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