Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard

Błaszczyk, Małgorzata; Laska, Michał; Sivertsen, Agnar Holten; Jawak, Shridhar D.

doi:10.3390/rs14030601

Cited by 14 publications

(5 citation statements)

References 47 publications

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“…(4) Most intense erosion induced STCs were detected within a one-year observed period within the gully headcut, where mass wasting resulted in the gradual uphill retreat of the headcut. (5) The developed systematic framework is applicable to all studies that require multitemporal TLS surveys for monitoring of STCs over complex geomorphic features.…”

Section: Supplementary Materialsmentioning

confidence: 99%

“…Terrestrial LiDAR scanning (TLS) has in preceding years emerged as one of the most accurate and reliable geospatial methods for creation of very-high resolution (VHR) models over complex geomorphic features [1][2][3][4][5]. TLS is an indirect measuring technique that actively emits laser beams from a terrestrial, tripod-based station while simultaneously measuring the distance to intersecting surfaces from received laser returns and forming a dense 3D point cloud [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology

et al. 2022

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Terrestrial LiDAR scanning (TLS) has in preceding years emerged as one of the most accurate and reliable geospatial methods for the creation of very-high resolution (VHR) models over gullies and other complex geomorphic features. Rough terrain morphology and rapid erosion induced spatio-temporal changes (STCs) can lead to significant challenges in multi-temporal field TLS surveys. In this study, we present a newly developed systematic framework for the optimization of multi-temporal terrestrial LiDAR surveys through the implementation of thorough systematic pre-survey planning and field preparation phases. The developed systematic framework is aimed at increase of accuracy and repeatability of multi-temporal TLS surveys, where optimal TLS positions are determined based on visibility analysis. The whole process of selection of optimal TLS positions was automated with the developed TLS positioning tool (TPT), which allows the user to adjust the parameters of visibility analysis to local terrain characteristics and the specifications of available terrestrial laser scanners. Application and validation of the developed framework were carried out over the gully Santiš (1226.97 m2), located at Pag Island (Croatia). Eight optimal TLS positions were determined by the TPT tool, from which planned coverage included almost 97% of the whole gully area and 99.10% of complex gully headcut morphology. In order to validate the performance of the applied framework, multi-temporal TLS surveys were carried out over the gully Santiš in December 2019 and 2020 using the Faro Focus M70 TLS. Field multi-temporal TLS surveys have confirmed the accuracy and reliability of the developed systematic framework, where very-high coverage (>95%) was achieved. Shadowing effects within the complex overhangs in the gully headcut and deeply incised sub-channels were successfully minimalized, thus allowing accurate detection and quantification of erosion induced STCs. Detection of intensive erosion induced STCs within the observed one-year period was carried out for the chosen part of the gully headcut. Most of the detected STCs were related to the mass collapse and gradual uphill retreat of the headcut, where in total 2.42 m2 of soil has been eroded. The developed optimization framework has significantly facilitated the implementation of multi-temporal TLS surveys, raising both their accuracy and repeatability. Therefore, it has great potential for further application over gullies and other complex geomorphic features where accurate multi-temporal TLS surveys are required for monitoring and detection of different STCs.

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Section: Supplementary Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology

et al. 2022

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“…The variation of the NBR index was described by equation ( 6) in relation to the MSAVI index (R 2 =0.965, p<0.001, F=329.39), by equation (7) (6) (7) (8) where:…”

Section: Resultsmentioning

confidence: 99%

Characterization of Vegetation and Prediction of Biomass Production in Silage Corn Crop Based on Satellite Images

Herbei¹,

POPESCU²,

Sala³

2022

LSSD

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The study used methods based on remote sensing to analyze the dynamics of a silage corn crop and predict biomass production. The corn crop, the Micado hybrid, was organized within the Didactic and Experimental Station of the "King Mihai I" University of Life Sciences in Timisoara, under the specific conditions of the 2021-2022 agricultural year. The soil in the crop plot was of chernozem type, and the crop was in a non-irrigated system. To obtain the satellite data, the Sentinel 2 system was used. To characterize the plot and the crop of silage corn, 14 sets of images were taken, in the interval March - July 2022. From the analysis of the images, the spectral data were obtained, and based on the consecrated formulas MSAVI, NDMI, NDVI and NBR indexes were calculated. The ANOVA test confirmed the reliability of the data and the presence of variance in the data set (F>Fcrit, p<0.001, for Alpha=0.001). The level of correlations identified between the calculated indices was between r=0.967 (NBR with NDVI) and r=0.996 (NDVI with MSAVI). The variation of the NBR index, in relation to the other indices, was described by linear equations, in terms of statistical safety (R2=0.965, p<0.001, in relation to MSAVI; R2=0.989, p<0.001 in relation to NDMI; R2=0.934, p<0.001 in relation to NDVI). The variation of indices in relation to time (t, days) during the study period was described by polynomial equations of the 3rd degree, in terms of statistical safety (R2=0.964 for MSAVI, R2=0.934 for NDMI, R2=0.941 for NDVI, and R2=0.961 for NBR). Regression analysis facilitated obtaining some equations, as models for predicting biomass production, under conditions of statistical safety. Based on the RMSEP parameter, it was found that the most reliable level of production prediction was obtained in the case of MSAVI and NDMI indices (RMSEP=0.05923).

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“…After the airborne LiDAR system completes the laser scanning, the data obtained includes the position, orientation, and laser scanning distance [122]. Among them, the position and orientation include differential GPS and IMU information.…”

Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Combined Use of Aerial Photogrammetry and Terrestrial Laser Scanning for Detecting Geomorphological Changes in Hornsund, Svalbard

Cited by 14 publications

References 47 publications

A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology

A New Systematic Framework for Optimization of Multi-Temporal Terrestrial LiDAR Surveys over Complex Gully Morphology

Characterization of Vegetation and Prediction of Biomass Production in Silage Corn Crop Based on Satellite Images

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

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