Terrestrial Laser Scanning Datasets Research Articles

3D modeling and volume measurement of bulk grains stored in large warehouses using bi-temporal multi-site terrestrial laser scanning data

Terrestrial laser scanning (TLS) is a promising technology for quantity checking huge grain stocks with low cost, light workload and high efficiency. Existing applications of TLS in bulk grain measurement and quantification lack the ability to capture complete structural information of grain bulks and thus will result in large errors. In this paper, we propose a bi-temporal TLS scheme for fast 3D modeling and accurate volume measurement of bulk grains stored in large warehouses. The scheme uses bi-temporal multi-site TLS datasets acquired under both empty and full or high loading conditions to obtain complete surface information about grain bulk’s structure. In order for a grain bulk’s all external surfaces and the 3D volumetric model reconstructed therefrom to be automatically derived from the bi-temporal TLS dataset, several dedicated methods are developed for the scheme. A fully automated marker-free strategy exploring structurally semantic information inherent in the large grain storehouses is adopted to register multi-scan TLS point cloud data captured in large-scale granary scenes. Also, a local minima-based region growing technique is devised to extract underlying surfaces from a granary scene point cloud model. Experiments show that the proposed 3D modeling and volume measurement scheme can work effectively and quickly in TLS-based granary field applications and repeated test data demonstrate its correctness, repeatability and accuracy. Compared with the conventional manual measurement approach, the bi-temporal TLS scheme can not only achieve much higher measurement precision, but also greatly improve efficiency by significantly reducing cost, workload, and manpower. It has good potential for use in the area of nation-wide grain inventory inspection in China.

Individual tree segmentation in occluded complex forest stands through ellipsoid directional searching and point compensation

Terrestrial laser scanning (TLS) accurately captures tree structural information and provides prerequisites for tree-scale estimations of forest biophysical attributes. Quantifying tree-scale attributes from TLS point clouds requires segmentation, yet the occlusion effects severely affect the accuracy of automated individual tree segmentation. In this study, we proposed a novel method using ellipsoid directional searching and point compensation algorithms to alleviate occlusion effects. Firstly, region growing and point compensation algorithms are used to determine the location of tree roots. Secondly, the neighbor points are extracted within an ellipsoid neighborhood to mitigate occlusion effects compared with k-nearest neighbor (KNN). Thirdly, neighbor points are uniformly subsampled by the directional searching algorithm based on the Fibonacci principle in multiple spatial directions to reduce memory consumption. Finally, a graph describing connectivity between a point and its neighbors is constructed, and it is utilized to complete individual tree segmentation based on the shortest path algorithm. The proposed method was evaluated on a public TLS dataset comprising six forest plots with three complexity categories in Evo, Finland, and it reached the highest mean accuracy of 77.5%, higher than previous studies on tree detection. We also extracted and validated the tree structure attributes using manual segmentation reference values. The RMSE, RMSE%, bias, and bias% of tree height, crown base height, crown projection area, crown surface area, and crown volume were used to evaluate the segmentation accuracy, respectively. Overall, the proposed method avoids many inherent limitations of current methods and can accurately map canopy structures in occluded complex forest stands.

INVESTIGATION OF THE SUITABILITY OF A PERSONAL LASER SCANNING DEVICE FOR THE MONITORING OF SMALL WATER BODIES

Abstract. This study investigates the applicability of a personal laser scanner (PLS) in surveying small creeks in urban areas. The surveying of the creek geometry can be done only with the PLS system or as a combination of terrestrial PLS measurement and airborne image sequence acquisition by an uncrewed aerial vehicle (UAV). For both methods, georeferencing is performed using RTK-GNSS. Based on the combination of the datasets and from additional terrestrial laser scanning (TLS) comparison, conclusions can be drawn about the accuracy of the PLS measurements. Point-to-mesh based distance calculations between the PLS and UAV measurement showed a variation of ± 0.10 m (96% of points) at selected naturally vegetated sites. The comparison of the PLS and TLS data set revealed differences with a standard deviation of 0.02 m at solid natural (tree trunk) structures and ground areas. The calculated differences grow with increasing distance to the PLS trajectory.

