Abstract

This study develops a workflow for forest carbon accounting in the West Qinling Mountains, China (2000–2025), integrating CLCD data, key-year forest subtype mapping, and InVEST-style carbon pools. Forest subtypes were classified with Random Forest under spatial block cross-validation (overall accuracy = 59.76%, Cohen’s Kappa = 0.3974, Macro-F1 = 0.5944). Under the baseline workflow, total carbon stock increased from 876.40 to 954.45 million Mg C, yielding a net gain of 78.05 million Mg C (+8.91%). The Hamed–Rao modified Mann–Kendall test identified a significant increase (Sen’s slope = 2.41 million Mg C year−1, p=2.99×10−5). A bounded parameter sensitivity analysis produced a 2025 total carbon range of 887.07–1021.84 million Mg C, whereas Scheme A yielded a more conservative net gain of 60.19 million Mg C. County-level patterns were broadly similar across scenarios, but fine-grained ranking remained uncertain. Because the annual series is a hybrid product, with annual forest/non-forest updates but a subtype structure refreshed only at key years, the results should not be interpreted as an independently reconstructed annual record of subtype transitions. The workflow therefore provides an uncertainty-aware regional accounting framework rather than a fully observed annual reconstruction of historical forest composition.

IPC Classification

Keywords