolutions N. Baurley et al. https://doi.org/10.3389/frsen.2022.1027065
- Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning A. Hojatimalekshah et al. https://doi.org/10.5194/tc-15-2187-2021
- Assessment of DSM Based on Radiometric Transformation of UAV Data M. Chaudhry et al. https://doi.org/10.3390/s21051649
- Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment A. Rauhala et al. https://doi.org/10.5194/tc-17-4343-2023
- Advances in image-based estimation of snow variable: A systematic literature review on recent studies G. Demil et al. https://doi.org/10.1016/j.jhydrol.2025.132855
- Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack R. Palomaki & E. Sproles https://doi.org/10.1016/j.rse.2023.113744
- Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar E. Cho et al. https://doi.org/10.1016/j.jhydrol.2021.126722
- Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions E. Enderlin et al. https://doi.org/10.1016/j.rse.2022.113307
- Exploring snow distribution dynamics in steep forested slopes with UAV-borne LiDAR K. Koutantou et al. https://doi.org/10.1016/j.coldregions.2022.103587
- Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data I. Alabi et al. https://doi.org/10.3389/frsen.2024.1481848
- UAS remote sensing applications to abrupt cold region hazards M. Verfaillie et al. https://doi.org/10.3389/frsen.2023.1095275
- Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar V. Dharmadasa et al. https://doi.org/10.5194/tc-17-1225-2023
- Marginal snowpacks: The basis for a global definition and existing research needs J. López-Moreno et al. https://doi.org/10.1016/j.earscirev.2024.104751
- Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar P. Harder et al. https://doi.org/10.5194/tc-18-3277-2024
- Light and Shadow in Mapping Alpine Snowpack With Unmanned Aerial Vehicles in the Absence of Ground Control Points J. Revuelto et al. https://doi.org/10.1029/2020WR028980
- Comparing high-resolution snow mapping approaches in palsa mires: UAS lidar vs. modelling A. Störmer et al. https://doi.org/10.5194/tc-19-3949-2025
- A new interpolation method to resolve under-sampling of UAV-lidar snow depth observations in coniferous forests V. Dharmadasa et al. https://doi.org/10.1016/j.coldregions.2024.104134
- Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables A. Szeitz & R. Moore https://doi.org/10.1002/hyp.14800
- High-resolution mapping of snow depth and snow thermal insulation using low-cost UAVs in complex Arctic Urban Terrains M. Mueller et al. https://doi.org/10.1139/as-2025-0062
- Monitoring Wet-Snow Avalanche Risk in Southeastern Tibet with a UAV-Based Multi-Sensor Framework S. Ye et al. https://doi.org/10.3390/rs17223698
- Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest J. Geissler et al. https://doi.org/10.1029/2023WR034460
- Understanding wind-driven melt of patchy snow cover L. van der Valk et al. https://doi.org/10.5194/tc-16-4319-2022
- Uncovering the melt: Unmanned Aircraft System (UAS) and in-situ sensor synergies reveal dissolved organic carbon (DOC) pathways in a northern peatland P. Korhonen et al. https://doi.org/10.5194/hess-30-3019-2026
- UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo S. Skiles et al. https://doi.org/10.3389/frsen.2022.1038287
- Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar A. Pouw et al. https://doi.org/10.5194/tc-17-2367-2023
- UAV LiDAR surveys and machine learning improve snow depth and water equivalent estimates in boreal landscapes M. Ylönen et al. https://doi.org/10.5194/tc-19-4585-2025
- Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment C. Marsh et al. https://doi.org/10.1088/2515-7620/acc56d
- Role of LiDAR remote sensing in identifying physiognomic traits of alpine treeline: a global review J. Mathew et al. https://doi.org/10.1007/s42965-023-00317-6
- Snow depth variability increases in a marginal snowpack as it transitions from accumulation to melt A. Marziliano et al. https://doi.org/10.1080/15230430.2025.2495518
- Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles J. Fernández-Guisuraga et al. https://doi.org/10.1007/s10980-024-01979-w
- Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape F. Sullivan et al. https://doi.org/10.3390/rs15215091
- Forest Snow Patterns Derived Using ClustSnow Are Temporally Persistent Under Variable Environmental Conditions J. Geissler et al. https://doi.org/10.1029/2024WR038442
