Time for 3D: UAV-based lidar for modelling splash erosion under vegetation

2021 ◽  
Author(s):  
Johannes Antenor Senn ◽  
Steffen Seitz ◽  
Fabian Ewald Fassnacht ◽  
Zahra Hosseini ◽  
Jannika Schäfer
Keyword(s):  
Kinetic Energy ◽  
Leaf Litter ◽  
Vegetation Structure ◽  
Plant Traits ◽  
3D Structure ◽  
Protective Layer ◽  
Point Clouds ◽  
Small Scale ◽  
Initial Results ◽  
Covering Vegetation

<p>Rain throughfall under vegetation is determined by characteristics of the vertical structure and the associated plant traits. It goes both ways: A protective layer of ground covering vegetation or leaf litter can decrease throughfall kinetic energy (TKE), whereas the formation of large drips in the canopy layers has been found to increase TKE. Abstracting the three-dimensional vegetation structure into usable quantitative metrics is challenging, and therefore these processes have not yet been sufficiently integrated into spatial erosion models. The vegetation splash factor (VSF) was designed to close this gap (Senn et al. 2020, DOI: 10.1002/esp.4820). The VSF quantifies the influence of vegetation on TKE and can be calculated from aerial lidar point clouds. In the first step, we derive the vegetation cover in a voxel space, which then allows modelling the proportional contribution of drips per layer to reach the ground. Hence, the approach is strictly based on the 3D structure rather than conventional forestry parameters, e.g. crown diameter or leaf sizes. Here, we present the result of the first application of the VSF in a small scale field study using splash cup measurements to validate and refine the concept.</p><p>We implemented the experiment in a mixed-broadleaf forest near Bretten, Germany with a beech and an oak-dominated plot to cover a variety of vertical forest structure configurations and a diverse composition of species. Each plot comprised two transects of ten splash cups to measure sand loss - as a proxy for TKE - during six individual rainfall events. In addition, we used micro-scale runoff plots to determine the effect of soil covering layers such as leaf litter or biological soil crusts in comparison to bare soil. The VSF was calculated in R with a voxel resolution of 0.5 x 0.5 x 0.25 m using a UAV lidar dataset. </p><p>Initial results from the splash cup measurements showed that young oak induced about 70 % higher TKE than adult beech trees. Among the individual cup positions, the lowest energy values were measured without canopy influence as freefall kinetic energy (FKE), TKE at positions with an intermediate young growth and shrub layer showed medium values. In near-trunk and mid-positions without intermediate layers, we measured TKE values more than twice as high as FKE. This resulted in significant sediment removal beneath the tree layer when the ground covering vegetation layer was removed, which is in accordance with studies from other ecosystems. Grouped according to these conventional vegetation structural criteria, we found that the calculated VSF values clustered around similar values and correlated with sand loss from splash cups. From these initial results, we assume general suitability of the VSF to reflect the influence of vegetation structure on TKE. Further, more detailed analysis will now be done to adjust and calibrate the VSF model to produce more indicative results. The preliminary findings presented here will be further expanded to be presented at vEGU21.</p>

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Paul-Mark DiFrancesco ◽  
David A. Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Computational Geometry ◽  
Surface Reconstruction ◽  
Power Law ◽  
Point Cloud ◽  
Point Clouds ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Small Scale ◽  
Reconstruction Methods ◽  
3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

scholarly journals Leveraging TLS as a Calibration and Validation Tool for MLS and ULS Mapping of Savanna Structure and Biomass at Landscape-Scales

2021 ◽  
Vol 13 (2) ◽  
pp. 257 ◽  
Author(s):  
Shaun R. Levick ◽  
Tim Whiteside ◽  
David A. Loewensteiner ◽  
Mitchel Rudge ◽  
Renee Bartolo
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Structural Diversity ◽  
Point Clouds ◽  
Large Area ◽  
Calibration And Validation ◽  
Savanna Vegetation ◽  
Landscape Scales

Savanna ecosystems are challenging to map and monitor as their vegetation is highly dynamic in space and time. Understanding the structural diversity and biomass distribution of savanna vegetation requires high-resolution measurements over large areas and at regular time intervals. These requirements cannot currently be met through field-based inventories nor spaceborne satellite remote sensing alone. UAV-based remote sensing offers potential as an intermediate scaling tool, providing acquisition flexibility and cost-effectiveness. Yet despite the increased availability of lightweight LiDAR payloads, the suitability of UAV-based LiDAR for mapping and monitoring savanna 3D vegetation structure is not well established. We mapped a 1 ha savanna plot with terrestrial-, mobile- and UAV-based laser scanning (TLS, MLS, and ULS), in conjunction with a traditional field-based inventory (n = 572 stems > 0.03 m). We treated the TLS dataset as the gold standard against which we evaluated the degree of complementarity and divergence of structural metrics from MLS and ULS. Sensitivity analysis showed that MLS and ULS canopy height models (CHMs) did not differ significantly from TLS-derived models at spatial resolutions greater than 2 m and 4 m respectively. Statistical comparison of the resulting point clouds showed minor over- and under-estimation of woody canopy cover by MLS and ULS, respectively. Individual stem locations and DBH measurements from the field inventory were well replicated by the TLS survey (R2 = 0.89, RMSE = 0.024 m), which estimated above-ground woody biomass to be 7% greater than field-inventory estimates (44.21 Mg ha−1 vs 41.08 Mg ha−1). Stem DBH could not be reliably estimated directly from the MLS or ULS, nor indirectly through allometric scaling with crown attributes (R2 = 0.36, RMSE = 0.075 m). MLS and ULS show strong potential for providing rapid and larger area capture of savanna vegetation structure at resolutions suitable for many ecological investigations; however, our results underscore the necessity of nesting TLS sampling within these surveys to quantify uncertainty. Complementing large area MLS and ULS surveys with TLS sampling will expand our options for the calibration and validation of multiple spaceborne LiDAR, SAR, and optical missions.

