Structure from Motion of Multi-Angle RPAS Imagery Complements Larger-Scale Airborne Lidar Data for Cost-Effective Snow Monitoring in Mountain Forests

Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE ~10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE ~10 cm vs ~10–20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE’s between these observed and estimated snow depths on the order of 10–15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.

Download Full-text

Advances in mapping sub-canopy snow depth with unmanned aerial vehicles using structure from motion and lidar techniques

10.5194/tc-2019-284 ◽

2019 ◽

Author(s):

Phillip Harder ◽

John W. Pomeroy ◽

Warren D. Helgason

Keyword(s):

Unmanned Aerial Vehicles ◽

Structure From Motion ◽

Snow Depth ◽

Point Clouds ◽

Airborne Lidar ◽

Canadian Prairie ◽

Widespread Application ◽

Aerial Vehicles ◽

Wide Range ◽

Consistent Error

Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are lacking. Unmanned Aerial Vehicles (UAV) have had recent widespread application to capture high resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV Structure from Motion (SfM) and airborne-lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds, measure returns from a wide range of scan angles, and so have a greater likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV-lidar and UAV-SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign in the Canadian Rockies Hydrological Observatory, Alberta and at Canadian Prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV-lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage, and consistent error metrics (RMSE

Download Full-text

Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques

The Cryosphere ◽

10.5194/tc-14-1919-2020 ◽

2020 ◽

Vol 14 (6) ◽

pp. 1919-1935

Author(s):

Phillip Harder ◽

John W. Pomeroy ◽

Warren D. Helgason

Keyword(s):

Unmanned Aerial Vehicles ◽

Structure From Motion ◽

Snow Depth ◽

Airborne Lidar ◽

Snow Surface ◽

Widespread Application ◽

Large Variability ◽

Error Metrics ◽

Aerial Vehicles ◽

Wide Range

Abstract. Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.

Download Full-text

Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry

The Cryosphere Discussions ◽

10.5194/tcd-9-333-2015 ◽

2015 ◽

Vol 9 (1) ◽

pp. 333-381 ◽

Cited By ~ 23

Author(s):

M. Nolan ◽

C. F. Larsen ◽

M. Sturm

Keyword(s):

Structure From Motion ◽

Snow Depth ◽

Digital Camera ◽

Field Measurements ◽

Airborne Lidar ◽

Ground Control ◽

Barriers To Entry ◽

Dual Frequency ◽

Ground Sample ◽

Landscape Scales

Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers-to-entry and now allow repeat-mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these techniques to the measurement of snow depth from manned aircraft. The main airborne hardware consists of a consumer-grade digital camera coupled to a dual-frequency GPS. The photogrammetric processing is done using a commercially-available implementation of the Structure from Motion (SfM) algorithm. The system hardware and software, exclusive of aircraft, costs less than USD 30 000. The technique creates directly-georeferenced maps without ground control, further reducing costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time-series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by another photogrammetric system. We empirically determined an accuracy of ± 30 cm and a precision of ± 8 cm (both 95% confidence) for our methods. We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and these depth distributions were statistically identical at 95% confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate isolated field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.

Download Full-text

Strengths and Limitations of UAV and Ground-based Structure from Motion Photogrammetry in a Gullied Savanna Catchment

10.20944/preprints201707.0030.v1 ◽

2017 ◽

Cited By ~ 2

Author(s):

Jack Koci ◽

Javier X. Leon ◽

Ben Jarihani ◽

Roy C. Sidle ◽

Scott N. Wilkinson ◽

...

Keyword(s):

