scholarly journals Estimating Floodplain Vegetative Roughness Using Drone-Based Laser Scanning and Structure from Motion Photogrammetry

2021 ◽  
Vol 13 (13) ◽  
pp. 2616
Author(s):  
Elizabeth M. Prior ◽  
Charles A. Aquilina ◽  
Jonathan A. Czuba ◽  
Thomas J. Pingel ◽  
W. Cully Hession
Keyword(s):  
Structure From Motion ◽  
Laser Scanning ◽  
Spatial Scales ◽  
High Flow ◽  
Model Simulations ◽  
Hydraulic Roughness ◽  
Vegetation Height ◽  
Large Trees ◽  
Patch Scale

Vegetation heights derived from drone laser scanning (DLS), and structure from motion (SfM) photogrammetry at the Virginia Tech StREAM Lab were utilized to determine hydraulic roughness (Manning’s roughness coefficients). We determined hydraulic roughness at three spatial scales: reach, patch, and pixel. For the reach scale, one roughness value was set for the channel, and one value for the entire floodplain. For the patch scale, vegetation heights were used to classify the floodplain into grass, scrub, and small and large trees, with a single roughness value for each. The roughness values for the reach and patch methods were calibrated using a two-dimensional (2D) hydrodynamic model (HEC-RAS) and data from in situ velocity sensors. For the pixel method, we applied empirical equations that directly estimated roughness from vegetation height for each pixel of the raster (no calibration necessary). Model simulations incorporating these roughness datasets in 2D HEC-RAS were validated against water surface elevations (WSE) from seventeen groundwater wells for seven high-flow events during the Fall of 2018 and 2019, and compared to marked flood extents. The reach method tended to overestimate while the pixel method tended to underestimate the flood extent. There were no visual differences between DLS and SfM within the pixel and patch methods when comparing flood extents. All model simulations were not significantly different with respect to the well WSEs (p > 0.05). The pixel methods had the lowest WSE RMSEs (SfM: 0.136 m, DLS: 0.124 m). The other methods had RMSE values 0.01–0.02 m larger than the DLS pixel method. Models with DLS data also had lower WSE RMSEs by 0.01 m when compared to models utilizing SfM. This difference might not justify the increased cost of a DLS setup over SfM (~150,000 vs. ~2000 USD for this study), though our use of the DLS DEM to determine SfM vegetation heights might explain this minimal difference. We expect a poorer performance of the SfM-derived vegetation heights/roughness values if we were using a SfM DEM, although further work is needed. These results will help improve hydrodynamic modeling efforts, which are becoming increasingly important for management and planning in response to climate change, specifically in regions were high flow events are increasing.

Terrestrial laser scanning and structure-from-motion photogrammetry concordance analysis for describing the surface layer of gravel beds

2019 ◽  
Vol 43 (2) ◽  
pp. 260-281 ◽  
Author(s):  
Andrew J Neverman ◽  
Ian C Fuller ◽  
Jon N Procter ◽  
Russell G Death
Keyword(s):  
Surface Layer ◽  
Structure From Motion ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Survey Method ◽  
Gravel Beds ◽  
Non Invasive ◽  
Sampling Biases

Terrestrial laser scanning (TLS) and structure-from-motion photogrammetry (SfMp) offer rapid, non-invasive surveying of in situ gravels. Numerous studies have used the point clouds derived from TLS or SfMp to quantify surface layer characteristics, but direct comparison of the methods for grain-scale analysis has received relatively little attention to date. Comparing equivalent products of different data capture methods is critical as differences in errors and sampling biases between the two methods may produce different outputs, effecting further analysis. The sampling biases and errors related to SfMp and TLS lead to differences in the point clouds produced by each method. The metrics derived from the point clouds are therefore likely to differ, potentially leading to different inputs for entrainment threshold models, different trends in surface layer development being identified and different trajectories for physical processes and habitat quality being predicted. This paper provides a direct comparison between TLS and SfMp surveys of an exposed gravel bar for three different survey periods following inundation and reworking of the bar surface during high flow events. The point clouds derived from the two methods are used to describe changes in the character of the surface layer between bar inundation events, and comparisons are made with descriptions derived from conventional pebble counts. The results found differences in the metrics derived using each method do exist, but the grid resolution used to detrend the surfaces and identify spatial variations in surface layer characteristics had a greater impact than survey method. Further research is required to understand the significance of these variations for quantifying surface texture and structure and for predicting entrainment thresholds and transport rates.