scholarly journals Surface kinetic energy transfer in surface quasi-geostrophic flows

2008 ◽  
Vol 604 ◽  
pp. 165-174 ◽  
Author(s):  
XAVIER CAPET ◽  
PATRICE KLEIN ◽  
BACH LIEN HUA ◽  
GUILLAUME LAPEYRE ◽  
JAMES C. MCWILLIAMS
Keyword(s):  
Kinetic Energy ◽  
Surface Velocity ◽  
Small Scale ◽  
Surface Dynamics ◽  
Spectral Flux ◽  
Advection Term ◽  
Wide Range ◽  
Geostrophic Flows ◽  
Scale Range ◽  
Density Variance

The relevance of surface quasi-geostrophic dynamics (SQG) to the upper ocean and the atmospheric tropopause has been recently demonstrated in a wide range of conditions. Within this context, the properties of SQG in terms of kinetic energy (KE) transfers at the surface are revisited and further explored. Two well-known and important properties of SQG characterize the surface dynamics: (i) the identity between surface velocity and density spectra (when appropriately scaled) and (ii) the existence of a forward cascade for surface density variance. Here we show numerically and analytically that (i) and (ii) do not imply a forward cascade of surface KE (through the advection term in the KE budget). On the contrary, advection by the geostrophic flow primarily induces an inverse cascade of surface KE on a large range of scales. This spectral flux is locally compensated by a KE source that is related to surface frontogenesis. The subsequent spectral budget resembles those exhibited by more complex systems (primitive equations or Boussinesq models) and observations, which strengthens the relevance of SQG for the description of ocean/atmosphere dynamics near vertical boundaries. The main weakness of SQG however is in the small-scale range (scales smaller than 20–30 km in the ocean) where it poorly represents the forward KE cascade observed in non-QG numerical simulations.

Optimizing Perimeter Surveillance Drones to enhance the security system of unmanned aerial vehicles

2021 ◽  
Vol 2 (2) ◽  
pp. 105-115
Author(s):  
Mahmod Al-Bkree
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Security System ◽  
Small Scale ◽  
Security Systems ◽  
Ongoing Research ◽  
Aerial Vehicles ◽  
Aerial Vehicle ◽  
Initial Results

This work is to optimize perimeter surveillance and explore the distribution of ground bases for unmanned aerial vehicles along the Jordanian border and optimize the set of technologies for each aerial vehicle. This model is part of ongoing research on perimeter security systems based on unmanned aerial vehicles. The suggested models give an initial insight about selecting technologies carried by unmanned aerial vehicles based on their priority; it runs for a small scale system that can be expanded, the initial results show the need for at least four ground bases along the length of the border, and a selected set of various technologies for each vehicle.

Application of close range photogrammetry in structural health monitoring by processing generated point cloud datasets

2021 ◽  
Author(s):  
Ali Mirzazade ◽  
Cosmin Popescu ◽  
Thomas Blanksvärd ◽  
Björn Täljsten
Keyword(s):  
Health Monitoring ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Optical Data ◽  
Small Scale ◽  
Relevant Parameter ◽  
Bridge Inspection ◽  
Close Range Photogrammetry ◽  
Close Range

<p>In bridge inspection, vertical displacement is a relevant parameter for both short and long-term health monitoring. Assessing change in deflections could also simplify the assessment work for inspectors. Recent developments in digital camera technology and photogrammetry software enables point cloud with colour information (RGB values) to be generated. Thus, close range photogrammetry offers the potential of monitoring big and small-scale damages by point clouds. The current paper aims to monitor geometrical deviations in Pahtajokk Bridge, Northern Sweden, using an optical data acquisition technique. The bridge in this study is scanned two times by almost one year a part. After point cloud generation the datasets were compared to detect geometrical deviations. First scanning was carried out by both close range photogrammetry (CRP) and terrestrial laser scanning (TLS), while second scanning was performed by CRP only. Analyzing the results has shown the potential of CRP in bridge inspection.</p>

scholarly journals OPTIMIZING RADIOMETRIC PROCESSING AND FEATURE EXTRACTION OF DRONE BASED HYPERSPECTRAL FRAME FORMAT IMAGERY FOR ESTIMATION OF YIELD QUANTITY AND QUALITY OF A GRASS SWARD

