Assessment and Dynamic Prediction of Green Space Ecological Service Value in Guangzhou City, China

Abstract

As an important part of the urban ecosystem, urban green space provides a variety of ecosystem services, including climate regulation, soil conservation, carbon sink and oxygen release, and biodiversity protection. However, existing remote sensing evaluation methods for ecological service value lack the evaluation indicators of ecosystem service value for Guangzhou, China, and the evaluation method depends on the land cover type. Based on remote sensing technology and random forest algorithm, this study addresses these gaps by integrating remote sensing technology with a random forest algorithm to enhance the accuracy and rationality of ESV assessments. Focusing on Guangzhou, China, we improved the ecological service value evaluation system and conducted dynamic predictions based on land-use change scenarios. Our results indicate that the total ESV of Guangzhou’s green space was USD 7.323 billion in 2020, with a projected decline to USD 6.496 billion by 2030, representing a 12.37% reduction due to urbanization-driven land-use changes. This research highlights the noticeable role of green spaces in urban sustainability and provides robust, data-driven insights for policymakers to design more effective green space protection and management strategies. The improved assessment framework offers a novel approach for accurately quantifying urban ecosystem services and predicting future trends.

Keywords

• urban green spaces
• ecosystem service value
• Guangzhou City
• random forest algorithm
• CA-Markov model
• dynamic prediction