A new algorithm for estimating ground elevation and vegetation characteristics in coastal salt marshes from high‐resolution UAV‐based LiDAR point clouds

2020 ◽  
Vol 45 (14) ◽  
pp. 3687-3701
Author(s):  
Daniele Pinton ◽  
Alberto Canestrelli ◽  
Benjamin Wilkinson ◽  
Peter Ifju ◽  
Andrew Ortega
Keyword(s):  
High Resolution ◽  
Salt Marshes ◽  
Point Clouds ◽  
Coastal Salt Marshes ◽  
Vegetation Characteristics

scholarly journals Estimating Ground Elevation and Vegetation Characteristics in Coastal Salt Marshes Using UAV-Based LiDAR and Digital Aerial Photogrammetry

2021 ◽  
Vol 13 (22) ◽  
pp. 4506
Author(s):  
Daniele Pinton ◽  
Alberto Canestrelli ◽  
Benjamin Wilkinson ◽  
Peter Ifju ◽  
Andrew Ortega
Keyword(s):  
Salt Marsh ◽  
High Resolution ◽  
Salt Marshes ◽  
Point Cloud ◽  
Point Clouds ◽  
Vegetation Density ◽  
Marsh Vegetation ◽  
Vegetation Height ◽  
Aerial Photogrammetry ◽  
Vegetation Characteristics

This study evaluates the skills of two types of drone-based point clouds, derived from LiDAR and photogrammetric techniques, in estimating ground elevation, vegetation height, and vegetation density on a highly vegetated salt marsh. The proposed formulation is calibrated and tested using data measured on a Spartina alterniflora-dominated salt marsh in Little Sapelo Island, USA. The method produces high-resolution (ground sampling distance = 0.40 m) maps of ground elevation and vegetation characteristics and captures the large gradients in the proximity of tidal creeks. Our results show that LiDAR-based techniques provide more accurate reconstructions of marsh vegetation (height: MAEVH = 12.6 cm and RMSEVH = 17.5 cm; density: MAEVD = 6.9 stems m−2 and RMSEVD = 9.4 stems m−2) and morphology (MAEM = 4.2 cm; RMSEM = 5.9 cm) than Digital Aerial Photogrammetry (DAP) (MAEVH = 31.1 cm; RMSEVH = 38.1 cm; MAEVD = 12.7 stems m−2; RMSEVD = 16.6 stems m−2; MAEM = 11.3 cm; RMSEM = 17.2 cm). The accuracy of the classification procedure for vegetation calculation negligibly improves when RGB images are used as input parameters together with the LiDAR-UAV point cloud (MAEVH = 6.9 cm; RMSEVH = 9.4 cm; MAEVD = 10.0 stems m−2; RMSEVD = 14.0 stems m−2). However, it improves when used together with the DAP-UAV point cloud (MAEVH = 21.7 cm; RMSEVH = 25.8 cm; MAEVD = 15.2 stems m−2; RMSEVD = 18.7 stems m−2). Thus, we discourage using DAP-UAV-derived point clouds for high-resolution vegetation mapping of coastal areas, if not coupled with other data sources.

scholarly journals Evaluation of UAV LiDAR for Mapping Coastal Environments

2019 ◽  
Vol 11 (24) ◽  
pp. 2893 ◽  
Author(s):  
Yi-Chun Lin ◽  
Yi-Ting Cheng ◽  
Tian Zhou ◽  
Radhika Ravi ◽  
Seyyed Hasheminasab ◽  
...  
Keyword(s):  
High Resolution ◽  
Lake Michigan ◽  
Point Clouds ◽  
Volume Loss ◽  
Coastal Environments ◽  
Average Volume ◽  
Survey Period ◽  
Shoreline Changes ◽  
Point Density ◽  
One Year

