At present, the dynamic change monitoring of urban ecological environment has became an important guarantee measure for urban management, planning and construction. In this paper, taking Nanchang city as a case study, the remote sensing ecological index (RSEI) which is based on the natural factors is used to study the changes of the urban ecological environment. The Landsat images in the three different time periods of 1996, 2005, and 2013 in Nanchang were selected. To extract the four factors of green level, moisture, dryness and heat respectively as sub-indexs of the ecological assessment, in which the single window algorithm was used to calculate the heat. Based on the four factors, the RSEI in each year was finally calculated. The results show that the ecological environment in Nanchang deteriorated in the past 17 years, the value of the RSEI has decreased from 0.385 in 1996 to 0.267 in 2005, falling by 30.65%, but the ecological environment has improved in the later period, with the value of RSEI value rising to 0.413, increased by 54.68% compared with the results in 2005. It is indicates that the urban ecological environment of Nanchang has been significantly improved after some effective measures such as urban greening, pollution control, environmental protection were taken.
In recent years, the effect of urban heat island (UHI) is increasingly obvious with moving forward in further
urbanization process, which has become one of the prominent issues of environment. The image data of
Nanchang city supplied by Landsat 5 Thematic Mapper (TM) in September 2006 is used in this paper, and the
land surface temperature (LST) over the same period has been retrieved by using a mono-window algorithm
based on remote sensing technology. The classification of LST is subsequently fulfilled by the method of proper
density cutting. Characteristics of intensity and spatial distribution of UHI effect in Nanchang, as well as its
relationships with land use type and vegetation coverage degree (VCD) are discussed in detail. The result shows
that the phenomena of UHI are significantly presented in urban area with an inhomogeneous distribution, and
the degree of influence of UHI depends on types of land uses. The intensity of UHI effect has a significant
negative linear correlation with normalized difference vegetation index (NDVI). It is deduced that suitably
optimizing land use types and raising VCR are obvious and effective ways to reduce UHI.
This paper proposes a method of image seam line based on the combination of Dijkstra algorithm and morphology. First, the method determines the differential images of the overlap part of two images. And then, morphological expansion processing is implemented based on the differential images and the eight-neighborhood sparse matrix is constructed according to the sparse matrix. Finally, the seam line with the optimal path can be obtained by automatic search of seam line on the differential images by Dijkstra algorithm. The experimental results show that compared to the Dijkstra method that is not subject to morphological processing, the improved algorithm can avoid the high brightness area as housing under the circumstance of less time (3.72s) in the process of automatic research seam line.