Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape

Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer prec...

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Published inRemote sensing (Basel, Switzerland) Vol. 15; no. 21; p. 5091
Main Authors Sullivan, Franklin B., Hunsaker, Adam G., Palace, Michael W., Jacobs, Jennifer M.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.11.2023
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Abstract Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover.
AbstractList Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s[sup.−1], as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s[sup.−1] and 4 m s[sup.−1] and snow-on flight speeds of 6 m s[sup.−1] over open fields and between 2 and 4 m s[sup.−1] over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover.
Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light detection and ranging (LiDAR) produces precise snow depth estimates for areas without vegetation cover, there has generally been poorer precision in forested areas. At a constant flight speed, the poorest precision within forests is observed beneath tree canopies that retain foliage into or through winter. The precision of lidar-derived elevation products is improved by increasing the sample size of ground returns but doing so reduces the spatial coverage of a mission due to limitations of battery power. We address the influence of flight speed on ground return density for baseline and snow-covered conditions and the subsequent effect on precision of snow depth estimates across a mixed landscape, while evaluating trade-offs between precision and bias. Prior to and following a snow event in December 2020, UAS flights were conducted at four different flight speeds over a region consisting of three contrasting land types: (1) open field, (2) deciduous forest, (3) conifer forest. For all cover types, we observed significant improvements in precision as flight speeds were reduced to 2 m s−1, as well as increases in the area over which a 2 cm snow depth precision was achieved. On the other hand, snow depth estimate differences were minimized at baseline flight speeds of 2 m s−1 and 4 m s−1 and snow-on flight speeds of 6 m s−1 over open fields and between 2 and 4 m s−1 over forest areas. Here, with consideration to precision and estimate bias within each cover type, we make recommendations for ideal flight speeds based on survey ground conditions and vegetation cover.
Audience Academic
Author Palace, Michael W
Jacobs, Jennifer M
Sullivan, Franklin B
Hunsaker, Adam G
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Snippet Recently, sensors deployed on unpiloted aerial systems (UAS) have provided snow depth estimates with high spatial resolution over watershed scales. While light...
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SubjectTerms Accuracy
Airspeed
Analysis
Bias
Clouds
Confidence intervals
Coniferous forests
Deciduous forests
Environmental protection
Estimates
Evaluation
Flight
Foliage
Forest management
Forests
Lasers
Lidar
Mapping
Missing data
Optical radar
Precipitation
Predation
Remote sensing
Sensors
Snow
Snow cover
Snow depth
Spatial discrimination
Spatial resolution
Sustainable forestry
UAS
Vegetation
Vegetation cover
Winter
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Title Evaluating the Effects of UAS Flight Speed on Lidar Snow Depth Estimation in a Heterogeneous Landscape
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