489 research outputs found
Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies
Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters
EVALUATING SATELLITE DERIVED BATHYMETRY IN REGARD TO TOTAL PROPAGATED UNCERTAINTY, MULTI-TEMPORAL CHANGE DETECTION, AND MULTIPLE NON-LINEAR ESTIMATION
Acoustic and electromagnetic hydrographic surveys produce highly-accurate bathymetric data that can be used to update and improve current nautical charts. For shallow-water surveys (i.e., less than 50m depths), this includes the use of single-beam echo-sounders (SBES), multi-beam echo-sounders (MBES), and airborne lidar bathymetry (ALB). However, these types of hydrographic surveys are time-consuming and require considerable financial and operational resources to conduct. As a result, some maritime regions are seldom surveyed due to their remote location and challenging logistics.
Satellite-derived bathymetry (SDB) provides a means to supplement traditional acoustic hydrographic surveys. In particular, Landsat 8 imagery: 1) provides complete coverage of the Earthโs surface every 16 days, 2) has an improved dynamic range (12-bits), and 3) is freely-available from the US Geological Survey. While the 30 m spatial resolution does not match MBES, ALB, or SBES coverage, SDB based on Landsat 8 can be regarded as a type of โreconnaissance surveyโ that can be used to identify potential hazards to navigation in areas that are seldom surveyed. It is also a useful means to monitor change detection in dynamic regions.
This study focused on developing improved image-processing techniques and time-series analysis for SDB from Landsat 8 imagery for three different applications:
1. An improved means to estimate total propagated uncertainty (TPU), mainly the vertical component, for single-image SDB;
2. Identifying the location and movement of dynamic shallow areas in river entrances based on multiple-temporal Landsat 8 imagery;
3. Using a multiple, nonlinear SDB approach to enhance depth estimations and enable bottom discrimination.
An improved TPU estimation was achieved based on the two most common optimization approaches (Dierssen et al., 2003 and Stumpf et al., 2003). Various single-image SDB band-ratio outcomes and associated uncertainties were compared against ground truth (i.e., recent Lidar surveys). Several parameters were tested, including various types of filters, kernel sizes, number of control points and their coverage, and recent vs. outdated control points. Based on the study results for two study sites (Cape Ann, MA and Ft Myers, FL), similar performance was observed for both the Stumpf and the Dierssen models. Validation was performed by comparing estimated depths and uncertainties to observed ALB data. The best performing configuration was achieved using low-pass filter (kernel size 3x3) with ALB control points that were distributed over the entire study site.
A change detection process using image processing was developed to identify the location and movement of dynamic shallow areas in riverine environments. Yukon River (Alaska) and Amazon River (Brazil) entrances were evaluated as study sites using multiple satellite imagery. A time-series analysis was used to identify probable shallow areas with no usable control points. By using an SDB ratio model with image processing techniques that includes feature extraction and a well-defined topological feature to describe the shoal feature, it is possible to create a time-series of the shoalโs motion, and predict its future location. A further benefit of this approach is that vertical referencing of the SDB ratio model to chart datum is not required.
