Intensity Data Correction for Long-Range Terrestrial Laser Scanners: A Case Study of Target Differentiation in an Intertidal Zone

Tan, Kai; Jin, Chen; Qian, Weiwei; Zhang, W.; Shen, Fang; Cheng, Xinbin

doi:10.3390/rs11030331

Cited by 13 publications

(9 citation statements)

References 42 publications

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“…Numerous studies [33,39,[45][46][47][48] revealed that the original backscattered intensity is inapplicable to discriminate the target surface properties directly. The radiometric correction is necessary to eliminate the impact of the distance and incidence angle on the intensity data and to convert the raw intensity into a corrected value that is proportional or equal to the target's reflectance [39,45,46,[49][50][51][52][53][54][55][56][57][58][59][60][61]. However, regarding the distance effect, due to the possible existence of automatic reducers for the near-distance backscattered signals and amplifiers for weak backscattered signals [46,62], only the part of the intensity data within a specific distance follows the theoretical LiDAR formula [39,48,61,63] and can be corrected effectively through 1/R 2 [33,34].…”

Section: Correction Of the Backscattered Intensitymentioning

confidence: 99%

“…In this study, an empirical correction method is adopted using sand collected from the selected research area. Based on the LiDAR formula, the effects of the distance and incidence angle on the intensity are independent from each other and could be corrected separately by a regression analysis [46,47,[58][59][60]65]. The original backscattered intensity I can be expressed as [46,47,[58][59][60]]…”

Section: Correction Of the Backscattered Intensitymentioning

confidence: 99%

“…where N 2 and N 3 denote the degree of the polynomials and β i and γ i are the polynomial coefficients. In previous studies, the intensity correction normally converts the original intensity into a corrected value that is proportional or equal to the target reflectance [46,47,[58][59][60]. In this study, we directly convert the original intensity into the target moisture contents, which are also independent of the scanning distance and incidence angle but exponentially correlate to the target reflectance.…”

Section: Correction Of the Backscattered Intensitymentioning

confidence: 99%

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Measuring Surface Moisture on a Sandy Beach based on Corrected Intensity Data of a Mobile Terrestrial LiDAR

et al. 2020

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Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 × 200 m per minute).

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Section: Correction Of the Backscattered Intensitymentioning

confidence: 99%

Section: Correction Of the Backscattered Intensitymentioning

confidence: 99%

Section: Correction Of the Backscattered Intensitymentioning

confidence: 99%

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Measuring Surface Moisture on a Sandy Beach based on Corrected Intensity Data of a Mobile Terrestrial LiDAR

et al. 2020

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“…In the present work, the intensity value is considered for the analysis. The intensity value is normalised with respect to a reference level for each single point, therefore the intensity data are dimensionless [ 44 ]. The output has been calibrated to allow the scan data to be range-independent [ 43 ].…”

Section: Methodsmentioning

confidence: 99%

Permanent Laser Scanner and Synthetic Aperture Radar Data: Correlation Characterisation at a Sandy Beach

Biase

Kuschnerus

Lindenbergh

2022

Sensors

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In recent years, our knowledge of coastal environments has been enriched by remotely sensed data. In this research, we co-analyse two sensor systems: Terrestrial Laser Scanning (TLS) and satellite-based Synthetic Aperture Radar (SAR). To successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common environment. TLS provides high-spatiotemporal-resolution information, but it has high economic costs and limited field of view. SAR systems, despite their lower resolution, provide complete, repeated, and frequent coverage. Moreover, Sentinel-1 SAR images are freely available. In the present work, Permanent terrestrial Laser Scanning (PLS) data, collected in Noordwijk (The Netherlands), are compared with simultaneous Sentinel-1 SAR images to investigate their combined use on coastal environments: knowing the relationship between SAR and PLS data, the SAR dataset could be correlated to beach characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. A generally positive linear correlation factor up to 0.5 exists between PLS and SAR data. This correlation occurs for low- or moderate-wind-speed conditions, whilst no particular correlation has been highlighted for high wind intensity. Furthermore, a dependence of the linear correlation on the wind direction has been detected.

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“…In the present work, the intensity value is considered for the analysis. The intensity value is normalized with respect to a reference level for each single point, therefore the intensity data is dimensionless [44]. The output have been calibrated to allow the scan data to be range-independent [45].…”

Section: Pls Data Setmentioning

confidence: 99%

Permanently Installed Terrestrial Laser Scanner And Synthetic Aperture Radar Data: Evaluation Of Correlation Factor Through Time Series Analysis On Coastal Area Under Varying Environmental Conditions

Biase¹,

Kuschnerus²,

Lindenbergh³

2022

Preprint

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In the past years, our knowledge of coastal environments has been enriched by remotely sensed data. However, to successfully extract information from a combination of different sensors systems, it should be understood how these interact with the common coastal environment. In this research we co-analyze two sensor systems: Terrestrial Laser Scanning (TLS) and satellite based Synthetic Aperture Radar (SAR). TLS shows large potential for examining coastal processes thanks to the possibility to retrieve repeated, accurate and dense topographic information in a rapid and non-invasive manner. However, TLS presents some limits due to its high economic costs and limited field of view. SAR systems are among the most used active remote sensor system for Earth Observation. Despite their relatively low resolution, SAR systems provide the ability to monitor and map coastal areas with complete, repeated and frequent coverage, penetrating through clouds and providing all weather monitoring. Moreover, Sentinel-1 SAR images are freely available. The availability of a permanently installed TLS system (PLS, Permanent Laser Scanner) allows us, to extensively compare Sentinel-1 SAR data and topographic laser scans during different conditions on a sandy beach. PLS data are compared with simultaneous Sentinel-1 SAR images in order to investigate the combined use of PLS and SAR in coastal environments. The purpose of this comparison is the investigation of a possible relation between PLS and SAR data: knowing their relation, SAR dataset could be correlated to beaches characteristics. Meteorological and surface roughness have also been taken into consideration in the evaluation of the correlation between PLS and SAR data. The permanently installed laser scanner for the present study is located in Noordwijk (the Netherlands). A generally positive but low correlation exists between the two variables. When considering weather phenomena, their correlation increases and shows a dependence on wind directions and speed. The correlation with the surface roughness, evaluated in terms of root-mean squared height, also depends on specific wind speed and directions.

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Intensity Data Correction for Long-Range Terrestrial Laser Scanners: A Case Study of Target Differentiation in an Intertidal Zone

Cited by 13 publications

References 42 publications

Measuring Surface Moisture on a Sandy Beach based on Corrected Intensity Data of a Mobile Terrestrial LiDAR

Measuring Surface Moisture on a Sandy Beach based on Corrected Intensity Data of a Mobile Terrestrial LiDAR

Permanent Laser Scanner and Synthetic Aperture Radar Data: Correlation Characterisation at a Sandy Beach

Permanently Installed Terrestrial Laser Scanner And Synthetic Aperture Radar Data: Evaluation Of Correlation Factor Through Time Series Analysis On Coastal Area Under Varying Environmental Conditions

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