A FULLY AUTOMATIC FOREST PARAMETERS EXTRACTION AT SINGLE-TREE LEVEL: A COMPARISON OF MLS AND TLS APPLICATIONS

Abstract. Forests are vital for ecological, economic, and social reasons, and adopting sustainable forest management practices is necessary. While traditional forest monitoring techniques provide detailed data, they are time-consuming; conversely, geomatic techniques can provide more detailed data for forest resource management. This study aims to assess the suitability of Mobile Mapping Systems (MMS) with simultaneous localisation and mapping (SLAM) technology for precision forestry purposes in challenging environments. We compared the performance of MMS data with Terrestrial Laser Scanning (TLS) data and evaluated the Forest Structural Complexity Tool (FSCT), which was developed for TLS datasets, on MMS data. The case study area is a highly sloped coniferous forest in the Italian Alps affected by a severe fire in 2017. Data were processed using a fully automated open-source Python tool that detects each tree's position, Diameter at Breast Height (DBH), and height. The validation procedure was conducted with respect to the TLS point cloud manually segmented. The results show that using MMS with SLAM technology is suitable for precision forestry purposes in challenging environments and that FSCT performs well on MMS data.

Debris-Flow Channel Headwater Dynamics: Examining Channel Recharge Cycles With Terrestrial Laser Scanning

Debris-flows present a natural hazard to the safe operation of linear infrastructure in mountainous environments. The most significant contributor to debris-flow occurrence is a supply of readily erodible material, often created by rockfalls and other shallow landslides. The spatial distribution and total volume of storage are also critical factors, controlling the initiation location, predominant flow type, and termination location of debris-flow surges. Therefore, there is a need to be able to systematically incorporate debris recharge processes and timeframes into the monitoring and characterization of debris-flow hazards. In this work, the authors present the results of 7 years of terrestrial laser scanning (TLS) captured at the White Canyon. The White Canyon represents an analog to large scale, steep catchments to investigate the role of sediment supply on debris-flow processes. The TLS dataset was collected at monthly to quarterly intervals, providing a basis for analysis of debris transfer processes occurring on the study slope. A rockfall database of over 72,000 events was generated from 52 change detection analyses and is linked to catchment recharge and transfer processes. The results indicate that the 17 channels analyzed in the White Canyon do not directly match the conceptual models proposed from the supply theory. The channels display a variety of behaviors when exposed to the same climate signature. The temporal data acquisition rate was found to have a significant influence on the dynamics of movement that can be interpreted from TLS change detection analysis. The work highlights the need for higher frequency monitoring and the integration of climate data into the analysis, in order to better understand these dynamic processes.

3D point cloud fusion from UAV and TLS to assess temperate managed forest structures

Light detection and ranging (LiDAR) technology has become one of the most dominant acquisition methods for modeling forest attributes, such as very accurate tree structure information, which is necessary for qualitative forest management and research activities. In this study, we evaluated the efficacy of standalone unmanned aerial vehicle-laser scanning (UAV-LS) and terrestrial laser scanning (TLS) data to accurately estimate forest tree metrics under differing management types. Furthermore, we combined the UAV-LS and TLS data to test whether fusion can improve the mapping of the three-dimensional (3D) structure of individual trees to favor accurate estimates of tree metrics. We initially calculated the percentage of point density per square meter aboveground in each height class at intervals of 1 m for the UAV-LS, TLS, and fusion datasets. This helped illustrate the vertical point density distribution that reflects the structural complexity between broadleaf and conifer trees. We then used tree-level point clouds to assess several tree metrics, such as diameter at breast height (DBH), total tree height (HT), crown projection area (PAC), crown width (WC), crown length (LC), 3D crown surface (SC), and 3D crown volume (VC). Our results indicated that LiDAR fusion can increase the estimation accuracy of DBH and HT, especially in broadleaves (97.8% accuracy). In addition, fusion significantly reshaped the modeled crown structures in both plots, which led to improved estimates for all crown metrics. The results show empirical evidence that LiDAR fusion can also have a determining role in supporting ecosystem services. For example, detailed 3D mapping of tree crowns can be used to assess the mitigation of rainfall`s kinetic energy by tree crowns concerning soil erosion and sedimentation near habitable zones.