In order to enhance the capabilities of the SDB approach to estimate depth in non-uniform conditions, Dierssenโs band ration SDB algorithm was transformed into a full non-linear SDB model. The model was evaluated in the Simeonof Island, AK, using Lidar control points from a previous NOAA ALB survey. Linear and non-linear SDB models were compared using the ALB survey for performance evaluation. The multi-nonlinear SDB model provides an enhanced performance compared to the more traditional linear SDB method. This is most noticeable in the very shallow waters (0-2 m), where a linear model does not provide a good correlation to the control points. In deep-waters close to the extinction depth, the multi-nonlinear SDB method is also able to better detect bottom features than the linear SDB method. By recognizing the water column contributions to the SDB solution, it is possible to achieve a more accurate estimate of the bathymetry in remote areas
Hyperspectral benthic mapping from underwater robotic platforms
We live on a planet of vast oceans; 70% of the Earth's surface is covered in water. They are integral to supporting life, providing 99% of the inhabitable space on Earth. Our oceans and the habitats within them are under threat due to a variety of factors. To understand the impacts and possible solutions, the monitoring of marine habitats is critically important. Optical imaging as a method for monitoring can provide a vast array of information however imaging through water is complex. To compensate for the selective attenuation of light in water, this thesis presents a novel light propagation model and illustrates how it can improve optical imaging performance. An in-situ hyperspectral system is designed which comprised of two upward looking spectrometers at different positions in the water column. The downwelling light in the water column is continuously sampled by the system which allows for the generation of a dynamic water model. In addition to the two upward looking spectrometers the in-situ system contains an imaging module which can be used for imaging of the seafloor. It consists of a hyperspectral sensor and a trichromatic stereo camera. New calibration methods are presented for the spatial and spectral co-registration of the two optical sensors. The water model is used to create image data which is invariant to the changing optical properties of the water and changing environmental conditions. In this thesis the in-situ optical system is mounted onboard an Autonomous Underwater Vehicle. Data from the imaging module is also used to classify seafloor materials. The classified seafloor patches are integrated into a high resolution 3D benthic map of the surveyed site. Given the limited imaging resolution of the hyperspectral sensor used in this work, a new method is also presented that uses information from the co-registered colour images to inform a new spectral unmixing method to resolve subpixel materials
๊ณ ํด์๋ ์ด๋ถ๊ด์์์ ํ์ฉํ ํ์ฒ ๋ถ์ ์ฌ๋๋ ๊ณ์ธก๊ธฐ๋ฒ ๊ฐ๋ฐ
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ๊ฑด์คํ๊ฒฝ๊ณตํ๋ถ, 2022. 8. ์์ผ์.๊ธฐ์กด์ ํ์ฒ ๋ถ์ ์ฌ ๋๋ ๊ณ์ธก์ ์ํ๋ง ๊ธฐ๋ฐ ์ง์ ๊ณ์ธก ๋ฐฉ์์ ์์กดํ์ฌ ์๊ณต๊ฐ์ ๊ณ ํด์๋ ์๋ฃ ์ทจ๋์ด ์ด๋ ค์ด ์ค์ ์ด๋ค. ์ด๋ฌํ ํ๊ณ์ ์ ๊ทน๋ณตํ๊ธฐ ์ํด ์ต๊ทผ ์์ฑ๊ณผ ๋๋ก ์ ํ์ฉํ์ฌ ์ดฌ์๋ ๋ค๋ถ๊ด ํน์ ์ด๋ถ๊ด ์์์ ํตํด ๊ณ ํด์๋์ ๋ถ์ ์ฌ๋๋ ์๊ณต๊ฐ๋ถํฌ๋ฅผ ๊ณ์ธกํ๋ ๊ธฐ๋ฒ์ ๋ํ ์ฐ๊ตฌ๊ฐ ํ๋ฐํ ์งํ๋๊ณ ์๋ค. ํ์ง๋ง, ๋ค๋ฅธ ํ์ฒ ๋ฌผ๋ฆฌ๋ ๊ณ์ธก์ ๋นํด ๋ถ์ ์ฌ ๊ณ์ธก ์ฐ๊ตฌ๋ ํ์ฒ์ ๋ฐ๋ผ ๋ถ์ ์ฌ๊ฐ ๋ค์ํ๊ฒ ๋ถํฌํ๊ณ ๋ค๋ฅธ ๋ถ์ ๋ฌผ์ง ํน์ ํ์์ ์ํ ๋ฐ๋ฅ ๋ฐ์ฌ์ ์ํฅ ๋๋ฌธ์ ๋ถ๊ด ์๋ฃ๋ฅผ ํตํด ์ ํํ ๋ถ์ ์ฌ๋๋ ๋ถํฌ๋ฅผ ์ฌํํ๊ธฐ ์ด๋ ค์ด ์ค์ ์ด๋ค. ํนํ, ๋ถ์ ์ฌ ๋ถ๊ด ํน์ฑ์ ์ํฅ์ ๋ฏธ์น๋ ์
๋๋ถํฌ, ๊ด๋ฌผํน์ฑ, ์นจ๊ฐ์ฑ ๋ฑ์ด ํ์ฒ์ ๋ฐ๋ผ ๊ฐํ ์ง์ญ์ฑ์ ๋ํ๋ด๊ธฐ์ ์ด๋ฌํ ์์ธ์์ ์ผ๊ธฐ๋๋ ๋ถ๊ด๋ค์์ฑ์ผ๋ก ์ธํด ํน์ ์๊ธฐ์ ์ง์ญ์๋ง ์ ํฉํ ์๊ฒฉํ์ฌ ๊ธฐ๋ฐ ๊ณ์ธก ๋ชจํ๋ค์ด ๊ฐ๋ฐ๋์ด ์๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ์ด๋ฌํ ๋ถ๊ด๋ค์์ฑ์ ๋ฐ์ํ์ฌ ๋ค์ํ ํ์ฒ ๋ฐ ์ ์ฌ ์กฐ๊ฑด์์ ์ ์ฉ ๊ฐ๋ฅํ ๊ณ ํด์๋ ์ด๋ถ๊ด์์์ ํ์ฉํ ํ์ฒ ๋ถ์ ์ฌ๋๋ ๊ณ์ธก๋ฐฉ๋ฒ์ ๊ฐ๋ฐํ๊ธฐ ์ํด ์ด๋ถ๊ด ๊ตฐ์งํ ๊ธฐ๋ฒ๊ณผ ๋ค์ํ ํ์ฅ๋์ ๋ถ๊ด ๋ฐด๋๋ฅผ ํ์ตํ ์ ์๋ ๊ธฐ๊ณํ์ต ํ๊ท ๋ชจํ์ ๊ฒฐํฉํ์ฌ CMR-OV๋ผ๋ ๋ฐฉ๋ฒ๋ก ์ ์ ์ํ์๋ค.