High Resolution ◽

Structure From Motion ◽

Digital Camera ◽

Cost Effective ◽

Point Clouds ◽

Airborne Lidar ◽

Tropical Savanna ◽

Ground Survey ◽

Rtk Gps ◽

Point Data

Structure from Motion with Multi-View Stereo photogrammetry (SfM) is increasingly utilised in geoscience investigations as a cost-effective method of acquiring high resolution (sub-meter) topographic data, but has not been thoroughly tested in gullied savanna systems. The aim of this study was to test the accuracy of topographic models derived from aerial (via an Unmanned Aerial Vehicle, ‘UAV’) and ground-based (via a handheld digital camera, ‘Ground’) SfM in modelling a hillslope gully system in dry-tropical savanna, and to assess the strengths and limitations of the approach at different scales. A UAV survey covered an entire hillslope gully system (0.715 km2), whereas a Ground survey covered a single gully within the broader system (650 m2). SfM topographic models, including Digital Surface Models (DSM) and dense point clouds, were compared against RTK-GPS point data and a pre-existing airborne LiDAR Digital Elevation Model (DEM). Results indicate UAV SfM can deliver topographic models with a resolution and accuracy suitable to define gully systems at a hillslope scale (e.g., 0.1 m resolution with ~ 0.5 – 1.3 m elevation error), while ground-based SfM is more capable of quantifying gully morphology (e.g., 0.01 m resolution with ~ 0.1 m elevation error). Key strengths of SfM for these applications include: the production of high resolution 3D topographic models and ortho-photo mosaics, low survey instrument costs (< $AUD 3,000); and rapid survey time (4 and 2 hours for UAV and Ground survey respectively). Current limitations of SfM include: difficulties in reconstructing vegetated surfaces; uncertainty as to optimal survey and processing designs; and high computational demands. Overall, this study has demonstrated great potential for SfM to be used as a cost-effective tool to aid in the mapping, modelling and management of hillslope gully systems at different scales, in tropical savanna landscapes and elsewhere.

Download Full-text

Inferring Watershed-scale Mean Snow Magnitude and Distribution Using Multidecadal Snow Reanalysis Patterns and Snow Pillow Observations

10.22541/au.163250588.84772578/v1 ◽

2021 ◽

Author(s):

Justin Pflug ◽

Steven Margulis ◽

Jessica Lundquist

Keyword(s):

Spatial Heterogeneity ◽

Sierra Nevada ◽

Snow Depth ◽

Snow Accumulation ◽

Airborne Lidar ◽

Watershed Scale ◽

Mountainous Regions ◽

Snow Covered Area ◽

Covered Area ◽

Snow Deposition

The magnitude and spatial heterogeneity of snow deposition are difficult to model in mountainous terrain. Here, we investigated how snow patterns from a 32-year (1985 – 2016) snow reanalysis in the Tuolumne, Kings, and Sagehen Creek, California Sierra Nevada watersheds could be used to improve simulations of winter snow deposition. Remotely-sensed fractional snow-covered area (fSCA) from dates following peak-snowpack timing were used to identify dates from different years with similar fSCA, which indicated similar snow accumulation and depletion patterns. Historic snow accumulation patterns were then used to 1) relate snow accumulation observed by snow pillows to watershed-scale estimates of mean snowfall, and 2) estimate 90 m snow deposition. Finally, snow deposition fields were used to force snow simulations, the accuracy of which were evaluated versus airborne lidar snow depth observations. Except for water-year 2015, which had the shallowest snow estimated in the Sierra Nevada, normalized snow accumulation and depletion patterns identified from historic dates with spatially correlated fractional snow-covered area agreed on average, with absolute differences of less than 10%. Watershed-scale mean winter snowfall inferred from the relationship between historic snow accumulation patterns and snow pillow observations had a ±13% interquartile range of biases between 1985 and 2016. Finally, simulations using 1) historic snow accumulation patterns, and 2) snow accumulation observed from snow pillows, had snow depth coefficients of correlations and mean absolute errors that improved by 70% and 27%, respectively, as compared to simulations using a more common forcing dataset and downscaling technique. This work demonstrates the real-time benefits of satellite-era snow reanalyses in mountainous regions with uncertain snowfall magnitude and spatial heterogeneity.

Download Full-text

Mapping snow depth from manned aircraft on landscape scales at centimeter resolution using structure-from-motion photogrammetry

The Cryosphere ◽

10.5194/tc-9-1445-2015 ◽

2015 ◽

Vol 9 (4) ◽

pp. 1445-1463 ◽

Cited By ~ 86

Author(s):

M. Nolan ◽

C. Larsen ◽

M. Sturm

Keyword(s):

