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Abstract:The response of vegetation to regional climate change was quantified between 1982 and 2010 in the Mongolian plateau by integrating the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000NDVI ( -2010. Average NDVI values for the growing season (April-October) were extracted from the AVHRR and MODIS NDVI datasets after cross-calibrating and consistency checking the dataset, based on the overlapping period of 2000-2006. Correlations between NDVI and climatic variables (temperature and precipitation) were analyzed to understand the impact of climate change on vegetation dynamics in the plateau. The results indicate that the growing-season NDVI generally exhibited an upward trend with both temperature and precipitation before the mid-or late 1990s. However, a downward trend in the NDVI with significantly decreased precipitation has been observed since the mid-or late 1990s. This is an apparent reversal in the NDVI trend from 1982 to 2010. Pixel-based analysis further indicated that the timing of the NDVI trend reversal varied across different regions and for different vegetation types. We found that approximately 66% of the plateau showed an OPEN ACCESS Remote Sens. 2014, 6 8338 increasing trend before the reversal year, whereas 60% showed a decreasing trend afterwards. The vegetation decline in the last decade is mostly attributable to the recent tendency towards a hotter and drier climate and the associated widespread drought stress. Monitoring precipitation stress and associated vegetation dynamics will be important for raising the alarm and performing risk assessments for drought disasters and other related natural disasters like sandstorms.

A rigorous analysis of blood flow must be based on the branching pattern and vascular geometry of the full vascular circuit of interest. It is experimentally difficult to reconstruct the entire vascular circuit of any organ because of the enormity of the vessels. The objective of the present study was to develop a novel method for the reconstruction of the full coronary vascular tree from partial measurements. Our method includes the use of data on those parts of the tree that are measured to extrapolate the data on those parts that are missing. Specifically, a two-step approach was employed in the reconstruction of the entire coronary arterial tree down to the capillary level. Vessels > 40 microm were reconstructed from cast data while vessels < 40 microm were reconstructed from histological data. The cast data were reconstructed one-bifurcation at a time while histological data were reconstructed one-sub-tree at a time by "cutting" and "pasting" of data from measured to missing vessels. The reconstruction algorithm yielded a full arterial tree down to the first capillary bifurcation with 1.9, 2.04 and 1.15 million vessel segments for the right coronary artery (RCA), left anterior descending (LAD) and left circumflex (LCx) trees, respectively. The node-to-node connectivity along with the diameter and length of every vessel segment was determined. Once the full tree was reconstructed, we automated the assignment of order numbers, according to the diameter-defined Strahler system, to every vessel segment in the tree. Consequently, the diameters, lengths, number of vessels, segments-per-element ratio, connectivity and longitudinal matrices were determined for every order number. The present model establishes a morphological foundation for future analysis of blood flow in the coronary circulation.

Based on the vegetation map of Mongolia, Global Inventory Monitoring and Modelling Studies (GIMMS) normalized difference vegetation index (NDVI) (1982–2006), the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000–2010), and temperature and precipitation data derived from 60 meteorological stations, this study has thoroughly examined vegetation dynamics in Mongolia and their responses to regional climate change at biome scale. To ensure continuity and consistency between the two NDVI datasets, the MODIS NDVI was first calibrated to the GIMMS NDVI based on the overlapping period of 2000–2006. Good calibration results with R2 values of 0.86–0.98 between the two NDVI datasets were obtained and can detect subtle trends in the long‐term vegetation dynamics of Mongolia. The results indicated that for various biomes, although NDVI changes during 1982–2010 showed great variation, vegetation greening for all biomes in Mongolia seem to have stalled or even decreased since 1991–1994, particularly for meadow steppe (0.0015 year−1), typical steppe (−0.0010 year−1), and desert steppe (−0.0008 year−1), which is an apparent turning point (TP) of the vegetation growth trend in Mongolia. A pronounced drying trend (from −4.399 mm year−1 in meadow steppe since 1990 to −2.445 mm year−1 in alpine steppe since 1993) occurred between 1990 and 1994, and persistently warming temperatures (0.015 °C year−1 in alpine steppe to 0.070 °C year−1 in forest and meadow steppe) until recently have likely played a major role in this NDVI trend reversal. However, the NDVI TP varied by biome, month, and climate and was not coupled exactly with climatic variables. The impact on climate of both same‐time and lagged‐time temperature and precipitation effects also varied strongly across biomes and months. On the whole, climate‐related vegetation decline and associated potential desertification trends will likely be among the major sources of ecological pressure for each biome in Mongolia, which could intensify environmental problems like sandstorms in other East Asian regions.

The Loess positive and negative terrains (P-N terrains), which are widely distributed on the Loess Plateau, are discussed for the first time by introducing its characteristic, demarcation as well as extraction method from high-resolution Digital Elevation Models. Using 5 m-resolution DEMs as original test data, P-N terrains of 48 geomorphological units in different parts of Shaanxi Loess Plateau are extracted accurately. Then six indicators for depicting the geomorphologic landscape and spatial configuration characteristic of P-N terrains are proposed. The spatial distribution rules of these indicators and the relationship between the P-N terrains and Loess relief are discussed for further understanding of Loess landforms. Finally, with the integration of P-N terrains and traditional terrain indices, a series of un-supervised classification methods are applied to make a proper landform classification in northern Shaanxi. Results show that P-N terrains are an effect clue to reveal energy and substance distribution rules on the Loess Plateau. A continuous change of P-N terrains from south to north in Shaanxi Loess Plateau shows an obvious spatial difference of Loess landforms and the positive terrain area only accounted for 60.5% in this region. The P-N terrains participant landform classification method increases validity of the result, especially in the Loess tableland, Loess tableland-ridge and the Loess low-hill area. This research is significant on the study of Loess landforms with the Digital Terrains Analysis methods.

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