CMR-OV ๊ฐ๋ฐ ๋ฐ ๊ฒ์ฆ์ 1) ์คํ์ ์ฐ๊ตฌ๋ฅผ ํตํ ํ์ฒ ๋ถ์ ์ฌ ๋ถ๊ด ํน์ฑ์ ์ฃผ์ ๊ต๋ ์์ธ ๋ถ์, 2) ์ต์ ํ๊ท๋ชจํ ์ ์ ๋ฐ ์ด๋ถ๊ด ํด๋ฌ์คํฐ๋ง๊ณผ์ ๊ฒฐํฉ, 3) ํ์ฅ์ ์ฉ์ฑ ํ๊ฐ์ ๊ณผ์ ์ ๊ฑฐ์ณ ์ํ๋์๋ค. ์คํ์ ์ฐ๊ตฌ์์๋ ์ฐ์ ์ค๋ด ์คํ์ค์์ ํก๋ฐฉํฅ ํผํฉ๊ธฐ๋ฅผ ํ์ฉํ์ฌ ๋ฐ๋ฅ ๋ฐ์ฌ๋ฅผ ์ ๊ฑฐํ๊ณ ์์ ํผํฉ๋ ์ํ์์ ๋ถ์ ์ฌ์ ๊ณ ์ ์ด๋ถ๊ด ์คํํธ๋ผ ์๋ฃ๋ฅผ ์์งํ์๋ค. ์ด๋ฅผ ๋ฐํ์ผ๋ก ์ค์ ํ์ฒ๊ณผ ์ ์ฌํ ์กฐ๊ฑด์ ์ค๊ท๋ชจ ์ฅ์ธ ์๋ก ์คํ์์ ๋ค์ํ ์ ์ฌ ํน์ฑ(์
๋ ๋ฐ ๊ด๋ฌผ)๊ณผ ํ์ ํน์ฑ(์์ ๋ฐ ๋ชจ๋)์ ๋ํ ์ด๋ถ๊ด ์๋ฃ๋ฅผ ์์งํ์ฌ ๊ณ ์ ์ด๋ถ๊ด ์คํํธ๋ผ๊ณผ ๋น๊ตํ์๋ค. ๊ทธ ๊ฒฐ๊ณผ, ๋ถ์ ์ฌ์ ๋ถ๊ด ํน์ฑ์ ์ ์ฌ์ ์ข
๋ฅ ๋ฐ ์
๋์ ๋ฐ๋ผ ๋๋ ์ฆ๊ฐ์ ๋ฐ๋ฅธ ์ด๋ถ๊ด ์คํํธ๋ผ์ ๋ฐ์ฌ์จ ๋ณํ๊ฐ ์์ดํ๊ฒ ๋ํ๋ฌ๋ค. ๋ํ, 1 m ์ดํ์ ์์ ์์ฌ ์กฐ๊ฑด์์๋ ๋ฐ๋ฅ ๋ฐ์ฌ์ ์ํฅ์ผ๋ก ํ์ ์ข
๋ฅ์ ๋ฐ๋ผ ์ด๋ถ๊ด ์คํํธ๋ผ์ ๊ฐํ์ด ํฌ๊ฒ ๋ณํํ์์ผ๋ฉฐ, ๊ณ ๋๋์ ๋ถ์ ์ฌ๊ฐ ๋ถํฌํ ๋๋ ๋ฐ๋ฅ ๋ฐ์ฌ๊ฐ ํฌ๊ฒ ์ํฅ์ ๋ฏธ์น๋ ๊ฒ์ ํ์ธํ์๋ค.