- Monitoring snow depth with ICESat-2 at mid-latitudes: a synergistic multi-scale framework integrating ground-airborne-satellite observations in northern Xinjiang, China L. Zhu et al. https://doi.org/10.1016/j.jhydrol.2026.135183
- Drone applications in hydrogeophysics: Recent examples and a vision for the future A. Mangel et al. https://doi.org/10.1190/tle41080540.1
- New opportunities for low-cost LiDAR-derived snow depth estimates from a consumer drone-mounted smartphone F. King et al. https://doi.org/10.1016/j.coldregions.2022.103757
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing R. Bonnell et al. https://doi.org/10.3390/rs13214223
- Multitemporal UAV lidar detects seasonal heave and subsidence on palsas C. Renette et al. https://doi.org/10.5194/tc-18-5465-2024
- Applicability of alpine snow depth estimation based on multitemporal UAV-LiDAR data: A case study in the Maxian Mountains, Northwest China T. Feng et al. https://doi.org/10.1016/j.jhydrol.2022.129006
- Characterizing the spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry E. Cho et al. https://doi.org/10.5194/hess-29-4539-2025
- Influence of forest canopy structure and wind flow on patterns of sub‐canopy snow accumulation in montane needleleaf forests J. Staines & J. Pomeroy https://doi.org/10.1002/hyp.15005
- Automated high-resolution 3D crevasse extraction and dynamic linkages: an integrated UAV-LiDAR, photogrammetry, and C-TransUNet framework Y. Duan et al. https://doi.org/10.1016/j.jag.2025.104881
- BVLOS Unmanned Aircraft Operations in Forest Environments R. Hartley et al. https://doi.org/10.3390/drones6070167
68 citations as recorded by crossref.
- Unpiloted Aerial Vehicle Retrieval of Snow Depth Over Freshwater Lake Ice Using Structure From Motion G. Gunn et al. https://doi.org/10.3389/frsen.2021.675846
- Snow Depth Retrieval With an Autonomous UAV-Mounted Software-Defined Radar S. Prager et al. https://doi.org/10.1109/TGRS.2021.3117509
- Unmanned Aerial Vehicles in Hydrology and Water Management: Applications, Challenges, and Perspectives B. Acharya et al. https://doi.org/10.1029/2021WR029925
- Review article: using spaceborne lidar for snow depth retrievals: recent findings and utility for hydrologic applications Z. Fair et al. https://doi.org/10.5194/tc-19-5671-2025
- Mountain Roads Across the Globe Significantly Alter Local Soil Thermal Microclimates R. Lejeune et al. https://doi.org/10.1111/geb.70237
- Location Dictates Snow Aerodynamic Roughness S. Fassnacht et al. https://doi.org/10.3390/glacies1010001
- Measurement of snow depth and avalanche accumulation distribution in avalanche zones using unmanned aerial vehicle photogrammetry with post-processing kinematic positioning S. ADACHI et al. https://doi.org/10.5331/seppyo.84.5_439
- Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States J. Jacobs et al. https://doi.org/10.5194/tc-15-1485-2021
- Snow Surface Roughness across Spatio-Temporal Scales S. Fassnacht et al. https://doi.org/10.3390/w15122196
- Analyzing vegetation effects on snow depth variability in Alaska's boreal forests with airborne lidar L. May et al. https://doi.org/10.5194/tc-19-3477-2025
- Mapping snow depth and volume at the alpine watershed scale from aerial imagery using Structure from Motion J. Meyer et al. https://doi.org/10.3389/feart.2022.989792
- Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 2: Snow processes and snow–canopy interactions L. Meriö et al. https://doi.org/10.5194/tc-17-4363-2023
- Spatially continuous snow depth mapping by aeroplane photogrammetry for annual peak of winter from 2017 to 2021 in open areas L. Bührle et al. https://doi.org/10.5194/tc-17-3383-2023
- Intercomparison of UAV platforms for mapping snow depth distribution in complex alpine terrain J. Revuelto et al. https://doi.org/10.1016/j.coldregions.2021.103344
- Winters are changing: snow effects on Arctic and alpine tundra ecosystems C. Rixen et al. https://doi.org/10.1139/as-2020-0058
- An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar V. Dharmadasa et al. https://doi.org/10.3390/rs14071649
- Drone-based ground-penetrating radar (GPR) application to snow hydrology E. Valence et al. https://doi.org/10.5194/tc-16-3843-2022
