2) The RESTREND method
We
used the residual trend analysis (RESTREND) method to explore the degree
and scope of the effect of anthropogenic and climatic factors on
regional vegetation (Evans and Geerken, 2004; Ma et al., 2017).
NPP-A, which is directly observed by remote sensing and
selected from a set of remote sensing datasets, monitors the real growth
conditions of local vegetation. NPP-A records the
combined effects of human activity and climate change.
NPP-P is calculated from the actual annual
evapotranspiration measurements, and this index represents the NPP value
under ideal conditions with no human activity; that is, the
NPP-P value influenced only by climatic factors. The
change in NPP-P can be interpreted as reflecting a
climatic control on vegetation dynamics. A positive value of the
NPP-P change rate indicates that climate change is
beneficial to vegetation recovery, whereas a negative value indicates a
detrimental effect.
The residuals of (i.e., differences between) the NPP-Aand NPP-P values are defined as the residual NPP value
(NPP-RES) and can be interpreted as reflecting the
influence of human activity on the value of NPP:
NPP-RES = (NPP-P) –
(NPP-A)
Using the RESTREND method, the NPP-p reflects the absence of
anthropogenic influence, while the NPP-RES value
represents only the extent to which human activity contributes to or
damages regional vegetation recovery, and is interpreted in terms of the
anthropogenic influence on
vegetation dynamics. A positive value of the NPP-RESregression slope indicates that human activity has been conducive to
vegetation recovery, whereas a negative value indicates that human
activity has caused degradation.
The slope of the linear regression fit of the NDVI, NPP-P, and NPP-RES
time series can be either >0 or <0. Taking all
permutations of the NDVI, NPP-P, and NPP-RES slope values into account,
we can generate eight potential scenarios that reflect the main driving
factors controlling vegetation change and these are detailed in Table 1.
3 Results