์ด๋ฌํ ๋ถ๊ด๋ค์์ฑ์ด ๋ฐ์๋ ๋ถ์ ์ฌ๋๋์ ์ด๋ถ๊ด ์๋ฃ์ ๊ด๊ณ๋ฅผ ๊ตฌ์ถํ๊ธฐ ์ํ์ฌ ๊ธฐ๊ณํ์ต ๊ธฐ๋ฐ ๋๋คํฌ๋ ์คํธ ํ๊ท ๋ชจํ๊ณผ ๊ฐ์ฐ์์ ํผํฉ ๋ชจํ ๊ธฐ๋ฐ ์ด๋ถ๊ด ๊ตฐ์ง ๊ธฐ๋ฒ์ ๊ฒฐํฉํ CMR-OV๋ฅผ ์ ์ฉํ ๊ฒฐ๊ณผ, ๊ธฐ์กด ์ฐ๊ตฌ๋ค์์ ์ฃผ๋ก ํ์ฉ๋ ๋ฐด๋๋น ๊ธฐ๋ฐ์ ๋ชจํ๊ณผ ๋จ์ผ ๊ธฐ๊ณํ์ต๋ชจํ์ ๋นํด ์ ํ๋๊ฐ ํฌ๊ฒ ํฅ์ํ์๋ค. ํนํ, ๊ธฐ์กด ์ต์ ๋ฐด๋๋น ๋ถ์ (OBRA) ๋ฐฉ๋ฒ์ ๋น์ ํ์ฑ์ ๊ณ ๋ คํด๋ ์ข์ ์์ญ์ ํ์ฅ๋๋ง์ ๊ณ ๋ คํ๋ ํ๊ณ์ ์ผ๋ก ์ธํด ๋ถ๊ด๋ค์์ฑ์ ๋ฐ์ํ์ง ๋ชปํ๋ ๊ฒ์ผ๋ก ๋ฐํ์ก๋ค. ํ์ง๋ง, CMR-OV๋ ํญ ๋์ ํ์ฅ๋ ์์ญ์ ๊ณ ๋ คํจ๊ณผ ๋์์ ๋์ ์ ํ๋๋ฅผ ์ฐ์ถํ์๋ค.
์ต์ข
์ ์ผ๋ก CMR-OV๋ฅผ ํฉ๊ฐ์ ์ง์ ๊ตฌ๊ฐ ๋ฐ ์ฌํ๊ตฌ๊ฐ๊ณผ ๋๋๊ฐ๊ณผ ํฉ๊ฐ์ ํฉ๋ฅ๋ถ์ ์ ์ฉํ์ฌ ํ์ฅ๊ฒ์ฆ์ ์ํํ ๊ฒฐ๊ณผ, ๊ธฐ์กด ๋ชจํ ๋๋น ์ ํ๋์ ๋ถ์ ์ฌ ๋๋ ๋งตํ์ ์ ๋ฐ์ฑ์์ ํฐ ๊ฐ์ ์ด ์์์ผ๋ฉฐ, ๋นํ์ต์ง์ญ์์๋ ๋์ ์ ํ๋๋ฅผ ์ฐ์ถํ์๋ค. ํนํ, ํ์ฒ ํฉ๋ฅ๋ถ์์๋ ์ด๋ถ๊ด ๊ตฐ์ง์ ํตํด ๋ ํ์ฒ ํ๋ฆ์ ๊ฒฝ๊ณ์ธต์ ๋ช
ํํ ๊ตฌ๋ณํ์์ผ๋ฉฐ, ์ด๋ฅผ ๋ฐํ์ผ๋ก ์ง๋ฅ์ ๋ณธ๋ฅ์ ๋ํด ๊ฐ๊ฐ ๋ถ๋ฆฌ๋ ํ๊ท๋ชจํ์ ๊ตฌ์ถํ์ฌ ๋ณต์กํ ํฉ๋ฅ๋ถ ๊ทผ์ญ ๊ฒฝ๊ณ์ธต์์์ ๋ถ์ ์ฌ ๋ถํฌ๋ฅผ ๋ณด๋ค ์ ํํ๊ฒ ์ฌํํ์๋ค. ๋ํ, ๋์๊ฐ์ ์ฌํ๋ ๊ณ ํด์๋์ ๋ถ์ ์ฌ ๊ณต๊ฐ๋ถํฌ๋ฅผ ๋ฐํ์ผ๋ก ํผํฉ๋๋ฅผ ์ฐ์ ํ ๊ฒฐ๊ณผ, ๊ธฐ์กด ์ ๊ณ์ธก ๋๋น ์์ธํ๊ฒ ๋ถ์ ์ฌ ํผํฉ์ ๋ํ ์ ๋์ ํ๊ฐ๊ฐ ๊ฐ๋ฅํ ๊ฒ์ผ๋ก ๋ํ๋ฌ๋ค. ๋ฐ๋ผ์, ๋ณธ ์ฐ๊ตฌ์์ ๊ฐ๋ฐํ ์ด๋ถ๊ด์์ ๊ธฐ๋ฐ ๋ถ์ ์ฌ ๊ณ์ธก ๊ธฐ์ ์ ํตํด ์ถํ ํ์ฒ ์กฐ์ฌ ๋ฐ ๊ด๋ฆฌ ์ค๋ฌด์ ์ ํ์ฑ ๋ฐ ํจ์จ์ฑ์ ํฌ๊ฒ ์ฆ์งํ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.The conventional measurement method of suspended sediment concentration (SSC) in the riverine system is labor-intensive and time-consuming since it has been conducted using the sampling-based direct measurement method. For this reason, it is challenging to collect high-resolution datasets of SSC in rivers. In order to overcome this limitation, remote sensing-based techniques using multi- or hyper-spectral images from satellites or UAVs have been recently carried out to obtain high-resolution SSC distributions in water environments. However, suspended sediment in rivers is more dynamic and spatially heterogeneous than those in other fields. Moreover, the sediment and streambed properties have strong regional characteristics depending on the river type; thus, only models suitable for a specific period and region have been developed owing to the increased spectral variability of the water arising from various types of suspended matter in the water and the heterogeneous streambed properties.