Investigating Surface Fractures and Materials Behavior of Cultural Heritage Buildings Based on the Attribute Information of Point Clouds Stored in the TLS Dataset

To date, the potential development of 3D laser scanning has enabled the capture of high-quality and high-precision reality-based datasets for both research and industry. In particular, Terrestrial Laser Scanning (TLS) technology has played a key role in the documentation of cultural heritage. In the existing literature, the geometric properties of point clouds are still the main focus for 3D reconstruction, while the surface performance of the dataset is of less interest due to the partial and limited analysis performed by certain disciplines. As a consequence, geometric defects on surface datasets are often identified when visible through physical inspection. In response to that, this study presents an integrated approach for investigating the materials behavior of heritage building surfaces by making use of attribute point cloud information (i.e., XYZ, RGB, reflection intensity). To do so, fracture surface analysis and material properties are computed to identify vulnerable structures on the existing dataset. This is essential for architects or conservators so that they can assess and prepare preventive measures to minimize microclimatic impacts on the buildings.

Automatic Pairwise Coarse Registration of Terrestrial Point Clouds Using 3D Line Features

The fully automatic registration of 3D terrestrial laser scanning (TLS) point clouds, which is the first step in the usage of point clouds, is a highly challenging task in Light Detection and Ranging (LiDAR) remote sensing applications. Here, an automatic algorithm for pairwise coarse registration of TLS point clouds using 3D line features is proposed. First, the 3D line sets were extracted from the original pair of point clouds respectively, and two arbitrary lines from a point cloud were used to construct a 2-line base; Then, a pair of conjugate 2-line bases were identified from the source and target 3D line sets at a time, based on which a 3D rotation matrix together with its corresponding overlap between the pairwise 3D line sets was calculated; Third, a series of 3D rotation matrixes together with their overlaps were obtained using the traversing strategy to identify conjugate 2-line base pairs, and the 3D rotation matrix with the highest overlap was outputted as the final 3D rotation matrix, which was further used to compute the corresponding lines set from the pairwise 3D line sets; Next, based on the set of common perpendicular midpoints between the line correspondences, the 3D translation vector was computed; Finally, the 3D transformation matrix between pairwise point clouds was obtained by combining the 3D rotation matrix and the 3D translation vector. The proposed method was tested on three different TLS datasets, with experimental results demonstrating that the proposed algorithm could perform well on registering pairwise TLS point clouds. The rotation and translation errors of aligning the nine experimental pairwise point clouds were all less than 0.50° and 0.55m, respectively. This registration framework was also shown to be superior to state-of-the-art methods in terms of registration accuracy.

Classification of Typical Tree Species in Laser Point Cloud Based on Deep Learning

We propose the Point Cloud Tree Species Classification Network (PCTSCN) to overcome challenges in classifying tree species from laser data with deep learning methods. The network is mainly composed of two parts: a sampling component in the early stage and a feature extraction component in the later stage. We used geometric sampling to extract regions with local features from the tree contours since these tend to be species-specific. Then we used an improved Farthest Point Sampling method to extract the features from a global perspective. We input the intensity of the tree point cloud as a dimensional feature and spatial information into the neural network and mapped it to higher dimensions for feature extraction. We used the data obtained by Terrestrial Laser Scanning (TLS) and Unmanned Aerial Vehicle Laser Scanning (UAVLS) to conduct tree species classification experiments of white birch and larch. The experimental results showed that in both the TLS and UAVLS datasets, the input tree point cloud density and the highest feature dimensionality of the mapping had an impact on the classification accuracy of the tree species. When the single tree sample obtained by TLS consisted of 1024 points and the highest dimension of the network mapping was 512, the classification accuracy of the trained model reached 96%. For the individual tree samples obtained by UAVLS, which consisted of 2048 points and had the highest dimension of the network mapping of 1024, the classification accuracy of the trained model reached 92%. TLS data tree species classification accuracy of PCTSCN was improved by 2–9% compared with other models using the same point density, amount of data and highest feature dimension. The classification accuracy of tree species obtained by UAVLS was up to 8% higher. We propose PCTSCN to provide a new strategy for the intelligent classification of forest tree species.

A novel and efficient method for wood–leaf separation from terrestrial laser scanning point clouds at the forest plot level

