Automatic processing of Sentinel-2 image for Kerch peninsula lake areas extraction using QGIS and Python

Krivoguz, Denis; Mal’ko, S; Borovskaya, R.V.; Semenova, Anna

doi:10.1051/e3sconf/202020303011

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…Furthermore, the Kerch Peninsula is not unique solely within the Russian context; it possesses global value in terms of its rich biodiversity and natural beauty [16,[20][21][22]. Therefore, the findings of this research may have far-reaching consequences, being valuable not only for the region but also for the global understanding of the interplay between climate and water resources.…”

Section: Introductionmentioning

confidence: 95%

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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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Section: Introductionmentioning

confidence: 95%

“…Previous research touched upon the influence of temperature and precipitation on the state of the Kerch Peninsula's lakes in the year 2018 [20]. The study described three primary methods of monitoring aquatic bodies: field, remote, and combined.…”

Section: Introductionmentioning

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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“…For example, there are several mud lakes in the area that may affect land zoning and vegetation distribution. Moreover, different types of landscapes, such as steppes, forest steppes, rocky outcrops, and sand dunes, can also affect the results of LULC classification [32]. The Kerch Peninsula was chosen for the study for several reasons.…”

Section: Research Areamentioning

confidence: 99%

Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models

Krivoguz,

Chernyi,

Zinchenko

et al. 2023

Data

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This study investigates the application of various machine learning models for land use and land cover (LULC) classification in the Kerch Peninsula. The study utilizes archival field data, cadastral data, and published scientific literature for model training and testing, using Landsat-5 imagery from 1990 as input data. Four machine learning models (deep neural network, Random Forest, support vector machine (SVM), and AdaBoost) are employed, and their hyperparameters are tuned using random search and grid search. Model performance is evaluated through cross-validation and confusion matrices. The deep neural network achieves the highest accuracy (96.2%) and performs well in classifying water, urban lands, open soils, and high vegetation. However, it faces challenges in classifying grasslands, bare lands, and agricultural areas. The Random Forest model achieves an accuracy of 90.5% but struggles with differentiating high vegetation from agricultural lands. The SVM model achieves an accuracy of 86.1%, while the AdaBoost model performs the lowest with an accuracy of 58.4%. The novel contributions of this study include the comparison and evaluation of multiple machine learning models for land use classification in the Kerch Peninsula. The deep neural network and Random Forest models outperform SVM and AdaBoost in terms of accuracy. However, the use of limited data sources such as cadastral data and scientific articles may introduce limitations and potential errors. Future research should consider incorporating field studies and additional data sources for improved accuracy. This study provides valuable insights for land use classification, facilitating the assessment and management of natural resources in the Kerch Peninsula. The findings contribute to informed decision-making processes and lay the groundwork for further research in the field.

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“…In previous studies, a method for extracting the water surface areas of the lakes of the Kerch Peninsula using QGIS and Python was tested [14]. The methodology was based on the use of the mNDW I index, which helped to separate water bodies from land.…”

Section: Introductionmentioning

confidence: 99%

A Deep Neural Network Method for Water Areas Extraction Using Remote Sensing Data

Krivoguz

Bespalova

Zhilenkov

et al. 2022

JMSE

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Water bodies on the Earth’s surface are an important part of the hydrological cycle. The water resources of the Kerch Peninsula at this moment can be described as a network with temporary streams and small rivers that dry up in summer. Partially, they are often used in fisheries. But since permanent field monitoring is quite financially and resource-intensive, it becomes necessary to find a way for the automated remote monitoring of water bodies using remote sensing data. In this work, we used remote sensing data obtained using the Sentinel-2 satellite in the period from 2017 to 2022 during the days of field expeditions to map the water bodies of the Kerch Peninsula. As a training data set for surface water prediction, field expeditions data were used. The area for test data collection is located near Lake Tobechikskoye, where there are five water bodies. The Keras framework, written in Python, was used to build the architecture of a deep neural network. The architecture of the neural network consisted of one flattened and four dense layers fully connected. As a result, it achieved a model prediction accuracy of 96% when solving the problem of extracting the area of the water surface using remote sensing data. The obtained model showed quite good results in the task of identifying water bodies using remote sensing data, which will make it possible to fully use this technology in the future both in hydrological studies and in the design and forecasting of fisheries.

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Automatic processing of Sentinel-2 image for Kerch peninsula lake areas extraction using QGIS and Python

Cited by 4 publications

References 14 publications

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

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

Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models

A Deep Neural Network Method for Water Areas Extraction Using Remote Sensing Data

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