Structure From Motion ◽

Snow Depth ◽

Digital Camera ◽

Field Measurements ◽

Airborne Lidar ◽

Ground Control ◽

Barriers To Entry ◽

Dual Frequency ◽

Ground Sample ◽

Landscape Scales

Abstract. Airborne photogrammetry is undergoing a renaissance: lower-cost equipment, more powerful software, and simplified methods have significantly lowered the barriers to entry and now allow repeat mapping of cryospheric dynamics at spatial resolutions and temporal frequencies that were previously too expensive to consider. Here we apply these advancements to the measurement of snow depth from manned aircraft. Our main airborne hardware consists of a consumer-grade digital camera directly coupled to a dual-frequency GPS; no inertial motion unit (IMU) or on-board computer is required, such that system hardware and software costs less than USD 30 000, exclusive of aircraft. The photogrammetric processing is done using a commercially available implementation of the structure from motion (SfM) algorithm. The system is simple enough that it can be operated by the pilot without additional assistance and the technique creates directly georeferenced maps without ground control, further reducing overall costs. To map snow depth, we made digital elevation models (DEMs) during snow-free and snow-covered conditions, then subtracted these to create difference DEMs (dDEMs). We assessed the accuracy (real-world geolocation) and precision (repeatability) of our DEMs through comparisons to ground control points and to time series of our own DEMs. We validated these assessments through comparisons to DEMs made by airborne lidar and by a similar photogrammetric system. We empirically determined that our DEMs have a geolocation accuracy of ±30 cm and a repeatability of ±8 cm (both 95 % confidence). We then validated our dDEMs against more than 6000 hand-probed snow depth measurements at 3 separate test areas in Alaska covering a wide-variety of terrain and snow types. These areas ranged from 5 to 40 km2 and had ground sample distances of 6 to 20 cm. We found that depths produced from the dDEMs matched probe depths with a 10 cm standard deviation, and were statistically identical at 95 % confidence. Due to the precision of this technique, other real changes on the ground such as frost heave, vegetative compaction by snow, and even footprints become sources of error in the measurement of thin snow packs (< 20 cm). The ability to directly measure such small changes over entire landscapes eliminates the need to extrapolate limited field measurements. The fact that this mapping can be done at substantially lower costs than current methods may transform the way we approach studying change in the cryosphere.

Download Full-text

Snow Depth Distribution Patterns and Consistency from Airborne Lidar Time Series

10.18122/td/1731/boisestate ◽

2020 ◽

Author(s):

Megan A. Mason

Keyword(s):

Time Series ◽

Sierra Nevada ◽

Snow Depth ◽

Canopy Cover ◽

Absolute Magnitude ◽

Distribution Patterns ◽

Airborne Lidar ◽

Time Series Dataset ◽

Basin Scale ◽

Tuolumne River

Snow provides fresh meltwater to over a billion people worldwide. Snow dominated watersheds drive western US water supply and are increasingly important as demand depletes reservoir and groundwater recharge capabilities. This motivates our inter- and intra-annual investigation of snow distribution patterns, leveraging the most comprehensive airborne lidar survey (ALS) dataset for snow. Validation results for ALS from both the NASA SnowEx 2017 campaign in Grand Mesa, Colorado and the time series dataset from the Tuolumne River Basin in the Sierra Nevada, in California, are presented. We then assess the consistency in the snow depth patterns for the entire basin (at 20-m resolution) and for subbasin regions (at 3-m resolution) from a collection of 51 ALS that span a six-year period (2013-2018) in the Tuolumne Basin. Strong correlations between ALS from different years near peak SWE confirm that spatial patterns exist between snow seasons. Year-to-year snow depth differs in absolute magnitude, but relative differences are consistent spatially, such that deep and shallow zones occur in the same location. We further show that elevation is the terrain parameter with the largest correlation to snow depth at the basin scale, and we map the expected pattern distribution for periods with similar snow-covered extents. Lastly, we show at a subbasin scale that distribution patterns are more consistent in vegetation-limited areas (bedrock dominated terrain and open meadows) compared to vegetation-rich zones (valley hillslopes and dense canopy cover). The maps of snow patterns and their consistency can be used to determine optimal locations of new long-term monitoring sites, design sampling strategies for future snow surveys, and to improve high resolution snow models.

