Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Borovskaya, R.V.; Krivoguz, Denis; Chernyi, Sergei; Kozhurin, Efim; Khorosheltseva, Victoria; Zinchenko, Elena

doi:10.3390/jmse10020257

Cited by 10 publications

(6 citation statements)

References 28 publications

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“…We determine the errors of representativeness and the number of degrees of freedom as: The value of t obtained in the experiment is greater than the tabular value of t 0.01 ; therefore, the differences between P 1 and P 2 can be considered significant at p < 0.01. Thus, the reliability of the differences between options P 1 and P 2 suggests that the developed information system for technical diagnostics and monitoring of steel-pouring ladles turned out to be more effective than the standard system in determining the main operational characteristic-the number of permissible pouring of liquid metal into ladles [30].…”

Section: Discussionmentioning

confidence: 99%

Information System for Diagnosing the Condition of the Complex Structures Based on Neural Networks

et al. 2022

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In this paper, we describe the relevance of diagnosing the lining condition of steel ladles in metallurgical facilities. Accidents with steel ladles lead to losses and different types of damage in iron and steel works. We developed an algorithm for recognizing thermograms of steel ladles to identify burnout zones in the lining based on the technology and design of neural networks. A diagnostic system structure for automated evaluating of the technical conditions of steel ladles without taking them out of service has been developed and described.

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Section: Discussionmentioning

confidence: 99%

Information System for Diagnosing the Condition of the Complex Structures Based on Neural Networks

et al. 2022

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“…In this study, two variables with an absolute correlation coefficient below 0.3 are considered to have no correlation. Its results can be interpreted using the Chaddock scale (Table 2) [73]. In this study, a DWT was used to decompose the original hyperspectral data of the moso bamboo leaves into coarse-scale approximation coefficients (cA) and fine-scale detail coefficients (cD).…”

Section: Evaluation Of the Correlation Between Hvi And V 25 Cmaxmentioning

confidence: 99%

Wavelet Vegetation Index to Improve the Inversion Accuracy of Leaf V25cmax of Bamboo Forests

Guo

et al. 2023

Remote Sensing

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Maximum carboxylation rate (Vcmax) is a key parameter to characterize the forest carbon cycle process. Hence, monitoring the Vcmax of different forest types is a hot research topic at home and abroad, and hyperspectral remote sensing is an important technique for Vcmax inversion. Moso bamboo is a unique forest type with a high carbon sequestration capacity in subtropical regions, but the lack of Vcmax data is a major limitation to accurately modeling carbon cycling processes in moso bamboo forests. Our study area was selected in the moso bamboo forest carbon sink research base in Shanchuan Township, Anji County, Zhejiang Province, China, which has a typical subtropical climate and is widely distributed with moso bamboo forests. In this study, the hyperspectral reflectance and V25cmax (Vcmax converted to 25 °C) of leaves of newborn moso bamboo (I du bamboo) and 2- to 3-year-old moso bamboo (II du bamboo) were measured at different canopy positions, i.e., the top, middle and bottom, in the moso bamboo forest. Then, we applied a discrete wavelet transform (DWT) to the obtained leaf hyperspectral reflectance to construct the wavelet vegetation index (WVI), analyzed the relationship between the WVI and moso bamboo leaf V25cmax, and compared the WVI to the existing hyperspectral vegetation index (HVI). The ability of the WVI to characterize the moso bamboo V25cmax was interpreted. Finally, the partial least squares regression (PLSR) method was used to construct a model to invert the V25cmax of moso bamboo leaves. We showed the following: (1) There are significant leaf V25cmax differences between I du and II du bamboo, and there are also significant leaf V25cmax differences between the top, middle and bottom canopy positions of I du bamboo. (2) Compared to that with the HVI, the detection wavelength of the V25cmax of the WVI expanded to the shortwave infrared region, and thus the WVI had a higher correlation with the V25cmax. The absolute value of the correlation coefficient between the V25cmax of I du bamboo and SR2148,2188 constructed by cD1 was 0.75, and the absolute value of the correlation coefficient between the V25cmax of II du bamboo and DVI2069,407 constructed by cD2 was 0.67. The highest absolute value of the correlation coefficient between V25cmax and WVI at the three different canopy positions was also 13–21% higher than that with the HVI. The longest wavelength used by the WVI was 2441 nm. (3) The validation accuracies of the V25cmax inversion models constructed with the WVI as a variable were all higher than those of the models constructed with the HVI as a variable for all ages and positions, with the highest R2 value of 0.97 and a reduction of 20–60% in the root mean square error (RMSE) value. After the wavelet decomposition of the hyperspectral reflectance of moso bamboo leaves, the low-frequency components contained no noise, and the high-frequency components highlighted the original spectral detail features. The WVI constructed by these components increases the wavelength range of V25cmax interpretation. Therefore, the V25cmax retrieval model based on the WVI encompasses different resolutions and levels of spectral characteristics, which can better reflect the changes in bamboo leaves and can provide a new method for the inversion of the V25cmax of moso bamboo forests based on hyperspectral remote sensing.

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“…Regression analysis is a statistical method used to assess and model the relationships between a dependent variable (in this case, lake area, denoted as Y) and one or more independent variables (annual mean temperature, denoted as X, and annual mean precipitation, denoted as Z) [33][34][35]. In this context, we investigated the correlation and influence of annual mean temperature and precipitation on the lake area in the Kerch Peninsula.…”

Section: Regression Analysismentioning

confidence: 99%

Assessing Long-Term Lake Dynamics in Response to Climatic Variability: A Comprehensive Statistical Analysis

Krivoguz,

Ioshpa,

Bespalova

et al. 2023

JMSE

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This comprehensive study delves into the intricate relationship between climatic factors and the dynamic changes in lakes across the Kerch Peninsula. By analyzing annual mean temperature, annual mean precipitation, and their impact on lake area, this research uncovers significant insights. Key findings include a strong inverse correlation between rising annual mean temperature and reductions in the lake area. With every 1-degree Celsius increase in temperature, the average lake area decreases by 0.302 square kilometers. The analysis indicates higher temperatures are consistently associated with diminishing lake areas, a trend commonly observed in water bodies. While annual precipitation also influences lake areas, the effect is less pronounced, with a correlation coefficient of 0.141, signifying a positive connection between the two variables. Temporal analysis reveals climate impact exhibits a one-year time lag, meaning changes in the current year’s climate manifest in alterations in lake areas in the subsequent year. Generalized Additive Models provide further insights, emphasizing the complex, non-linear nature of the relationship between climatic factors and lake areas. Pseudo-R2 values for lakes on the Kerch Peninsula range from 0.0913 to 0.2769, indicating the proportion of variability in lake area explained by the models. These values emphasize the significance of essential factors, though some unexplained variability remains. In summary, this research highlights the critical role of climate factors in shaping the dynamics of lakes in the Kerch Peninsula. The findings underscore the need for continued monitoring and adaptive management to address the multifaceted challenges posed by climate change and other contributing factors in this region.

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Surface Water Salinity Evaluation and Identification for Using Remote Sensing Data and Machine Learning Approach

Cited by 10 publications

References 28 publications

Information System for Diagnosing the Condition of the Complex Structures Based on Neural Networks

Information System for Diagnosing the Condition of the Complex Structures Based on Neural Networks

Wavelet Vegetation Index to Improve the Inversion Accuracy of Leaf V25cmax of Bamboo Forests

Assessing Long-Term Lake Dynamics in Response to Climatic Variability: A Comprehensive Statistical Analysis

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