Dynamic Carbon Stock Mapping Reveals a Shift from Rapid Accumulation to Decelerating Carbon Growth After Ecological Restoration on the Loess Plateau
Large-scale ecological restoration has greatly boosted carbon sequestration across China’s Loess Plateau, yet the long-term sustainability of restoration-fueled carbon growth remains unclear. This study constructs a dynamic carbon stock mapping framework integrating GEDI LiDAR, Landsat time-series data and the dynamic InVEST model for ecosystem carbon accounting to analyze carbon accumulation trends from 2000 to 2025. Regional total carbon storage rose from 8088.65 Tg C to 9269.54 Tg C, with a net gain of 1180.89 Tg C, but carbon growth slowed markedly. The share of regions with notable carbon growth dropped sharply from 77.92% (2000–2013) to merely 1.50% (2013–2025). Logistic modeling shows 2025 carbon storage hit roughly 96.3% of its estimated saturation threshold, meaning room for fast carbon accumulation is shrinking. SHAP analysis identifies SWIR1 (31.1%) and NDVI (15.9%) as primary predictors contributing to biomass carbon variation, with nonlinear relationships proving moisture and vegetation jointly shape carbon accumulation. PLUS model simulations show ecological priority land-use scenarios deliver stronger carbon storage capacity than cropland protection or natural development schemes, though future carbon increments will be far smaller than historical gains. Ultimately, this study confirms that the Loess Plateau carbon sink is transitioning from restoration-facilitated rapid expansion to environmentally constrained slow growth, with current carbon storage approaching 96.3% of the regional ecohydrological carrying capacity.
Authors
- Quanfu Niu (ORCID: https://orcid.org/0000-0003-0001-4093)
- Yuan Zhang (ORCID: https://orcid.org/0000-0003-1056-3280)
- Qiong Fang
- Youjun Xiong
Institutions
- Lanzhou University of Technology (CN)
Publication Details
- Journal
- Sustainability
- Published
- 2026-09-17
- DOI
- https://doi.org/10.3390/su18189538
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Gansu Province