Therefore, to overcome the limitations of the existing monitoring system, this study proposed a robust hyperspectral imagery-based SSC measurement method, termed cluster-based machine learning regression with optical variability (CMR-OV). This method dealt with the spectral variability problem by combining hyperspectral clustering and machine learning regression with the Gaussian mixture model (GMM) and Random forest (RF) regression. The hyperspectral clustering separated the complex dataset into several homogeneous datasets according to spectral characteristics. Then, the machine learning regressors corresponding to clustered datasets were built to construct the relationship between the hyperspectral spectrum and SSC.
The development and validation of the proposed method were carried out through the following processes: 1) analysis of confounding factors in the spectral variability through experimental studies, 2) selection of an optimal regression model and validation of hyperspectral clustering, and 3) evaluation of field applicability. In the experimental studies, the intrinsic hyperspectral spectra of suspended sediment were collected in a completely mixed state after removing the bottom reflection using a horizontal rotating cylinder. Then, hyperspectral data on various sediment properties (particle size and mineral contents) and river bed properties (sand and vegetation) were collected from sediment tracer experiments in field-scale open channels under different hydraulic conditions and compared with intrinsic hyperspectral spectra. Consequently, the change of the hyperspectral spectrum was different according to the sediment type and particle size distribution. In addition, under the shallow water depth condition of 1 m or less, the shape of the hyperspectral spectrum changed significantly depending on the bed type due to the bottom reflectance. The bottom reflectance substantially affected the hyperspectral spectrum even when the high SSC was distributed.
As a result of combining the GMM and RF regression with building a relationship between the SSC and hyperspectral data reflecting the spectral variability, the accuracy was substantially improved compared to the other methods. In particular, even when nonlinearity is considered based on the existing optimal band ratio analysis (OBRA) method, spectral variability could not be reflected due to the limitation of considering only a narrow wavelength range. On the other hand, CMR-OV showed high accuracy while considering a wide range of wavelengths with clusters having distinct spectral characteristics.
