As a typical semiarid farming-pastoral ecotone sensitive to the environmennt, the Plain of West Liaohe Basin (WLBP) is currently experiencing drastic environmental changes. To identify how environmental change affect vegetation in the WLBP, we analysed spatiotemporal variation characteristics of Ecological environment factors based on monthly and annual air temperature (T), precipitation (P) and Normalized Difference Vegetation Index (NDVI) from 1982 to 2015. And the correlations between them were investigated by correlation analysis (Simple correlation, partial correlation and complex correlation) at temporal and spatial scale. The results showed that: (1) the vegetation growth of the WLBP showed ameliorated trend, with a change rate of 0.004/yr.; (2) P was more sensitive to NDVI than T; (3) and the influence of hydrothermal changes on vegetation growth was more significant than that of the change of single climate factors at time scales; (4) the effects of anthropogenic factors on vegetation change were 75.07% (1982–1993) and 98.08% (1994–2015), respectively. At the temp-special scales, P&T and land use type change (LUCC) were the main climatic and anthropogenic factors that affect vegetation changes, respectively. 相似文献
Intense geomagnetic storms (Dst < −100 nT) usually occur when a large interplanetary duskward-electric field (with Ey > 5 mV m−1) lasts for more than 3 h. In this article, a self-organizing map (SOM) neural network is used to recognize different patterns in the temporal variation of hourly averaged Ey data and to predict intense storms. The input parameters of SOM are the hourly averaged Ey data over 3 h. The output layer of the SOM has a total of 400 neurons. The hourly Ey data are calculated from solar wind data, which are provided by NSSDC OMNIWeb and ACE spacecraft and contain information on 143 intense storms and a fair number of moderate storms, weak storms and quiet periods between September 3, 1966 and June 30, 2002. Our results show that SOM is able to classify solar wind structures and therefore to give timely intense storm alarms. In our SOM, 21 neurons out of 400 are identified to be closely associated with the intense storms and they successfully predict 134 intense storms out of the 143 ones selected. In particular, there are 14 neurons for which, if one or more of them are present, the occurrence probability of intense storms is about 90%. In addition, several of these 14 neurons can predict big magnetic storm (Dst −180 nT). In summary, our method achieves high accuracy in predicting intense geomagnetic storms and could be applied in space environment prediction. 相似文献
We derive bias-corrected X-ray luminosity functions (XLFs) of LMXBs detected in 14 E and S0 galaxies observed with Chandra. After correcting for incompleteness, the individual XLFs are statistically consistent with a single power-law. A break at or near LX,Eddington , as previously reported, is not required in any individual case. The combined XLF with a reduced error, however, suggests a possible break at LX = 5 × 1038 erg s−1, which may be consistent with the Eddington luminosity of neutron stars with the largest possible mass (3 M), or of He-enriched neutron star binaries. We confirm that the total X-ray luminosity of LMXBs is correlated with the the near-IR luminosities, but the scatter exceeds that expected from measurement errors. The scatter in LX(LMXB)/LK appears to be correlated with the specific frequency of globular clusters, as reported earlier.
We cross-correlate X-ray binaries with globular clusters determined by ground-based optical and HST observations in 6 giant elliptical galaxies. With the largest sample reported so far (300 GC LMXBs with a 5:2 ratio between red and blue GCs), we compare their X-ray properties, such as X-ray hardness, XLF and LX/LB and find no statistically significance difference between different groups of LMXBs. Regardless of their association with GCs, both GC and field LMXBs appear to follow the radial profile of the optical halo light, rather than that of more extended GCs. This suggests that while metallicity is a primary factor in the formation of LMXBs in GCs, there may be a secondary factor (e.g., encounter rate) playing a non-negligible role. 相似文献