Effects of improved land-cover mapping on predicted ecosystem service outcomes in a lowland river catchment

Abstract

Reliable quantification of ecosystem service (ES) provision in agricultural landscapes depends on accurate mapping of the spatial configuration of land-use and land cover (LULC). In this paper we explore the benefits of enhanced spatial and thematic resolution in LULC mapping in terms of predicting ecosystem services and associated natural capital-based land-use policies. Copernicus Sentinel-2 satellite images were processed using Google Earth Engine (GEE) to generate a LULC map at 10 m resolution, which was compared to existing datasets at 20 m, 25 m, and 100 m resolution in the River Welland catchment (Eastern England). Spatial resolution had a significant effect on the abundance and spatial configuration of land cover types. For example, detected woodland cover in the finest resolution dataset was 2x that in the coarsest data. Finer spatial resolution also allowed small, fragmented patches of woodland and grassland to be identified. ES provision (crop yield, carbon storage and pollinator abundance) was estimated from each map using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The finest resolution map resulted in 21% lower predicted wheat production (due to lower estimates of cultivated land cover), 7% higher predicted carbon stocks and 43% higher predicted wild bee abundance compared to the coarsest resolution map. The estimated monetary value of ES provision increased by 23.2% between the 10 and 100 m dataset. We recommend that a LULC resolution of at least 10 m should be employed in agricultural landscapes to accurately capture ES provision. This can be achieved using GEE and could be used as a basis for the development of future natural capital policy.

Keywords

• Natural capital
• Ecosystem services
• Google earth engine
• Land-cover mapping