Spatial and temporal changes in the southern species component of North Sea Bottom fish assemblages

2001 ◽  
Vol 31 (2) ◽  
pp. 143-150 ◽  
Author(s):  
Siegfried Ehrich ◽  
Christoph Stransky
Keyword(s):  
North Sea ◽  
Fish Assemblages ◽  
Temporal Changes ◽  
Sea Bottom ◽  
Spatial And Temporal Changes ◽  
Bottom Fish ◽  
Southern Species

Spatial and temporal changes in reef fish assemblages on disturbed coral reefs, north Pacific coast of Costa Rica

2019 ◽  
Vol 40 (1) ◽  
pp. e12532
Author(s):  
Gustavo Arias‐Godínez ◽  
Carlos Jiménez ◽  
Carlos Gamboa ◽  
Jorge Cortés ◽  
Mario Espinoza ◽  
...  
Keyword(s):  
Costa Rica ◽  
Coral Reefs ◽  
Reef Fish ◽  
North Pacific ◽  
Pacific Coast ◽  
Fish Assemblages ◽  
Temporal Changes ◽  
Spatial And Temporal Changes

scholarly journals Linking spatial pattern of bottom fish assemblages with water masses in the North Sea

2009 ◽  
Vol 18 (1) ◽  
pp. 36-50 ◽  
Author(s):  
SIEGFRIED EHRICH ◽  
VANESSA STELZENMÜLLER ◽  
SARA ADLERSTEIN
Keyword(s):  
Spatial Pattern ◽  
North Sea ◽  
Fish Assemblages ◽  
Water Masses ◽  
The North ◽  
Bottom Fish ◽  
The North Sea

Energy Sharing Boundaries Integrating Buildings and Vehicles Tangled in Spatial and Temporal Changes

2020 ◽  
Author(s):  
Soowon Chang ◽  
Takahiro Yoshida ◽  
Robert Brent Binder ◽  
Yoshiki Yamagata ◽  
Daniel Castro-Lacouture
Keyword(s):  
Temporal Changes ◽  
Spatial And Temporal Changes ◽  
Energy Sharing

Identification and Mapping of Bottom Fish Assemblages in Northern Baffin Bay

2011 ◽  
Vol 43 ◽  
pp. 65-79 ◽  
Author(s):  
O A Jorgensen ◽  
C Hvingel ◽  
M A Treble
Keyword(s):  
Fish Assemblages ◽  
Baffin Bay ◽  
Bottom Fish

scholarly journals Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach

2021 ◽  
Vol 13 (7) ◽  
pp. 1340
Author(s):  
Shuailong Feng ◽  
Shuguang Liu ◽  
Lei Jing ◽  
Yu Zhu ◽  
Wende Yan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Environmental Impacts ◽  
Land Surface ◽  
Vegetation Index ◽  
Highway Construction ◽  
Temporal Changes ◽  
Moisture Index ◽  
Buffer Zones ◽  
Wide Range ◽  
Spatial And Temporal Changes

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

scholarly journals Spatial and Temporal Changes in Vegetation in the Ruoergai Region, China

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 76
Author(s):  
Yahui Guo ◽  
Jing Zeng ◽  
Wenxiang Wu ◽  
Shunqiang Hu ◽  
Guangxu Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Linear Trend ◽  
Spectral Characteristics ◽  
Growth Conditions ◽  
Temporal Changes ◽  
Vegetation Coverage ◽  
Dynamic Changes ◽  
Spatial And Temporal Changes ◽  
Temporal And Spatial

Timely monitoring of the changes in coverage and growth conditions of vegetation (forest, grass) is very important for preserving the regional and global ecological environment. Vegetation information is mainly reflected by its spectral characteristics, namely, differences and changes in green plant leaves and vegetation canopies in remote sensing domains. The normalized difference vegetation index (NDVI) is commonly used to describe the dynamic changes in vegetation, but the NDVI sequence is not long enough to support the exploration of dynamic changes due to many reasons, such as changes in remote sensing sensors. Thus, the NDVI from different sensors should be scientifically combined using logical methods. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI from the Advanced Very High Resolution Radiometer (AVHRR) and Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI are combined using the Savitzky–Golay (SG) method and then utilized to investigate the temporal and spatial changes in the vegetation of the Ruoergai wetland area (RWA). The dynamic spatial and temporal changes and trends of the NDVI sequence in the RWA are analyzed to evaluate and monitor the growth conditions of vegetation in this region. In regard to annual changes, the average annual NDVI shows an overall increasing trend in this region during the past three decades, with a linear trend coefficient of 0.013/10a, indicating that the vegetation coverage has been continuously improving. In regard to seasonal changes, the linear trend coefficients of NDVI are 0.020, 0.021, 0.004, and 0.004/10a for spring, summer, autumn, and winter, respectively. The linear regression coefficient between the gross domestic product (GDP) and NDVI is also calculated, and the coefficients are 0.0024, 0.0015, and 0.0020, with coefficients of determination (R2) of 0.453, 0.463, and 0.444 for Aba, Ruoergai, and Hongyuan, respectively. Thus, the positive correlation coefficients between the GDP and the growth of NDVI may indicate that increased societal development promotes vegetation in some respects by resulting in the planting of more trees or the promotion of tree protection activities. Through the analysis of the temporal and spatial NDVI, it can be assessed that the vegetation coverage is relatively large and the growth condition of vegetation in this region is good overall.

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