Abstract
The present study classified global Ecosystem Functional Types (EFTs) derived from seasonal vegetation dynamics of the GIMMS3g NDVI time-series. Rotated Principal Component Analysis (PCA) was run on the derived phenological and productivity variables, which selected the Standing Biomass (approximation of Net Primary Productivity), the Cyclic Fraction (seasonal vegetation productivity), the Permanent Fraction (permanent surface vegetation), the Maximum Day (day of maximum vegetation development) and the Season Length (length of vegetation growing season) variables, describing 98% of the variation in global ecosystems. EFTs were created based on Isodata classification of the spatial patterns of the Principal Components and were interpreted via gradient analysis using the selected remote sensing variables and climatic constraints (radiation, temperature, and water) of vegetation growth. The association of the EFTs with existing climate and land cover classifications was demonstrated via Detrended Correspondence Analysis (DCA). The ordination indicated good description of the global environmental gradient by the EFTs, supporting the understanding of phenological and productivity dynamics of global ecosystems. Climatic constraints of vegetation growth explained 50% of variation in the phenological data along the EFTs showing that part of the variation in the global phenological gradient is not climate related but is unique to the Earth Observation derived variables. DCA demonstrated good correspondence of the EFTs to global climate and also to land use classification. The results show the great potential of Earth Observation derived parameters for the quantification of ecosystem functional dynamics and for providing reference status information for future assessments of ecosystem changes.
Highlights
There is growing evidence that ecosystem degradation is largely due to intensive human resource use together with increasing awareness of the dependence of the global population on the globe’s ecosystem services [1,2,3]
The final Principal Component Analysis (PCA) was run with the five variables loading highest on the first five rotated axes: Standing Biomass (SB), Cyclic Fraction (CF), the Maximum Day (MXD), the Season Length (SL) and the Permanent Fraction (PF) (Figure 1)
Variations in vegetation signals are driven by climatic cycles but among others, by anthropogenic management practices [7]. This suggests that the functional types of global ecosystems might be substantially influenced by anthropogenic activities as well and that the Ecosystem Functional Types (EFTs), to be valuable indicators of ecosystem functioning, should reflect differences in land use
Summary
There is growing evidence that ecosystem degradation is largely due to intensive human resource use together with increasing awareness of the dependence of the global population on the globe’s ecosystem services [1,2,3]. The need for improved information on the status of lands and on degradation processes, along with standardized methodologies and understandable mapping, has to be addressed urgently. A useful monitoring and assessment system will supply indicators that account for the climate dependence of ecosystem functioning that is responsive to land cover and land use change while supplying knowledge of the temporal and spatial patterns of ecosystem dynamics at larger spatial scales. At global scales, such indicators are best derived from information on vegetation/land cover dynamics as core components to assess the state of ecosystem degradation [5]. Various aspects of vegetation productivity and phenology dynamics, reflecting land cover/use transitions that might lead to ecosystem degradation, need to be considered in a spatio-temporal context [6]
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