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Most lakes in eastern China are exploited in the form of enclosure culture. The high density enclosure culture area is easy to cause lake eutrophication and deteriorate the water quality. So the lake enclosure culture area is focused by people and is usually extracted by using the multi-temporal remote sensing data and the multi-spectral classification or the manual digitization means. The multi-spectral classification is easy to confuse enclosure culture area with the natural water area and the manual digitization demands much time. This paper suggests a new method using the image texture information to extract lake enclosure area. The result is more accurate and the method demands less expense. Lake Baima is the study area and the image of the area is multi-spectral data of China Brazil Earth Resources Satellite 02. First the water area、the man-made area and the bare soil which has the similar spectral characteristics with the water are extracted by using the normalized difference water index. Then the principal component analysis was used to compress the multi-spectral data of the study area and enhance the texture information of the image. The third step Grey Level Co-occurrence Matrix (GLCM) was used to analyze the image, select a proper threshold of mean value which quantifies the GLCM. Finally, the lake enclosure culture area was extracted by using the decision tree.

The spectra were measured with hand-spectral instrument in Lake Taihu, and the water samples were collected in situ and analyzed in the lab. The correlations between chlorophyll-a (CHL) and total suspended matter (TSM) and the measured spectra reflectivity were analyzed, on the basis of which, CHL and TSM concentrations were estimated by using the combination of the field bands, respectively. Then by the comparison of the bands of Landsat ETM, the most practicable ones were selected, which would be validated for the estimation of CHL and TSM content by using the Landsat ETM images. Finally, lots of regression models were discussed to educe the estimation models. Two of the most precise ones were used to estimate the CHL and TSM concentrations. The conclusion are as follows: (1) Landsat TM/ETM is one of the appropriate data resources in the multi-spectral remote sensing, but it isnt the most appropriate, (2) it is more precise to estimate CHL content with the third band of ETM by some certain function. In addition, it is also better to estimate CHL content with the combination of ETM3 and ETM1, (3) the correlation between ETM4 and TSM is also very close, and the most precise band to estimate TSM is ETM4/ETM1 , (4) the image window size has an effect on the precision to estimate the water quality parameters, and 5 by 5 or 7 by 7 is more appropriate to estimate CHL content, while 3 by 3 or less is more appropriate to estimate TSM content.