Global relative ecosystem service budget mapping using the Google Earth Engine and land cover datasets

Abstract Ecosystem service mapping (ESM) studies are receiving increasing attention due to the imbalance between supply of and demand for ecosystem services (ES). Global scale ESM is still scarce, but high computing power Google Earth Engine (GEE) cloud platform significantly increases efficiency. Based on global-scale land cover datasets GEE, an ES matrix model based-expert constructed in this paper map supply, demand, relative budgets. The net primary productivity (NPP), enhanced vegetation index (EVI), nighttime light (NTL), world population (Pop) were acquired, NPP EVI NTL Pop used revise ESs, respectively. We discovered that capacity exhibits a double-peaked distribution with latitude, peaks located at equator 50° N. global ESs have spatial heterogeneity 2.405 times higher than demand; however, trend about 3.36% per decade, only southern Asia has more supply. produced push-pull effect, is, it forced humans move closer surplus regions (ESSRs) farther away from deficit (ESDRs), destruction ecological environment promoted phenomenon. terrestrial area divided into eight sub-regions, targeted management, urban planning, environmental remediation policies proposed.