Scavenging ant foraging behavior and variation in the scale of nutrient redistribution among semi-arid grasslands

2003 ◽  
Vol 53 (3) ◽  
pp. 373-386 ◽  
Author(s):  
Brandon T. Bestelmeyer ◽  
John A. Wiens
Keyword(s):  
Foraging Behavior ◽  
Ant Foraging ◽  
Arid Grasslands ◽  
Semi Arid

scholarly journals Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

2020 ◽  
Vol 12 (22) ◽  
pp. 3826 ◽  
Author(s):  
Yuhong He ◽  
Jian Yang ◽  
Xulin Guo
Keyword(s):  
Time Series ◽  
Vegetation Cover ◽  
Driving Forces ◽  
Spectral Unmixing ◽  
Landsat Images ◽  
Green Vegetation ◽  
Arid Grasslands ◽  
Spectral Mixture ◽  
The Impact ◽  
Semi Arid

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R2 = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes.

Interacting effects of urea and water addition on soil mineral‐bound phosphorus dynamics in semi‐arid grasslands with different land‐use history

2020 ◽  
Vol 72 (2) ◽  
pp. 946-962
Author(s):  
Ruzhen Wang ◽  
Heyong Liu ◽  
Jordi Sardans ◽  
Xue Feng ◽  
Zhuwen Xu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use History ◽  
Soil Mineral ◽  
Water Addition ◽  
Phosphorus Dynamics ◽  
Arid Grasslands ◽  
Semi Arid

Spatial and temporal variation of soil moisture in dependence of multiple environmental parameters in semi-arid grasslands

2011 ◽  
Vol 340 (1-2) ◽  
pp. 73-88 ◽  
Author(s):  
Katrin Schneider ◽  
Ulrich Leopold ◽  
Friederike Gerschlauer ◽  
Frauke Barthold ◽  
Marcus Giese ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Variation ◽  
Environmental Parameters ◽  
Spatial And Temporal Variation ◽  
Arid Grasslands ◽  
Semi Arid
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