Download Full-text

Comparison of Multi-Temporal PlanetScope Data with Landsat 8 and Sentinel-2 Data for Estimating Airborne LiDAR Derived Canopy Height in Temperate Forests

Remote Sensing ◽

10.3390/rs12111876 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1876 ◽

Cited By ~ 1

Author(s):

Katsuto Shimizu ◽

Tetsuji Ota ◽

Nobuya Mizoue ◽

Hideki Saito

Keyword(s):

Time Series ◽

Prediction Accuracy ◽

Time Series Data ◽

Airborne Lidar ◽

Canopy Height ◽

Forest Monitoring ◽

Series Data ◽

Landsat 8 ◽

Multi Temporal ◽

Sentinel 2

Developing accurate methods for estimating forest structures is essential for efficient forest management. The high spatial and temporal resolution data acquired by CubeSat satellites have desirable characteristics for mapping large-scale forest structural attributes. However, most studies have used a median composite or single image for analyses. The multi-temporal use of CubeSat data may improve prediction accuracy. This study evaluates the capabilities of PlanetScope CubeSat data to estimate canopy height derived from airborne Light Detection and Ranging (LiDAR) by comparing estimates using Sentinel-2 and Landsat 8 data. Random forest (RF) models using a single composite, multi-seasonal composites, and time-series data were investigated at different spatial resolutions of 3, 10, 20, and 30 m. The highest prediction accuracy was obtained by the PlanetScope multi-seasonal composites at 3 m (relative root mean squared error: 51.3%) and Sentinel-2 multi-seasonal composites at the other spatial resolutions (40.5%, 35.2%, and 34.2% for 10, 20, and 30 m, respectively). The results show that RF models using multi-seasonal composites are 1.4% more accurate than those using harmonic metrics from time-series data in the median. PlanetScope is recommended for canopy height mapping at finer spatial resolutions. However, the unique characteristics of PlanetScope data in a spatial and temporal context should be further investigated for operational forest monitoring.

Download Full-text

The use of Otsu algorithm and multi-temporal airborne LiDAR data to detect building changes in urban space

Applied Geomatics ◽

10.1007/s12518-021-00371-6 ◽

2021 ◽

Author(s):

Renato César dos Santos ◽

Mauricio Galo ◽

André Caceres Carrilho ◽

Guilherme Gomes Pessoa

Keyword(s):

Urban Space ◽

Airborne Lidar ◽

Lidar Data ◽

Multi Temporal ◽

Airborne Lidar Data

Download Full-text

Effect of bench terracing on ground dwelling arthropods

10.5194/egusphere-egu21-12387 ◽

2021 ◽

Author(s):

Liliana B Simões ◽

Martinho A S Martins ◽

João R L Puga ◽

Jan J Keizer ◽

Nelson Abrantes

Keyword(s):

Water Infiltration ◽

Total Abundance ◽

Conservation Practice ◽

Mountainous Regions ◽

Erosion Rates ◽

Short Rotation ◽

Eucalypt Plantations ◽

Spider Community ◽

Forested Areas ◽

To Receive

Eucalypt trees are the most planted tree in the world, and in Portugal these plantations occupy 26% of the forested area. The area of Eucalypt monoculture is growing since the 50&#8217;s due to the importance of this tree for the pulp and paste industry. With short rotation cycles, it is important to facilitate the cut and transport of the logged trees. In this sense, many forested areas in mountainous regions are being terraced with bulldozers.Terracing is a well know soil conservation practice, reducing runoff peak flows, increasing water infiltration and subsequent low soil erosion rates. Nevertheless, the impacts of terracing for eucalypt plantations are still unknown, especially in terms of biodiversity of soil fauna. Hence, to address this research gap, the present study aimed to assess the impacts of terracing on the ground dwelling arthropods in eucalypt plantations.This study took place in a mountain slope with old eucalypt trees that were logged (May 2019) and then terraced (July) as ground preparation to receive a new eucalypt plantation. The community of ground dwelling arthropods were accessed using pitfall traps. &#160;The arthropods were collected before the terracing process, in Spring 2019, and then seasonally after terracing until the Spring of 2020Total abundance and richness at order level, as well as, abundance, richness, Shannon-Wiener diversity and Pielou&#8217;s Evenness indexes, at Family level of Coleoptera, Araneae and Hymenoptera, were used to depict differences between pre- and post-terracing. The results showed that although terracing did not reduce the total abundance or richness, it changed the community structure. In particular, it was observed an increase in opportunist and generalist families after terracing such as Staphylinidae and Myrmicinae. The spider community also changed, with more hunter families captured after the terrace construction. In overall, the results of our study reveal that although the total abundance and richness of arthropods was not altered by the construction of terraces, their structure was modified.

Download Full-text