| With the rapid development of society, economy and population, human exploitation and utilization of nature has gradually restricted the development, and caused many kinds of ecological environment problems, such as water pollution, air pollution, soil heavy metal pollution, ecological degradation, and desertification. At the same time, the contradiction between economic development and environmental protection, will greatly reduce the sustainable development capacity of economy and society.This phenomenon, will continue to aggravate with the global economic development. Therefore, protecting and improving the eco-environmental quality have become an important and urgent task.Based on the social development research project of Shaanxi province (2013K14-01-03), under the background of rapid urbanization, this paper is based on the principle of data’s availability. we selected the 1992,2000,2007 and 2014 those four years’data in 10 districts of Xi’an including multi-source remote sensing image data, daily value meteorological data, statistical yearbook data and DEM data. This paper constructed the eco-environmental quality evaluation system by the principles of dominant, classification and operability. On the basis of data standardization, the principal component analysis was used to calculate the weigh. The comprehensive index method of raster data was applied to evaluate and to analyze the 1992,2000,2007, and 2014’s eco-environmental quality. We have gotten the evaluation classification figure and dynamic changes of eco-environmental quality. The main contents of this paper were as follows:(1) Compared two atmospheric correction methods between the FLAASH model and 6S module and selected the appropriate image correction data to improve the accuracy of quantitative remote sensing. (2) Obtained the landuse data by interpreting the remote sensing images. (3) Retrieved the land surface temperature and the vegetation index, soil index, dryness index and calculated the heat island effect. (4) Calculated the landscape diversity index and landscape fragmentation index. (5) The population density and GDP data were distributed in the 30m*30m grid cell sizes. (6) Calculated weights and built the comprehensive evaluation model. (7)Divided the eco-environmental quality level and explored the rule of variation.The results indicated:(1)The eco-environmental qualities were basically stable without the trend of extreme deterioration in 10 districts of Xi’an, but there were still some problems. Some fluctuations were existed within the scope of the recoverable ecological environment. Types of eco-environmental quality focused on "medium" and "good", and the areas of "bad" and "good" were very little. The main trends were "mild deterioration" and "mild improvement". The eco-environmental quality rose after the first decrease and was wavied at a tread of dynamic and time-varying from 1992 to 2014. There were no obvious ups and downs and no clear improvement.The eco-environmental quality issues were still highly valued.(2) Those areas which are similar in population, economy, and topography also easily had the similar distribution pattern of eco-environmental quality. The southern of Chang’an district was Qinling Mountains, which had the high vegetation cover and stable eco-environmental quality. The eco-environmental quality of Beilin, Lianhu and Xincheng district which were plain, was worse than Qinling mountain.But the level of economic development was opposite. The development of eco-environmental quality in those districts was related to the transformation of urban construction centers and the expansion of construction land areas. Therefore, the development of the surrounding area is driven by the central area in Xi’an. Qinling, the natural ecological security barrier should be protected while economy grows, and the prevention of urban pollution should be strengthened.(3)It should pay attention to balance the relationship among the economy, population and ecological environment. Xi’an’s economic development primarily relied on industry in the early time, while the supporting environmental protection measures were incomplete and the eco-environmental quality declined. Then, under the policy driven, the tourism and high-tech industries were vigorously developed, the development’s pace of the economy and population was increased, but the ecological environment was caused a threat. Within a certain threshold range, the environment still can be adjusted by itself. The relationship between economic growth and ecological environmental protection should be balanced and pay more attention.(4) This paper was made use of the approach that combined multi-source, multi-temporal remote sensing images with statistical data to evaluate the regional eco-environmental quality and to discuss the variation laws. Compared with the traditional methods, this method avoided spending a lot of manpower, material and other resources. Remote sensing data was easy to obtain, and had the advantages of high timeliness, abundant information and more objective evaluation results.This paper intends to innovate:(1) A comprehensive evaluation of the combination of nature and social economy. Previous index system used remote sensing data as a single data source. In this paper, the remote sensing data and statistical data were combined to develop an evaluation index system. Remote sensing data based on grid scale, but statistical data based on administrative divisions. This study intends to by gridding method, simulated the spatial distribution of population and GDP with 30m* 30m grid scale in Xi’an area, To better reflect the distribution of population and GDP spatial patterns and regional differences. Different indicators of raster data in the same position with relative field location were haved a good spatial coincidence. Algebra operation of raster data met needs of query evaluation; All raster data elements haved the same size as 30m*30m grid scale which can improve the universality and precision of index selection, also, increased the evaluation of rationality.(2) Quantitative remote sensing inversion was based on model contrast. Quantitative remote sensing in how to improve the prediction accuracy of the model, to achieve precise inversion lied in the accurate estimation of surface reflectance. This paper tried to explore a variety of advantages and disadvantages of atmospheric correction methods, and used the two methods of FLAASH module and 6S model.Through the typical object spectral curve method (including water, vegetation, soil, man-made features) after contrast correction of spectral data, we selected a suitable atmospheric correction data, improved the accuracy of quantitative remote sensing inversion. |
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