Abstract
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 the carbon density dynamics to improve bookkeeping in the carbon model and applied to subtropical forest activities in Guangzhou, southern China, during the period of 1995 to 2014. Based on the overall accuracy of 87.5% ± 1.7% for forest change products using Landsat time series (LTS), we found that this is a typical period of deforestation conversion to reforestation activity accompanied with urbanization. Additionally, linear regression, random forest regression and allometric growth fitting were proposed by using forest field plots to obtain reliable per-pixel carbon density estimations. The cross-validation (CV) of random forest with LTS-derived parameters reached the highest accuracy of R2 and RMSE of 0.763 and 7.499 Mg ha−1. The RMES of the density estimation ranged between 78 and 84% of the mean observed biomass in the study area, which outperformed previous studies. Over the 20-year period, the study results showed that the explicit carbon emissions were (6.82 ± 0.26) × 104 Mg C yr−1 from deforestation; emissions increased to (1.02 ± 0.04) × 105 Mg C yr−1 given the implicit carbon not yet released to the atmosphere in the form of decomposing slash and wood products. In addition, a carbon uptake of about 1.91 ± 0.73 × 105 Mg C yr−1, presented as the net carbon pool. Based on the continuous detection capability, biennial reforestation activity has increased carbon density by a growth rate of 1.55 Mg ha−1, and the emission factors can be identified with LTS-derived parameters. In general, the study realizes the spatiotemporal improvement of carbon density and flux dynamics tracking, including the abrupt and graduate change based on fine-scale forest activity. It can provide more comprehensive and detailed feedback on the carbon source and sink change process of forest activities and disturbances.
Highlights
Accurate accounting of forest carbon storage in vegetation is essential for global carbon budgets
ETM+ data, which meets geometric and radiometric quality requirements. (ii) Change detection by the CCDC algorithm [26] and forest activity were identified according to the transition of biennial land cover and land-use classes. (iii) The dynamics of emission factors were linked with the forest activity process to promote fine-scale bookkeeping model development
This study proposed a reliable estimation of temporal continuous carbon density using random forest regression to generate data in 2007, based on which the linear regression could generate data for other years
Summary
Accurate accounting of forest carbon storage in vegetation is essential for global carbon budgets. Tropical forests are an important carbon sink and have been estimated to remove 1.2 Pg C yr−1 from the atmosphere in recent decades [1,2]. Forest management was found to be a new force in the formation of carbon sinks, including tropical and subtropical forests in India and China [3]. The total aboveground biomass (AGB) across Guangdong in southern China, even with the strongest economic strength, markedly increased by 55.9% from 1986 to 2016 due to reforestation projects in the last three decades [4,5]. Land-use change is the primary source of ecosystem services loss [6], carbon emissions [7], increased temperature [8], and so on. Land cover and land-use change present a more multidirectional transition than the bi-directional
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