- Fine-scale Antarctic grounded ice cliff 3D calving monitoring based on multi-temporal UAV photogrammetry without ground control S. Zhang et al. https://doi.org/10.1016/j.jag.2025.104620
- Mapping snow depth and spatial variability using SFM photogrammetry of UAV images over rugged mountainous regions of the Western Himalaya S. Dewali et al. https://doi.org/10.1080/10106049.2022.2127923
- Improving the snowpack monitoring in the mountainous areas of Sweden from space: a machine learning approach J. Zhang et al. https://doi.org/10.1088/1748-9326/abfe8d
- Spatial patterns of snow distribution in the sub-Arctic K. Bennett et al. https://doi.org/10.5194/tc-16-3269-2022
- The Role of Basin Geometry in Mountain Snowpack Responses to Climate Change J. Shea et al. https://doi.org/10.3389/frwa.2021.604275
- UAV-borne LiDAR revolutionizing groundwater level mapping S. García-López et al. https://doi.org/10.1016/j.scitotenv.2022.160272
- Local-scale heterogeneity of soil thermal dynamics and controlling factors in a discontinuous permafrost region C. Wang et al. https://doi.org/10.1088/1748-9326/ad27bb
- Spatio‐temporal analysis of snow depth and snow water equivalent in a mountainous catchment: Insights from in‐situ observations and statistical modelling T. Çitgez et al. https://doi.org/10.1002/hyp.15260
- Assessing UAV-based laser scanning for monitoring glacial processes and interactions at high spatial and temporal resolutions N. Baurley et al. https://doi.org/10.3389/frsen.2022.1027065
- Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning A. Hojatimalekshah et al. https://doi.org/10.5194/tc-15-2187-2021
- Assessment of DSM Based on Radiometric Transformation of UAV Data M. Chaudhry et al. https://doi.org/10.3390/s21051649
- Measuring the spatiotemporal variability in snow depth in subarctic environments using UASs – Part 1: Measurements, processing, and accuracy assessment A. Rauhala et al. https://doi.org/10.5194/tc-17-4343-2023
- Advances in image-based estimation of snow variable: A systematic literature review on recent studies G. Demil et al. https://doi.org/10.1016/j.jhydrol.2025.132855
- Assessment of L-band InSAR snow estimation techniques over a shallow, heterogeneous prairie snowpack R. Palomaki & E. Sproles https://doi.org/10.1016/j.rse.2023.113744
- Maximum entropy modeling to identify physical drivers of shallow snowpack heterogeneity using unpiloted aerial system (UAS) lidar E. Cho et al. https://doi.org/10.1016/j.jhydrol.2021.126722
- Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions E. Enderlin et al. https://doi.org/10.1016/j.rse.2022.113307
- Exploring snow distribution dynamics in steep forested slopes with UAV-borne LiDAR K. Koutantou et al. https://doi.org/10.1016/j.coldregions.2022.103587
- Advancing terrestrial snow depth monitoring with machine learning and L-band InSAR data: a case study using NASA’s SnowEx 2017 data I. Alabi et al. https://doi.org/10.3389/frsen.2024.1481848
- UAS remote sensing applications to abrupt cold region hazards M. Verfaillie et al. https://doi.org/10.3389/frsen.2023.1095275
- Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar V. Dharmadasa et al. https://doi.org/10.5194/tc-17-1225-2023
- Marginal snowpacks: The basis for a global definition and existing research needs J. López-Moreno et al. https://doi.org/10.1016/j.earscirev.2024.104751
- Measuring prairie snow water equivalent with combined UAV-borne gamma spectrometry and lidar P. Harder et al. https://doi.org/10.5194/tc-18-3277-2024
- Light and Shadow in Mapping Alpine Snowpack With Unmanned Aerial Vehicles in the Absence of Ground Control Points J. Revuelto et al. https://doi.org/10.1029/2020WR028980
- Comparing high-resolution snow mapping approaches in palsa mires: UAS lidar vs. modelling A. Störmer et al. https://doi.org/10.5194/tc-19-3949-2025
- A new interpolation method to resolve under-sampling of UAV-lidar snow depth observations in coniferous forests V. Dharmadasa et al. https://doi.org/10.1016/j.coldregions.2024.104134
- Modelling snowpack bulk density using snow depth, cumulative degree‐days, and climatological predictor variables A. Szeitz & R. Moore https://doi.org/10.1002/hyp.14800
- High-resolution mapping of snow depth and snow thermal insulation using low-cost UAVs in complex Arctic Urban Terrains M. Mueller et al. https://doi.org/10.1139/as-2025-0062
