[Remote sensing analysis of ecological change caused by construction of the new island city: Pingtan Comprehensive Experimental Zone, Fujian Province].

Pingtan Island was officially established as the 'Pingtan Comprehensive Experimental Zone of Fujian' in 2010, and it led to a surge of construction in the island city. Based on the Landsat-5 images for 2007 and the latest Landsat-8 images for 2013, this paper studied the ecological status, the temporal trends of the ecological changes and the reasons for those changes in Pingtan Comprehensive Experimental Zone at its early stage of construction, by using the remote sensing of ecological index (RSEI). The results showed that as an ecologically fragile area, Pingtan Island had a moderate level of overall ecological status. In the early construction period (from 2007 to 2013), the ecological status of the island showed a downward trend, with a 14% drop of RSEI from 0.511 in 2007 down to 0.450 in 2013, and approximately 36.5% of the area of the island faced the degradation of ecological status, which mainly occurred in the central and southwestern parts of the island. The reason for the degradation was mainly due to the large-scale construction which further damaged the scarce vegetation on the island. Therefore, in order to curb the downward trend of the ecological quality of Pingtan Comprehensive Experimental Zone, some effective ecological protection measures must be developed and implemented during the construction.

[Quantitative estimation of suspended solid concentration in the lower Min River based on multi-source synchronal data].

Three synchronal data collected on 2006-09-18 have been used in the study of the suspended solid concentration (SSC) of the lower Min River, which are in situ sampled water data, field-spectrometer measured spectral data and Landsat TM spectral data. Two models for predicting SSC have been proposed, one of which is based on field-spectrometer measured data and the other is on Landsat TM data. The statistical analysis of the field-spectroreter measured data has revealed that the reflectance of the SSC at the 690 nm has the strongest correlation with the in situ-sampled SSC data. The regression model can be expressed as SS = 116.2 (R690/R530) - 33.4. Furthermore, the model built upon the ratio of the reflectance at 690 nm to 530 nm has the best fitness with the in situ sampled SSC data. While the best predicting model for the Landsat TM data is achieved using the band combination of (TM2 + TM3)2 and is defined as SS = 3793.7 (R(TM3) + R(TM2)2 - 16.5. The assessment of the two models shows that the model on the field-spectrometer data has higher accuracy than that on the Landsat TM data but the difference is not big. This suggests that the Landsat TM data are still valuable in the prediction of the SSC if the field-spectrometer data are not available. Consequently, the predicting model based on the Landsat data has been applied in the study of the SSC of the lower Min River. The result shows that the model can efficiently reveal the SSC with its spatial distributional pattern features.