Research on ecosystem service value and landscape ecological risk prediction and zoning: Taking Fujian province as an example

Abstract

Rapid urbanization drives urban expansion while threatening sustainable development through declining ecosystem service value (ESV) and rising ecological risk (ERI). This study developed an integrated SD-PLUS model combining historical analysis and future projections to evaluate ESV-ERI dynamics, establishing ecological zoning frameworks with policy implications. Using Fujian Province as a testbed, we established a Business-as-Usual (BAU) scenario through historical pattern extrapolation, compliant with EEA technical guidelines to create a policy-neutral baseline. This framework enables quantitative evaluation of anthropogenic regulation effects and early identification of ecological risk thresholds. Our simulations reveal over 15 years, construction land expanded by 48% (1757.42 km²), primarily through forest and farmland conversion. ESV showed initial growth followed by decline, exhibiting southeast-northwest spatial gradients (lower SE, higher NW). Conversely, ERI progressively increased with medium-high risk transitions, displaying inverse spatial concentration (high SE, low NW). Model validation showed strong performance with SD prediction errors <5% and PLUS simulations achieving Kappa=0.90/accuracy=0.94, confirming SD-PLUS effectiveness in land change modeling. Spatial analysis identified four functional ecological zones: 1) expanding strict control zones (high ERI), 2) key control zones showing initial expansion then contraction, 3) stable general control zones, and 4) continuously shrinking ecological protection zones (high ESV). These findings enable targeted spatial governance by aligning economic development with ecological conservation priorities. The integrated methodology provides policymakers with a scientifically robust framework for balancing urban growth with ecosystem preservation, particularly valuable for rapidly developing regions facing similar sustainability challenges.