Assessment and Classification of Cloud Coverage Using K-Means Clustering Algorithm for the Sentinel-3 LST Data: A Case Study in the Fujairah Region
Manar Ahmed Mohammed Alblooshi,
Sirajul Huda Kalathingal,
Shaher Bano Mirza,
Fouad Lamghari Ridouane
Issue:
Volume 11, Issue 2, December 2023
Pages:
32-35
Received:
16 June 2023
Accepted:
7 July 2023
Published:
17 July 2023
Abstract: Clouds have a significant impact on the planet's energy balance, climate, and weather. They serve as the primary temperature regulator and function as a blanket to absorb thermal energy or longwave radiation. The present study estimates the percentage of rainfall clouds within a 100-kilometer radius of Fujairah City on the Gulf of Oman using image processing based on machine learning and digital image processing. The data for 9 months starting from January 2022 to October 2022 has been retrieved from the Copernicus satellite data component through the Sentinel 3 LST F2 channel. K-mean cluster analysis has been used to validate the accuracy of an algorithm which is applied to determine cloud cover, with a precision rate of 99.9% for clear weather and 95.5% for overcast conditions. The findings indicate that most of the rainy clouds were observed during the months of January and July. The remaining duration of the year exhibits a reduced occurrence of these clouds. Beginning in February, the region of interest experiences cloud cover accompanied by precipitation subsequent to the month of January. Similarly, the month of July exhibited cloud covers with moisture. Throughout the year, dry clouds are observed with moderate coverage percentages. However, there are no observations of any of these clouds during the months of May and December. In summary, automated systems for observing clouds in the atmosphere are a valuable method for detecting cloud cover and predicting climatic patterns in diverse geographical locations.
Abstract: Clouds have a significant impact on the planet's energy balance, climate, and weather. They serve as the primary temperature regulator and function as a blanket to absorb thermal energy or longwave radiation. The present study estimates the percentage of rainfall clouds within a 100-kilometer radius of Fujairah City on the Gulf of Oman using image ...
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Mangrove Information Extraction and Precision Analysis Based on Multi-Feature Combination
Mingli Zhou,
Angying Xu,
Chengming Yang,
Lifeng Liang
Issue:
Volume 11, Issue 2, December 2023
Pages:
36-43
Received:
24 July 2023
Accepted:
8 August 2023
Published:
28 August 2023
Abstract: Extracting information of mangroves at different tide levels from remote sensing images is challenging. In this study, we investigated the use of multiple features for mangrove information extraction, including spectral features, vegetation indices (NDVI, NIMI), and texture features. The accuracy of the extraction was also analyzed. We collected remote sensing images covering mangrove areas at different tide levels and conducted a comprehensive analysis of these images and extracted the desired features. The collected data were then used to train and evaluate classification models for accurate mangrove identification. The results showed that: (1) The integration of NDVI, NIMI, and band features effectively enhanced the classification accuracy of mangroves. These features provided valuable information about the vegetation cover and health of mangroves, enabling better differentiation from other land cover types. (2) The introduction of texture features for classification resulted in a significant decrease in user classification accuracy of mangroves. This suggests that texture features may not be as reliable in distinguishing mangroves from other land cover types, possibly due to the complex and heterogeneous nature of mangrove ecosystems. (3) Feature selection methods played a crucial role in improving the accuracy of mangrove extraction. By selecting an appropriate number of relevant features, these methods helped to avoid data redundancy and reduce the influence of weak features. This was particularly beneficial for the extraction of submerged mangroves, which are often challenging to detect accurately. These findings contribute to the development of improved methods for monitoring and managing mangrove ecosystems, which are vital for their conservation and sustainable management.
Abstract: Extracting information of mangroves at different tide levels from remote sensing images is challenging. In this study, we investigated the use of multiple features for mangrove information extraction, including spectral features, vegetation indices (NDVI, NIMI), and texture features. The accuracy of the extraction was also analyzed. We collected re...
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Research Article
Effect of Anthropogenic Activities on the Presence of Nitrogen Dioxide (NO2) Through Remote Sensing and Ground Based Observations over Emirate of Fujairah, UAE
Reem Yaqoub Yousif Ahmed Abdalla,
Aseela Al Moalla,
Mohamed Ateeg,
Marwa Hossny,
Shaher Bano Mirza*,
Fouad Lamghari Ridouane
Issue:
Volume 11, Issue 2, December 2023
Pages:
44-51
Received:
25 October 2023
Accepted:
21 November 2023
Published:
29 November 2023
Abstract: Air pollution is one of the biggest problems of this age, not only it contributes to climate change but also it has a negative effect on public and individual health due to rising morbidity and mortality. There are various contaminants that play a significant role in human disease. Numerous toxins are released into the environment as a result of anthropogenic activities including burning fossil fuels for transportation and electricity generation. In order to assist decision-makers in finding a long-term solution for improving environmental quality and population health status, NO2 pollution monitoring and regulation is an essential task. Moreover, efforts to reduce NO2 emissions are important for improving air quality and public health. Based on data acquired from Sentinel-5P, the study presented a comparison of NO2 measurements in Sentinel-5P and ground-based stations over the urban area of Fujairah during 2021 and comparison analysis of the tropospheric NO2 column spatial configuration over urban area of Fujairah during comparable periods of pre, mid and post COVID-19 pandemic era as 2019, 2020 and 2021, respectively. These findings demonstrate remarkable tropospheric NO2 column number density decreases even of 85% in the busier areas of emirate of Fujairah during the pandemic era. Sentinel-5P measurements draw attention to the significant reduction in NO2 pollution over Fujairah during the COVID-19 lockdown where we had less traffic, which is supported by ground stations-based calculations.
Abstract: Air pollution is one of the biggest problems of this age, not only it contributes to climate change but also it has a negative effect on public and individual health due to rising morbidity and mortality. There are various contaminants that play a significant role in human disease. Numerous toxins are released into the environment as a result of an...
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