[Spatiotemporal dynamic fuzzy evaluation of wetland environmental pollution risk in Dayang estuary of Liaoning Province, Northeast China based on remote sensing].

Based on the aerial image data of Dayang estuary in 2008, and by virtue of Analytic Hierarchy Process (AHP) , remote sensing technology, and GIS spatial analysis, a spatiotemporal evaluation was made on the comprehensive level of wetland environmental pollution risk in Dayang estuary, with the impacts of typical human activities on the dynamic variation of this comprehensive level discussed. From 1958 to 2008, the comprehensive level of the environmental pollution risk in study area presented an increasing trend. Spatially, this comprehensive level declined from land to ocean, and showed a zonal distribution. Tourism development activities unlikely led to the increase of the comprehensive level, while human inhabitation, transportation, and aquaculture would exacerbate the risk of environmental pollution. This study could provide reference for the sea area use planning, ecological function planning, and pollutants control of estuary region.

[Changes of wetland landscape pattern in Dayang River Estuary based on high-resolution remote sensing image].

Based on the comprehensive consideration of the high resolution characteristics of remote sensing data and the current situation of land cover and land use in Dayang River Estuary wetland, a classification system with different resolutions of wetland landscape in the Estuary was established. The landscape pattern indices and landscape transition matrix were calculated by using the high resolution remote sensing data, and the dynamic changes of the landscape pattern from 1984 to 2008 were analyzed. In the study period, the wetland landscape components changed drastically. Wetland landscape transferred from natural wetland into artificial wetland, and wetland core regional area decreased. Natural wetland's largest patch area index descended, and the fragmentation degree ascended; while artificial wetland area expanded, its patch number decreased, polymerization degree increased, and the maximum patch area index had an obvious increasing trend. Increasing human activities, embankment construction, and reclamation for aquaculture were the main causes for the decrease of wetland area and the degradation of the ecological functions of Dayang River Estuary. To constitute long-term scientific and reasonable development plan, establish wetland nature reserves, protect riverway, draft strict inspective regimes for aquaculture reclamation, and energetically develop resource-based tourism industry would be the main strategies for the protection of the estuarine wetland.

[Research on remote sensing inversion biomass method based on the Suaeda salsa's measured spectrum].

Suaeda salsa is one of characteristic vegetation of wetlands in Northern China. By measuring the spectral data and leaf area index (LAI) of the Suaeda salsa in the ShuangTai Estuary of Liaodong Bay by the use of portable spectrometer and vegetation canopy analyzer, collecting the biomass of the Suaeda salsa samples, setting up the spectral reflectance curve of the Suaeda salsa, probing into the relationship between the vegetation index and the leaf area index of the Suaeda salsa, carrying out regression analysis of LAI and biomass and constructing the function equation, some conclusion were drawn: (1) By the end of September the spectral characteristics of Suaeda salsa show that at the red band 630 nm there is a clear reflection of the peak with a reflection rate of 12%-15%; there is a clear "red valley" configuration between 680 and 700 nm, and there is a clear "red edge" reflection rate of 25%-30% about 760 nm. (2) It was found that there is best correlation between vegetation index (SAVI and MSAVI) and LAI compared to other vegetation index in the regression analysis of the LAI and vegetation index. The correlation coefficient R2 is 0.711. By comparison of vegetation index linear regression equations, the correlation coefficient (SAVI and LADI) R2 is 0.696; the value of R2 (LAI and MSAV) is 0.695; the value of R2 (RVI) is 0.664; the value of R2(NDVI) is 0.649 and the value of R2 (PVI) is 0.466. (3) The value of correlation coefficient is low between the biomass and the vegetation indexes (RVI and NDVI) and the value of linear regression equation's R2 is 0.342 and 0.316, and the Logarithmic regression equation's R2 is 0.319 and 0.21, and the quadratic equation's R2 is 0.589 and 0.568, the value of correlation coefficient is high between the biomass and the vegetation indexes (PVI, SAVI and MSAVI), the value of linear regression equation is 0.626, 0.698 and 0.679, that of logarithmic regression equation is 0.592, 0.706 and 0.683 and that of the quadratic equation is 0.688, 0.711 and 0.683.