Work

Assessing Long-Term Impacts of Afforestation on Soil Conservation and Carbon Sequestration: A Spatially Explicit Analysis of China’s Shelterbelt Program Zones

Year: 2025

Type: article

Abstract: Afforestation is a critical nature-based strategy for enhancing ecological resilience and supporting cleaner land-use systems. This study presents a spatially explicit modeling framework to evaluate the long-term impacts of potential afforestation amendments on two key ecosystem services—soil conservation and carbon sequestration—across China’s major shelterbelt program areas under the SSP245 scenario (2020–2070). Using a zonal approach, we integrated Random Forest models, Bayesian belief networks, and Geodetector analysis to identify region-specific afforestation suitability and quantify ecological service gains across eight national shelterbelt program zones. The results reveal pronounced spatial heterogeneity in ecosystem service improvements. (1) High-quality potential afforestation lands, totaling approximately 2.33 × 105 km2, are primarily concentrated near the Hu Line (a geographical boundary that divides China into two distinct climatic regions), with the shelterbelt program for upper and middle reaches of Yangtze River accounting for 45.94%. (2) Based on the amended annual afforestation target of 0.47 × 105 km2, the adjusted land use projections indicate a significant increase in forest cover. By 2070, the afforestation program for Taihang Mountain exhibits the most significant improvements, with a 47.56% increase in soil conservation and a 10.15% increase in carbon sequestration. (3) Optimization areas differ across zones, with the Taihang mountain area (99.2%) and Pearl river area (70.1%) achieving the highest improvements in soil and carbon services, respectively. These findings provide robust scientific support for data-driven, region-specific afforestation planning under future land-use change scenarios.

Source: Remote Sensing

DOI: 10.3390/rs17203455

Authors: Lanqing Zhang, Xinyuan Zhang, Zhipeng Zhang, Xiaoyuan Zhang, Huihui Huang, Zong Wang