This study aimed to demonstrate change in spatial correlation between Korean pine （Pinus koraiensis Sieb. et Zucc.） and three rare species, and change in spatial distribution of four species in response to a range o...This study aimed to demonstrate change in spatial correlation between Korean pine （Pinus koraiensis Sieb. et Zucc.） and three rare species, and change in spatial distribution of four species in response to a range of selective cutting intensities. We sampled three plots of mixed Korean pine and broad-leaf forest in Lushuihe Forestry Bureau of Jilin province, China. Plot 1, a control, was unlogged Korean pine broad-leaf forest. In plots 2 and 3, Korean pine was selectively cut at 15 and 30 % intensity, respectively, in the 1970s. Other species were rarely cut. We used point-pattern analysis to research the spatial distributions of four tree species and quantify spatial correlations between Korean pine and the other three species, Amur linden （Tilia amurensis Rupr.）, Manchurian ash （Fraxinus mandshurica Rupr.）, and Mongolian oak （Quercus mongolica Fisch.） in all three plots. The results of the study show that selective cutting at 15 % intensity did not significantly change either the species spatial patterns or the spatial correlation between Korean pine and broadleaf species. Selective cutting at 30 % intensity slightly affected the growth of Korean pine and valuable species in forest communities, and the effect was considered nondestructive and recoverable.展开更多
This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture f...This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.展开更多
基金funded by China National Science and Technology Support Program(Grant No.2012BAD21B02)
文摘This study aimed to demonstrate change in spatial correlation between Korean pine （Pinus koraiensis Sieb. et Zucc.） and three rare species, and change in spatial distribution of four species in response to a range of selective cutting intensities. We sampled three plots of mixed Korean pine and broad-leaf forest in Lushuihe Forestry Bureau of Jilin province, China. Plot 1, a control, was unlogged Korean pine broad-leaf forest. In plots 2 and 3, Korean pine was selectively cut at 15 and 30 % intensity, respectively, in the 1970s. Other species were rarely cut. We used point-pattern analysis to research the spatial distributions of four tree species and quantify spatial correlations between Korean pine and the other three species, Amur linden （Tilia amurensis Rupr.）, Manchurian ash （Fraxinus mandshurica Rupr.）, and Mongolian oak （Quercus mongolica Fisch.） in all three plots. The results of the study show that selective cutting at 15 % intensity did not significantly change either the species spatial patterns or the spatial correlation between Korean pine and broadleaf species. Selective cutting at 30 % intensity slightly affected the growth of Korean pine and valuable species in forest communities, and the effect was considered nondestructive and recoverable.
基金supported by the National Natural Science Foundation of China (Grant Nos. 60573172 and 60973152)the Superior University Doctor Subject Special Scientific Research Foundation of China (Grant No. 20070141014)the Natural Science Foundation of Liaoning Province of China (Grant No. 20082165)
文摘This paper utilizes a spatial texture correlation and the intelligent classification algorithm （ICA） search strategy to speed up the encoding process and improve the bit rate for fractal image compression. Texture features is one of the most important properties for the representation of an image. Entropy and maximum entry from co-occurrence matrices are used for representing texture features in an image. For a range block, concerned domain blocks of neighbouring range blocks with similar texture features can be searched. In addition, domain blocks with similar texture features are searched in the ICA search process. Experiments show that in comparison with some typical methods, the proposed algorithm significantly speeds up the encoding process and achieves a higher compression ratio, with a slight diminution in the quality of the reconstructed image; in comparison with a spatial correlation scheme, the proposed scheme spends much less encoding time while the compression ratio and the quality of the reconstructed image are almost the same.
