Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry

Zhao, Xinglei; Zhao, Jianhu; Zhang, Hongmei; Zhou, Fengnian

doi:10.3390/rs10050681

Cited by 10 publications

(8 citation statements)

References 30 publications

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“…One of the key tasks in SSC estimation using RF algorithm is the selection of the input variables. Numerous studies suggested that five group variables, including the band reflectance (Band(i)), enhanced vegetation index (EVI), difference vegetation index (DVI DVI(i,j)), ratio vegetation index (RVI(i,j)), and NDVI(i,j), are the most widely used variables [14][15][16], where i and j are band number. There are four bands reflectance for MOD13Q1, resulting in four band reflectance variables (Band…”

Section: Data Processing and Selection Of Variablesmentioning

confidence: 99%

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

et al. 2018

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Spanning the Yangtze River of China, the Three Gorges Dam (TGD) has received considerable concern worldwide with its potential impacts on the downstream side of the dam. This work investigated the spatio-temporal variations of suspended sediment concentration (SSC) at the downstream section of Yichang-to-Chenglingji from 2002 to 2015. A random forest model was developed to estimate SSC using MODIS ground reflectance products, and the spatio-temporal distributions of SSC were retrieved with this model to investigate the characteristics of water-silt variation. Our results revealed that, relatively, SSC before 2003 was evenly distributed in the downstream Yangtze River, while this spatial distribution pattern changed ce 2003 when the dam started storing water. Temporally, the SSC demonstrated a W-shaped curve of seasonal variation as one peak occurred in September and two troughs in March and November, and showed a significantly decreasing trend after three-stage impoundment. After official operation of the TGD in 2009, the SSC was reduced by over 40% than before 2003. Spatially, the most significant changes occurred in the upper Jingjiang section, where the SSC dropped by 45%. During all stages of impoundment, the water impoundment to 135 m in 2003 had the most significant impact on suspended sediment. The decreased SSC has led to emerging risks of bank failure, aggravated erosion of water front and aggressive down-cutting erosion along the downstream of the dam, as well as other ecological and environmental issues that require urgent attention by the government.

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Section: Data Processing and Selection Of Variablesmentioning

confidence: 99%

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

et al. 2018

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“…As a consequence, this parameter has also been recognised by the Global Climate Observing System as a fundamental climate variable for inland waters [13]. SPM is another key parameter for describing water characteristics and can contribute to assessing aquatic ecosystems' quality [14][15][16][17][18][19][20][21]. It is a bio-optical parameter consisting of a mix of inorganic substances (e.g., mineral sediments), organic constituents (e.g., algal particles and vegetation debris) and water-insoluble microorganisms.…”

Section: Introductionmentioning

confidence: 99%

The Long-Term Detection of Suspended Particulate Matter Concentration and Water Colour in Gravel and Sand Pit Lakes through Landsat and Sentinel-2 Imagery

Ghirardi,

Pinardi,

Nizzoli

et al. 2023

Remote Sensing

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Over the past half century, the demand for sand and gravel has led to extensive quarrying activities, creating many pit lakes (PLs) which now dot floodplains and urbanized regions globally. Despite the potential importance of these environments, systematic data on their location, morphology and water quality remain limited. In this study, we present an extensive assessment of the physical and optical properties in a large sample of PLs located in the Po River basin (Italy) from 1990 to 2021, utilizing a combined approach of remote sensing (Landsat constellation and Sentinel-2) and traditional limnological techniques. Specifically, we focused on the concentration of Suspended Particulate Matter (SPM) and the dominant wavelength (λdom, i.e., water colour). This study aims to contribute to the analysis of PLs at a basin scale as an opportunity for environmental rehabilitation and river floodplain management. ACOLITE v.2022, a neural network particularly suitable for the analysis of turbid waters and small inland water bodies, was used to atmospherically correct satellite images and to obtain SPM concentration maps and the λdom. The results show a very strong correlation between SPM concentrations obtained in situ and those obtained from satellite images, both for data derived from Landsat (R2 = 0.85) and Sentinel-2 images (R2 = 0.82). A strong correlation also emerged from the comparison of spectral signatures obtained in situ via WISP-3 and those derived from ACOLITE, especially in the visible spectrum (443–705 nm, SA = 10.8°). In general, it appeared that PLs with the highest mean SPM concentrations and the highest mean λdom are located along the main Po River, and more generally near rivers. The results also show that active PLs exhibit a poor water quality status, especially those of small sizes (<5 ha) and directly connected to a river. Seasonal comparison shows the same trend for both SPM concentration and λdom: higher values in winter gradually decreasing until spring–summer, then increasing again. Finally, it emerged that the end of quarrying activity led to a reduction in SPM concentration from a minimum of 43% to a maximum of 72%. In this context, the combined use of Landsat and Sentinel-2 imagery allowed for the evaluation of the temporal evolution of the physical and optical properties of the PLs in a vast area such as the Po River basin (74,000 km2). In particular, the Sentinel-2 images consistently proved to be a reliable resource for capturing episodic and recurring quarrying events and portraying the ever-changing dynamics of these ecosystems.

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“…Airborne LiDAR bathymetry (ALB) uses scanning and pulsed laser beams from air to detect shallow waters, showing high efficiency, accuracy, and resolution and economy, safety, and flexibility [1,2]. Apart from water depth measurement [3][4][5][6][7][8], ALB has been applied to water-land discrimination [9][10][11][12], suspended sediment concentration monitoring [13,14], ocean wave pattern analysis [15][16][17][18], and seabed classification [19][20][21]. Integrated infrared (IR) and green ALB systems use green laser with a wavelength of 532 nm to detect water bottom and IR laser with a wavelength of 1064 nm to detect water surface.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser

Liang

Zhao

et al. 2021

Remote Sensing

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The discrimination of water–land waveforms is a critical step in the processing of airborne topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser waveforms of airborne LiDAR, have been used in identifying water–land interfaces in coastal waters through waveform clustering. However, water–land discrimination using other IR waveform features, such as full width at half maximum, area, width, and combinations of different features, has not been evaluated and compared with other methods. Furthermore, false alarms often occur when water–land discrimination in coastal areas is conducted using IR laser waveforms because of environmental factors. This study provides an optimal feature for water–land discrimination using an IR laser by comparing the performance of different waveform features and proposes a dual-clustering method integrating K-means and density-based spatial clustering applications with noise algorithms to improve the accuracy of water–land discrimination through the clustering of waveform features and positions of IR laser spot centers. The proposed method is used for practical measurement with Optech Coastal Zone Mapping and Imaging LiDAR. Results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms among the researched features. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The amplitudes of IR laser waveform and the proposed dual-clustering method are recommended for water–land discrimination in coastal and inland waters because of the high accuracy, resolution, and automation of the methods.

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Remote Sensing of Sub-Surface Suspended Sediment Concentration by Using the Range Bias of Green Surface Point of Airborne LiDAR Bathymetry

Cited by 10 publications

References 30 publications

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

Spatio-Temporal Patterns and Impacts of Sediment Variations in Downstream of the Three Gorges Dam on the Yangtze River, China

The Long-Term Detection of Suspended Particulate Matter Concentration and Water Colour in Gravel and Sand Pit Lakes through Landsat and Sentinel-2 Imagery

Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser

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