Modelling groundwater-dependent vegetation index using Entropy theory

Abstract

An ecosystem is vulnerable to the scarcity of water resources and sparse vegetation cover in arid regions. Groundwater plays an important role in maintaining ecological environment and strongly impacts the ecosystem through influencing vegetation structure and species distribution. It is therefore important to clearly understand the relationship between vegetation patterns and groundwater depth(GWD). In this paper, Tsallis entropy theory was applied to derive a functional relationship between GWD and vegetation distribution. The theory was tested using observed data from arid regions in northwestern China. Results showed that higher vegetation coverage exist at places of shallow GWD. The values of NDVI gradually increase with increasing GWD until reaching a maximum at the optimum depth, after which they decrease with increasing groundwater depth when GWD is less than approximately 10 m. Beyond that depth, a low level of vegetation coverage is maintained. The correlation coefficients between measured and simulated values of NDVI were above 0.9 (p < 0.01) in the Ejina, Qaidam and Hailiutu basins. The theory is applicable to different regions and vegetation types and may improve our ability to sustainably manage land and groundwater resources in arid regions, especially where the vegetation is groundwater-dependent.