- Monitoring Wet-Snow Avalanche Risk in Southeastern Tibet with a UAV-Based Multi-Sensor Framework S. Ye et al. https://doi.org/10.3390/rs17223698
- Combining Daily Sensor Observations and Spatial LiDAR Data for Mapping Snow Water Equivalent in a Sub‐Alpine Forest J. Geissler et al. https://doi.org/10.1029/2023WR034460
- Understanding wind-driven melt of patchy snow cover L. van der Valk et al. https://doi.org/10.5194/tc-16-4319-2022
- Uncovering the melt: Unmanned Aircraft System (UAS) and in-situ sensor synergies reveal dissolved organic carbon (DOC) pathways in a northern peatland P. Korhonen et al. https://doi.org/10.5194/hess-30-3019-2026
- UAV hyperspectral imaging for multiscale assessment of Landsat 9 snow grain size and albedo S. Skiles et al. https://doi.org/10.3389/frsen.2022.1038287
- Mapping snow depth on Canadian sub-arctic lakes using ground-penetrating radar A. Pouw et al. https://doi.org/10.5194/tc-17-2367-2023
- UAV LiDAR surveys and machine learning improve snow depth and water equivalent estimates in boreal landscapes M. Ylönen et al. https://doi.org/10.5194/tc-19-4585-2025
- Validation of FABDEM, a global bare-earth elevation model, against UAV-lidar derived elevation in a complex forested mountain catchment C. Marsh et al. https://doi.org/10.1088/2515-7620/acc56d
- Role of LiDAR remote sensing in identifying physiognomic traits of alpine treeline: a global review J. Mathew et al. https://doi.org/10.1007/s42965-023-00317-6
- Snow depth variability increases in a marginal snowpack as it transitions from accumulation to melt A. Marziliano et al. https://doi.org/10.1080/15230430.2025.2495518
- Estimating vegetation and litter biomass fractions in rangelands using structure-from-motion and LiDAR datasets from unmanned aerial vehicles J. Fernández-Guisuraga et al. https://doi.org/10.1007/s10980-024-01979-w
- Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape F. Sullivan et al. https://doi.org/10.3390/rs15215091
- Forest Snow Patterns Derived Using ClustSnow Are Temporally Persistent Under Variable Environmental Conditions J. Geissler et al. https://doi.org/10.1029/2024WR038442
- Monitoring snow depth with ICESat-2 at mid-latitudes: a synergistic multi-scale framework integrating ground-airborne-satellite observations in northern Xinjiang, China L. Zhu et al. https://doi.org/10.1016/j.jhydrol.2026.135183
- Drone applications in hydrogeophysics: Recent examples and a vision for the future A. Mangel et al. https://doi.org/10.1190/tle41080540.1
- New opportunities for low-cost LiDAR-derived snow depth estimates from a consumer drone-mounted smartphone F. King et al. https://doi.org/10.1016/j.coldregions.2022.103757
- Recent Advances in Snow Monitoring from Local to Global Scales J. Revuelto et al. https://doi.org/10.1007/s40641-025-00207-0
- Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing R. Bonnell et al. https://doi.org/10.3390/rs13214223
- Multitemporal UAV lidar detects seasonal heave and subsidence on palsas C. Renette et al. https://doi.org/10.5194/tc-18-5465-2024
- Applicability of alpine snow depth estimation based on multitemporal UAV-LiDAR data: A case study in the Maxian Mountains, Northwest China T. Feng et al. https://doi.org/10.1016/j.jhydrol.2022.129006
- Characterizing the spatial distribution of field-scale snowpack using unpiloted aerial system (UAS) lidar and structure-from-motion (SfM) photogrammetry E. Cho et al. https://doi.org/10.5194/hess-29-4539-2025
- Influence of forest canopy structure and wind flow on patterns of sub‐canopy snow accumulation in montane needleleaf forests J. Staines & J. Pomeroy https://doi.org/10.1002/hyp.15005
- Automated high-resolution 3D crevasse extraction and dynamic linkages: an integrated UAV-LiDAR, photogrammetry, and C-TransUNet framework Y. Duan et al. https://doi.org/10.1016/j.jag.2025.104881
- BVLOS Unmanned Aircraft Operations in Forest Environments R. Hartley et al. https://doi.org/10.3390/drones6070167
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in western Canada collected in 2019. It is demonstrated that UAV lidar can measure the sub-canopy snow depth at a high accuracy, while UAV-SfM cannot. UAV lidar promises to quantify snow–vegetation interactions at unprecedented accuracy and resolution.
Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to...
