Effect of Turbidity, Temperature and Salinity of Waters on Depth Data from Airborne LiDAR Bathymetry

The influence of seawater parameters cannot be ignored when conducting bathymetric LiDAR (Laser Induced Detection and Ranging or Light Detection and Ranging) surveys such as turbidity, temperature, and salinity. Turbidity affects the attenuation diffusion coefficient of the green laser is penetrating the air column. The comparison of LiDAR bathymetric depth with Secchi disk depth is used as a reference in determining the effect of turbidity. The results are in locations with primarily clear water the ability of LiDAR can penetrate up to 7m, while in turbid waters up to 3m. On average, the ability of the green laser LiDAR bathymetry can penetrate the waters of 1.5-2 times the depth at the location of this study around the bay of Lampung Indonesia. Other water parameters are temperature and salinity. These parameters are used to calculate the refractive index value of water. The Different temperature and salinity values in a water column can result in differences in the accuracy of the bathymetry LiDAR depth of 4-6mm. The influence of water column parameters can be a concern in planning and processing airborne LiDAR altimetry (ALB) surveys.

Comparison of multichannel signal deconvolution algorithms in airborne LiDAR bathymetry based on wavelet transform

Airborne LiDAR bathymetry offers low cost and high mobility, making it an ideal option for shallow-water measurements. However, due to differences in the measurement environment and the laser emission channel, the received waveform is difficult to extract using a single algorithm. The choice of a suitable waveform processing method is thus of extreme importance to guarantee the accuracy of the bathymetric retrieval. In this study, we use a wavelet-denoising method to denoise the received waveform and subsequently test four algorithms for denoised-waveform processing, namely, the Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation and measured multichannel databases are used to evaluate the algorithms, with focus on improving their performance after data-denoising and their capability of extracting water depth. Results show that applying wavelet denoising before deconvolution improves the extraction accuracy. The four algorithms perform better for the shallow-water orthogonal polarization channel (PMT2) than for the shallow horizontal row polarization channel (PMT1). Of the four algorithms, RLD provides the best signal-detection rate, and RFD is the most robust; BD has low computational efficiency, and WFD performs poorly in deep water (< 25 m).

WATER TURBIDITY ESTIMATION FROM LIDAR BATHYMETRY DATA BY FULL-WAVEFORM ANALYSIS – COMPARISON OF TWO APPROACHES

Airborne LiDAR bathymetry is an efficient technique for surveying the bottom of shallow waters. In addition, the measurement data contain valuable information about the local turbidity conditions in the water body. The extraction of this information requires appropriate evaluation methods examining the decay of the recorded waveform signal. Existing approaches are based on several assumptions concerning the influence of the ALB system on the waveform signal, the extraction of the volume backscatter, and the directional independence of turbidity. The paper presents a novel approach that overcomes the existing limitations using two alternative turbidity estimation methods as well as different variants of further processed full-waveform data. For validation purposes, the approach was applied to a data set of a shallow inland water. The results of the quantitative evaluation show, which method and which data basis is best suited for the derivation of area wide water turbidity information.

Potential of a Non-linear Full-Waveform Stacking Technique in Airborne LiDAR Bathymetry

Airborne LiDAR bathymetry is an efficient measurement method for area-wide acquisition of water bottom topography in shallow water areas. However, the method has a limited penetration depth into water bodies due to water turbidity. This affects the accuracy and reliability of the determination of water bottom points in waters with high turbidity or larger water depths. Furthermore, the coverage of the water bottom topography is also limited. In this contribution, advanced processing methods are presented with the goal of increasing the evaluable water depth, resulting in an improved coverage of the water bottom by measurement points. The methodology moves away from isolated evaluation of individual signals to a determination of water bottom echoes, taking into account information from closely adjacent measurements, assuming that these have similar or correlated characteristics. The basic idea of the new processing approach is the combination of closely adjacent full-waveform data using full-waveform stacking techniques. In contrast to established waveform stacking techniques, we do not apply averaging, which entails low-pass filtering effects, but a modified majority voting technique. This has the effect of amplification of repeating weak characteristics and an improvement of the signal-noise-ratio. As a consequence, it is possible to detect water bottom points that cannot be detected by standard methods. The results confirm an increased penetration water depth by about 27% with a high reliability of the additionally extracted water bottom points along with a larger coverage of the water bottom topography.

