Inland Lakes Mapping for Monitoring Water Quality Using a Detail/Smoothing-Balanced Conditional Random Field Based on Landsat-8/Levels Data

An advanced remote sensing retrieval method for urban non-optically active water quality parameters: An example from Shanghai

The optical insensitivity of non-optically active water quality parameters (NAWQPs) presents a significant challenge for remote sensing-based quantitative monitoring, which is an important tool for water quality assessment and management. Based on the analysis of the samples from Shanghai, China, it was found that the spectral morphological characteristics of the water body were obviously different under the combined effect of multiple NAWQPs. In view of this, in this paper, a machine learning method was proposed for the retrieval of urban NAWQPs by using multi-spectral scale morphological combined feature (MSMCF). The proposed method integrates both local and global spectral morphological features, and employs a multi-scale approach to enhance its applicability and stability, providing a more accurate and robust solution. To explore the applicability of the MSMCF method in retrieving urban NAWQPs, different methods were tested in terms of the retrieval accuracy and stability on the measured data and three different hyperspectral data. As can be seen from the results, the proposed method has good retrieval performance, which can be applied to hyperspectral data with different spectral resolutions with certain ability to suppress noise. Further analysis indicates that the sensitivity of each NAWQP to spectral morphological features varies. The research methods and findings in this paper can promote the development of hyperspectral and remote sensing technology in the prevention and treatment of urban water quality deterioration, and provide reference for related research.

Mapping Archaeal Diversity in Soda Lakes by Coupling 16S rRNA PCR-DGGE Analysis with Remote Sensing and GIS Technology

The haloarchaeal diversity of four hypersaline alkaline lakes from the Wadi El-Natrun depression (Northern Egypt) was investigated using culture-independent polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) of 16S rRNA gene phylotypes, which was combined with remote sensing and geographic information system (GIS) data to highlight the distribution pattern of the microbial diversity in water and sediment samples. The majority of archaeal sequences identified in all four lakes belonged to the phyla Euryarchaeota and Crenarchaeota. Sediment samples from Beida Lake and water samples from El-Hamra Lake showed the highest levels of archaeal diversity. Sequence similarities ≥ 95% were found between six of the acquired clones and uncultured Halorhabdus, Euryarchaeota, and archaeon clones. In addition, two clones shared a high level of sequence similarity (97%) with unclassified archaea, while other nine clones exhibited 96% to 99% sequence similarity with uncultured archaeon clones, and only one clone showed 97% identity with an uncultured Crenarchaeota. Likewise, 7 DGGE bands presented a sequence similarity of 90 to 98% to Halogranum sp., Halalkalicoccus tibetensis, Halalkalicoccus jeotgali, uncultured Halorubrum, Halobacteriaceae sp., or uncultured haloarchaeon. In conclusion, while the variety of alkaliphilic haloarchaea in the examined soda lakes was restricted, the possibility of uncovering novel species for biotechnological applications from these extreme habitats remains promising.

Design of Water Quality Monitoring System in Shaanxi Section of Weihe River Basin Based on the Internet of Things

Monitoring environmental water quality in an efficient, cheap, and sustainable way can better serve the country's strategic requirements for water resources and water ecological protection. This paper takes the Shaanxi section of the Weihe River Basin as a pilot project and aims to use the Internet of Things technology to develop water quality monitoring sensors, so as to realize the construction of low-cost, high-reliability water quality monitoring demonstration applications. First of all, we established the design of the water quality collection terminal, designed the low-power water quality sensor node, supported the Internet of Things protocol and the collection of various water quality parameters, and used networking for data transmission. Secondly, we use the ant colony algorithm-based system clustering model to obtain a cluster map of water quality monitoring tasks in a certain section of the Weihe River Basin. We take the task clustering graph as an example for analysis, optimize the monitoring model through the ant colony algorithm, and obtain the weight of the optimization index. The weight of the scheduled task limit of the monitoring point becomes larger, so the release of the monitoring task mainly affects the limit of the scheduled task of the monitoring point. Through the above work, we designed and implemented a set of online water quality monitoring system based on the Internet of Things and data mining technology. The system can provide reference for large-scale water resource protection and water environment governance.