This study selects the typical middle and lower reaches of Han River as the study area and focuses on water quality
evaluation methods and water quality evaluation of the surface water of the river basin. On the basis of the field survey,
the author conducted a water quality sampling survey in the study area in spring and summer in 2012. The main
excessive factors in the study area are determined as TN and TP. Using HJ1A/1B CCD multi-spectral data, the multiple
linear regression inversion model and neural network inversion model are established for content of TN and TP. In
accordance with these inversion results, the single factor water quality identification indexes in the study area are
obtained. The results show that, BP neural network model boasts the highest inversion accuracy and that the single factor
water quality identification indexes resulting from its inversion results are highly accurate, reliable and applicable, which
can really reflect the changes in water quality and better realize the evaluation of water quality in the study area. Water
quality evaluation results show that the water pollution in the study area is organic pollution; the water quality of Han
River experiences large differences in different regions and seasons; downstream indexes are superior to upstream
indexes, and the indexes in summer are superior to those in spring; the TN index seriously exceeds the standard in spring
and the TP index seriously exceeds the standard in some regions.
Lake water temperature is one of the most important parameters determining ecological conditions in lake water. With the recent development of satellite remote sensing, remotely sensed data instead of traditional sampling measurement can be used to retrieve the lake surface temperature. The East Lake located in the Wuhan city was selected as research region in this paper. The mono window algorithm has been applied to retrieve the lake water temperature of East lake basin with Landsat TM data. Through three groups of field survey data, the outcome shows that the retrieval results using the mono window model are quite approximate to the same period of the experimental region historical temperature data. So, it is feasible to utilize the remote sensing method to obtain the lake temperature. Meanwhile, the retrieval results also demonstrate that the East Lake surface temperatures from different years have the similar distribution regularity. Generally speaking, the temperature of the lake center is higher than the surrounding area. The west of lake is mostly higher than the east mainly due to the vegetation density and urbanization distribution condition. This conclusion is important to the further study on monitoring the East Lake temperature particularly in large scale.
The floods due to dam failure usually do great harm to human beings and social economy. The aim of this paper is to apply geographical information system (GIS) technology, and the loss estimation model to estimate the loss of the hypothetical dam break in Lushui reservoir basin. The general predictive loss evaluation scheme of dam-break flood is illustrated. The empirical formula method is used to determine the flood submerged region. The final loss assessment result is mapped out on GIS software. The result shows that the combination of GIS and loss estimation model can make the overall procedure for loss assessment efficient and effective, which could do favor to long-term basin flood control planning and disaster preparedness.
The research in this paper is to recognize and locate sand dredges in Changjiang River based on ASAR remote sensing
data. Chenglingji is selected to be experimental area. Adopting advanced radar remote sensing image can satisfy the
requirement to monitor and locate sand dredges in Changjiang River ignoring the effect of weather condition and night
limit. The program is developed with the IDL which property is a visual language facing matrix. Some key technologies
in this paper, such as filtering and noise reduction, the process of image based on morphology, edge detection, clustering,
extraction of ship's shape feature and so on, are researched. Comparing with the information gathering from land
monitoring station, this method can achieve satisfied accurateness.
Data fusion techniques of pixel level are widely used to integrate a lower spatial resolution multi-spectral image with a
higher spatial resolution panchromatic image. Four data fusion techniques, smoothing filter-based intensity modulation
(SFIM), high pass-filter transform (HPT), wavelet transform (WT) and multiplication transform (MT), are adapted to
fuse low spatial resolution multi-spectral data and panchromatic image of Quick-Bird sensor. The visual evaluation and
quantitative analysis results show that HPT and MT models provide relatively higher spatial frequency information
gaining from panchromatic channel while SFIM provides the best spectral information preservation between merged
production and original multi-spectral image.