Unmanned Aerial Vehicle (UAV)-based remote sensing techniques have demonstrated great potential for monitoring rapid shoreline changes. With image-based approaches utilizing Structure from Motion (SfM), high-resolution Digital Surface Models (DSM), and orthophotos can be generated efficiently using UAV imagery. However, image-based mapping yields relatively poor results in low textured areas as compared to those from LiDAR. This study demonstrates the applicability of UAV LiDAR for mapping coastal environments. A custom-built UAV-based mobile mapping system is used to simultaneously collect LiDAR and imagery data. The quality of LiDAR, as well as image-based point clouds, are investigated and compared over different geomorphic environments in terms of their point density, relative and absolute accuracy, and area coverage. The results suggest that both UAV LiDAR and image-based techniques provide high-resolution and high-quality topographic data, and the point clouds generated by both techniques are compatible within a 5 to 10 cm range. UAV LiDAR has a clear advantage in terms of large and uniform ground coverage over different geomorphic environments, higher point density, and ability to penetrate through vegetation to capture points below the canopy. Furthermore, UAV LiDAR-based data acquisitions are assessed for their applicability in monitoring shoreline changes over two actively eroding sandy beaches along southern Lake Michigan, Dune Acres, and Beverly Shores, through repeated field surveys. The results indicate a considerable volume loss and ridge point retreat over an extended period of one year (May 2018 to May 2019) as well as a short storm-induced period of one month (November 2018 to December 2018). The foredune ridge recession ranges from 0 m to 9 m. The average volume loss at Dune Acres is 18.2 cubic meters per meter and 12.2 cubic meters per meter within the one-year period and storm-induced period, respectively, highlighting the importance of episodic events in coastline changes. The average volume loss at Beverly Shores is 2.8 cubic meters per meter and 2.6 cubic meters per meter within the survey period and storm-induced period, respectively.

2D quantitative analysis of fractures from high-resolution photos for the geomechanical characterization of rock masses

2021 ◽  
Author(s):  
Emmanuel Wyser ◽  
Lidia Loiotine ◽  
Charlotte Wolff ◽  
Gioacchino Francesco Andriani ◽  
Michel Jaboyedoff ◽  
...  
Keyword(s):  
High Resolution ◽  
Point Clouds ◽  
Mitigation Measures ◽  
Future Perspective ◽  
Rock Masses ◽  
Sampling Errors ◽  
Discontinuity Sets ◽  
Adverse Weather ◽  
Geomechanical Characterization

<p>The identification of discontinuity sets and their properties is among the key factors for the geomechanical characterization of rock masses, which is fundamental for performing stability analyses, and for planning prevention and mitigation measures as well.<br>In practice, discontinuity data are collected throughout difficult and time-consuming field surveys, especially when dealing with areas of wide extension, difficult accessibility, covered by dense vegetation, or with adverse weather conditions. Consequently, even experienced operators may introduce sampling errors or misinterpretations, leading to biased geomechanical models for the investigated rock mass.<br>In the last decades, new remote techniques such as photogrammetry,<em> Light Detection and Ranging</em> (LiDAR), <em>Unmanned Aerial Vehicle</em> (UAV) and <em>InfraRed Thermography </em>(IRT) have been introduced to overcome the limits of conventional surveys. We propose here a new tool for extracting information on the fracture pattern in rock masses, based on <em>remote sensing </em>methods, with particular reference to the analysis of high-resolution georeferenced photos. The first step consists in applying the <em>Structure from Motion</em> (SfM) technique on photos acquired by means of digital cameras and UAV techniques. Once aligned and georeferenced, the orthophotos are exported in a GIS software, to draw the fracture traces at an appropriate scale. We developed a MATLAB routine to extract information on the geostructural setting of rock masses by performing a quantitative 2D analysis of the fracture traces, based on formulas reported in the literature. The code was written by testing few experimental and simple traces and was successively validated on an orthophoto from a real case study.<br>Currently, the script plots the fracture traces as polylines and calculates their orientation (strike) and length. Subsequently, it detects the main discontinuity sets by fitting an experimental composite Gaussian curve on histograms showing the number of discontinuities according to their orientation, and splitting the curve in simpler Gaussian curves, with peaks corresponding to the main discontinuity sets.<br>Then, for each set, a linear scanline intersecting the highest number of traces is plotted, and the apparent and real spacing are calculated. In a second step, a grid of circular scanlines covering the whole area where the traces are located is plotted, and the mean trace intensity, trace density and trace length estimators are calculated.<br>It is expected to test the presented tools on other case studies, in order to optimize them and calculate additional metrics, such as persistence and block sizes, useful to the geomechanical characterization of rock masses.<br>As a future perspective, a similar approach could be investigated for 3D analyses from point clouds.</p>

