Search Results (3)

Green development is a concept of sustainable development, aiming to protect the environment and ecosystems while meeting economic development needs. In the field of agriculture, green development has emerged as a crucial pathway for reconciling the conflicts between agricultural development and ecological conservation. To investigate the level of green development in Chinese agriculture, regional variations, and the evolutionary patterns, this paper is based on the framework of sustainable development theory. This study establishes a comprehensive evaluation system for agricultural green development and applies methods such as entropy-weighted TOPSIS, Dagum’s Gini coefficient, kernel density estimation, Moran’s I index, and Markov chains to analyze the level of agricultural green development, regional disparities, and dynamic evolution in China. The findings of this study reveal that: (1) The overall level of agricultural green development in China is steadily improving, with notable differences in the level of agricultural green development among different regions and provinces. There are significant disparities in agricultural green development between regions, and the overall disparities exhibit a fluctuating downward trend characterized by periods of increase followed by decrease. The regional disparities are identified as the primary cause of the overall disparities in agricultural green development in China. (2) The eight major economic regions in China are experiencing steady development in agricultural green practices, but there are varying degrees of polarization due to different development speeds. (3) This study also highlights a clear spatial positive correlation in the level of agricultural green development in China, with most provinces showing clustering in the first and third quadrants, indicating a “high–high” (H-H) and “low–low” (L-L) agglomeration pattern. (4) The study reveals that the level of agricultural green development in China exhibits a certain degree of stability. Over time, the probability of transitioning from lower-level regions to neighboring higher-level regions increases, and the agricultural green development level in neighboring regions can influence the spatial transfer probability within a given region. Therefore, agricultural green development demonstrates significant spatial dependence. Full article

Star identification plays a key role in spacecraft attitude measurement. Currently, most star identification algorithms tend to perform well only in a scene without noise and are highly sensitive to noise. To solve this problem, this paper proposes a star identification algorithm based on the maximum spanning tree (MST) index and multi-order continuous cycle angle (CCA) intended for the lost-in-space mode. In addition, a neighboring star selection method named dynamic eight-quadrant (DEQ) is developed. First, the DEQ method is used to select high-confidence neighboring stars for the main star. Then, the star image is regarded as a graph, and the Prim algorithm is employed to construct the MST pattern for each guide star, which is then combined with the K vector index to perform the main star candidate search. Finally, the Jackard similarity voting for the multi-order CCA of the main star is used to identify the main star, and the global neighboring star identification is conducted by the multi-order CCA of neighboring stars. The simulated and real star images test results show that compared with five mainstream algorithms, when the position noise is 1 pixel, the number of false stars is five, the magnitude noise is 0.5, and the identification accuracy of the proposed algorithm is higher than 98.5%. Therefore, the proposed algorithm has excellent anti-noise ability in comparison to other algorithms. Full article

Nighttime light (NTL) images obtained by the Visible Infrared Imaging Radiometer (VIIRS) mounted on the National Polar-orbiting Partnership (NPP) could objectively represent human activities and instantly identify urban shapes on a temporal and spatial scale. From 2013 to 2020, the built-up areas of eight provincial capital cities were extracted using NPP/VIIRS NTL data to examine the dynamic changes in city expansion and socioeconomic development in the Yellow River Basin during the urbanization process. The spatial characteristics of urban built-up area expansion were generated using the eight-quadrant analysis method and combined with the statistical data of population and (gross domestic product) GDP to analyze the correlations between the light intensity of built-up areas, population and GDP; this enables an understanding of the changes in population and economy in the development of urban built-up area expansion. The findings show that: (1) unbalanced city development existed in the Yellow River Basin’s upper, middle, and lower reaches, and the expansion and light intensity of cities in the upper reaches were slower than those in the middle and lower reaches; (2) the spatial differentiation of urban expansion was significant between each of the reaches in the Yellow River Basin, and greatly influenced by natural geographical elements; and (3) positive correlation exists between light intensity, population, and GDP in the built-up areas of the middle and lower reaches, while the correlations in the upper reaches were not stable. In conclusion, light data indirectly reflects urban development and could be used as a substitute variable for socioeconomic development indicators under certain conditions. Full article