Finally, the CMR-OV was applied to the straight and meandering reaches of the Hwang River and the confluence of the Nakdong and Hwang Rivers in South Korea to assess field applicability. There was a remarkable improvement in the accuracy and precision of SSC mapping under various river conditions compared to the existing models, and CMR-OV showed robust performance even with non-calibrated datasets. At the river confluence, the mixing pattern between the main river and tributary was apparently retrieved from CMR-OV under optically complex conditions. Compared to the non-clustered model, hyperspectral clustering played a primary role in improving the performance by separating the water bodies originating from both rivers. It was also possible to quantitatively evaluate the complicated mixing pattern in detail compared to the existing point measurement. Therefore, it is expected that the accuracy and efficiency of river investigation will be significantly improved through the SSC measurement method presented in this study.Abstract of dissertation i
List of figures ix
List of tables xvii
List of abbreviations xix
List of symbols xxii
1. Introduction 1
2. Theoretical research 13
2.1.1 Pre-processing of hyperspectral image (HSI) 19
2.1.2 Optical characteristics of suspended sediment in rivers 28
2.1.2.1 Theory of solar radiation transfer in rivers 28
2.1.2.2 Heterogeneity of sediment properties 33
2.1.2.3 Effects of bottom reflectance 38
2.1.2.4 Vertical distribution of suspended sediment 41
2.1.3 Retrieval of suspended sediment from remote sensing data 46
2.1.3.1 Remote sensing-based regression approach 46
2.1.3.2 Clustering of remote sensing data 52
2.2 Mapping of suspended sediment concentration in rivers 56
2.2.1 Traditional method for spatial measurement 56
2.2.2 Spatial measurement at river confluences 57
2.2.2.1 Dynamics of flow and mixing at river confluences 57
2.2.2.2 Field experiments in river confluences 64
3. Experimental studies 68
3.1 Experimental cases 68
3.2 Laboratory experiment 74
3.2.1 Experimental setup 74
3.2.2 Experimental method 78
3.3 Field-scale experiments in River Experiment Center 83
3.3.1 Experiments in the straight channel 83
3.3.2 Experiments in the meandering channel 96
3.4 Field survey 116
3.4.1 Study area and field measurement 116
3.4.2 Hydraulic and sediment data in rivers with simple geometry 122
3.4.3 Hydraulic and sediment data in river confluences 126
3.5 Analysis of hyperspectral data of suspended sediment 141
3.5.1 Hyperspectral data of laboratory experiment 142
3.5.2 Hyperspectral data of field-scale experiments 146
3.5.2.1 Effect of bottom reflectance 146
3.5.2.2 Principal component analysis of the effect of suspended sediment properties 154
4. Development of suspended sediment concentration estimator using UAV-based hyperspectral imagery 164
4.1 Outline of Cluster-based Machine learning Regression with Optical Variability (CMR-OV) 164
4.2 Pre-processing of hyperspectral images 168
4.3 Regression models and clustering technique 173
4.3.1 Index-based regression models 173
4.3.2 Machine learning regression models 175
4.3.3 Relevant band selection 183
4.3.4 Gaussian mixture model for clustering 185
4.3.5 Performance criteria 188
4.4 Model development and evaluation 189
4.4.1 Comparison of regression models 189
4.4.1.1 OBRA-based explicit models 189
4.4.1.2 Machine learning-based implicit models 194
4.4.2 Assessment of hyperspectral clustering 200
4.4.3 Spatio-temporal SSCV mapping using CMR-OV 215
5. Evaluation of field applicability of CMR-OV 225
5.1 Outline of field applicability test 225
5.2 Cross-applicability validation of CMR-OV 227
5.3 Assessment of field applicability in rivers with simple geometry 234
5.4 Assessment of field applicability in river confluences 241
5.4.1 Classification of river regions using hyperspectral clustering 241
5.4.2 Retrievals of SSCV map 258
5.4.3 Mixing pattern extraction from SSCv map 271
6. Conclusions and future study 274
6.1 Conclusions 274
6.2 Future directions 278
Reference 280
Appendix 308
Appendix A. Breakthrough curve (BTC) analysis 308
Appendix B. Experimental data 310
Appendix B. 1. BTCs of in-situ measured SSC from field-scale experiments 310
Appendix B. 2. Dataset of spectra from hyperspectral images and corresponding SSC in rivers 330
Appendix C. CMR-OV code 331
๊ตญ๋ฌธ์ด๋ก 337๋ฐ
Technique for validating remote sensing products of water quality
Remote sensing of water quality is initiated as an additional part of the on going activities of the EAGLE2006 project.
Within this context intensive in-situ and airborne measurements campaigns were carried out over the Wolderwijd and
Veluwemeer natural waters. However, in-situ measurements and image acquisitions were not simultaneous. This poses
some constraints on validating air/space-borne remote sensing products of water quality. Nevertheless, the detailed insitu
measurements and hydro-optical model simulations provide a bench mark for validating remote sensing products.
That is realized through developing a stochastic technique to quantify the uncertainties on the retrieved aquatic inherent
optical properties (IOP).