Coastal Salt Marshes

2020 ◽  
pp. 1-39
Author(s):  
Maria Sarika ◽  
Andreas Zikos
Keyword(s):  
Salt Marshes ◽  
Coastal Salt Marshes

scholarly journals High resolution carbon stock and soil data for three salt marshes along the northeastern coast of North America

Data in Brief ◽  
2018 ◽  
Vol 19 ◽  
pp. 2438-2441
Author(s):  
Lee B. van Ardenne ◽  
Serge Jolicoeur ◽  
Dominique Bérubé ◽  
David Burdick ◽  
Gail L. Chmura
Keyword(s):  
High Resolution ◽  
North America ◽  
Salt Marshes ◽  
Carbon Stock ◽  
Northeastern Coast

scholarly journals Evaluation of the Apple iPhone 12 Pro LiDAR for an Application in Geosciences

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregor Luetzenburg ◽  
Aart Kroon ◽  
Anders A. Bjørk
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Cost Effective ◽  
Point Clouds ◽  
3D Models ◽  
Absolute Accuracy ◽  
Wide Range ◽  
Financial Investments ◽  
Coastal Cliff ◽  
High Resolution Models

AbstractTraditionally, topographic surveying in earth sciences requires high financial investments, elaborate logistics, complicated training of staff and extensive data processing. Recently, off-the-shelf drones with optical sensors already reduced the costs for obtaining a high-resolution dataset of an Earth surface considerably. Nevertheless, costs and complexity associated with topographic surveying are still high. In 2020, Apple Inc. released the iPad Pro 2020 and the iPhone 12 Pro with novel build-in LiDAR sensors. Here we investigate the basic technical capabilities of the LiDAR sensors and we test the application at a coastal cliff in Denmark. The results are compared to state-of-the-art Structure from Motion Multi-View Stereo (SfM MVS) point clouds. The LiDAR sensors create accurate high-resolution models of small objects with a side length > 10 cm with an absolute accuracy of ± 1 cm. 3D models with the dimensions of up to 130 × 15 × 10 m of a coastal cliff with an absolute accuracy of ± 10 cm are compiled. Overall, the versatility in handling outweighs the range limitations, making the Apple LiDAR devices cost-effective alternatives to established techniques in remote sensing with possible fields of application for a wide range of geo-scientific areas and teaching.

Coastal salt-marshes plant communities of the Salicornietea fruticosae class in Apulia (Italy)

Biologia ◽  
2014 ◽  
Vol 69 (1) ◽  
Author(s):  
Saverio Sciandrello ◽  
Valeria Tomaselli
Keyword(s):  
Salt Marsh ◽  
Plant Communities ◽  
Salt Marshes ◽  
Field Work ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Apulia Region ◽  
Coastal Salt Marshes ◽  
Ecological Features ◽  
Halocnemum Strobilaceum

AbstractAn overview of the salt-marsh herbland and scrub vegetation belonging to the class Salicornietea fruticosae Br.-Bl. et Tx. ex A. Bolòs y Vayreda 1950 in Apulia is presented. Data available from literature have been supplemented with original relevés performed in different locations of the Apulia region. On the basis of a total of 297 relevés, fifteen communities have been defined, according to the traditional phytosociological system based on dominant and/or diagnostic taxa. For comparison purposes, the salt-marsh vegetation has been classified using numerical methods. The results obtained show that most of the clusters correspond to specific associations, and confirm the division into vegetation alliances and orders. Numerical analysis also allowed us to assign the proper allocation of some associations and plant communities drawn from literature. Five alliances, with plant communities characterized by specific ecological features, have been discriminated: Sarcocornion alpini and Arthrocnemion glauci (lower marshes), Salicornion fruticosae (middle marshes), Inulion crithmoidis and Suaedion brevofoliae (upper marshes). In addition, during the field work, a population of Halocnemum strobilaceum (Arthrocnemo-Halocnemetum strobilacei), new record for the Apulia region, has been found.

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