Comparison of Multichannel Signal Deconvolution Algorithms in Airborne LiDAR Bathymetry Based on Wavelet Transform

Airborne LiDAR bathymetry offers low cost and high mobility, making it an ideal option for shallow-water measurements. However, due to differences in the measurement environment and the laser emission channel, the received waveform is difficult to extract using a single algorithm. The choice of a suitable waveform processing method is thus extremely important to guarantee the accuracy of the bathymetric retrieval. In this work, we use a wavelet-denoising method to denoise the received waveform and then test four algorithms for denoised-waveform processing: Richardson–Lucy deconvolution (RLD), blind deconvolution (BD), Wiener filter deconvolution (WFD), and constrained least-squares filter deconvolution (RFD). The simulation database and the measured multichannel database are used to evaluate the algorithms, with the focus on improving their performance after the data-denoising preprocessing and their capability of extracting water depth. The results show that applying wavelet denoising before deconvolution improves the extraction accuracy. The four algorithms perform better for the shallow water orthogonal polarization channel (PMT2) than the shallow horizontal row polarization channel (PMT1). Of the four algorithms, RLD provides the best signal-detection rate, and RFD is the most robust. BD has low computational efficiency, and WFD performs poorly in deep water (<25 m).

Refined Geometric Modeling of Laser Pulse Propagation in Airborne LiDAR Bathymetry

To achieve a geometrically accurate representation of the water bottom, airborne LiDAR bathymetry (ALB) requires the correction of the raw 3D point coordinates due to refraction at the air–water interface, different signal velocity in air and water, and further propagation induced effects. The processing of bathymetric LiDAR data is based on a geometric model of the laser bathymetry pulse propagation describing the complex interactions of laser radiation with the water medium and the water bottom. The model comprises the geometric description of laser ray, water surface, refraction, scattering in the water column, and diffuse bottom reflection. Conventional geometric modeling approaches introduce certain simplifications concerning the water surface, the laser ray, and the bottom reflection. Usually, the local curvature of the water surface and the beam divergence are neglected and the travel path of the outgoing and the returned pulse is assumed to be identical. The deviations between the applied geometric model and the actual laser beam path cause a coordinate offset at the water bottom, which affects the accuracy potential of the measuring method. The paper presents enhanced approaches to geometric modeling which are based on a more accurate representation of water surface geometry and laser ray geometry and take into account the diffuse reflection at the water bottom. The refined geometric modeling results in an improved coordinate accuracy at the water bottom. The impact of the geometric modeling methods on the accuracy of the water bottom points is analyzed in a controlled manner using a laser bathymetry simulator. The findings will contribute to increase the accuracy potential of modern ALB systems.

Pengolahan Data Airborne Lidar Bathymetry untuk Perairan Dangkal (Studi Kasus : Perairan Kolaka Sulawesi Tenggara)

Batimetri adalah teknik mengukur kedalaman di bawah air dan studi tentang tiga dimensi lantai samudra atau danau. Sebuah peta batimetri umumnya menampilkan relief lantai atau dataran dengan garis-garis kontur (contour lines) yang disebut kontur kedalaman (depth contours atau isobath), dan dapat memiliki informasi tambahan berupa informasi navigasi permukaan. Dengan semakain majunya teknologi pada saat ini menuntut tersedianya informasi dan data yang cepat, tepat dan akurat, dengan tidak mengorbankan banyak elemen yang terkait baik personil maupun material itu semua merupakan factor penentu keberhasilan dalam suatu kegiatan survey bathymetry yang telah didilakukan Untuk memenuhi kebutuhan tersebut, maka dibutuhkan suatu teknologi yang memadai. Light Detection and Ranging (LiDAR) merupakan teknologi baru yang cukup fenomenal dibidang geospasial, pemetaan dengan menggunakan sinar laser yang dibawa pada pesawat udara ini merupakan sistem pemetaan yang paling efisien dalam hal waktu dan merupakan salah satu metode yang dapat digunakan dalam menjawab tantangan kebutuhan data tersebut. LiDAR adalah sebuah teknologi peraba jarak jauh optik yang mengukur properti cahaya yang tersebar untuk menemukan jarak dan/atau informasi lain dari target yang jauh, metode untuk menentukan jarak menuju objek atau permukaan adalah dengan menggunakan pulsa laser. Seperti teknologi radar, yang menggunakan gelombang radio daripada cahaya, jarak menuju objek ditentukan dengan mengukur selang waktu antara transmisi pulsa dan deteksi sinyal yang dipancarkan.