Abstract With the increasing use of terrestrial laser scanning (TLS) technology in the field of forest ecology, a large number of studies have been carried out on the separation of wood and leaves based on TLS point cloud data. However, most wood–leaf separation methods adopt the point‐wise classification strategy, which is not efficient for processing large‐volume TLS datasets acquired at the forest plot level. In this study, we proposed a segment‐wise classification strategy to improve the efficiency of the wood–leaf separation from large‐volume TLS point cloud datasets collected at the forest plot. The proposed method first decomposes the point cloud into three parts based on the threshold values of its local curvature. Then, the first two parts with lower local curvatures were segmented respectively by a connected component labelling algorithm. Finally, the segmented point clouds were classified into wood or leaf segments according to the segment‐wise geometric features of each segment. We tested our method on both needleleaf and broadleaf forest plots in temperate and tropical forests. We also compared our method with two other state‐of‐the‐art wood–leaf separation methods, that is, the CANUPO and LeWoS. The results showed that our method was more than 10 times faster than the compared methods while maintaining comparable and even higher accuracy. Our study demonstrates that the segment‐wise classification strategy applies to the large‐volume TLS datasets and can greatly improve the efficiency of the classification. The proposed method is simple, fast and universally applicable to the TLS data from various tree species and forest types at the plot level, which may facilitate the adoption of TLS technology by forest ecologists in their studies.

Extraction of discontinuity sets of rocky slopes using iPhone-12 derived 3DPC and comparison to TLS and SfM datasets

Characterisation of a rock mass requires data from the intact rock along with the discontinuities. Assuming that the discontinuities are planar, its characterisation requires its number and orientation. This leads to the analysis of the normal spacing, the persistence and the roughness, among others. The geometrical analysis of the surface enables the calculation of the parameters to characterise the discontinuities, and the use of digital datasets enhance them. Remote sensing techniques, such as the Terrestrial Laser Scanning (TLS) instruments of Structure from Motion (SfM) technique, provide 3D point clouds that enable the geometrical analysis. The scientific community has been testing both techniques since the 2000s, and companies are introducing their use in their workflows. However, the cost of the TLS instrument could still be a barrier to its use to most scholars. Because of this, the community shows a growing interest in Remotely Piloted Aircraft Systems (RPAS) equipped with digital cameras or in smartphones equipped with high-quality cameras to capture digital datasets of rocky slopes. The SfM workflow processes the captured images, reconstructing the rocky slope through a 3D point cloud and textured meshes. Although previous studies show that the SfM-derived point clouds present less quality than TLS-derived datasets in terms of accuracy, the use of SfM is still of interest because of its cost. In 2020 Apple launched the Iphone-12 device, which is equipped with a LiDAR sensor that is not used to capture the surface coordinates but to enhance the photo’s quality. Since then, the community has developed several applications to reconstruct 3D surfaces using this device. This leads to consider this device as an intermediate option between the TLS and SfM to characterise rocky slopes and their discontinuities. In this communication we explore the digitalisation of a rocky slope via TLS instruments, SfM technique and using the Iphone-12 device. It comprises a 26 meter high mechanically excavated rocky slope in Cretaceous marlstones and limestones. To capture the surface, we used three configurations, and we found that to scan ground surface the distance device-surface had to be less than 3 meters. The discontinuities are characterised using the three sources of information using the DSE software. The results show a promising match compared to the TLS or SfM. This evidences that these devices will soon be widely employed for evaluating rocky slopes.

Tree species richness promotes an early increase of stand structural complexity in young subtropical plantations

Abstract Forest structural complexity has been identified as an important driver for promoting simultaneously biodiversity across trophic levels and multiple ecosystem services. However, we still have a limited understanding of the processes that lead to structural complex stands and how they evolve over time. Using terrestrial laser scanning (TLS), we quantified a three‐dimensional (3D) stand structural complexity index (SSCI) in an experimental plantation with a long gradient of tree species richness (1–24 species). The plantation was established in 2009, and we made use of a multi‐temporal TLS dataset recorded during 2012–2019. We found a positive relationship between tree species richness and structural complexity. This relationship became stronger over time. Ten years after planting, SSCI was on average two‐fold higher in 16‐ and 24‐species mixtures than in monocultures. Furthermore, we demonstrate that tree species richness promotes 3D stand structural complexity indirectly by fostering a high vertical heterogeneity and thus greater spatial complementarity in canopy space. Synthesis and applications. Our findings indicate that tree species richness plays a crucial role in promoting stand structural complexity in young plantations, and this role becomes more important already during early stand development. Thus, afforestation measures would benefit from planting multiple native tree species to initiate structurally complex stands.

