Spatial and Temporal Distribution Patterns of Mafic Dyke Swarms in Central Asia: Results from Remote-Sensing Interpretation and Regional Geology

2018 ◽  
pp. 315-333
Author(s):  
Qianwen Feng ◽  
Jinyi Li ◽  
Jianfeng Liu ◽  
Kefa Zhou
Keyword(s):  
Remote Sensing ◽  
Central Asia ◽  
Temporal Distribution ◽  
Distribution Patterns ◽  
Spatial And Temporal Distribution ◽  
Mafic Dyke ◽  
Regional Geology ◽  
Dyke Swarms ◽  
Remote Sensing Interpretation

Spatial and temporal distribution patterns of nitrogen in marsh soils from an inland alkaline wetland – A case study of Fulaowenpao wetland, China

2010 ◽  
Vol 30 (4) ◽  
pp. 210-215 ◽  
Author(s):  
Jun-Hong Bai ◽  
Qinggai Wang ◽  
Gao Hai-Feng ◽  
Xiao Rong ◽  
Deng Wei ◽  
...  
Keyword(s):  
Temporal Distribution ◽  
Distribution Patterns ◽  
Spatial And Temporal Distribution

Delimitation of subtidal macrobenthos associations at the mouth of the Brisbane River

1976 ◽  
Vol 27 (2) ◽  
pp. 217 ◽  
Author(s):  
TS Hailstone
Keyword(s):  
Temporal Distribution ◽  
Community Analysis ◽  
Distribution Patterns ◽  
Spatial And Temporal Distribution ◽  
Hierarchical Analysis ◽  
Faunal Distribution

One hundred and fifty dredge samples of macrobenthic animals from the mouth of the Brisbane River were analysed by zonation analysis, Petersen-type community analysis, and hierarchical classificatory analysis. The methods were compared in terms of the faunal distribution patterns that they yielded. Hierarchical analysis gave the most acceptable results. Although the normal dendro- gram could be interpreted meaningfully to at least 16 sample groups, it has been interpreted to six groups here in preparation for subsequent, more detailed analyses. Spatial and temporal distribution patterns have been recognized and the consideration of the sample groupings as faunal states has been emphasized.

scholarly journals Urban Vitality Area Identification and Pattern Analysis from the Perspective of Time and Space Fusion

2019 ◽  
Vol 11 (15) ◽  
pp. 4032 ◽  
Author(s):  
Liu ◽  
Zhang ◽  
Long
Keyword(s):  
Land Use ◽  
Urban Space ◽  
Pattern Analysis ◽  
Temporal Distribution ◽  
Distribution Patterns ◽  
Mobile Phone Data ◽  
Spatial And Temporal Distribution ◽  
Strong Correlations ◽  
Variation Characteristics ◽  
Nanjing City

Urban vitality provides an important basis for evaluating urban development and spatial balance. In the era of big data, the quantitative analysis of urban vitality has become a research hotspot in the field of urban sustainability and planning research. However, time variation characteristics are often neglected, which leads to one-sidedness in the pattern analysis of urban vitality. In this paper, a method for extracting vitality areas and integrating spatiotemporal features clustering is proposed. The method is used to divide urban space into multiple vitality areas scientifically. The spatial and temporal distribution patterns of urban vitality areas are found, and the driving factors of various vitality patterns are analyzed by combining points of interest (POI)-based land use characteristics. To illustrate this method, this paper takes Nanjing city as an example. One week's worth of mobile phone data indicated that Nanjing has 10 and 8 vitality areas on weekdays and weekends, respectively. The spatial and temporal distribution patterns of the vitality areas and their correlation with land use were analyzed, which proved that POI density and entropy have strong correlations with urban vitality.

scholarly journals Snow depth derived from passive microwave remote-sensing data in China

2008 ◽  
Vol 49 ◽  
pp. 145-154 ◽  
Author(s):  
Tao Che ◽  
Xin Li ◽  
Rui Jin ◽  
Richard Armstrong ◽  
Tingjun Zhang
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Snow Depth ◽  
Temporal Distribution ◽  
Remote Sensing Data ◽  
Passive Microwave ◽  
Spatial And Temporal Distribution ◽  
Sensing Data ◽  
Seasonal Snow ◽  
Snow Mass

AbstractIn this study, we report on the spatial and temporal distribution of seasonal snow depth derived from passive microwave satellite remote-sensing data (e.g. SMMR from 1978 to 1987 and SMM/ I from 1987 to 2006) in China. We first modified the Chang algorithm and then validated it using meteorological observation data, considering the influences from vegetation, wet snow, precipitation, cold desert and frozen ground. Furthermore, the modified algorithm is dynamically adjusted based on the seasonal variation of grain size and snow density. Snow-depth distribution is indirectly validated by MODIS snow-cover products by comparing the snow-extent area from this work. The final snow-depth datasets from 1978 to 2006 show that the interannual snow-depth variation is very significant. The spatial and temporal distribution of snow depth is illustrated and discussed, including the steady snow-cover regions in China and snow-mass trend in these regions. Though the areal extent of seasonal snow cover in the Northern Hemisphere indicates a weak decrease over a long period, there is no clear trend in change of snow-cover area extent in China. However, snow mass over the Qinghai–Tibetan Plateau and northwestern China has increased, while it has weakly decreased in northeastern China. Overall, snow depth in China during the past three decades shows significant interannual variation, with a weak increasing trend.

A SURVEY FOR THE AMBROSIA BEETLES TRYPODENDRON LINEATUM AND GNATHOTRICHUS RETUSUS (COLEOPTERA: SCOLYTIDAE) IN A SAWMILL USING PHEROMONE-BAITED TRAPS

1985 ◽  
Vol 117 (1) ◽  
pp. 49-55 ◽  
Author(s):  
T. L. Shore ◽  
J. A. McLean
Keyword(s):  
Temporal Distribution ◽  
Distribution Patterns ◽  
Mass Trapping ◽  
Spatial And Temporal Distribution ◽  
Trypodendron Lineatum ◽  
Ambrosia Beetles ◽  
Baited Traps

AbstractThe pheromones lineatum and (+)-sulcatol were used in traps in a sawmill to survey the ambrosia beetles Trypodendron lineatum (Olivier) and Gnathotrichus retusus (LeConte) respectively. Spatial and temporal distribution patterns for both species were identified. This information can be used for the establishment of a pheromone-based mass-trapping program for ambrosia beetles. The addition of ethanol and α-pinene to traps baited with (+)-sulcatol significantly increased the catches of G. retusus.

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