Proposing a GEE-Based Spatiotemporally Adjusted Value Transfer Method to Assess Land-Use Changes and Their Impacts on Ecosystem Service Values in the Shenyang Metropolitan Area

Abstract

Understanding land-use dynamics and their impacts on ecosystem service values (ESVs) is critical to conservation and environmental decision-making. This work used the Google Earth Engine (GEE) platform and an adjusted value transfer method to investigate spatiotemporal ESV changes in the Shenyang Metropolitan Area (SMA), a National Reform Pilot Zone in northeast China. First, we obtained land-use classification maps for 2000, 2005, 2010, 2015, and 2020 using a GEE-based Landsat dense stacking methodology. Then, we employed four spatiotemporal correction factors (net primary productivity, fractional vegetation cover, precipitation, and crop yield) in the value transfer method, and analyzed the ESV dynamics. The results showed that forest land and cropland were the two dominant land-use types, jointly occupying 75–89% of the total area. The built-up areas expanded rapidly from 2727 km2 in 2000 to 3597 km2 in 2020, while the cropland kept decreasing, and suffered the most area loss (−1305.09 km2). The ESV of the SMA rose substantially from 814.04 hundred million Chinese Yuan (hmCYN) in 2000 to 1546.82 hmCYN in 2005, then kept decreasing in 2005–2010 (−17.01%) and 2010–2015 (−10.75%), and finally increased to 1329.81 hmCYN in 2020. The ESVs of forest comprised most of the total ESVs, with the percentage ranging from 72.65% to 77.18%, followed by water bodies, ranging from 11.61% to 15.64%. The ESV changes for forest land and water bodies were the main drivers for the total ESV dynamics. Overall, this study illustrated the feasibility of combining the GEE platform and the spatiotemporal adjusted value transfer method into the ESV analysis. Additionally, the results could provide essential references to future environmental management policymaking in the SMA.