The output of the proposed technique is applied to validate remote sensing products of water quality. In this processing
phase, simulations of the radiative transfer in the coupled atmosphere-water system are performed to generate spectra
at-sensor-level. The upper and the lower boundaries of perturbations, around each recorded spectrum, are then modelled
as function of residuals between simulated and measured spectra. The perturbations are parameterized as a function of
model approximations/inversion, sensor-noise and atmospheric residual signal. All error sources are treated as being of
stochastic nature. Three scenarios are considered: spectrally correlated (i.e. wavelength dependent) perturbations,
spectrally uncorrelated perturbations and a mixed scenario of the previous two with equal probability of occurrence.
Uncertainties on the retrieved IOP are quantified with the relative contribution of each perturbation component to the
total error budget of the IOP.
This technique can be used to validate earth observation products of water quality in remote areas where few or no inโ
situ measurements are available
Spatial Prediction of Coastal Bathymetry Based on Multispectral Satellite Imagery and Multibeam Data
The coastal shallow water zone can be a challenging and costly environment in
which to acquire bathymetry and other oceanographic data using traditional survey methods.
Much of the coastal shallow water zone worldwide remains unmapped using recent
techniques and is, therefore, poorly understood. Optical satellite imagery is proving to be a
useful tool in predicting water depth in coastal zones, particularly in conjunction with other
standard datasets, though its quality and accuracy remains largely unconstrained. A common
challenge in any prediction study is to choose a small but representative group of predictors,
one of which can be determined as the best. In this respect, exploratory analyses are used to
guide the make-up of this group, where we choose to compare a basic non-spatial model
versus four spatial alternatives, each catering for a variety of spatial effects. Using one
instance of RapidEye satellite imagery, we show that all four spatial models show better
adjustments than the non-spatial model in the water depth predictions, with the best predictor
yielding a correlation coefficient of actual versus predicted at 0.985. All five predictors also
factor in the influence of bottom type in explaining water depth variation. However, the
prediction ranges are too large to be used in high accuracy bathymetry products such as
navigation charts; nevertheless, they are considered beneficial in a variety of other applications in sensitive disciplines such as environmental monitoring, seabed mapping, or
coastal zone management
Estimating the concentration of physico chemical parameters in hydroelectric power plant reservoir
The United Nations Educational, Scientific and Cultural Organization (UNESCO) defines
the amazon region and adjacent areas, such as the Pantanal, as world heritage territories, since
they possess unique flora and fauna and great biodiversity. Unfortunately, these regions have
increasingly been suffering from anthropogenic impacts. One of the main anthropogenic impacts
in the last decades has been the construction of hydroelectric power plants.
As a result, dramatic altering of these ecosystems has been observed, including changes in
water levels, decreased oxygenation and loss of downstream organic matter, with consequent
intense land use and population influxes after the filling and operation of these reservoirs. This,
in turn, leads to extreme loss of biodiversity in these areas, due to the large-scale deforestation.
The fishing industry in place before construction of dams and reservoirs, for example, has become
much more intense, attracting large populations in search of work, employment and income.
Environmental monitoring is fundamental for reservoir management, and several studies
around the world have been performed in order to evaluate the water quality of these ecosystems.
The Brazilian Amazon, in particular, goes through well defined annual hydrological cycles, which
are very importante since their study aids in monitoring anthropogenic environmental impacts
and can lead to policy and decision making with regard to environmental management of this
area. The water quality of amazon reservoirs is greatly influenced by this defined hydrological
cycle, which, in turn, causes variations of microbiological, physical and chemical characteristics.
Eutrophication, one of the main processes leading to water deterioration in lentic environments,
is mostly caused by anthropogenic activities, such as the releases of industrial and domestic
effluents into water bodies.
Physico-chemical water parameters typically related to eutrophication are, among others,
chlorophyll-a levels, transparency and total suspended solids, which can, thus, be used to assess
the eutrophic state of water bodies.
Usually, these parameters must be investigated by going out to the field and manually
measuring water transparency with the use of a Secchi disk, and taking water samples to the
laboratory in order to obtain chlorophyll-a and total suspended solid concentrations. These
processes are time- consuming and require trained personnel. However, we have proposed other
techniques to environmental monitoring studies which do not require fieldwork, such as remote
sensing and computational intelligence.
Simulations in different reservoirs were performed to determine a relationship between these
physico-chemical parameters and the spectral response. Based on the in situ measurements,
empirical models were established to relate the reflectance of the reservoir measured by the
satellites. The images were calibrated and corrected atmospherically.
Statistical analysis using error estimation was used to evaluate the most accurate methodology.
