Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

Hojatimalekshah, Ahmad; Uhlmann, Zach; Glenn, Nancy F.; Hiemstra, Christopher A.; Tennant, Christopher J.; Graham, Jake D.; Spaete, L.; Gelvin, A.; Marshall, Hans‐Peter; McNamara, J. P.; Enterkine, Josh

doi:10.5194/tc-15-2187-2021

Cited by 26 publications

(4 citation statements)

References 50 publications

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“…Then, we applied the M3C2 method to estimate snow depth on a 1 m grid. The relative accuracy of the snow depth measurement was estimated at 7 cm, based on the maximum standard deviation of the M3C2 method, which agrees well with previous lidar error assessments (Hojatimalekshah et al, 2021). After computing the snow depth, the 3 m ASO bare-earth and vegetation data products were resampled to the 1 m resolution of the snow-covered SnowEx…”

Section: Lidar Snow Depthsupporting

confidence: 67%

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Meehan,

Hojatimalekshah,

Marshall

et al. 2023

Preprint

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Abstract. Spaceborne remote sensing of snow currently enables landscape-scale snow covered area, but estimating snow mass in the mountains remains a major challenge from space. Airborne LiDAR can retrieve snow depth, and some promising results have recently been shown from spaceborne platforms, yet density estimates are required to convert snow depth to snow water equivalent (SWE). However, the retrieval of snow bulk density remains unsolved, and limited data is available to evaluate model estimates of density in mountainous terrain. Knowledge of the spatial patterns and predictors of density is critical for accurate assessment of SWE and essential snow physics, such as energy balance and mechanics related to hazards and over-snow mobility. Toward the goal of landscape-scale retrievals of snow density, we estimated bulk density and length-scale variability by combining ground-penetrating radar (GPR) two-way travel-time observations and airborne LiDAR snow depths collected during the mid-winter NASA SnowEx 2020 campaign at Grand Mesa, Colorado, USA. Key advancements of our approach include an automated layer picking method that leverages co- and cross-polarization coherence and distributed LiDAR–GPR inferred bulk density with machine learning. The root-mean-square error between the distributed estimates is 12 cm for depth, 27 kg/m3 for density, and 42 mm for SWE, and the median relative uncertainty in distributed SWE is 7 %. Wind, terrain, and vegetation interactions display corroborated controls on bulk density that show model and observation agreement. The spatially continuous snow density and SWE estimated over approximately 16 km2 represents the next step towards broad-scale SWE retrieval.

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Section: Lidar Snow Depthsupporting

confidence: 67%

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Meehan,

Hojatimalekshah,

Marshall

et al. 2023

Preprint

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“…Since then, numerous measurement campaigns have been conducted (e.g., Webster et al 2016Webster et al , 2018Malle, et al, 2019;Mazzotti et al, 2019;Hojatimalekshah et al, 2021) and snow routines in hydrological and land surface models have been enhanced to incorporate more accurate representations of forest snow processes (e.g., Ellis et al, 2013;Gouttevin et al, 2015;Boone et al, 2017;Mazzotti et al, 2020). However, these improved routines still represent canopy as one homogeneous layer without accounting for all the effects of particularly vertical canopy heterogeneity on snow accumulation and ablation processes.…”

Section: Introductionmentioning

confidence: 99%

Snow accumulation and ablation measurements in a mid-latitude mountain coniferous forest (Col de Porte, France, 1325 m alt.): The Snow Under Forest field campaigns dataset

Sicart,

Ramseyer,

Picard

et al. 2023

Preprint

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Abstract. Forests strongly modify the accumulation, metamorphism and melting of snow in mid and high-latitude regions. Recently, snow routines in hydrological and land surface models have been improved to incorporate more accurate representations of forest snow processes, but model inter-comparison projects have identified deficiencies, partly due to incomplete knowledge of the processes controlling snow cover in forests. The Snow Under Forest (SnoUF) project was initiated to enhance knowledge of the complex interactions between snow and vegetation. Two field campaigns, during the winters 2016–17 and 2017–18, were conducted in a coniferous forest bordering the snow study at Col de Porte (1325 m a.s.l, French Alps) to document the snow accumulation and ablation processes. This paper presents the field site, instrumentation, and collection methods. The observations include distributed forest characteristics (tree inventory, LIDAR measurements of forest structure, sub-canopy hemispherical photographs), meteorology (automatic weather station and radiometers array), snow cover and depth (snow poles transect and laser scan), and snow interception by the canopy during precipitation events. The weather station installed under dense canopy during the first campaign has been maintained since then and provides continuous measurements throughout the year since 2018. Data are publicly available from the repository of the Observatoire des Sciences de l’Univers de Grenoble (OSUG) data center at http://dx.doi.org/10.17178/SNOUF.2022 (Sicart et al., 2022).

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“…The most notorious feature of DL is its ability in reducing computer hardware and software manipulation, making advancements in computational capabilities, machine learning, and signal processing. Furthermore, it is proved to be a highly applicable solution in objects recognition [ 6 – 8 ], speech recognition [ 9 – 12 ], SAR image processing [ 13 – 16 ], and a highly viable method in medical image processing for the detection of potential drug molecules activities [ 17 , 18 ], liver and lung tumor segmentation [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

Saadi

Sarshar

Sadeghi

et al. 2022

Journal of Healthcare Engineering

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One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN’s weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism.

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Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning

Cited by 26 publications

References 50 publications

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA

Snow accumulation and ablation measurements in a mid-latitude mountain coniferous forest (Col de Porte, France, 1325 m alt.): The Snow Under Forest field campaigns dataset

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

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