Characterising Termite Mounds in a Tropical Savanna with UAV Laser Scanning

Termite mounds are found over vast areas in northern Australia, delivering essential ecosystem services, such as enhancing nutrient cycling and promoting biodiversity. Currently, the detection of termite mounds over large areas requires airborne laser scanning (ALS) or high-resolution satellite data, which lack precise information on termite mound shape and size. For detailed structural measurements, we generally rely on time-consuming field assessments that can only cover a limited area. In this study, we explore if unmanned aerial vehicle (UAV)-based observations can serve as a precise and scalable tool for termite mound detection and morphological characterisation. We collected a unique data set of terrestrial laser scanning (TLS) and UAV laser scanning (UAV-LS) point clouds of a woodland savanna site in Litchfield National Park (Australia). We developed an algorithm that uses several empirical parameters for the semi-automated detection of termite mounds from UAV-LS and used the TLS data set (1 ha) for benchmarking. We detected 81% and 72% of the termite mounds in the high resolution (1800 points m−2) and low resolution (680 points m−2) UAV-LS data, respectively, resulting in an average detection of eight mounds per hectare. Additionally, we successfully extracted information about mound height and volume from the UAV-LS data. The high resolution data set resulted in more accurate estimates; however, there is a trade-off between area and detectability when choosing the required resolution for termite mound detection Our results indicate that UAV-LS data can be rapidly acquired and used to monitor and map termite mounds over relatively large areas with higher spatial detail compared to airborne and spaceborne remote sensing.

An automatic method for the measurement of coarse particle movement in a mountain riverbed

In this paper, we propose an automatic approach that is able to recognize the corresponding particles in multi-temporal point clouds and to determine 3D displacement vectors and the rotation parameters between them. The core of this method is a moving window approach combined with the Iterative Closest Point (ICP) algorithm. The particle-wise ICP algorithm is supplemented by initial transformation parameters obtained by key point matching. The routine is designed to be applied to natural objects, which are characterized by their complex geometry. The method’s performance is verified using terrestrial laser scanning data sets representing a mountain riverbed. The experiments performed show that the method enables us to recognize corresponding particles with an effectiveness of 85%. The mean absolute distances between the tile of a point cloud and the particle after alignment are used as the accuracy of the alignment. The median of these values is equal to 2 mm.

An Iterative-Mode Scan Design of Terrestrial Laser Scanning in Forests for Minimizing Occlusion Effects

Occlusion effect, an inherent problem of terrestrial laser scanning (TLS) measurements, limits the potential of TLS data in tree attribute estimation. Multiple scans seek to mitigate this effect to provide enhanced scan completeness. However, the numbers and locations of the scans (i.e., the scan design) are usually determined via a subjective assessment of the tree density, spatial patterns of trees, and attributes to be derived. These could cause suboptimal scan completeness and limit tree attribute estimation. This study proposed an iterative-mode scan design to minimize the occlusion effect. First, we introduced a PoTo index based on visibility analysis to evaluate how many trees can be scanned from a location and to select effective candidates for the optimal TLS location. Second, we introduced a cumulative degree of ring closure (CDRC) to quantify the scan completeness for each candidate and determine the optimal TLS location. The TLS data sets of virtual forests with field-measured and synthetic plot parameter settings were simulated according to iterative- and regular-mode designs by using a Heidelberg light detection and ranging (LiDAR) Operations Simulator (HELIOS). The results demonstrated that an iterative-mode design can improve the scan completeness of trees compared to the regular-mode design. The tree attribute (diameter at breast height (DBH), tree height, stem curve, and crown volume) estimates of the iterative-mode design were less erroneous than those of the regular-mode design (e.g., the root-mean-square error (RMSE) could decrease the stem curve estimation by 38% and the crown volume estimation by 15%). This study suggests that the iterative-mode design can obtain an improved quality of the TLS data, especially for dense stands.

EVALUATING THE QUALITY OF PHOTOGRAMMETRIC POINT-CLOUDS IN CHALLENGING GEO-ENVIRONMENTS – A CASE STUDY IN AN ALPINE VALLEY

Abstract. Precise and accurate three-dimensional geospatial data has become increasingly available thanks to advances in both Terrestrial Laser Scanning (TLS) and Structure-from-Motion Photogrammetry (SfM). These tools provide valuable information for mapping geomorphological features and detect surface changes in mountainous environments. The exploitation of 3D point-clouds has been proven tremendously useful in the field of geosciences. It remains, however, controversial whether cost efficient photogrammetry can provide as accurate and reliable geospatial information as the significantly more expensive laser scanning or not. In this study, a rockfall case site in the territory of Obergurgl, Austria, is investigated in order to provide answers to the above question in a complex environment. The analysis includes different terrestrial photogrammetry configurations aiming to comprehensively define the strengths and limitations of terrestrial photogrammetry over TLS. The latter constitutes an optimized methodology that provides guidelines for costly future assessments as part of the site investigation phase in geohazard management. There are no doubts that compared to traditional and conventional surveying methods TLS and Photogrammetry both offer products much faster and with a much higher data density. In the current study, we show that when photogrammetry is applied following a well-defined optimized strategy, it can be potentially an adequate alternative to more costly TLS datasets for mass movement assessment and monitoring purposes.

