Remote Detection of Moisture and Bio-Deterioration of Building Walls by Time-Of-Flight and Phase-Shift Terrestrial Laser Scanners

Suchocki, Czesław; Damięcka-Suchocka, Marzena; Katzer, Jacek; Janicka, Joanna; Rapiński, Jacek; Stałowska, Paulina

doi:10.3390/rs12111708

Cited by 26 publications

(13 citation statements)

References 39 publications

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“…As with laser scanning, which was not profitable (e.g., high cost of the device, weight of collected data), and yet still found various applications [83][84][85], bathymetric scanning will also be utilised in the analysis of the natural environment, rather than focusing on the technology itself. The data obtained can often discourage researchers, due to the required processing techniques.…”

Section: Discussionmentioning

confidence: 99%

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Tysiąc

2020

Remote Sensing

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One of the major tasks in environmental protection is monitoring the coast for negative impacts due to climate change and anthropopressure. Remote sensing techniques are often used in studies of impact assessment. Topographic and bathymetric procedures are treated as separate measurement methods, while methods that combine coastal zone analysis with underwater impacts are rarely used in geotechnical analyses. This study presents an assessment of the bathymetry airborne system used for coastal monitoring, taking into account environmental conditions and providing a comparison with other monitoring methods. The tests were carried out on a section of the Baltic Sea where, despite successful monitoring, coastal degradation continues. This technology is able to determine the threat of coastal cliff erosion (based on the geotechnical analyses). Shallow depths have been reported to be a challenge for bathymetric Light Detection and Ranging (LiDAR), due to the difficulty in separating surface, water column and bottom reflections from each other. This challenge was overcome by describing the classification method used which was the CANUPO classification method as the most suitable for the point cloud processing. This study presents an innovative approach to identifying natural hazards, by combining analyses of coastal features with underwater factors. The main goal of this manuscript is to assess the suitability of using bathymetry scanning in the Baltic Sea to determine the factors causing coastal erosion. Furthermore, a geotechnical analysis was conducted, taking into account geometrical ground change underwater. This is the first study which uses a coastal monitoring approach, combining geotechnical computations with remote sensing data. This interdisciplinary scientific research can increase the awareness of the environmental processes.

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Section: Discussionmentioning

confidence: 99%

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Tysiąc

2020

Remote Sensing

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“…Due to this, the water molecules contained in the ground-along with the salts dissolved in them-gradually penetrate into the wall's components, in turn moisturizing and salting it. The level of dampness and salinity of walls in such buildings usually significantly exceeds the permissible levels determined on the basis of classifications commonly accepted in technical literature [2][3][4][5][6], which are presented in Figure 1. [8][9][10][11][12][13][14]).…”

Section: Introductionmentioning

confidence: 95%

“…The problem of excessive moisture in brick walls usually concerns old buildings that have been used for several dozen years or more [1][2][3][4][5][6]. A special group of such old buildings concerns those that are included in the register of monuments and those that are subjected to special conservation protection.…”

Section: Introductionmentioning

confidence: 99%

Verification of a Nondestructive Method for Assessing the Humidity of Saline Brick Walls in Historical Buildings

Hoła

Sadowski

2020

Applied Sciences

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The paper presents the results of the verification of the neural method for assessing the humidity of saline brick walls. The method was previously developed by the authors and can be useful for the nondestructive assessment of the humidity of walls in historic buildings when destructive intervention during testing is not possible due to conservation restrictions. However, before being implemented in construction practice, this method requires validation by verification on other historic buildings, which to date has not been done. The paper presents the results of such verification, which has never been carried out before, and thus extends the scope of knowledge related to the issue. For experimental verification of the artificial neural network (ANN), the results of moisture tests of two selected historic buildings, other than those used for ANN learning and testing processes, were used. An artificial unidirectional multilayer neural network with backward error propagation and the algorithm for learning conjugate gradient (CG) was found to be useful for this purpose. The obtained satisfactory value of the linear correlation coefficient R of 0.807 and low average absolute error |Df| of 1.16% confirms this statement. The values of average relative error |RE| of 19.02%, which were obtained in this research, were not very high for an in-situ study. Moreover, the relative error values |RE| were mostly in the range of 15% to 25%.

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“…1. Terrestrial level (Terrestrial Laser Scanning, TLS) for, among others, architectural heritage [17,18], buildings [19][20][21], roads [22], and bridges [23,24]; or 2. airborne level (Airborne Laser Scanning, ALS), using a flying platform (aircraft, helicopter, or Unmanned Aerial Vehicle; UAV), for aerial objects such as forests [25][26][27] or urban environments [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

2021

Self Cite

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Remote sensing techniques, developed over the past four decades, have enabled large-scale forest inventory. Light Detection and Ranging (LiDAR), as an active remote sensing technology, allows for the acquisition of three-dimensional point clouds of scanned areas, as well as a range of features allowing for increased performance of object extraction and classification approaches. As many publications have shown, multiple LiDAR-derived metrics, with the assistance of classification algorithms, contribute to the high accuracy of tree species discrimination based on data obtained by laser scanning. The aim of this article is to review studies in the species classification literature which used data collected by Airborne Laser Scanning. We analyzed these studies to figure out the most efficient group of LiDAR-derived features in species discrimination. We also identified the most powerful classification algorithm, which maximizes the advantages of the derived metrics to increase species discrimination performance. We conclude that features extracted from full-waveform data lead to the highest overall accuracy. Radiometric features with height information are also promising, generating high species classification accuracies. Using random forest and support vector machine as classifiers gave the best species discrimination results in the reviewed publications.

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Remote Detection of Moisture and Bio-Deterioration of Building Walls by Time-Of-Flight and Phase-Shift Terrestrial Laser Scanners

Cited by 26 publications

References 39 publications

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Bringing Bathymetry LiDAR to Coastal Zone Assessment: A Case Study in the Southern Baltic

Verification of a Nondestructive Method for Assessing the Humidity of Saline Brick Walls in Historical Buildings

A Review of Tree Species Classification Based on Airborne LiDAR Data and Applied Classifiers

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