scholarly journals Relationship of Abrupt Vegetation Change to Climate Change and Ecological Engineering with Multi-Timescale Analysis in the Karst Region, Southwest China

2019 ◽  
Vol 11 (13) ◽  
pp. 1564 ◽  
Author(s):  
Xiaojuan Xu ◽  
Huiyu Liu ◽  
Zhenshan Lin ◽  
Fusheng Jiao ◽  
Haibo Gong
Keyword(s):  
Climate Change ◽  
Southwest China ◽  
Abrupt Change ◽  
Karst Region ◽  
The West ◽  
Term Trend ◽  
Relationship Of ◽  
The Relationship ◽  
Long Term Trend

Vegetation is known to be sensitive to both climate change and anthropogenic disturbance in the karst region. However, the relationship between an abrupt change in vegetation and its driving factors is unclear at multiple timescales. Based on the non-parametric Mann-Kendall test and the ensemble empirical mode decomposition (EEMD) method, the abrupt changes in vegetation and its possible relationships with the driving factors in the karst region of southwest China during 1982–2015 are revealed at multiple timescales. The results showed that: (1) the Normalized Difference Vegetation Index (NDVI) showed an overall increasing trend and had an abrupt change in 2001. After the abrupt change, the greening trend of the NDVI in the east and the browning trend in the west, both changed from insignificant to significant. (2) After the abrupt change, at the 2.5-year time scale, the correlation between the NDVI and temperature changed from insignificantly negative to significantly negative in the west. Over the long-term trend, it changed from significantly negative to significantly positive in the east, but changed from significantly positive to significantly negative in the west. The abrupt change primarily occurred on the long-term trend. (3) After the abrupt change, 1143.32 km2 farmland was converted to forests in the east, and the forest area had significantly increased. (4) At the 2.5-year time scale, the abrupt change in the relationships between the NDVI and climate factors was primarily driven by climate change in the west, especially rising temperatures. Over the long-term trend, it was caused by ecological protection projects in the east, but by rising temperatures in the west. The integration of the abrupt change analysis and multiple timescale analysis help assess the relationship of vegetation changes with climate changes and human activities accurately and comprehensively, and deepen our understanding of the driving mechanism of vegetation changes, which will further provide scientific references for the protection of fragile ecosystems in the karst region.

Nonlinear characteristics of the vegetation change and its response to climate change in the karst region of southwest China

2021 ◽  
pp. 030913332110662
Author(s):  
Mingyang Zhang ◽  
Zhenhua Deng ◽  
Yuemin Yue ◽  
Kelin Wang ◽  
Huiyu Liu ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Change ◽  
Southwest China ◽  
Abrupt Change ◽  
Ecological Engineering ◽  
Karst Region ◽  
Vegetation Changes ◽  
Nonlinear Characteristics ◽  
Relationship Of ◽  
The Relationship

The vegetation is known to be sensitive to both climate change and anthropogenic disturbance. However, the relationship between changes in vegetation and climate is unclear in karst regions. The nonlinear characteristics of vegetation change and its possible relationships with driving factors in the karst region of southwest China are revealed, using methods of Ensemble Empirical Mode Decomposition, Mann-Kendall, and Partial Least Squares Regression. The results show that: (1) vegetation changes demonstrate an increasing trend with an abrupt change in 2002. Multiple time scales of 3, 6, 10, and 25-year are observed in vegetation variations, dominated by long-term trend and the short time scale of 3-year with variance contributions of 58.10% and 28.63%. (2) The relationship of climate indexes with vegetation changes shows r2 = 0.78 ( p < 0.01) based on the reconstruction of characteristic scales, indicating significant great relationship. In space, the area percentage with relationship of climate to vegetation is more than 50%, and the impact is much greater after the abrupt change of vegetation in 2002 ( r2 are 0.24–0.91 and 0.42–0.99, respectively). In addition, the correlation between vegetation change and ecological engineering is 0.15 ( p < 0.01). The results indicate that climate change is the main impact factor of vegetation change, ecological engineering has positive influences in improving vegetation condition, and methods of scales decomposition and abrupt detection could reveal some hidden information for better understanding ecosystems in karst regions.

scholarly journals Impact of climate change and man-made irrigation systems on the transmission risk, long-term trend and seasonality of human and animal fascioliasis in Pakistan

2014 ◽  
Vol 8 (2) ◽  
pp. 317 ◽  
Author(s):  
Kiran Afshan ◽  
Cesar A. Fortes-Lima ◽  
Patricio Artigas ◽  
M. Adela Valero ◽  
Mazhar Qayyum ◽  
...  
Keyword(s):  
Climate Change ◽  
Transmission Risk ◽  
Irrigation Systems ◽  
Term Trend ◽  
Impact Of Climate Change ◽  
Long Term Trend

Long term trend of snow depth at Sonnblick (Austrian Alps) and its relation to climate change

2009 ◽  
Vol 23 (7) ◽  
pp. 1052-1063 ◽  
Author(s):  
W. Schöner ◽  
I. Auer ◽  
R. Böhm
Keyword(s):  
Climate Change ◽  
Snow Depth ◽  
Term Trend ◽  
Austrian Alps ◽  
Long Term Trend

NAEP Trends: Main NAEP vs. Long-Term Trend

2010 ◽  
Author(s):  
Albert E. Beaton ◽  
James R. Chromy
Keyword(s):  
Term Trend ◽  
Long Term Trend

scholarly journals Domain randomization-enhanced deep learning models for bird detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Mao ◽  
Jun Kang Chow ◽  
Pin Siang Tan ◽  
Kuan-fu Liu ◽  
Jimmy Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Continuous Monitoring ◽  
Bird Species ◽  
Training Data ◽  
Learning Models ◽  
Fine Grained ◽  
Bird Detection ◽  
Relationship Of ◽  
The Relationship

AbstractAutomatic bird detection in ornithological analyses is limited by the accuracy of existing models, due to the lack of training data and the difficulties in extracting the fine-grained features required to distinguish bird species. Here we apply the domain randomization strategy to enhance the accuracy of the deep learning models in bird detection. Trained with virtual birds of sufficient variations in different environments, the model tends to focus on the fine-grained features of birds and achieves higher accuracies. Based on the 100 terabytes of 2-month continuous monitoring data of egrets, our results cover the findings using conventional manual observations, e.g., vertical stratification of egrets according to body size, and also open up opportunities of long-term bird surveys requiring intensive monitoring that is impractical using conventional methods, e.g., the weather influences on egrets, and the relationship of the migration schedules between the great egrets and little egrets.

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