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A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China
With escalating human activities and the substantial emissions of greenhouse gases, global warming intensifies. This phenomenon has led to increased occurrences of various extreme hydrological events, precipitating significant changes in lakes and rivers across the Qinghai Tibet Plateau. Therefore, accurate information extraction about and delineation of water bodies are crucial for lake monitoring. This paper proposes a methodology based on the Normalized Difference Water Index (NDWI) and Gumbel distribution to determine optimal segmentation thresholds. Focusing on Qinghai Lake, this study utilizes multispectral characteristics from the US Landsat satellite for analysis. Comparative assessments with seven alternative methods are conducted to evaluate accuracy. Employing the proposed approach, information about water bodies in Qinghai Lake is extracted over 38 years, from 1986 to 2023, revealing trends in area variation. Analysis indicates a rising trend in Qinghai Lake’s area following a turning point in 2004. To investigate this phenomenon, Pearson correlation analysis of temperature and precipitation over the past 38 years is used and unveils the fact that slight precipitation impacts on area and that there is a positive correlation between temperature and area. In conclusion, this study employs remote sensing data and statistical analysis to comprehensively investigate mechanisms driving changes in Qinghai Lake’s water surface area, providing insights into ecological shifts in lake systems against the backdrop of global warming, thereby offering valuable references for understanding and addressing these changes.
A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China
With escalating human activities and the substantial emissions of greenhouse gases, global warming intensifies. This phenomenon has led to increased occurrences of various extreme hydrological events, precipitating significant changes in lakes and rivers across the Qinghai Tibet Plateau. Therefore, accurate information extraction about and delineation of water bodies are crucial for lake monitoring. This paper proposes a methodology based on the Normalized Difference Water Index (NDWI) and Gumbel distribution to determine optimal segmentation thresholds. Focusing on Qinghai Lake, this study utilizes multispectral characteristics from the US Landsat satellite for analysis. Comparative assessments with seven alternative methods are conducted to evaluate accuracy. Employing the proposed approach, information about water bodies in Qinghai Lake is extracted over 38 years, from 1986 to 2023, revealing trends in area variation. Analysis indicates a rising trend in Qinghai Lake’s area following a turning point in 2004. To investigate this phenomenon, Pearson correlation analysis of temperature and precipitation over the past 38 years is used and unveils the fact that slight precipitation impacts on area and that there is a positive correlation between temperature and area. In conclusion, this study employs remote sensing data and statistical analysis to comprehensively investigate mechanisms driving changes in Qinghai Lake’s water surface area, providing insights into ecological shifts in lake systems against the backdrop of global warming, thereby offering valuable references for understanding and addressing these changes.
A Remote Sensing Water Information Extraction Method Based on Unsupervised Form Using Probability Function to Describe the Frequency Histogram of NDWI: A Case Study of Qinghai Lake in China
Shiqi Liu (author) / Jun Qiu (author) / Fangfang Li (author)
2024
Article (Journal)
Electronic Resource
Unknown
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