Developing statistically driven eco-engineering designs from LiDAR and structure from motion surveys for marine artificial structures.

2020 ◽  
Author(s):  
Peter Lawrence ◽  
Ally Evans ◽  
Paul Brooks ◽  
Tim D'Urban Jackson ◽  
Stuart Jenkins ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Habitat Loss ◽  
Structure From Motion ◽  
Laser Scanning ◽  
Large Scale ◽  
Spatial Scales ◽  
Coastal Development ◽  
Ecological Design ◽  
Artificial Structures ◽  
Engineering Research

<p>Coastal ecosystems are threatened by habitat loss and anthropogenic “smoothing” as hard engineering approaches to sea defence, such as sea-walls, rock armouring, and offshore reefs, become common place. These artificial structures use homogenous materials (e.g. concrete or quarried rock) and as a result, lack the surface heterogeneity of natural rocky shoreline known to play a key role in niche creation and higher species diversity. Despite significant investment and research into soft engineering and ecologically sensitive approaches to coastal development, there are still knowledge gaps, particularly in relation to how patterns that are observed in nature can be utilised to improve artificial shores.</p><p>Given the technical improvements and significant reductions in cost within the portable remote sensing field (structure from motion and laser scanning), we are now able to plug gaps in our understanding of how habitat heterogeneity can influence overall site diversity. These improvements represent an excellent opportunity to improve our understanding of the spatial scales and complexity of habitats that species occur within and ultimately improve the ecological design of engineered structures in areas experiencing “smoothing” and habitat loss.</p><p>In this talk, I will highlight how advances in remote sensing techniques can be applied to context-specific ecological problems, such as low diversity and loss of rare species within marine infrastructure. I will describe our approach to combining large-scale ecological, 3D geophysical and engineering research to design statistically-derived ecologically-inspired solutions to smooth artificial surfaces. We created experimental concrete enhancement units and deployed them at a number of coastal locations. I will present preliminary ecological results, provide a workflow of unit development and statistical approaches, and finally discuss how these advances may improve future ecological intervention and design options.</p>

scholarly journals Sedimentological characterization of Antarctic moraines using UAVs and Structure-from-Motion photogrammetry

2015 ◽  
Vol 61 (230) ◽  
pp. 1088-1102 ◽  
Author(s):  
Matthew J. Westoby ◽  
Stuart A. Dunning ◽  
John Woodward ◽  
Andrew S. Hein ◽  
Shasta M. Marrero ◽  
...  
Keyword(s):  
Grain Size ◽  
Structure From Motion ◽  
Laser Scanning ◽  
Particle Size Analysis ◽  
Size Analysis ◽  
Traditional Methods ◽  
Continuous Map ◽  
Scale Modelling ◽  
Grain Size Distributions ◽  
Patch Scale

AbstractIn glacial environments particle-size analysis of moraines provides insights into clast origin, transport history, depositional mechanism and processes of reworking. Traditional methods for grain-size classification are labour-intensive, physically intrusive and are limited to patch-scale (1 m2) observation. We develop emerging, high-resolution ground- and unmanned aerial vehicle-based ‘Structure-from-Motion’ (UAV-SfM) photogrammetry to recover grain-size information across a moraine surface in the Heritage Range, Antarctica. SfM data products were benchmarked against equivalent datasets acquired using terrestrial laser scanning, and were found to be accurate to within 1.7 and 50 mm for patch- and site-scale modelling, respectively. Grain-size distributions were obtained through digital grain classification, or ‘photo-sieving’, of patch-scale SfM orthoimagery. Photo-sieved distributions were accurate to <2 mm compared to control distributions derived from dry-sieving. A relationship between patch-scale median grain size and the standard deviation of local surface elevations was applied to a site-scale UAV-SfM model to facilitate upscaling and the production of a spatially continuous map of the median grain size across a 0.3 km2 area of moraine. This highly automated workflow for site-scale sedimentological characterization eliminates much of the subjectivity associated with traditional methods and forms a sound basis for subsequent glaciological process interpretation and analysis.

scholarly journals Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana

Forests ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 527 ◽  
Author(s):  
Alvaro Lau ◽  
Kim Calders ◽  
Harm Bartholomeus ◽  
Christopher Martius ◽  
Pasi Raumonen ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Tree Height ◽  
Point Clouds ◽  
Validation Data ◽  
Terrestrial Lidar ◽  
Allometric Models ◽  
Crown Diameter ◽  
National Monitoring ◽  
Large Trees

Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R 2 = 0.92–0.93) than traditional pantropical models ( R 2 = 0.85–0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R 2 = 0.89) and predicted AGB accurately across all size classes—which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

Habitat attributes and chestnut-backed chickadee nest site selection in uncut and partial-cut forests

2007 ◽  
Vol 37 (7) ◽  
pp. 1272-1285 ◽  
Author(s):  
C. Lisa Mahon ◽  
Kathy Martin ◽  
J.D. Steventon
Keyword(s):  
Site Selection ◽  
Resource Selection ◽  
Nest Site ◽  
Spatial Scales ◽  
Nest Site Selection ◽  
Large Trees ◽  
Poecile Rufescens ◽  
The Relationship ◽  
Patch Scale ◽  
Selection Of

We examined the relationship between habitat attributes and nest-site selection by chestnut-backed chickadees ( Poecile rufescens (Townsend, 1837); hereinafter chickadees) in uncut and partial-cut forests in northwest British Columbia. We described the characteristics of uncut sites and compared them with structurally modified partial-cut sites (mature and old forests). We then compared the use and selection of habitat by chickadees at uncut and partial-cut sites at three spatial scales: (1) the stand (19–24 ha uncut or partial-cut stand), (2) the nest patch (a 0.031 ha patch centered on nest trees), and (3) the nest tree. At the stand scale, we found no correlation between the density of breeding chickadees and the characteristics of uncut and partial-cut sites. At the nest-patch scale, chickadees in uncut and old partial-cut sites selected nest patches with higher densities of broken-top trees compared with available habitat within territories. At the nest-tree scale, chickadees selected nest trees with boring insects and broken tops in uncut and mature partial-cut sites and large trees with boring insects in old partial-cut sites. Our results suggest that chickadees exhibited flexibility in resource selection but also selected resources with similar attributes at the nest-patch and nest-tree scales. Managed stands that maintain a range of tree species and conditions, including live trees with areas of disease, insect attack, and damage, will provide the specific structural attributes used for nesting by weak cavity excavators such as the chickadee.

3-D imaging of cells using a confocal laser scanning microscope and digital image processing

1988 ◽  
Vol 46 ◽  
pp. 96-97
Author(s):  
Thomas M. Jovin ◽  
Michel Robert-Nicoud ◽  
Donna J. Arndt-Jovin ◽  
Thorsten Schormann
Keyword(s):  
Laser Scanning ◽  
Confocal Laser Scanning Microscope ◽  
Laser Scanning Microscopy ◽  
Confocal Laser Scanning ◽  
Confocal Laser ◽  
Scanning Microscope ◽  
Laser Scanning Microscope ◽  
Biochemical Processes ◽  
Adjacent Structures

Light microscopic techniques for visualizing biomolecules and biochemical processes in situ have become indispensable in studies concerning the structural organization of supramolecular assemblies in cells and of processes during the cell cycle, transformation, differentiation, and development. Confocal laser scanning microscopy offers a number of advantages for the in situ localization and quantitation of fluorescence labeled targets and probes: (i) rejection of interfering signals emanating from out-of-focus and adjacent structures, allowing the “optical sectioning” of the specimen and 3-D reconstruction without time consuming deconvolution; (ii) increased spatial resolution; (iii) electronic control of contrast and magnification; (iv) simultanous imaging of the specimen by optical phenomena based on incident, scattered, emitted, and transmitted light; and (v) simultanous use of different fluorescent probes and types of detectors.We currently use a confocal laser scanning microscope CLSM (Zeiss, Oberkochen) equipped with 3-laser excitation (u.v - visible) and confocal optics in the fluorescence mode, as well as a computer-controlled X-Y-Z scanning stage with 0.1 μ resolution.

scholarly journals Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products

2021 ◽  
Vol 13 (2) ◽  
pp. 228
Author(s):  
Jian Kang ◽  
Rui Jin ◽  
Xin Li ◽  
Yang Zhang
Keyword(s):  
Soil Moisture ◽  
Large Scale ◽  
Spatial Scales ◽  
Microwave Remote Sensing ◽  
In Situ Measurements ◽  
Spatial Density ◽  
Linear Regression Method ◽  
Spatiotemporal Resolution ◽  
Pixel Scale

In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

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.

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