2018 ◽  
Vol XLII-3 ◽  
pp. 1305-1310 ◽  
Author(s):  
R. Näsi ◽  
N. Viljanen ◽  
R. Oliveira ◽  
J. Kaivosoja ◽  
O. Niemeläinen ◽  
...  
Keyword(s):  
Feature Extraction ◽  
3D Structure ◽  
Point Clouds ◽  
Estimation Accuracy ◽  
Spectral Features ◽  
Data Set ◽  
Area Of Interest ◽  
Evaluation Task ◽  
Corrected Data ◽  
Grass Sward

Light-weight 2D format hyperspectral imagers operable from unmanned aerial vehicles (UAV) have become common in various remote sensing tasks in recent years. Using these technologies, the area of interest is covered by multiple overlapping hypercubes, in other words multiview hyperspectral photogrammetric imagery, and each object point appears in many, even tens of individual hypercubes. The common practice is to calculate hyperspectral orthomosaics utilizing only the most nadir areas of the images. However, the redundancy of the data gives potential for much more versatile and thorough feature extraction. We investigated various options of extracting spectral features in the grass sward quantity evaluation task. In addition to the various sets of spectral features, we used photogrammetry-based ultra-high density point clouds to extract features describing the canopy 3D structure. Machine learning technique based on the Random Forest algorithm was used to estimate the fresh biomass. Results showed high accuracies for all investigated features sets. The estimation results using multiview data provided approximately 10&amp;thinsp;% better results than the most nadir orthophotos. The utilization of the photogrammetric 3D features improved estimation accuracy by approximately 40&amp;thinsp;% compared to approaches where only spectral features were applied. The best estimation RMSE of 239&amp;thinsp;kg/ha (6.0&amp;thinsp;%) was obtained with multiview anisotropy corrected data set and the 3D features.

scholarly journals Impact of small-scale vegetation structure on tephra layer preservation

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Nick A. Cutler ◽  
Olivia M. Shears ◽  
Richard T. Streeter ◽  
Andrew J. Dugmore
Keyword(s):  
Vegetation Structure ◽  
Small Scale ◽  
Tephra Layer

Evaluation of Turbulent Kinetic Energy Dissipation Rate in a Free Jet Flow from PIV Measurements

2012 ◽  
Vol 7 (1) ◽  
pp. 53-69
Author(s):  
Vladimir Dulin ◽  
Yuriy Kozorezov ◽  
Dmitriy Markovich
Keyword(s):  
Energy Dissipation ◽  
Kinetic Energy ◽  
Turbulent Kinetic Energy ◽  
Dissipation Rate ◽  
Energy Dissipation Rate ◽  
Turbulent Velocity ◽  
Small Scale ◽  
Velocity Fluctuations ◽  
Piv Measurements ◽  
Free Jet

The present paper reports PIV (Particle Image Velocimetry) measurements of turbulent velocity fluctuations statistics in development region of an axisymmetric free jet (Re = 28 000). To minimize measurement uncertainty, adaptive calibration, image processing and data post-processing algorithms were utilized. On the basis of theoretical analysis and direct measurements, the paper discusses effect of PIV spatial resolution on measured statistical characteristics of turbulent fluctuations. Underestimation of the second-order moments of velocity derivatives and of the turbulent kinetic energy dissipation rate due to a finite size of PIV interrogation area and finite thickness of laser sheet was analyzed from model spectra of turbulent velocity fluctuations. The results are in a good agreement with the measured experimental data. The paper also describes performance of possible ways to account for unresolved small-scale velocity fluctuations in PIV measurements of the dissipation rate. In particular, a turbulent viscosity model can be efficiently used to account for the unresolved pulsations in a free turbulent flow

scholarly journals High Level 3D Structure Extraction from a Single Image Using a CNN-Based Approach

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 563 ◽  
Author(s):  
J. Osuna-Coutiño ◽  
Jose Martinez-Carranza
Keyword(s):  
Level Structure ◽  
3D Structure ◽  
Depth Estimation ◽  
Point Clouds ◽  
Graph Analysis ◽  
Single Image ◽  
3D Data ◽  
3D Elements ◽  
High Level ◽  
Structure Extraction

High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In contrast and motivated by the extensive work developed for the problem of depth estimation in a single image, where parallax constraints are not required, in this work, we propose a novel methodology towards HLS extraction from a single image with promising results. For that, our method has four steps. First, we use a CNN to predict the depth for a single image. Second, we propose a region-wise analysis to refine depth estimates. Third, we introduce a graph analysis to segment the depth in semantic orientations aiming at identifying potential HLS. Finally, the depth sections are provided to a new CNN architecture that predicts HLS in the shape of cubes and rectangular parallelepipeds.

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