###### Abstract
However, these models have not gained widespread application in analyzing the effects of CDHEs on forest carbon dynamics. Simultaneously, few efforts have been made to quantify changes in regional forest carbon sequestration due to CDHEs. In recent years, optical, radar, and LiDAR remote sensing observations have facilitated the understanding and monitoring of land cover changes and vegetation dynamics worldwide and on regional scales. They have refined carbon flux models and enhanced quantitative predictions of ecosystem carbon balance impacts [9; 18]. In recent decades, numerous approaches and models have been utilized to assess forest carbon fluxes and reservoirs. Observing changes in atmospheric CO\({}_{2}\) concentrations can help to infer forest carbon sources and sinks [19], albeit with significant uncertainties. Inventory methods relying on forest survey data, such as [PERSON] et al. [20], used the National Forest Inventory (NFI) to assess changes in forest carbon stocks in China over 70 years (1948-2018). However, the uncertainty is significant on a regional scale. Process-based ecosystem models can simulate changes in forest carbon stocks but often operate at coarse spatial resolutions, leading to significant discrepancies between simulated and observed values [21; 22]. For example, [PERSON] and [PERSON] [23] utilized the Peatland version of the Terrestrial Ecosystem Model (PTEM) to estimate carbon dynamics in peatlands across northern high latitudes, encompassing the timeframe from 1990 to 2300. However, the model's spatial resolution of 0.5\({}^{\circ}\) is insufficient to capture the effects of regional-scale forest cover changes. Bookkeeping models track carbon fluxes generated by land use change and forest disturbance and perform well in regional or global ecosystem assessments [24; 25]. However, they lack spatially distributed carbon flux data and assume that carbon density remains constant over time, resulting in significant differences from observational data. Due to classification errors and other factors in regional land use/land cover change data, there is considerable uncertainty in estimating ecosystem carbon budgets at finer local and regional scales [26; 27]. The CASA model is a light energy utilization model combined with the principle of photosynthesis, and its parameters are small and easy to obtain, making it easy to apply [28]. It can be combined with remote sensing data to obtain different spatial scale net primary productivity (NPP) data for small-scale regions. Therefore, we intend to incorporate the CASA model with the bookkeeping model to assess forest carbon dynamics due to forest cover change at the regional scale. So far, numerous scholars have employed diverse methods and models, successfully quantifying the impacts of forest cover changes and CDHEs on the forest carbon cycle within the Yangtze River Basin and across China [29; 30; 31]. However, few studies accurately quantify regional forest cover change and its effects on forest dynamics with high spatial resolution. Both Nanjing and Shaoguan have subtropical monsoon climates (Table 1). However, the former belongs to the North subtropical zone, and the latter belongs to the Middle subtropical zone. The primary land use type in Shaoguan is forest, while it is farmland in Nanjing. Shaoguan, as a typical forest city in Guangdong Province, has a low level of urbanization and slow economic development. Nanjing is a major financial city in the Yangtze River Delta, with a high level of urbanization and a developed economy. Meanwhile, the forest coverage rate ranks first in Jiangsu Province, China. Since 2000, forest cover changes and CDHEs have occurred more frequently in these two regions. Therefore, quantitatively comparing and exploring the difference between forest cover change and CDHEs on forest carbon dynamics in these regions is of particular social, economic, and ecological value.