Syntheses, structures, and magnetic properties of mixed-ligand complexes based on 3,6-bis(benzimidazol-1-yl)pyridazine.

Six new metal-organic coordination polymers (CPs) [Ni(L)(2,5-TDC)(H2O)] n (1), [Ni(L)(1,3-BDC)(H2O)] n (2), [Ni(L)(1,4-BDC)(H2O)] n (3), [Mn(L)(2,5-TDC)(H2O)] n (4), [Mn(L)(2,6-PYDC)(H2O)] n (5) and [Mn(L)(1,4-NDC)] n (6) were achieved by reactions of the corresponding metal salt with mixed organic ligands (L = 3,6-bis(benzimidazol-1-yl)pyridazine, 2,5-H2TDC = thiophene-2,5-dicarboxylic acid, 1,3-H2BDC = isophthalic acid, 1,4-H2BDC = terephthalic acid, 2,6-H2PYDC = pyridine-2,6-dicarboxylic acid, 1,4-H2NDC = naphthalene-1,4-dicarboxylic acid) under solvothermal condition. CPs 1-6 were characterized by single-crystal X-ray diffraction, IR, TG, XRD and elemental analyses. Their structures range from the intricate 3D CPs 1, 3, 4 and 6 to the 2D coordination polymer 2 and the infinite 1D chain 5. The CPs 1-4 and 6 underlying networks were classified from the topological viewpoint, disclosing the distinct sql (in 1), pcu (in 3 and 6), new topology (in 2), and dia (in 4) topological nets. Moreover, analysis of thermal stability shows that they had good thermal stability. Finally, magnetic properties of CPs 1-6 have been studied, the results showed that complex 2 had ferromagnetic coupling and complexes 1, 3-6 were antiferromagnetic.

[Dynamic changes in vegetation NDVI from 1982 to 2012 and its responses to climate change and human activities in Xinjiang, China].

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. Therefore, it has significance to monitor the growth of vegetation. Based on AVHRR GIMMS NDVI and MODIS NDVI datasets, we analyzed the spatiotemporal patterns of change in NDVI and their linkage with climate change and human activity from 1982 to 2012 in the typical arid region, Xinjiang of northwestern China, at pixel and regional scales. At regional scale, although a statistically significant positive trend of growing season NDVI with a rate of 4.09 x 10⁻4· a⁻¹ was found during 1982-2012, there were two distinct periods with opposite trends in growing season NDVI before and after 1998, respectively. NDVI in growing season first significantly increased with a rate of 10 x 10⁻4· a⁻¹ from 1982 to 1998, and then decreased with a rate of -3 x 10⁻4· a⁻¹ from 1998 to 2012. The change in trend of NDVI from increase to decrease mainly occurred in summer, followed by autumn, and the reversal wasn't observed in spring. At pixel scale, the NDVI in farmland significantly increased; the NDVI changes in the growing season and all seasons showed polarization: Areas with significant change mostly increased in size as the NDVI record grown in length. The rate of increase in size of areas with significantly decreasing NDVI was larger than that with significantly increasing NDVI, which led to the NDVI increase obviously slowing down or stopping at regional scale. The vegetation growth in the study area was regulated by both climate change and human activity. Temperature was the most important driving factor in spring and autumn, whereas precipitation in summer. Extensive use of fertilizers and increased farmland irrigated area promoted the vegetation growth. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation might reduce spring NDVI in the part of farmlands, and the increase in stocking levels of livestock might lead to a decrease in NDVI in some grasslands.

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

Consistent NDVI time series are basic and prerequisite in long-term monitoring of land surface properties. Advanced very high resolution radiometer (AVHRR) measurements provide the longest records of continuous global satellite measurements sensitive to live green vegetation, and moderate resolution imaging spectroradiometer (MODIS) is more recent typical with high spatial and temporal resolution. Understanding the relationship between the AVHRR-derived NDVI and MODIS NDVI is critical to continued long-term monitoring of ecological resources. NDVI time series acquired by the global inventory modeling and mapping studies (GIMMS) and Terra MODIS were compared over the same time periods from 2000 to 2006 at four scales of Qinghai-Tibet Plateau (whole region, sub-region, biome and pixel) to assess the level of agreement in terms of absolute values and dynamic change by independently assessing the performance of GIMMS and MODIS NDVI and using 495 Landsat samples of 20 km x20 km covering major land cover type. High correlations existed between the two datasets at the four scales, indicating their mostly equal capability of capturing seasonal and monthly phenological variations (mostly at 0. 001 significance level). Simi- larities of the two datasets differed significantly among different vegetation types. The relative low correlation coefficients and large difference of NDVI value between the two datasets were found among dense vegetation types including broadleaf forest and needleleaf forest, yet the correlations were strong and the deviations were small in more homogeneous vegetation types, such as meadow, steppe and crop. 82% of study area was characterized by strong consistency between GIMMS and MODIS NDVI at pixel scale. In the Landsat NDVI vs. GIMMS and MODIS NDVI comparison of absolute values, the MODIS NDVI performed slightly better than GIMMS NDVI, whereas in the comparison of temporal change values, the GIMMS data set performed best. Similar with comparison results of GIMMS and MODIS NDVI, the consistency across the three datasets was clearly different among various vegetation types. In dynamic changes, differences between Landsat and MODIS NDVI were smaller than Landsat NDVI vs. GIMMS NDVI for forest, but Landsat and GIMMS NDVI agreed better for grass and crop. The results suggested that spatial patterns and dynamic trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI. It might be feasible to successfully integrate historical GIMMS and more recent MODIS NDVI to provide continuity of NDVI products. The accuracy of merging AVHRR historical data recorded with more modern MODIS NDVI data strongly depends on vegetation type, season and phenological period, and spatial scale. The integration of the two datasets for needleleaf forest, broadleaf forest, and for all vegetation types in the phenological transition periods in spring and autumn should be treated with caution.