The Neural Networks were trained by hydrological cycle, and were useful to estimate the physicalchemical
parameters of the water from the reflectance of visible bands and NIR of satellite images,
with better results for the period with few clouds in the regions analyzed.
The present study shows the application of wavelet neural network to estimate water quality
parameters using concentration of the water samples collected in the Amazon reservoir and Cefni
reservoir, UK. Sattelite imagens from Landsats and Sentinel-2 were used to train the ANN by
hydrological cycle.
The trained ANNs demonstrated good results between observed and estimated after Atmospheric
corrections in satellites images. The ANNs showed in the results are useful to estimate
these concentrations using remote sensing and wavelet transform for image processing.
Therefore, the techniques proposed and applied in the present study are noteworthy since
they can aid in evaluating important physico-chemical parameters, which, in turn, allows for identification of possible anthropogenic impacts, being relevant in environmental management
and policy decision-making processes.
The tests results showed that the predicted values have good accurate. Improving efficiency
to monitor water quality parameters and confirm the reliability and accuracy of the approaches
proposed for monitoring water reservoirs.
This thesis contributes to the evaluation of the accuracy of different methods in the estimation
of physical-chemical parameters, from satellite images and artificial neural networks. For future
work, the accuracy of the results can be improved by adding more satellite images and testing
new neural networks with applications in new water reservoirs
Estimating Chlorophyll a Concentrations of Several Inland Waters with Hyperspectral Data and Machine Learning Models
Water is a key component of life, the natural environment and human health.
For monitoring the conditions of a water body, the chlorophyll a concentration
can serve as a proxy for nutrients and oxygen supply. In situ measurements of
water quality parameters are often time-consuming, expensive and limited in
areal validity. Therefore, we apply remote sensing techniques. During field
campaigns, we collected hyperspectral data with a spectrometer and in situ
measured chlorophyll a concentrations of 13 inland water bodies with different
spectral characteristics. One objective of this study is to estimate
chlorophyll a concentrations of these inland waters by applying three machine
learning regression models: Random Forest, Support Vector Machine and an
Artificial Neural Network. Additionally, we simulate four different
hyperspectral resolutions of the spectrometer data to investigate the effects
on the estimation performance. Furthermore, the application of first order
derivatives of the spectra is evaluated in turn to the regression performance.
This study reveals the potential of combining machine learning approaches and
remote sensing data for inland waters. Each machine learning model achieves an
R2-score between 80 % to 90 % for the regression on chlorophyll a
concentrations. The random forest model benefits clearly from the applied
derivatives of the spectra. In further studies, we will focus on the
application of machine learning models on spectral satellite data to enhance
the area-wide estimation of chlorophyll a concentration for inland waters.Comment: Accepted at ISPRS Geospatial Week 2019 in Ensched
Development of a Semi-Analytical Model for Seagrass Mapping using Cloud-Based Computing and Open Sourced Optical Satellite Data
Seagrasses provide USD169 million arising solely from blue carbon sequestration, the absorption and storage of carbon emissions from these coastal vegetated ecosystems. Unfortunately, 51,000 km2 or 29% of the known global seagrasses were lost between 1879 and 2006. The best global seagrass map is an assemblage of known areas since the 1930s. With growing interests in blue carbon, a standardised approach to map seagrass is needed. Aquatic remote sensing introduces the water column as a second medium and other aquatic-specific challenges. Solutions include the computationally expensive physics-based or analytical approach, which is less data-dependent than the conventional statistical approach, or the hybrid semi-analytical approach which combines the strengths of both. Fortunately, the advent of cloud computing services such as the Google Earth Engine (GEE) brings easy access to computational power. This study aims to implement a semi-analytical approach on GEE to map seagrasses in Mozambique. A forward Hyperspectral Optimisation Process Exemplar (HOPE) model based on Sentinel-2 was implemented and supplemented by a bathymetry log-linear regression and published intrinsic optical properties of water (IOPs) values and/or equations. Support Vector Machine and Random Forest were used for classification. Support Vector Machine produced the best areal estimate of 3518.37 km2 with a seagrass producerโs accuracy of 51.02%, a seagrass userโs accuracy of 65.79% and an overall accuracy of 60.27%. The best bathymetry estimate featured an R2 of 0.68. Although there was no validation for IOPs, external validation showed that the total absorption had less than 25% difference from the Case-2 Regional / Coast Colour (C2RCC) processor. While requiring further improvements, this model has shown potential for seagrass mapping, especially in remote or understudied regions, and is a step towards a global seagrass map
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