EFFICIENT ESTIMATION OF 3D SHIFTS BETWEEN POINT CLOUDS USING LOW-FREQUENCY COMPONENTS OF PHASE CORRELATION

Abstract. Registration of multiple point clouds acquired via terrestrial laser scanning (TLS) is usually compulsory to obtain the scanned data covering a whole urban scene. However, the automated processing of aligning multiple scans is still a concern because of the complex urban environment. To this end, we propose a fast and sturdy estimation of 3D shifts between point clouds by an automated markerfree process using global features, converting translation measurement between two point clouds in the space domain to the frequency domain and estimating the phase difference. By using the low-frequency components from the normalized cross-power spectrum, accurate 3D shifts are calculated by solving parameters in the linear equation representing phase difference angles, with the help of a robust estimator. The results of experiments using TLS datasets of different scenes show that the proposed approach is both practical and efficient. In particular, the proposed approach can achieve results with a translation error of less than about 1.0 m on test datasets.

An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data

Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa ≥ 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA > 84.26% (Kappa ≥ 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy.

Pattern and structure of microtopography implies autogenic origins in forested wetlands

Abstract. Wetland microtopography is a visually striking feature, but also critically influences biogeochemical processes at both the scale of its observation (10−2–102 m2) and at aggregate scales (102–104 m2). However, relatively little is known about how wetland microtopography develops or the factors influencing its structure and pattern. Growing research across different ecosystems suggests that reinforcing processes may be common between plants and their environment, resulting in self-organized patch features, like hummocks. Here, we used landscape ecology metrics and diagnostics to evaluate the plausibility of plant–environment feedback mechanisms in the maintenance of wetland microtopography. We used terrestrial laser scanning (TLS) to quantify the sizing and spatial distribution of hummocks in 10 black ash (Fraxinus nigra Marshall) wetlands in northern Minnesota, USA. We observed clear elevation bimodality in our wettest sites, indicating microsite divergence into two states: elevated hummocks and low elevation hollows. We coupled the TLS dataset to a 3-year water level record and soil-depth measurements, and showed that hummock height (mean = 0.31±0.06 m) variability is largely predicted by mean water level depth (R2=0.8 at the site scale, R2=0.12–0.56 at the hummock scale), with little influence of subsurface microtopography on surface microtopography. Hummocks at wetter sites exhibited regular spatial patterning (i.e., regular spacing of ca. 1.5 m, 25 %–30 % further apart than expected by chance) in contrast to the more random spatial arrangements of hummocks at drier sites. Hummock size distributions (perimeters, areas, and volumes) were lognormal, with a characteristic patch area of approximately 1 m2 across sites. Hummocks increase the effective soil surface area for redox gradients and exchange interfaces in black ash wetlands by up to 32 %, and influence surface water dynamics through modulation of specific yield by up to 30 %. Taken together, the data support the hypothesis that vegetation develops and maintains hummocks in response to anaerobic stresses from saturated soils, with a potential for a microtopographic signature of life.

Understanding Lateral Marsh Edge Erosion with Terrestrial Laser Scanning (TLS)

Coastal wetlands are a crucial buffer zone between land and sea but lateral erosion threatens their long-term sustainability. Better understanding of the forces leading to lateral marsh retreat will benefit the assessment of management options applied to mitigate the erosion. Terrestrial Laser Scanning (TLS), Light Detection and Ranging (lidar), and associated technologies are increasingly being used to assess this erosion. The central objective of this study was to identify a methodology for measuring marsh edge erosion with a TLS and correlate that erosion with exposed roots and incident wave energy. We quantified edge erosion across multiple temporal and spatial scales using a TLS, showing greater than one meter of lateral erosion over a 318-day period. We then evaluated the relationships between the erosion and incident wave energy along with erosion and vegetation roots. Wave height and erosion was strongly related (r2 = 0.99), while vegetation roots did not show an apparent effect. We discuss the challenges that arise from using TLS equipment, TLS data sets, and the use of voxels to measure marsh edge erosion.