The drainage evolution and valley development of the Jinsha River is an important issue constantly concerned by researchers in geology and geomorphology. Despite hundreds of years of research, there is a big dispute on the formation time and the evolution process of the fluvial valley. Fluvial terraces are very important geomorphic markers for studying the formation and evolution of the fluvial valley. Through field investigation combined with Electron Spin Resonance (ESR) dating, we confirmed that 5 fluvial terraces were formed, and then preserved, along the course of the Jinsha River near the Longjie, which are all strath terraces. Among them, T5 developed on the base rock, with an age of (78±12) ka; all T4~T1 developed on the lacustrine sediments, named Longjie Group by Chinese, with an age of (29±1.4) ka, (26±2.4) ka, (23±1.4) ka, (18±1.7) ka, respectively. Compared with the global and regional climate change history, the terraces are all the result of the river responding to the climate change. T5 formed at MIS 5/4, and T4~T1 formed at the period of regional climate fluctuation. The relationship of terraces and the Longjie Formation, combined with sedimentary characteristics analysis demonstrate that the Longjie Formation is landslide dammed lake sediment. The landslide and blocking events.seriously influenced the valley evolution, inhibiting the river incising, and making the valley evolution defer to the mode of “cut-landside-damming-fill-cut” in the period of Late Pleistocene. Synthesized studies of the terraces and the correlative sediments indicate that the formation of the Jinsha River valley may have begun in the late Early Pleistocene. 相似文献
We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (Glob-Cover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.
防风林带结构是影响防风效能的主要因素。建立不同宽度、不同株行距林带防风效能与林带后距离之间的统计模型，可以为防风林建设提供指导性意见。通过风洞实验，在11 m·s-1风速下，对4种宽度、5种株行距林带的背风面0~10H（H为林带高度）的风速进行测定，采用曲线参数估计法、傅立叶模型、SSF模型（Sum of Sin Functions），构建了不同结构林带防风效能与林带后距离间的统计模型。结果表明：傅立叶模型拟合不同宽度林带的防风效能与林带后距离的关系效果最优，可决系数（R2）均在98%以上；SSF模型拟合不同株行距林带的防风效能与林带后距离的关系效果最优（R2>0.98）。根据构建的统计模型，风速为11 m·s-1左右时，林带宽度8 m（两行一带）的防风林的防风效能存在明显优势；5种株行距的林带中，株行距为8 m×8 m的防风林带本试验条件下防风效果最好。 相似文献
The discovery of spatial clusters formed by proximal spatial units with similar non-spatial attribute values plays an important role in spatial data analysis. Although several spatial contiguity-constrained clustering methods are currently available, almost all of them discover clusters in a geographical dataset, even though the dataset has no natural clustering structure. Statistically evaluating the significance of the degree of homogeneity within a single spatial cluster is difficult. To overcome this limitation, this study develops a permutation test approach Specifically, the homogeneity of a spatial cluster is measured based on the local variance and cluster member permutation, and two-stage permutation tests are developed to determine the significance of the degree of homogeneity within each spatial cluster. The proposed permutation tests can be integrated into the existing spatial clustering algorithms to detect homogeneous spatial clusters. The proposed tests are compared with four existing tests (i.e., Park’s test, the contiguity-constrained nonparametric analysis of variance (COCOPAN) method, spatial scan statistic, and q-statistic) using two simulated and two meteorological datasets. The comparison shows that the proposed two-stage permutation tests are more effective to identify homogeneous spatial clusters and to determine homogeneous clustering structures in practical applications. 相似文献
