8 research outputs found
κ³ ν΄μλ μ΄λΆκ΄μμμ νμ©ν νμ² λΆμ μ¬λλ κ³μΈ‘κΈ°λ² κ°λ°
νμλ
Όλ¬Έ(λ°μ¬) -- μμΈλνκ΅λνμ : 곡과λν 건μ€ν경곡νλΆ, 2022. 8. μμΌμ.κΈ°μ‘΄μ νμ² λΆμ μ¬ λλ κ³μΈ‘μ μνλ§ κΈ°λ° μ§μ κ³μΈ‘ λ°©μμ μμ‘΄νμ¬ μ곡κ°μ κ³ ν΄μλ μλ£ μ·¨λμ΄ μ΄λ €μ΄ μ€μ μ΄λ€. μ΄λ¬ν νκ³μ μ 극볡νκΈ° μν΄ μ΅κ·Ό μμ±κ³Ό λλ‘ μ νμ©νμ¬ μ΄¬μλ λ€λΆκ΄ νΉμ μ΄λΆκ΄ μμμ ν΅ν΄ κ³ ν΄μλμ λΆμ μ¬λλ μ곡κ°λΆν¬λ₯Ό κ³μΈ‘νλ κΈ°λ²μ λν μ°κ΅¬κ° νλ°ν μ§νλκ³ μλ€. νμ§λ§, λ€λ₯Έ νμ² λ¬Όλ¦¬λ κ³μΈ‘μ λΉν΄ λΆμ μ¬ κ³μΈ‘ μ°κ΅¬λ νμ²μ λ°λΌ λΆμ μ¬κ° λ€μνκ² λΆν¬νκ³ λ€λ₯Έ λΆμ λ¬Όμ§ νΉμ νμμ μν λ°λ₯ λ°μ¬μ μν₯ λλ¬Έμ λΆκ΄ μλ£λ₯Ό ν΅ν΄ μ νν λΆμ μ¬λλ λΆν¬λ₯Ό μ¬ννκΈ° μ΄λ €μ΄ μ€μ μ΄λ€. νΉν, λΆμ μ¬ λΆκ΄ νΉμ±μ μν₯μ λ―ΈμΉλ μ
λλΆν¬, κ΄λ¬ΌνΉμ±, μΉ¨κ°μ± λ±μ΄ νμ²μ λ°λΌ κ°ν μ§μμ±μ λνλ΄κΈ°μ μ΄λ¬ν μμΈμμ μΌκΈ°λλ λΆκ΄λ€μμ±μΌλ‘ μΈν΄ νΉμ μκΈ°μ μ§μμλ§ μ ν©ν μ격νμ¬ κΈ°λ° κ³μΈ‘ λͺ¨νλ€μ΄ κ°λ°λμ΄ μλ€.
λ³Έ μ°κ΅¬μμλ μ΄λ¬ν λΆκ΄λ€μμ±μ λ°μνμ¬ λ€μν νμ² λ° μ μ¬ μ‘°κ±΄μμ μ μ© κ°λ₯ν κ³ ν΄μλ μ΄λΆκ΄μμμ νμ©ν νμ² λΆμ μ¬λλ κ³μΈ‘λ°©λ²μ κ°λ°νκΈ° μν΄ μ΄λΆκ΄ κ΅°μ§ν κΈ°λ²κ³Ό λ€μν νμ₯λμ λΆκ΄ λ°΄λλ₯Ό νμ΅ν μ μλ κΈ°κ³νμ΅ νκ· λͺ¨νμ κ²°ν©νμ¬ CMR-OVλΌλ λ°©λ²λ‘ μ μ μνμλ€.
CMR-OV κ°λ° λ° κ²μ¦μ 1) μ€νμ μ°κ΅¬λ₯Ό ν΅ν νμ² λΆμ μ¬ λΆκ΄ νΉμ±μ μ£Όμ κ΅λ μμΈ λΆμ, 2) μ΅μ νκ·λͺ¨ν μ μ λ° μ΄λΆκ΄ ν΄λ¬μ€ν°λ§κ³Όμ κ²°ν©, 3) νμ₯μ μ©μ± νκ°μ κ³Όμ μ κ±°μ³ μνλμλ€. μ€νμ μ°κ΅¬μμλ μ°μ μ€λ΄ μ€νμ€μμ ν‘λ°©ν₯ νΌν©κΈ°λ₯Ό νμ©νμ¬ λ°λ₯ λ°μ¬λ₯Ό μ κ±°νκ³ μμ νΌν©λ μνμμ λΆμ μ¬μ κ³ μ μ΄λΆκ΄ μ€ννΈλΌ μλ£λ₯Ό μμ§νμλ€. μ΄λ₯Ό λ°νμΌλ‘ μ€μ νμ²κ³Ό μ μ¬ν 쑰건μ μ€κ·λͺ¨ μ₯μΈ μλ‘ μ€νμμ λ€μν μ μ¬ νΉμ±(μ
λ λ° κ΄λ¬Ό)κ³Ό νμ νΉμ±(μμ λ° λͺ¨λ)μ λν μ΄λΆκ΄ μλ£λ₯Ό μμ§νμ¬ κ³ μ μ΄λΆκ΄ μ€ννΈλΌκ³Ό λΉκ΅νμλ€. κ·Έ κ²°κ³Ό, λΆμ μ¬μ λΆκ΄ νΉμ±μ μ μ¬μ μ’
λ₯ λ° μ
λμ λ°λΌ λλ μ¦κ°μ λ°λ₯Έ μ΄λΆκ΄ μ€ννΈλΌμ λ°μ¬μ¨ λ³νκ° μμ΄νκ² λνλ¬λ€. λν, 1 m μ΄νμ μμ μμ¬ μ‘°κ±΄μμλ λ°λ₯ λ°μ¬μ μν₯μΌλ‘ νμ μ’
λ₯μ λ°λΌ μ΄λΆκ΄ μ€ννΈλΌμ κ°νμ΄ ν¬κ² λ³ννμμΌλ©°, κ³ λλμ λΆμ μ¬κ° λΆν¬ν λλ λ°λ₯ λ°μ¬κ° ν¬κ² μν₯μ λ―ΈμΉλ κ²μ νμΈνμλ€.
