Carbon storage in terrestrial ecosystems is largely a function of land cover. Forests hold vast quantities of carbon in trunks, roots and soils; grasslands store smaller but still significant amounts; croplands cycle carbon quickly through seasonal growth and harvest; and built-up areas store comparatively little. When farmland is converted to housing estates or forests are cleared for mining, the carbon accounting of an entire region can shift dramatically. Tracking those shifts requires maps of land use that are both accurate and finely resolved in time, which is precisely what the research team set out to produce for the Fen River Basin.

The first pillar of the methodological chain is PLUS, the Patch-generating Land Use Simulation model. PLUS is designed to mine the rules of land-use change from historical maps and then project future landscapes under alternative scenarios. It works by analyzing how individual land patches expand or contract in response to terrain, accessibility, population and policy constraints, and it uses a random-forest-based rule discovery engine to capture the nonlinear relationships between those drivers and observed land transitions. In the Fen River Basin, PLUS allowed the researchers to generate internally consistent land-use maps for multiple time points and to simulate plausible futures under different development pathways.

The second pillar is the Integrated Valuation of Ecosystem Services and Tradeoffs, or InVEST, model developed with support from the Natural Capital Project. Its carbon storage module estimates the carbon held in four pools—above-ground biomass, below-ground biomass, dead organic matter and soil organic carbon—by assigning each land-cover class a set of carbon densities and multiplying those densities by mapped areas. The result is a spatially explicit picture of where carbon is concentrated and how the regional total moves as land cover changes. By coupling InVEST to the PLUS-generated land maps, the researchers could translate every hectare of forest loss or gain into a quantified change in stored carbon.

The third pillar, GeoDetector, addresses a question that pure mapping cannot answer: which factors actually explain the observed spatial variation in carbon storage. GeoDetector is a statistical technique rooted in spatial variance analysis. It compares the variance of a variable within spatial strata defined by a potential driving factor with the variance of the whole study area. If carbon storage values are far more homogeneous inside, say, zones of a given land-use type or precipitation band than across the basin as a whole, that factor is judged to have strong explanatory power. The method is particularly valuable because it captures nonlinear interactions without imposing a predefined functional form, and it quantifies how pairs of factors act together.

Applying this three-part chain to the Fen River Basin revealed a landscape under measurable stress. The basin’s carbon stock, the analysis indicates, has been shaped by the expansion of construction land and the pressures of cultivated agriculture on one side, and by afforestation and grassland restoration programs on the other. China’s sweeping ecological initiatives, including the Grain for Green Program that converts sloping cropland back to forest and grassland, have pushed in the direction of greater carbon storage, while urbanization and mining-related land disturbance have pushed against it. The net trajectory reflects the balance of these opposing forces, and the study quantifies that balance in tonnage terms for successive time windows.

The spatial detail matters as much as the totals. The InVEST maps show that carbon storage is concentrated in the basin’s forested uplands and along vegetated margins, while the densely settled valley floor, where Taiyuan and other industrial cities sit, functions as a persistent carbon deficit zone. This uneven geography means that a hectare of forest protected in the loess hills yields far more carbon benefit than a hectare of marginal grassland elsewhere, and it gives provincial planners a concrete basis for prioritizing restoration investment. The maps also expose hotspots where carbon losses have accelerated, often coinciding with mining expansion or the outward growth of urban built-up areas along transport corridors.

The GeoDetector analysis adds explanatory depth by ranking the drivers behind this spatial pattern. Land-use type itself emerges as the dominant single factor, an expected but important confirmation that vegetation class controls the carbon ledger. Beyond land use, climatic variables such as precipitation and temperature, topographic features including elevation and slope, soil properties, and human-intensity measures such as population density and gross domestic product all contribute, with their relative influence varying by sub-period. Crucially, the method shows that interactions between factors—between precipitation and land use, or between slope and human activity—often explain more of the spatial variance than any single factor alone, underscoring that carbon storage in the basin is governed by coupled natural and human systems rather than by any one lever.

Perhaps the most forward-looking element of the study is its use of scenario simulation. By driving the PLUS model with different assumptions about future development—ranging from trajectories that prioritize economic growth and continued urban expansion to ones emphasizing ecological protection and cropland preservation—the researchers generated contrasting projections of the basin’s land system and, through InVEST, of its carbon future. The comparisons suggest that policy choices made in the coming years will have consequences measurable in millions of tons of carbon: under protection-oriented scenarios, continued afforestation and constrained urban sprawl raise projected storage, while growth-oriented scenarios erode it, particularly around existing urban cores. These scenario outputs give decision-makers a quantified trade-off table rather than a vague warning.

The significance of the work extends beyond one river basin. The Fen River drains a portion of the Yellow River system and typifies the ecological fragility of China’s loess plateau region, where soil erosion, water scarcity and industrial land use intersect. A transferable PLUS-InVEST-GeoDetector framework, validated in such a demanding setting, can be redeployed in other basins facing similar tensions between development and carbon goals. As China pursues its commitments to peak carbon emissions and to enhance carbon sinks, tools that connect land-use planning to carbon accounting at landscape scale are likely to become standard instruments of regional policy. This study demonstrates that when land change modeling, ecosystem service valuation and driver detection are chained together, the result is not just a set of maps but an actionable diagnosis of where a landscape’s carbon is going, why it is moving, and what choices could keep more of it in the ground.

Subject of Research: Spatio-temporal evolution and driving forces of terrestrial carbon storage in the Fen River Basin using an integrated PLUS-InVEST-GeoDetector modeling framework

Article Title: Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China

Article References: Chen, J., Gao, P., & Hou, Y. (2026). Spatio-temporal evolution and driving force analysis of carbon storage coupled with PLUS-InVEST-GeoDetector in the Fen River Basin of China. Scientific Reports. https://doi.org/10.1038/s41598-026-71784-9

Image Credits: AI Generated

DOI: 10.1038/s41598-026-71784-9

Keywords: carbon storage, Fen River Basin, PLUS model, InVEST model, GeoDetector, land use simulation, ecosystem services, Shanxi Province, afforestation, urbanization, carbon sinks, spatial analysis