4.1 Errors in the residual method
The vegetation change trend, evaluated with respect to the driving factors and NDVI indicator, showed restoration in the MP and each in subregion, but there were some differences in the restoration ratios between those two evaluation methods. This section focuses on this phenomenon, which can partly explain the “Errors” listed in Tables 1 and 3.
We classified possible scenarios as A, B, C, and D (see Table 1). Scenario A occurs when the negative effect of climate change is greater than the positive effect of human activity, resulting in vegetation degradation. Scenario C occurs when the negative effect of human activity is less than the positive effect of climate change, resulting in vegetation recovery.
Results yielding scenarios B and D were defined as “Errors” here. NDVI and actual NPP are both indexes that can reveal and track vegetation growth and dynamics, and these two indexes are closely correlated. For example, the research of De et al. (2013) captured the spatial pattern of NPP, and the global trend of NPP is consistent with the NDVI for the same period of time.
Rashid et al. (2016) revealed that about 68% of the global land area has positive NPP values with an increasing trend, which corresponds closely to the 67% of the MP land area with positive NDVI values and an increasing trend. This phenomenon provides theoretical support for our use of NDVI and NPP in the RESTREND method to validate the driving forces of vegetation dynamics. However, there are inconsistencies between NDVI and NPP values in some regions and situations. For example, if grassland or forest is transformed into farmland/plantations, the actual NPP could show a huge increase because of artificial fertilization and irrigation. Consequently, it is possible that changes in NDVI < 0 and changes in NPP-RES> 0 could co-occur if NPP-P increases but NPP-A increases more, and vice versa. We believe that this can explain part of the error scenarios outlined above from a data perspective.