μ΄λ¬ν λΆκ΄λ€μμ±μ΄ λ°μλ λΆμ μ¬λλμ μ΄λΆκ΄ μλ£μ κ΄κ³λ₯Ό ꡬμΆνκΈ° μνμ¬ κΈ°κ³νμ΅ κΈ°λ° λλ€ν¬λ μ€νΈ νκ· λͺ¨νκ³Ό κ°μ°μμ νΌν© λͺ¨ν κΈ°λ° μ΄λΆκ΄ κ΅°μ§ κΈ°λ²μ κ²°ν©ν CMR-OVλ₯Ό μ μ©ν κ²°κ³Ό, κΈ°μ‘΄ μ°κ΅¬λ€μμ μ£Όλ‘ νμ©λ λ°΄λλΉ κΈ°λ°μ λͺ¨νκ³Ό λ¨μΌ κΈ°κ³νμ΅λͺ¨νμ λΉν΄ μ νλκ° ν¬κ² ν₯μνμλ€. νΉν, κΈ°μ‘΄ μ΅μ λ°΄λλΉ λΆμ (OBRA) λ°©λ²μ λΉμ νμ±μ κ³ λ €ν΄λ μ’μ μμμ νμ₯λλ§μ κ³ λ €νλ νκ³μ μΌλ‘ μΈν΄ λΆκ΄λ€μμ±μ λ°μνμ§ λͺ»νλ κ²μΌλ‘ λ°νμ‘λ€. νμ§λ§, CMR-OVλ ν λμ νμ₯λ μμμ κ³ λ €ν¨κ³Ό λμμ λμ μ νλλ₯Ό μ°μΆνμλ€.
μ΅μ’
μ μΌλ‘ CMR-OVλ₯Ό ν©κ°μ μ§μ κ΅¬κ° λ° μ¬νꡬκ°κ³Ό λλκ°κ³Ό ν©κ°μ ν©λ₯λΆμ μ μ©νμ¬ νμ₯κ²μ¦μ μνν κ²°κ³Ό, κΈ°μ‘΄ λͺ¨ν λλΉ μ νλμ λΆμ μ¬ λλ 맡νμ μ λ°μ±μμ ν° κ°μ μ΄ μμμΌλ©°, λΉνμ΅μ§μμμλ λμ μ νλλ₯Ό μ°μΆνμλ€. νΉν, νμ² ν©λ₯λΆμμλ μ΄λΆκ΄ κ΅°μ§μ ν΅ν΄ λ νμ² νλ¦μ κ²½κ³μΈ΅μ λͺ
νν ꡬλ³νμμΌλ©°, μ΄λ₯Ό λ°νμΌλ‘ μ§λ₯μ λ³Έλ₯μ λν΄ κ°κ° λΆλ¦¬λ νκ·λͺ¨νμ ꡬμΆνμ¬ λ³΅μ‘ν ν©λ₯λΆ κ·Όμ κ²½κ³μΈ΅μμμ λΆμ μ¬ λΆν¬λ₯Ό λ³΄λ€ μ ννκ² μ¬ννμλ€. λν, λμκ°μ μ¬νλ κ³ ν΄μλμ λΆμ μ¬ κ³΅κ°λΆν¬λ₯Ό λ°νμΌλ‘ νΌν©λλ₯Ό μ°μ ν κ²°κ³Ό, κΈ°μ‘΄ μ κ³μΈ‘ λλΉ μμΈνκ² λΆμ μ¬ νΌν©μ λν μ λμ νκ°κ° κ°λ₯ν κ²μΌλ‘ λνλ¬λ€. λ°λΌμ, λ³Έ μ°κ΅¬μμ κ°λ°ν μ΄λΆκ΄μμ κΈ°λ° λΆμ μ¬ κ³μΈ‘ κΈ°μ μ ν΅ν΄ μΆν νμ² μ‘°μ¬ λ° κ΄λ¦¬ μ€λ¬΄μ μ νμ± λ° ν¨μ¨μ±μ ν¬κ² μ¦μ§ν μ μμ κ²μΌλ‘ κΈ°λλλ€.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
κ΅λ¬Έμ΄λ‘ 337λ°