The east Taihu lake region is characterized by high-density and large areas of enclosure culture area which tend to cause
eutrophication of the lake and worsen the quality of its water. This paper takes an area (380×380) of the east Taihu Lake
from image as an example and discusses the extraction method of combing texture feature of high resolution image with
spectrum information. Firstly, we choose the best combination bands of 1, 3, 4 according to the principles of the
maximal entropy combination and OIF index. After applying algorithm of different bands and principal component
analysis (PCA) transformation, we realize dimensional reduction and data compression. Subsequently, textures of the
first principal component image are analyzed using Gray Level Co-occurrence Matrices (GLCM) getting statistic Eigen
values of contrast, entropy and mean. The mean Eigen value is fixed as an optimal index and a appropriate conditional
thresholds of extraction are determined. Finally, decision trees are established realizing the extraction of enclosure
culture area. Combining the spectrum information with the spatial texture feature, we obtain a satisfied extracted result
and provide a technical reference for a wide-spread survey of the enclosure culture area.
Based on in situ water sampling and field spectral measurement from June to September 2004 in Lake Chagan, this
paper partly addressed to develop a new approach named inverse continuum removal to isolate fluorescence peak for the
comparison of water reflectance spectra with different Chl-a concentration during the summer. Next, an attempt was
made to link the reflectance changes including band depth and band area with Chl-a concentration and evaluate the
potential of remote sensing data for inversion. Results show that the Chl-a determined from band depth and band area of
fluorescence peak with the determination coefficient (R2) higher than 0.74. The study also proves that inverse continuum
removal analysis can be used to effectively determine the Chl-a concentration of Lake Chagan in Northeast China.
There is important significance for hydrophytes extraction. It is the basis of water pollution control decision. For the purpose of hydrophytes extraction, the vegetation is classified into two species: submersed vegetation and emerged vegetation. And to obtain a better categorization map, three different classification methods as ISODATA, MLC and Decision tree are put forward in the paper. The analysis is performed on the Landsat TM image of Taihu lake acquired in 7, 2002. The result shows that the decision tree classification acquires the best extraction effect.
When multispectral images are used to extract the area of aquatic vegetation in Taihu Lake, because of the influence of
suspended matter and algae, different objects may have the same spectrum and make it difficult to mapping the
distribution of aquatic vegetation exactly. Many different methods, including unsupervised classification and supervised
classification, are used, but the classification accuracy didn't improve obviously. The growth of aquatic vegetation is
closely to the water depth. So we try to use water depth data to increase the extraction accuracy. The whole Taihu Lake is
classified into three types: open water, emerged vegetation and submersed aquatic vegetation. Suppose the DN (Digital
number) of each type satisfies normal distribution. Numbers of sample points of each type in single band or combined
bands are selected and put down there DNs, and then statistical method is adopted to acquire the maximum and
minimum which are used to build decision tree to fulfill the classification. The single band or combined bands in which
maximum and minimum interval of each type have small intersect set are considered as the suitable bands for
classification. Two methods, classification based on spectral characteristics and classification based on spectral
characteristics and water depth data, are used. The classification accuracies of the two methods are compared. The results
show the water depth data can improve the classification accuracy and resolve the different objects with same spectrum
problem partially.
Methods and techniques for mapping the trophic state of water bodies in Taihu Lake based on synchronous Landsat TM images were studied. The rapid deterioration of water quality of Taihu Lake in recent years demanded effective monitoring methods. The remote sensing technology had provided effective and low-cost means of monitoring synoptic water quality over inland waters. The Landsat TM images acquired on July 13th, 2002, together with in situ measurements of chl-a, were used to retrieve chl-a concentration in Taihu Lake. The visible bands of TM images were carefully corrected for atmospheric effects using clear-water approach, and the remotely sensed reflectance of water at these bands were estimated. Then, chl-a concentration in Taihu Lake was estimated by the statistical relationship between the atmospherically corrected water reflectance at these bands and in situ measurements. In accordance with the definition of Carlson's trophic state and his formula from his previous studies, TSI (chl) was expressed as TSI(chl) = 9.81* ln(chl-a) + 30.6. The Taihu Lake map of trophic state was generated. The spatial distribution of trophic state in Taihu Lake was analyzed, as well as the errors in estimation of chl-a content and trophic state of Taihu Lake from remotely sensed data.
KEYWORDS: Data modeling, Geographic information systems, Databases, Data mining, Mining, Knowledge discovery, Data analysis, Statistical analysis, Autoregressive models, Geography
Spatial data mining and knowledge discovery (SDMKD) is a whole process of discovering implicit but useful knowledge
from GIS databases. From the first law of geography, spatial association patterns are the realizations of processes that
operate across the geographic space. This paper attempts to present a decision tree framework to assist in analyzing
spatial association patterns. Based on the problem, the representation of data or data model should be identified firstly.
Secondly, geostatistical, lattice and point pattern data can be distinguished through the characteristics of spatial domain.
The main task of third level of the decision tree is to apply different spatial data analysis methods to different spatial data
types. For lattice data, the work is to apply exploratory spatial data analysis (ESDA) to find spatial association patterns,
and then identify the driving forces which cause the observed spatial association patterns by confirmatory spatial data
analysis (CSDA). The fourth level is to verify the precision and accuracy of spatial association models. All in all, spatial
association pattern analysis is a process of acquiring useful spatial patterns by circulation and repetition.
Three campaigns including 37 valid samplings were made to measure field spectra together with some water quality parameters including suspended substance concentration and other data. Field spectra were measured with a portable Field Spec FR spectroradiometer (ASD Inc.), in a wavelength range of 350-2500 nm. And the concentrations of water quality parameters were measured according to the corresponding investigation criteria about lakes of China. Based on the correlative analysis between field spectra and suspended substance concentrations of different ranges, we divided all the 37 samples into different groups according to the analyzed threshold concentrations. Then some reflectance variables at some bands were used to do the correlative analysis with suspended substance concentrations of different groups to find out the better indicative bands. Because of no evident indicative bands existing with the general method in some groups, the first and second derivative method was also used. The results showed that the best variable is not a constant when the concentration is low and the range or span is narrow. Otherwise, the best variable is the reflectance first derivative near 878 nm, and then the average reflectivity in the range of 810-820 nm, or the reflectivity at 820 nm. Generally, the derivative method is better to estimate suspended substance concentration with hyperspectra.
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