Fine-Scale Improved Carbon Bookkeeping Model Using Landsat Time Series for Subtropical Forest, Southern China

Subtropical forests easily suffer anthropogenic disturbance, including deforestation and reforestation management, which both highly affect the carbon pools. This study proposes spatial-temporal tracking of density dynamics to improve bookkeeping in model applied subtropical forest activities Guangzhou, southern China, during period 1995 2014. Based on overall accuracy 87.5% ± 1.7% for change products using Landsat time series (LTS), we found that this is a typical conversion activity accompanied with urbanization. Additionally, linear regression, random regression allometric growth fitting were proposed by field plots obtain reliable per-pixel estimations. The cross-validation (CV) LTS-derived parameters reached highest R2 RMSE 0.763 7.499 Mg ha?1. RMES estimation ranged between 78 84% mean observed biomass area, outperformed previous studies. Over 20-year period, results showed explicit emissions (6.82 0.26) × 104 C yr?1 from deforestation; increased (1.02 0.04) 105 given implicit not yet released atmosphere form decomposing slash wood products. In addition, uptake about 1.91 0.73 yr?1, presented as net pool. continuous detection capability, biennial has rate 1.55 ha?1, emission factors can be identified parameters. general, realizes spatiotemporal improvement flux tracking, abrupt graduate based fine-scale activity. It provide more comprehensive detailed feedback source sink process disturbances.