ABSTRACT:With the rapid development of the regional economy, water pollution has gradually become an environmental problem that cannot be ignored. As an important water source in central China, the Han River should strengthen water quality monitoring and management in order to ensure the sustainable development of watershed and related areas. Taking typical sections of middle and lower reaches of the Han River as the study area, this paper focuses on rapid river water quality assessment using multispectral remote sensing images.Based on measured water quality data and synchronous spatial high and medium-resolution remote sensing data (multi-spectral data of ZY3 and HJ1A) in 2013, neural network algorithm is used to establish water quality index retrieval model for the study area, and then water quality status is assessed accordingly. The results show that BP neural network retrieval model of water quality index that is established based on multispectral data of ZY3 satellite has higher accuracy and that its assessment results are of high credibility and strong applicability, which can really reflect changes in water quality and better achieve water quality assessment for the study area. In addition, water quality assessment results show that major excessive factors in the study area are total nitrogen and total phosphorus; the polluting type is organic pollution; water quality varies greatly with seasons. INTRODUCTIONWater quality evaluation is a fundamental link in water environment management and monitoring. Only through water quality monitoring can water quality be reasonably evaluated and targeted water environment management planning and scheme be developed. In terms of water quality evaluation, traditional methods like water sample collection, indicator analysis and grade evaluation can only provide water quality status at the sampling point instead of large area of waters, while large-scale field sampling will consume a large amount of manpower, materials and financial resources. In recent years, with the rapid development of remote sensing technique, more and more researchers carried out fast, continuous and dynamic monitoring on waters by means of remote sensing technique.Further, this technique has been adopted by lots of domestic and foreign scholars on water quality evaluation (Wu, 2012, Gu, 2014, Zhu, 2013, Bitelli, 2010, Markogianni , 2014, Syahreza ,2012, Alparslan, 2007, Thiemann , 2000, and most of these studies used remote-sensing data to quantitatively retrieve concentration of water quality parameter and then establish a water quality evaluation model on this basis. The difficulty of this method mainly lies in the establishment of a definite linear relationship between remote sensing data and water quality parameter. Existing studies have shown that neural networks can better simulate the complex nonlinear relationship between remote sensing signal and water quality parameter concentration and have significantly higher retrieval accuracy than empirical models (Keiner,1998, Buckton, 1999, ...
ABSTRACT:In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.
THEME: DATA -Data and information systems and spatial data infrastructure.KEY WORDS: Cloud detecting, Brightness temperature, FY-2 VISSR data, Dynamic threshold, Temporal-Spatial scale ABSTRACT:The traditional statistical methods and radiation transfer theory methods for cloud detecting have a high adaptability just only in those areas with a uniform surface coverage and noncomplex terrain. Therefore, adapted to large spatial and temporal scales, in this work a cloud detection method is developed, seeking the main influencing factors of the change of Brightness Temperature(BT) of clear sky and their relationships, researching the change regularity and normal fluctuation range of BT on the basis of function fitting, setting the cloud detecting dynamic threshold depending on the cloud spectral characteristics, and making accuracy assessment in order to ensure higher adaptability and accuracy of this cloud detecting method. In this paper, a dynamic threshold algorithm is presented for cloud detection using daytime imagery from the VISSR sensor on board FY-2C/D/E, which is the first generation geostationary satellite. And the land surface/brightness temperature influence functions are analysis and established, including latitude, longitude, altitude, time, land cover. The theoretical temperature value of clear sky can be calculated through these influence functions. Then, the dynamic threshold cloud detection model is proposed based on the high temporal resolution of VISSR data. Meanwhile, the land surface emissivity is considered as the main factor to the change range of brightness temperature which determines the dynamic threshold for cloud detection. Finally, the dynamic threshold cloud detecting model is evaluated using FY-2C/D/E VISSR data covering China, and the Kappa of dynamic method is maximum, equalling 0.6195, which is much higher than the indexes for the reflectivity and BT fixed methods, equalling 0.4511 and 0.403, respectively. Consequently, the dynamic threshold cloud detecting method provides an important improvement because the spatial, temporal and geographic characteristics were considered into the model.
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