Xiaoshan Ge

Exploring carbon emissions in Suzhou, a city with a significant heavy industry presence, and understanding the factors that influence these emissions are crucial in achieving China’s dual-carbon goals within the framework of global climate governance. This study utilized land use data and statistical data from 2008 to 2020 in Suzhou. The carbon emission coefficient method was employed to calculate carbon emissions, while GIS technology was used to analyze their temporal and spatial distribution, as well as carbon emission risk. Additionally, the LMDI model was applied to investigate the contribution of influencing factors and TAPIO was used to analyze the decoupling relationship between the main influencing factors and carbon emissions. The study yielded the following findings: (1) From 2008 to 2020, land use changes in all regions of Suzhou are obvious, and there are mutual transformations among different land types. (2) The overall carbon emission in Suzhou showed an upward trend, with a spatial distribution characterized by higher emissions in the northern regions and lower emissions in the southern regions. (3) The risk and pressure index of carbon emission in all regions of Suzhou are too large, and the amount of carbon emission and carbon absorption is seriously out of balance, resulting in an overall carbon imbalance. (4) Among the influencing factors on land use carbon emissions in Suzhou, energy intensity exerted the strongest negative effect, and economic growth showed the strongest positive effect. (5) Decoupling analysis showed that economic growth and carbon emissions are generally shifting towards a strong decoupling and, except for Zhangjiagang, other regions have a good development model. Based on the research findings, this paper proposes specific suggestions for reducing carbon emissions, aiming to provide actionable recommendations for Suzhou and other urban areas in achieving low-carbon and environmentally sustainable cities.

Imaging with different spectrums often leads to significant nonlinear differences in the intensity. Consequently, automatic matching of the multispectral images is a challenging task. Considering the limitations of using intensity information alone, this paper studies multispectral image matching based on the structure consistency (MMSC). Firstly, the Log-Gabor filter is extended to construct the phase congruency maps, which embody the structural properties of images and provide rich meaningful features. Then, a new algorithm is developed for salient feature point detection by performing nonlinear diffusion on the phase congruency. Those feature points reflect the characteristics of image structure which are illumination and contrast invariant. Finally, structural descriptors are built according to the orientation of the maximum Log-Gabor filter responses, and the matching can be performed by computing the correspondent descriptors. To evaluate the effectiveness of the proposed method, extensive experiments are carried out on three multispectral image datasets. The experimental results show that, the proposed method is not sensitive to the nonlinear intensity variance across spectrums, and outperforms the comparison methods in terms of the number of correct matches and the matching precision.

Spatial information plays an increasingly important role in our society. In many situations, spatial information semantic understanding has often created barriers in spatial information sharing and interoperability. In this paper, the inhere mechanism of spatial information semantics from the view of TFIG is studied. Based on this theory, spatial information semantic representation, equivalence, entailment are discussed, and an approach to achieve spatial information semantic computing is also proposed in this paper, which can better solve the barriers of semantic understanding in spatial information sharing and interoperability