Predicting land cover driven ecosystem service value using artificial neural network model

Understanding the synergies and trade-offs of major cities' ecosystem services is vital to mitigating regional ecological and environmental risks and enhancing human well-being in this era of rapid urbanization and global climate change. This study aimed to assess and predict the land use- and land cover (LULC)-driven ecosystem service value (ESV) dynamics in Arkansas's capital city, Little Rock. Historical LULC data were derived by applying support vector machine learning algorithms to Landsat satellite imagery. The benefit transfer method was utilized to identify nine types of ecosystem services and their corresponding economic values. A cellular automata artificial neural network model was used to simulate future potential LULC and ESV patterns. Vegetation accounted for more than 94% of total ESV over the past two decades. However, a 38.40% expansion of built-up areas resulted in a 45.28% decrease in vegetated areas, which reduced total ESV from $3619.73 × 10⁶ to $2563.81 × 10⁶ during 2003–2023. By 2033, the city's urban area will expand to 72.75% of the total area and will witness further declines of 30.35 km² in vegetation, 19.30 km² in barren soil, and 1.69 km² in waterbody areas. Consequently, the ESVs of these natural landscapes will decline by $708.58 × 10⁶, $44.87 × 10⁶, and $15.69 × 10⁶, respectively. Provisioning services will be most affected, followed by supporting, regulating, and cultural services. The study findings provide reference information to policymakers and the local government for use in adopting sustainable land management policies, thereby promoting the ecological value of Little Rock.