2021
DOI: 10.1016/j.scitotenv.2020.142567
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Impacts of extreme climate on Australia's green cover (2003–2018): A MODIS and mascon probe

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Cited by 11 publications
(7 citation statements)
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“…Currently, ecosystem vulnerability and biodiversity have become prominent topics in ecological research [7,8]. However, the impact of extreme climate events on vegetation may be more direct and significant than mean climate events [9]. For instance, the El Niño-Southern Oscillation (ENSO) caused rapid changes in African vegetation patterns [10].…”
Section: Introductionmentioning
confidence: 99%
“…Currently, ecosystem vulnerability and biodiversity have become prominent topics in ecological research [7,8]. However, the impact of extreme climate events on vegetation may be more direct and significant than mean climate events [9]. For instance, the El Niño-Southern Oscillation (ENSO) caused rapid changes in African vegetation patterns [10].…”
Section: Introductionmentioning
confidence: 99%
“…As an important proxy to characterize the earth vegetation coverage and analyze the response of vegetation to global climate change, the NDVI has been widely used in related fields ( Julien and Sobrino, 2009 ; Saleem et al., 2020 ). However, the NDVI data used in many studies contained noise and seasonal components, and it was difficult to show the true change trends of vegetation.…”
Section: Discussionmentioning
confidence: 99%
“…In order to investigate the leading fundamental modes of variances from the 20-year PV time series of images, principal component analysis (PCA) was implemented to eliminate redundant information so that only the most dominant spatiotemporal patterns are retained in which cumulative variance can account for >95% of the variability of the original dataset [86,87]. This multivariate statistical technique transforms a group of correlated PV images into a set of uncorrelated variables, called principal components (PCs) consisting of spatial patterns of eigenvectors derived from original time-series images, and temporal loadings the coefficients of linear combinations that presents the correlations of original variables in PCs [88].…”
Section: Principal Component Analysis Of Time-series Imagesmentioning
confidence: 99%